repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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CLMR | CLMR-master/clmr/datasets/magnatagatune.py | import os
import warnings
import subprocess
import torch
import numpy as np
import zipfile
from collections import defaultdict
from typing import Any, Tuple, Optional
from tqdm import tqdm
import soundfile as sf
import torchaudio
torchaudio.set_audio_backend("soundfile")
from torch import Tensor, FloatTensor
from torchaudio.datasets.utils import (
download_url,
extract_archive,
)
from clmr.datasets import Dataset
FOLDER_IN_ARCHIVE = "magnatagatune"
_CHECKSUMS = {
"http://mi.soi.city.ac.uk/datasets/magnatagatune/mp3.zip.001": "",
"http://mi.soi.city.ac.uk/datasets/magnatagatune/mp3.zip.002": "",
"http://mi.soi.city.ac.uk/datasets/magnatagatune/mp3.zip.003": "",
"http://mi.soi.city.ac.uk/datasets/magnatagatune/annotations_final.csv": "",
"https://github.com/minzwon/sota-music-tagging-models/raw/master/split/mtat/binary.npy": "",
"https://github.com/minzwon/sota-music-tagging-models/raw/master/split/mtat/tags.npy": "",
"https://github.com/minzwon/sota-music-tagging-models/raw/master/split/mtat/test.npy": "",
"https://github.com/minzwon/sota-music-tagging-models/raw/master/split/mtat/train.npy": "",
"https://github.com/minzwon/sota-music-tagging-models/raw/master/split/mtat/valid.npy": "",
"https://github.com/jordipons/musicnn-training/raw/master/data/index/mtt/train_gt_mtt.tsv": "",
"https://github.com/jordipons/musicnn-training/raw/master/data/index/mtt/val_gt_mtt.tsv": "",
"https://github.com/jordipons/musicnn-training/raw/master/data/index/mtt/test_gt_mtt.tsv": "",
"https://github.com/jordipons/musicnn-training/raw/master/data/index/mtt/index_mtt.tsv": "",
}
def get_file_list(root, subset, split):
if subset == "train":
if split == "pons2017":
fl = open(os.path.join(root, "train_gt_mtt.tsv")).read().splitlines()
else:
fl = np.load(os.path.join(root, "train.npy"))
elif subset == "valid":
if split == "pons2017":
fl = open(os.path.join(root, "val_gt_mtt.tsv")).read().splitlines()
else:
fl = np.load(os.path.join(root, "valid.npy"))
else:
if split == "pons2017":
fl = open(os.path.join(root, "test_gt_mtt.tsv")).read().splitlines()
else:
fl = np.load(os.path.join(root, "test.npy"))
if split == "pons2017":
binary = {}
index = open(os.path.join(root, "index_mtt.tsv")).read().splitlines()
fp_dict = {}
for i in index:
clip_id, fp = i.split("\t")
fp_dict[clip_id] = fp
for idx, f in enumerate(fl):
clip_id, label = f.split("\t")
fl[idx] = "{}\t{}".format(clip_id, fp_dict[clip_id])
clip_id = int(clip_id)
binary[clip_id] = eval(label)
else:
binary = np.load(os.path.join(root, "binary.npy"))
return fl, binary
class MAGNATAGATUNE(Dataset):
"""Create a Dataset for MagnaTagATune.
Args:
root (str): Path to the directory where the dataset is found or downloaded.
folder_in_archive (str, optional): The top-level directory of the dataset.
download (bool, optional):
Whether to download the dataset if it is not found at root path. (default: ``False``).
subset (str, optional): Which subset of the dataset to use.
One of ``"training"``, ``"validation"``, ``"testing"`` or ``None``.
If ``None``, the entire dataset is used. (default: ``None``).
"""
_ext_audio = ".wav"
def __init__(
self,
root: str,
folder_in_archive: Optional[str] = FOLDER_IN_ARCHIVE,
download: Optional[bool] = False,
subset: Optional[str] = None,
split: Optional[str] = "pons2017",
) -> None:
super(MAGNATAGATUNE, self).__init__(root)
self.root = root
self.folder_in_archive = folder_in_archive
self.download = download
self.subset = subset
self.split = split
assert subset is None or subset in ["train", "valid", "test"], (
"When `subset` not None, it must take a value from "
+ "{'train', 'valid', 'test'}."
)
self._path = os.path.join(root, folder_in_archive)
if download:
if not os.path.isdir(self._path):
os.makedirs(self._path)
zip_files = []
for url, checksum in _CHECKSUMS.items():
target_fn = os.path.basename(url)
target_fp = os.path.join(self._path, target_fn)
if ".zip" in target_fp:
zip_files.append(target_fp)
if not os.path.exists(target_fp):
download_url(
url,
self._path,
filename=target_fn,
hash_value=checksum,
hash_type="md5",
)
if not os.path.exists(
os.path.join(
self._path,
"f",
"american_bach_soloists-j_s__bach_solo_cantatas-01-bwv54__i_aria-30-59.mp3",
)
):
merged_zip = os.path.join(self._path, "mp3.zip")
print("Merging zip files...")
with open(merged_zip, "wb") as f:
for filename in zip_files:
with open(filename, "rb") as g:
f.write(g.read())
extract_archive(merged_zip)
if not os.path.isdir(self._path):
raise RuntimeError(
"Dataset not found. Please use `download=True` to download it."
)
self.fl, self.binary = get_file_list(self._path, self.subset, self.split)
self.n_classes = 50 # self.binary.shape[1]
# self.audio = {}
# for f in tqdm(self.fl):
# clip_id, fp = f.split("\t")
# if clip_id not in self.audio.keys():
# audio, _ = load_magnatagatune_item(fp, self._path, self._ext_audio)
# self.audio[clip_id] = audio
def file_path(self, n: int) -> str:
_, fp = self.fl[n].split("\t")
return os.path.join(self._path, fp)
def __getitem__(self, n: int) -> Tuple[Tensor, Tensor]:
"""Load the n-th sample from the dataset.
Args:
n (int): The index of the sample to be loaded
Returns:
tuple: ``(waveform, label)``
"""
clip_id, fp = self.fl[n].split("\t")
label = self.binary[int(clip_id)]
audio, _ = self.load(n)
label = FloatTensor(label)
return audio, label
def __len__(self) -> int:
return len(self.fl)
| 6,744 | 35.069519 | 99 | py |
CLMR | CLMR-master/clmr/datasets/million_song_dataset.py | import os
import pickle
import torch
import torchaudio
from collections import defaultdict
from pathlib import Path
from torch import Tensor, FloatTensor
from tqdm import tqdm
from typing import Any, Tuple, Optional
from clmr.datasets import Dataset
def load_id2gt(gt_file, msd_7d):
ids = []
with open(gt_file) as f:
id2gt = dict()
for line in f.readlines():
msd_id, gt = line.strip().split("\t") # id is string
id_7d = msd_7d[msd_id]
id2gt[msd_id] = eval(gt) # gt is array
ids.append(msd_id)
return ids, id2gt
def load_id2path(index_file, msd_7d):
paths = []
with open(index_file) as f:
id2path = dict()
for line in f.readlines():
msd_id, msd_path = line.strip().split("\t")
id_7d = msd_7d[msd_id]
path = os.path.join(id_7d[0], id_7d[1], f"{id_7d}.clip.mp3")
id2path[msd_id] = path
paths.append(path)
return paths, id2path
def default_indexer(ids, id2audio_path, id2gt):
index = []
track_index = defaultdict(list)
track_idx = 0
clip_idx = 0
for clip_id in ids:
fp = id2audio_path[clip_id]
label = id2gt[clip_id]
track_idx = clip_id
clip_id = clip_idx
clip_idx += 1
index.append([track_idx, clip_id, fp, label])
track_index[track_idx].append([clip_id, fp, label])
return index, track_index
def default_loader(path):
audio, sr = torchaudio.load(path)
audio = audio.mean(dim=0, keepdim=True)
return audio, sr
class MillionSongDataset(Dataset):
_base_dir = "million_song_dataset"
_ext_audio = ".wav"
def __init__(
self,
root: str,
base_dir: str = _base_dir,
download: bool = False,
subset: Optional[str] = None,
):
if download:
raise Exception("The Million Song Dataset is not publicly available")
self.root = root
self.base_dir = base_dir
self.subset = subset
assert subset is None or subset in ["train", "valid", "test"], (
"When `subset` not None, it must take a value from "
+ "{'train', 'valid', 'test'}."
)
self._path = os.path.join(self.root, self.base_dir)
if not os.path.exists(self._path):
raise RuntimeError(
"Dataset not found. Please place the MSD files in the {} folder.".format(
self._path
)
)
msd_processed_annot = Path(self._path, "processed_annotations")
if self.subset == "train":
self.annotations_file = Path(msd_processed_annot) / "train_gt_msd.tsv"
elif self.subset == "valid":
self.annotations_file = Path(msd_processed_annot) / "val_gt_msd.tsv"
else:
self.annotations_file = Path(msd_processed_annot) / "test_gt_msd.tsv"
with open(Path(msd_processed_annot) / "MSD_id_to_7D_id.pkl", "rb") as f:
self.msd_to_7d = pickle.load(f)
# int to label
with open(Path(msd_processed_annot) / "output_labels_msd.txt", "r") as f:
lines = f.readlines()
self.tags = eval(lines[1][lines[1].find("[") :])
self.n_classes = len(self.tags)
[audio_repr_paths, id2audio_path] = load_id2path(
Path(msd_processed_annot) / "index_msd.tsv", self.msd_to_7d
)
[ids, id2gt] = load_id2gt(self.annotations_file, self.msd_to_7d)
self.index, self.track_index = default_indexer(ids, id2audio_path, id2gt)
def file_path(self, n: int) -> str:
_, _, fp, _ = self.index[n]
return os.path.join(self._path, "preprocessed", fp)
def __getitem__(self, n: int) -> Tuple[Tensor, Tensor]:
track_id, clip_id, fp, label = self.index[n]
label = torch.FloatTensor(label)
try:
audio, _ = self.load(n)
except Exception as e:
print(f"Skipped {track_id, fp}, could not load audio: {e}")
return self.__getitem__(n + 1)
return audio, label
def __len__(self) -> int:
return len(self.index)
| 4,169 | 29.661765 | 89 | py |
CLMR | CLMR-master/clmr/datasets/gtzan.py | import torchaudio
from torchaudio.datasets.gtzan import gtzan_genres
from torch.utils.data import Dataset
class GTZAN(Dataset):
subset_map = {"train": "training", "valid": "validation", "test": "testing"}
def __init__(self, root, download, subset):
self.dataset = torchaudio.datasets.GTZAN(
root=root, download=download, subset=self.subset_map[subset]
)
self.labels = gtzan_genres
self.label2idx = {}
for idx, label in enumerate(self.labels):
self.label2idx[label] = idx
self.n_classes = len(self.label2idx.keys())
def __getitem__(self, idx):
audio, sr, label = self.dataset[idx]
label = self.label2idx[label]
return audio, label
def __len__(self):
return len(self.dataset)
| 802 | 26.689655 | 80 | py |
CLMR | CLMR-master/clmr/datasets/audio.py | import os
from glob import glob
from torch import Tensor
from typing import Tuple
from clmr.datasets import Dataset
class AUDIO(Dataset):
"""Create a Dataset for any folder of audio files.
Args:
root (str): Path to the directory where the dataset is found or downloaded.
src_ext_audio (str): The extension of the audio files to analyze.
"""
def __init__(
self,
root: str,
src_ext_audio: str = ".wav",
n_classes: int = 1,
) -> None:
super(AUDIO, self).__init__(root)
self._path = root
self._src_ext_audio = src_ext_audio
self.n_classes = n_classes
self.fl = glob(
os.path.join(self._path, "**", "*{}".format(self._src_ext_audio)),
recursive=True,
)
if len(self.fl) == 0:
raise RuntimeError(
"Dataset not found. Please place the audio files in the {} folder.".format(
self._path
)
)
def file_path(self, n: int) -> str:
fp = self.fl[n]
return fp
def __getitem__(self, n: int) -> Tuple[Tensor, Tensor]:
"""Load the n-th sample from the dataset.
Args:
n (int): The index of the sample to be loaded
Returns:
Tuple [Tensor, Tensor]: ``(waveform, label)``
"""
audio, _ = self.load(n)
label = []
return audio, label
def __len__(self) -> int:
return len(self.fl)
| 1,506 | 24.116667 | 91 | py |
CLMR | CLMR-master/clmr/datasets/dataset.py | import os
import subprocess
import torchaudio
from torch.utils.data import Dataset as TorchDataset
from abc import abstractmethod
def preprocess_audio(source, target, sample_rate):
p = subprocess.Popen(
["ffmpeg", "-i", source, "-ar", str(sample_rate), target, "-loglevel", "quiet"]
)
p.wait()
class Dataset(TorchDataset):
_ext_audio = ".wav"
def __init__(self, root: str):
pass
@abstractmethod
def file_path(self, n: int):
pass
def target_file_path(self, n: int) -> str:
fp = self.file_path(n)
file_basename, _ = os.path.splitext(fp)
return file_basename + self._ext_audio
def preprocess(self, n: int, sample_rate: int):
fp = self.file_path(n)
target_fp = self.target_file_path(n)
if not os.path.exists(target_fp):
preprocess_audio(fp, target_fp, sample_rate)
def load(self, n):
target_fp = self.target_file_path(n)
try:
audio, sample_rate = torchaudio.load(target_fp)
except OSError as e:
print("File not found, try running `python preprocess.py` first.\n\n", e)
return
return audio, sample_rate
| 1,201 | 25.130435 | 87 | py |
CLMR | CLMR-master/clmr/datasets/librispeech.py | import os
import torchaudio
from torch.utils.data import Dataset
class LIBRISPEECH(Dataset):
subset_map = {"train": "train-clean-100", "test": "test-clean"}
def __init__(self, root, download, subset):
self.dataset = torchaudio.datasets.LIBRISPEECH(
root=root, download=download, url=self.subset_map[subset]
)
self.speaker2idx = {}
if not os.path.exists(self.dataset._path):
raise RuntimeError(
"Dataset not found. Please use `download=True` to download it."
)
self.speaker_ids = list(map(int, os.listdir(self.dataset._path)))
for idx, speaker_id in enumerate(sorted(self.speaker_ids)):
self.speaker2idx[speaker_id] = idx
self.n_classes = len(self.speaker2idx.keys())
def __getitem__(self, idx):
(
audio,
sample_rate,
utterance,
speaker_id,
chapter_id,
utterance_id,
) = self.dataset[idx]
label = self.speaker2idx[speaker_id]
return audio, label
def __len__(self):
return len(self.dataset)
| 1,147 | 26.333333 | 79 | py |
CLMR | CLMR-master/clmr/utils/checkpoint.py | import torch
from collections import OrderedDict
def load_encoder_checkpoint(checkpoint_path: str, output_dim: int) -> OrderedDict:
state_dict = torch.load(checkpoint_path, map_location=torch.device("cpu"))
if "pytorch-lightning_version" in state_dict.keys():
new_state_dict = OrderedDict(
{
k.replace("model.encoder.", ""): v
for k, v in state_dict["state_dict"].items()
if "model.encoder." in k
}
)
else:
new_state_dict = OrderedDict()
for k, v in state_dict.items():
if "encoder." in k:
new_state_dict[k.replace("encoder.", "")] = v
new_state_dict["fc.weight"] = torch.zeros(output_dim, 512)
new_state_dict["fc.bias"] = torch.zeros(output_dim)
return new_state_dict
def load_finetuner_checkpoint(checkpoint_path: str) -> OrderedDict:
state_dict = torch.load(checkpoint_path, map_location=torch.device("cpu"))
if "pytorch-lightning_version" in state_dict.keys():
state_dict = OrderedDict(
{
k.replace("model.", ""): v
for k, v in state_dict["state_dict"].items()
if "model." in k
}
)
return state_dict
| 1,265 | 33.216216 | 82 | py |
RSFormer | RSFormer-master/utils.py | import torch.nn.functional as F
# pad
def pad(x, factor=16, mode='reflect'):
_, _, h_even, w_even = x.shape
padh_left = (factor - h_even % factor) // 2
padw_top = (factor - w_even % factor) // 2
padh_right = padh_left if h_even % 2 == 0 else padh_left + 1
padw_bottom = padw_top if w_even % 2 == 0 else padw_top + 1
x = F.pad(x, pad=[padw_top, padw_bottom, padh_left, padh_right], mode=mode)
return x, (padh_left, padh_right, padw_top, padw_bottom)
# reverse pad
def unpad(x, pad_size):
padh_left, padh_right, padw_top, padw_bottom = pad_size
_, _, newh, neww = x.shape
h_start = padh_left
h_end = newh - padh_right
w_start = padw_top
w_end = neww - padw_bottom
x = x[:, :, h_start:h_end, w_start:w_end]
return x | 776 | 31.375 | 79 | py |
RSFormer | RSFormer-master/datasets.py | import os
from PIL import Image
from torch.utils.data import Dataset
import torchvision.transforms.functional as ttf
class MyTestDataSet(Dataset):
def __init__(self, inputPathTest):
super(MyTestDataSet, self).__init__()
self.inputPath = inputPathTest
self.inputImages = os.listdir(inputPathTest)
def __len__(self):
return len(self.inputImages)
def __getitem__(self, index):
index = index % len(self.inputImages)
inputImagePath = os.path.join(self.inputPath, self.inputImages[index])
inputImage = Image.open(inputImagePath).convert('RGB')
input_ = ttf.to_tensor(inputImage)
return input_, self.inputImages[index]
| 702 | 28.291667 | 78 | py |
RSFormer | RSFormer-master/demo.py | import sys
import time
import torch
import torch.nn as nn
from tqdm import tqdm
from torch.utils.data import DataLoader
from torchvision.utils import save_image
from RSFormer import RSFormer
from datasets import *
from config import Options
from utils import pad, unpad
if __name__ == '__main__':
opt = Options()
inputPathTest = opt.Input_Path_Test
resultPathTest = opt.Result_Path_Test
modelPath = opt.MODEL_PATH
myNet = RSFormer()
myNet = nn.DataParallel(myNet)
if opt.CUDA_USE:
myNet = myNet.cuda()
datasetTest = MyTestDataSet(inputPathTest)
testLoader = DataLoader(dataset=datasetTest, batch_size=1, shuffle=False, drop_last=False,
num_workers=opt.Num_Works, pin_memory=True)
print('--------------------------------------------------------------')
# pretrained model
if opt.CUDA_USE:
myNet.load_state_dict(torch.load(modelPath))
else:
myNet.load_state_dict(torch.load(modelPath, map_location=torch.device('cpu')))
myNet.eval()
with torch.no_grad():
timeStart = time.time()
for index, (x, name) in enumerate(tqdm(testLoader, desc='Testing !!! ', file=sys.stdout), 0):
torch.cuda.empty_cache()
input_test = x.cuda() if opt.CUDA_USE else x
input_test, pad_size = pad(input_test, factor=16)
output_test = myNet(input_test)
output_test = unpad(output_test, pad_size)
save_image(output_test, resultPathTest + name[0])
timeEnd = time.time()
print('---------------------------------------------------------')
print("Testing Process Finished !!! Time: {:.4f} s".format(timeEnd - timeStart))
| 1,721 | 30.888889 | 101 | py |
RSFormer | RSFormer-master/RSFormer.py | import torch
import torch.nn as nn
class FeedForward(nn.Module):
def __init__(self, dim, mlp_ratio=4):
super().__init__()
hidden_features = int(dim * mlp_ratio)
self.norm = LayerNorm(dim)
self.fc1 = nn.Conv2d(dim, hidden_features, 1)
self.dwconv = nn.Conv2d(hidden_features, hidden_features, 3, padding=1, groups=hidden_features)
self.fc2 = nn.Conv2d(hidden_features, dim, 1)
self.act = nn.GELU()
def forward(self, x):
x = self.norm(x)
x = self.fc1(x)
x = self.act(x)
res = x
x = self.dwconv(x)
x = self.act(x) + res
x = self.fc2(x)
return x
class Attention(nn.Module):
def __init__(self, dim, bias=False):
super().__init__()
self.norm = LayerNorm(dim)
self.qk = nn.Conv2d(dim, dim, 1, bias=bias)
self.act = nn.GELU()
self.dwconv = nn.Conv2d(dim, dim, 11, padding=5, groups=dim, bias=bias)
self.v = nn.Conv2d(dim, dim, 1)
self.proj = nn.Conv2d(dim, dim, 1)
def forward(self, x):
x = self.norm(x)
qk = self.qk(x)
attn = self.act(qk)
attn = self.dwconv(attn)
attn = self.act(attn)
v = self.v(x)
x = attn * v
x = self.proj(x)
return x
class ConvolutionBlock(nn.Module):
def __init__(self, dim, mlp_ratio=4):
super().__init__()
self.attn = Attention(dim)
self.ffn = FeedForward(dim, mlp_ratio)
layer_scale_init_value = 1e-6
self.layer_scale_1 = nn.Parameter(
layer_scale_init_value * torch.ones((dim)), requires_grad=True)
self.layer_scale_2 = nn.Parameter(
layer_scale_init_value * torch.ones((dim)), requires_grad=True)
def forward(self, x):
x = x + self.layer_scale_1.unsqueeze(-1).unsqueeze(-1) * self.attn(x)
x = x + self.layer_scale_2.unsqueeze(-1).unsqueeze(-1) * self.ffn(x)
return x
class LayerNorm(nn.Module):
def __init__(self, dim):
super().__init__()
self.weight = nn.Parameter(torch.ones(dim))
self.bias = nn.Parameter(torch.zeros(dim))
self.eps = 1e-6
def forward(self, x):
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x
class PatchEmbed(nn.Module):
def __init__(self, in_c=3, embed_dim=48, bias=False):
super().__init__()
self.proj = nn.Conv2d(in_c, embed_dim, kernel_size=3, stride=1, padding=1, bias=bias)
def forward(self, x):
x = self.proj(x)
return x
class Downsample(nn.Module):
def __init__(self, dim, num_head=8, bias=False):
super().__init__()
self.num_head = num_head
self.temperature = nn.Parameter(torch.ones(num_head, 1, 1))
self.v = nn.Sequential(
nn.Conv2d(dim, dim, kernel_size=3, stride=2, padding=1, bias=bias),
LayerNorm(dim),
nn.Conv2d(dim, dim * 2, kernel_size=1, bias=bias)
)
self.v_hp = nn.Conv2d(dim * 2, dim * 2, kernel_size=3, stride=1, padding=1, groups=dim, bias=bias)
self.qk = nn.Conv2d(dim, dim * 4, kernel_size=1, bias=bias)
self.proj = nn.Conv2d(dim * 2, dim * 2, kernel_size=1, bias=bias)
def forward(self, x):
B, C, H, W = x.shape
out_shape = B, C * 2, H // 2, W // 2
qk = self.qk(x).reshape(B, 2, self.num_head, (C * 2) // self.num_head, -1).transpose(0, 1)
q, k = qk[0], qk[1]
v = self.v(x)
v_hp = self.v_hp(v)
v = v.reshape(B, self.num_head, (C * 2) // self.num_head, -1)
attn = (q @ k.transpose(-1, -2)) * self.temperature
attn = attn.softmax(dim=-1)
x = (attn @ v).reshape(out_shape) + v_hp
x = self.proj(x)
return x
class Upsample(nn.Module):
def __init__(self, dim, num_head=8, bias=False):
super().__init__()
self.num_head = num_head
self.temperature = nn.Parameter(torch.ones(num_head, 1, 1))
self.v = nn.Sequential(
nn.ConvTranspose2d(dim, dim, kernel_size=4, stride=2, padding=1, bias=bias),
LayerNorm(dim),
nn.Conv2d(dim, dim // 2, kernel_size=1, bias=False)
)
self.v_hp = nn.Conv2d(dim // 2, dim // 2, kernel_size=3, stride=1, padding=1, groups=dim // 2, bias=False)
self.qk = nn.Conv2d(dim, dim, kernel_size=1, bias=False)
self.proj = nn.Conv2d(dim // 2, dim // 2, kernel_size=1, bias=False)
def forward(self, x):
B, C, H, W = x.shape
out_shape = B, C // 2, H * 2, W * 2
qk = self.qk(x).reshape(B, 2, self.num_head, (C // 2) // self.num_head, -1).transpose(0, 1)
q, k = qk[0], qk[1]
v = self.v(x)
v_hp = self.v_hp(v)
v = v.reshape(B, self.num_head, (C // 2) // self.num_head, -1)
attn = (q @ k.transpose(-1, -2)) * self.temperature
attn = attn.softmax(dim=-1)
x = (attn @ v).reshape(out_shape) + v_hp
x = self.proj(x)
return x
class RSFormer(nn.Module):
def __init__(self,
in_channels=3,
dim=48,
num_blocks=(4, 6, 6, 8),
num_heads=(2, 4, 8), # sampling head
num_refinement_blocks=4,
mlp_ratios=(4, 4, 4, 4),
bias=False,
):
super(RSFormer, self).__init__()
self.patch_embed = PatchEmbed(in_channels, dim)
self.encoder1 = nn.Sequential(*[
ConvolutionBlock(dim=dim, mlp_ratio=mlp_ratios[0]) for i in range(num_blocks[0])])
self.down1 = Downsample(dim, num_head=num_heads[0])
self.encoder2 = nn.Sequential(*[
ConvolutionBlock(dim=int(dim * 2 ** 1), mlp_ratio=mlp_ratios[1]) for i in range(num_blocks[1])])
self.down2 = Downsample(int(dim * 2 ** 1), num_head=num_heads[1])
self.encoder3 = nn.Sequential(*[
ConvolutionBlock(dim=int(dim * 2 ** 2), mlp_ratio=mlp_ratios[2]) for i in range(num_blocks[2])])
self.down3 = Downsample(int(dim * 2 ** 2), num_head=num_heads[2])
self.latent = nn.Sequential(*[
ConvolutionBlock(dim=int(dim * 2 ** 3), mlp_ratio=mlp_ratios[3]) for i in range(num_blocks[3])])
self.up3 = Upsample(int(dim * 2 ** 3), num_head=num_heads[2])
self.reduce3 = nn.Conv2d(int(dim * 2 ** 3), int(dim * 2 ** 2), kernel_size=1, bias=bias)
self.decoder3 = nn.Sequential(*[
ConvolutionBlock(dim=int(dim * 2 ** 2), mlp_ratio=mlp_ratios[2]) for i in range(num_blocks[2])])
self.up2 = Upsample(int(dim * 2 ** 2), num_head=num_heads[1])
self.reduce2 = nn.Conv2d(int(dim * 2 ** 2), int(dim * 2 ** 1), kernel_size=1, bias=bias)
self.decoder2 = nn.Sequential(*[
ConvolutionBlock(dim=int(dim * 2 ** 1), mlp_ratio=mlp_ratios[1]) for i in range(num_blocks[1])])
self.up1 = Upsample(int(dim * 2 ** 1), num_head=num_heads[0])
self.decoder1 = nn.Sequential(*[
ConvolutionBlock(dim=int(dim * 2 ** 1), mlp_ratio=mlp_ratios[0]) for i in range(num_blocks[0])])
self.refinement = nn.Sequential(*[ConvolutionBlock(dim=int(dim * 2 ** 1), mlp_ratio=mlp_ratios[0]) for i in range(num_refinement_blocks)])
self.output = nn.Conv2d(int(dim * 2 ** 1), in_channels, kernel_size=3, stride=1, padding=1, bias=bias)
def forward(self, x):
input_ = x
x = self.patch_embed(x) # stage 1
x0 = self.encoder1(x)
x = self.down1(x0) # stage 2
x1 = self.encoder2(x)
x = self.down2(x1) # stage 3
x2 = self.encoder3(x)
x = self.down3(x2) # stage 4
x = self.latent(x)
x = self.up3(x)
x2 = torch.cat([x, x2], 1)
x2 = self.reduce3(x2)
x2 = self.decoder3(x2)
x = self.up2(x2)
x1 = torch.cat([x, x1], 1)
x1 = self.reduce2(x1)
x1 = self.decoder2(x1)
x = self.up1(x1)
x0 = torch.cat([x, x0], 1)
x0 = self.decoder1(x0)
x = self.refinement(x0)
x = self.output(x) + input_
return x
if __name__ == '__main__':
x = torch.randn((1, 3, 256, 256)).cuda()
net = RSFormer().cuda()
from thop import profile, clever_format
flops, params = profile(net, (x,))
flops, params = clever_format([flops, params], "%.3f")
print(flops, params) | 8,508 | 34.016461 | 146 | py |
DRT | DRT-master/external_libs/matconvnet/matconvnet/utils/layers.py | # file: layers.py
# brief: A number of objects to wrap caffe layers for conversion
# author: Andrea Vedaldi
from collections import OrderedDict
from math import floor, ceil
from operator import mul
import numpy as np
from numpy import array
import scipy
import scipy.io
import scipy.misc
import copy
import collections
# Recent Caffes just pass a string as a type; this is used for legacy support
layers_type = {}
layers_type[0] = 'none'
layers_type[1] = 'accuracy'
layers_type[2] = 'bnll'
layers_type[3] = 'concat'
layers_type[4] = 'conv'
layers_type[5] = 'data'
layers_type[6] = 'dropout'
layers_type[7] = 'euclidean_loss'
layers_type[8] = 'flatten'
layers_type[9] = 'hdf5_data'
layers_type[10] = 'hdf5_output'
layers_type[28] = 'hinge_loss'
layers_type[11] = 'im2col'
layers_type[12] = 'image_data'
layers_type[13] = 'infogain_loss'
layers_type[14] = 'inner_product'
layers_type[15] = 'lrn'
layers_type[25] = 'eltwise'
layers_type[29] = 'memory_data'
layers_type[16] = 'multinomial_logistic_loss'
layers_type[17] = 'pool'
layers_type[26] = 'power'
layers_type[18] = 'relu'
layers_type[19] = 'sigmoid'
layers_type[27] = 'sigmoid_cross_entropy_loss'
layers_type[20] = 'softmax'
layers_type[21] = 'softmax_loss'
layers_type[22] = 'split'
layers_type[23] = 'tanh'
layers_type[24] = 'window_data'
layers_type[39] = 'deconvolution'
layers_type[40] = 'crop'
def getFilterOutputSize(size, kernelSize, stride, pad):
return [floor((size[0] + pad[0]+pad[1] - kernelSize[0]) / stride[0]) + 1., \
floor((size[1] + pad[2]+pad[3] - kernelSize[1]) / stride[1]) + 1.]
def getFilterTransform(ks, stride, pad):
y1 = 1. - pad[0] ;
y2 = 1. - pad[0] + ks[0] - 1 ;
x1 = 1. - pad[2] ;
x2 = 1. - pad[2] + ks[1] - 1 ;
h = y2 - y1 + 1. ;
w = x2 - x1 + 1. ;
return CaffeTransform([h, w], stride, [(y1+y2)/2, (x1+x2)/2])
def reorder(aList, order):
return [aList[i] for i in order]
def row(x):
return np.array(x,dtype=float).reshape(1,-1)
def rowarray(x):
return x.reshape(1,-1)
def rowcell(x):
return np.array(x,dtype=object).reshape(1,-1)
def dictToMatlabStruct(d):
if not d:
return np.zeros((0,))
dt = []
for x in d.keys():
pair = (x,object)
if isinstance(d[x], np.ndarray): pair = (x,type(d[x]))
dt.append(pair)
y = np.empty((1,),dtype=dt)
for x in d.keys():
y[x][0] = d[x]
return y
# --------------------------------------------------------------------
# MatConvNet in NumPy
# --------------------------------------------------------------------
mlayerdt = [('name',object),
('type',object),
('inputs',object),
('outputs',object),
('params',object),
('block',object)]
mparamdt = [('name',object),
('value',object)]
minputdt = [('name',object),
('size',object)]
# --------------------------------------------------------------------
# Vars and params
# --------------------------------------------------------------------
class CaffeBlob(object):
def __init__(self, name):
self.name = name
self.shape = None
self.value = np.zeros(shape=(0,0), dtype='float32')
self.bgrInput = False
self.transposable = True # first two dimensions are spatial
def transpose(self):
if self.shape: self.shape = [self.shape[k] for k in [1,0,2,3]]
def toMatlab(self):
mparam = np.empty(shape=[1,], dtype=mparamdt)
mparam['name'][0] = self.name
mparam['value'][0] = self.value
return mparam
def toMatlabSimpleNN(self):
return self.value
def hasValue(self):
return reduce(mul, self.value.shape, 1) > 0
class CaffeTransform(object):
def __init__(self, size, stride, offset):
self.shape = size
self.stride = stride
self.offset = offset
def __str__(self):
return "<%s %s %s>" % (self.shape, self.stride, self.offset)
def composeTransforms(a, b):
size = [0.,0.]
stride = [0.,0.]
offset = [0.,0.]
for i in [0,1]:
size[i] = a.stride[i] * (b.shape[i] - 1) + a.shape[i]
stride[i] = a.stride[i] * b.stride[i]
offset[i] = a.stride[i] * (b.offset[i] - 1) + a.offset[i]
c = CaffeTransform(size, stride, offset)
return c
def transposeTransform(a):
size = [0.,0.]
stride = [0.,0.]
offset = [0.,0.]
for i in [0,1]:
size[i] = (a.shape[i] + a.stride[i] - 1.0) / a.stride[i]
stride[i] = 1.0/a.stride[i]
offset[i] = (1.0 + a.stride[i] - a.offset[i]) / a.stride[i]
c = CaffeTransform(size, stride, offset)
return c
# --------------------------------------------------------------------
# Errors
# --------------------------------------------------------------------
class ConversionError(Exception):
def __init__(self, value):
self.value = value
def __str__(self):
return repr(self.value)
# --------------------------------------------------------------------
# Basic Layers
# --------------------------------------------------------------------
class CaffeLayer(object):
def __init__(self, name, inputs, outputs):
self.name = name
self.inputs = inputs
self.outputs = outputs
self.params = []
self.model = None
def reshape(self, model):
pass
def display(self):
print "Layer \'{}\'".format(self.name)
print " +- type: %s" % (self.__class__.__name__)
print " +- inputs: %s" % (self.inputs,)
print " +- outputs: %s" % (self.outputs,)
print " +- params: %s" % (self.params,)
def getTransforms(self, model):
transforms = []
for i in enumerate(self.inputs):
row = []
for j in enumerate(self.outputs):
row.append(CaffeTransform([1.,1.], [1.,1.], [1.,1.]))
transforms.append(row)
return transforms
def transpose(self, model):
pass
def setBlob(self, model, i, blob):
assert(False)
def toMatlab(self):
mlayer = np.empty(shape=[1,],dtype=mlayerdt)
mlayer['name'][0] = self.name
mlayer['type'][0] = None
mlayer['inputs'][0] = rowcell(self.inputs)
mlayer['outputs'][0] = rowcell(self.outputs)
mlayer['params'][0] = rowcell(self.params)
mlayer['block'][0] = dictToMatlabStruct({})
return mlayer
def toMatlabSimpleNN(self):
mparam = collections.OrderedDict() ;
mparam['name'] = self.name
mparam['type'] = None
return mparam
class CaffeElementWise(CaffeLayer):
def reshape(self, model):
for i in range(len(self.inputs)):
model.vars[self.outputs[i]].shape = \
model.vars[self.inputs[i]].shape
class CaffeReLU(CaffeElementWise):
def __init__(self, name, inputs, outputs):
super(CaffeReLU, self).__init__(name, inputs, outputs)
def toMatlab(self):
mlayer = super(CaffeReLU, self).toMatlab()
mlayer['type'][0] = u'dagnn.ReLU'
mlayer['block'][0] = dictToMatlabStruct(
{'leak': float(0.0) })
# todo: leak factor
return mlayer
def toMatlabSimpleNN(self):
mlayer = super(CaffeReLU, self).toMatlabSimpleNN()
mlayer['type'] = u'relu'
mlayer['leak'] = float(0.0)
return mlayer
class CaffeLRN(CaffeElementWise):
def __init__(self, name, inputs, outputs,
local_size,
alpha,
beta,
norm_region,
kappa):
super(CaffeLRN, self).__init__(name, inputs, outputs)
self.local_size = local_size
self.alpha = alpha
self.beta = beta
self.norm_region = norm_region
self.kappa = kappa
assert(norm_region == 'across_channels')
def toMatlab(self):
mlayer = super(CaffeLRN, self).toMatlab()
mlayer['type'][0] = u'dagnn.LRN'
mlayer['block'][0] = dictToMatlabStruct(
{'param': row([self.local_size,
self.kappa,
self.alpha / self.local_size,
self.beta])})
return mlayer
def toMatlabSimpleNN(self):
mlayer = super(CaffeLRN, self).toMatlabSimpleNN()
mlayer['type'] = u'lrn'
mlayer['param'] = row([self.local_size,
self.kappa,
self.alpha / self.local_size,
self.beta])
return mlayer
class CaffeSoftMax(CaffeElementWise):
def __init__(self, name, inputs, outputs):
super(CaffeSoftMax, self).__init__(name, inputs, outputs)
def toMatlab(self):
mlayer = super(CaffeSoftMax, self).toMatlab()
mlayer['type'][0] = u'dagnn.SoftMax'
return mlayer
def toMatlabSimpleNN(self):
mlayer = super(CaffeSoftMax, self).toMatlabSimpleNN()
mlayer['type'] = u'softmax'
return mlayer
class CaffeSoftMaxLoss(CaffeElementWise):
def __init__(self, name, inputs, outputs):
super(CaffeSoftMaxLoss, self).__init__(name, inputs, outputs)
def toMatlab(self):
mlayer = super(CaffeSoftMaxLoss, self).toMatlab()
mlayer['type'][0] = u'dagnn.SoftMaxLoss'
return mlayer
def toMatlabSimpleNN(self):
mlayer = super(CaffeSoftMaxLoss, self).toMatlabSimpleNN()
mlayer['type'] = u'softmax'
return mlayer
class CaffeDropout(CaffeElementWise):
def __init__(self, name, inputs, outputs, ratio):
super(CaffeDropout, self).__init__(name, inputs, outputs)
self.ratio = ratio
def toMatlab(self):
mlayer = super(CaffeDropout, self).toMatlab()
mlayer['type'][0] = u'dagnn.DropOut'
mlayer['block'][0] = dictToMatlabStruct({'rate': float(self.ratio)})
return mlayer
def toMatlabSimpleNN(self):
mlayer = super(CaffeDropout, self).toMatlabSimpleNN()
mlayer['type'] = u'dropout'
mlayer['rate'] = float(self.ratio)
return mlayer
def display(self):
super(CaffeDropout, self).display()
print " c- ratio (dropout rate):", self.ratio
class CaffeData(CaffeLayer):
def __init__(self, name, inputs, outputs):
super(CaffeData, self).__init__(name, inputs, outputs)
def reshape(self, model):
# todo: complete otehr cases
shape = [layer.transform_param.crop_size,
layer.transform_param.crop_size,
3,
layer.batch_size]
model.vars[self.outputs[0]].shape = shape
def toMatlab(self):
return None
def toMatlabSimpleNN(self):
return None
# --------------------------------------------------------------------
# Convolution
# --------------------------------------------------------------------
class CaffeConv(CaffeLayer):
def __init__(self, name, inputs, outputs,
num_output,
bias_term,
pad,
kernel_size,
stride,
dilation,
group):
super(CaffeConv, self).__init__(name, inputs, outputs)
if len(kernel_size) == 1 : kernel_size = kernel_size * 2
if len(stride) == 1 : stride = stride * 2
if len(pad) == 1 : pad = pad * 4
elif len(pad) == 2 : pad = [pad[0], pad[0], pad[1], pad[1]]
self.num_output = num_output
self.bias_term = bias_term
self.pad = pad
self.kernel_size = kernel_size
self.stride = stride
self.dilation = dilation
self.group = group
self.params = [name + '_filter']
if bias_term: self.params.append(name + '_bias')
self.filter_depth = None
def display(self):
super(CaffeConv, self).display()
print " +- filter dimension:", self.filter_depth
print " c- num_output (num filters): %s" % self.num_output
print " c- bias_term: %s" % self.bias_term
print " c- pad: %s" % (self.pad,)
print " c- kernel_size: %s" % self.kernel_size
print " c- stride: %s" % (self.stride,)
print " c- dilation: %s" % (self.dilation,)
print " c- group: %s" % (self.group,)
def reshape(self, model):
varin = model.vars[self.inputs[0]]
varout = model.vars[self.outputs[0]]
if not varin.shape: return
varout.shape = getFilterOutputSize(varin.shape[0:2],
self.kernel_size,
self.stride,
self.pad) \
+ [self.num_output, varin.shape[3]]
self.filter_depth = varin.shape[2] / self.group
def getTransforms(self, model):
return [[getFilterTransform(self.kernel_size, self.stride, self.pad)]]
def setBlob(self, model, i, blob):
assert(i < 2)
if i == 0:
assert(blob.shape[0] == self.kernel_size[0])
assert(blob.shape[1] == self.kernel_size[1])
assert(blob.shape[3] == self.num_output)
self.filter_depth = blob.shape[2]
elif i == 1:
assert(blob.shape[0] == self.num_output)
model.params[self.params[i]].value = blob
model.params[self.params[i]].shape = blob.shape
def transpose(self, model):
self.kernel_size = reorder(self.kernel_size, [1,0])
self.stride = reorder(self.stride, [1,0])
self.pad = reorder(self.pad, [2,3,0,1])
if model.params[self.params[0]].hasValue():
print "Layer %s: transposing filters" % self.name
param = model.params[self.params[0]]
param.value = param.value.transpose([1,0,2,3])
if model.vars[self.inputs[0]].bgrInput:
print "Layer %s: BGR to RGB conversion" % self.name
param.value = param.value[:,:,: : -1,:]
def toMatlab(self):
size = self.kernel_size + [self.filter_depth, self.num_output]
mlayer = super(CaffeConv, self).toMatlab()
mlayer['type'][0] = u'dagnn.Conv'
mlayer['block'][0] = dictToMatlabStruct(
{'hasBias': self.bias_term,
'size': row(size),
'pad': row(self.pad),
'stride': row(self.stride)})
return mlayer
def toMatlabSimpleNN(self):
size = self.kernel_size + [self.filter_depth, self.num_output]
mlayer = super(CaffeConv, self).toMatlabSimpleNN()
mlayer['type'] = u'conv'
mlayer['weights'] = np.empty([1,len(self.params)], dtype=np.object)
mlayer['size'] = row(size)
mlayer['pad'] = row(self.pad)
mlayer['stride'] = row(self.stride)
for p, name in enumerate(self.params):
mlayer['weights'][0,p] = self.model.params[name].toMatlabSimpleNN()
return mlayer
# --------------------------------------------------------------------
# InnerProduct
# --------------------------------------------------------------------
# special case: inner product
class CaffeInnerProduct(CaffeConv):
def __init__(self, name, inputs, outputs, num_output, bias_term, axis):
super(CaffeInnerProduct, self).__init__(name, inputs, outputs,
num_output = num_output,
bias_term = bias_term,
pad = [0, 0, 0, 0],
kernel_size = [1, 1],
stride = [1, 1],
dilation = [],
group = 1)
self.axis = axis
assert(axis == 1)
def setBlob(self, model, i, blob):
assert(i < 1 + self.bias_term)
if i == 0:
self.filter_depth = blob.shape[0]
assert(blob.shape[1] == self.num_output)
blob = blob.reshape([1, 1, self.filter_depth, self.num_output])
elif i == 1:
assert(blob.shape[0] == self.num_output)
model.params[self.params[i]].value = blob
model.params[self.params[i]].shape = blob.shape
def reshape(self, model):
if not model.vars[self.inputs[0]].shape: return
s = model.vars[self.inputs[0]].shape
self.kernel_size = [s[0], s[1], s[2], self.num_output]
print "Layer %s: inner product converted to filter bank of shape %s" \
% (self.name, self.kernel_size)
param = model.params[self.params[0]]
if param.hasValue():
print "Layer %s: reshaping inner product paramters of shape %s into a filter bank" % (self.name, param.value.shape)
param.value = param.value.reshape(self.kernel_size, order='F')
super(CaffeInnerProduct, self).reshape(model)
# --------------------------------------------------------------------
# Deconvolution
# --------------------------------------------------------------------
class CaffeDeconvolution(CaffeConv):
def __init__(self, name, inputs, outputs,
num_output,
bias_term,
pad,
kernel_size,
stride,
dilation,
group):
super(CaffeDeconvolution, self).__init__(name, inputs, outputs,
num_output = num_output,
bias_term = bias_term,
pad = pad,
kernel_size = kernel_size,
stride = stride,
dilation = dilation,
group = group)
def setBlob(self, model, i, blob):
assert(i < 2)
if i == 0:
assert(blob.shape[0] == self.kernel_size[0])
assert(blob.shape[1] == self.kernel_size[1])
assert(blob.shape[2] == self.num_output)
self.filter_depth = blob.shape[3]
elif i == 1:
assert(blob.shape[0] == self.num_output)
model.params[self.params[i]].value = blob
model.params[self.params[i]].shape = blob.shape
def reshape(self, model):
inshape = model.vars[self.inputs[0]].shape
if not inshape: return
model.vars[self.outputs[0]].shape = \
getFilterOutputSize(inshape[0:2],
self.kernel_size, self.stride, self.pad) + \
[self.num_output, inshape[3]]
self.filter_depth = inshape[2]
def getTransforms(self, model):
t = getFilterTransform(self.kernel_size, self.stride, self.pad)
t = transposeTransform(t)
return [[t]]
def transpose(self, model):
self.kernel_size = reorder(self.kernel_size, [1,0])
self.stride = reorder(self.stride, [1,0])
self.pad = reorder(self.pad, [2,3,0,1])
if model.params[self.params[0]].hasValue():
print "Layer %s transposing filters" % self.name
param = model.params[self.params[0]]
param.value = param.value.transpose([1,0,2,3])
if model.vars[self.inputs[0]].bgrInput:
print "Layer %s BGR to RGB conversion" % self.name
param.value = param.value[:,:,:,: : -1]
def toMatlab(self):
size = self.kernel_size + [self.num_output, self.filter_depth / self.group]
mlayer = super(CaffeDeconvolution, self).toMatlab()
mlayer['type'][0] = u'dagnn.ConvTranspose'
mlayer['block'][0] = dictToMatlabStruct(
{'hasBias': self.bias_term,
'size': row(size),
'upsample': row(self.stride),
'crop': row(self.pad)})
return mlayer
def toMatlabSimpleNN(self):
size = self.kernel_size + [self.num_output, self.filter_depth / self.group]
mlayer = super(CaffeDeconvolution, self).toMatlabSimpleNN()
mlayer['type'] = u'convt'
mlayer['weights'] = np.empty([1,len(self.params)], dtype=np.object)
mlayer['size'] = row(size)
mlayer['upsample'] = row(self.stride)
mlayer['crop'] = row(self.pad)
for p, name in enumerate(self.params):
mlayer['weights'][0,p] = self.model.params[name].toMatlabSimpleNN()
return mlayer
# --------------------------------------------------------------------
# Pooling
# --------------------------------------------------------------------
class CaffePooling(CaffeLayer):
def __init__(self, name, inputs, outputs,
method,
pad,
kernel_size,
stride):
super(CaffePooling, self).__init__(name, inputs, outputs)
if len(kernel_size) == 1 : kernel_size = kernel_size * 2
if len(stride) == 1 : stride = stride * 2
if len(pad) == 1 : pad = pad * 4
elif len(pad) == 2 : pad = [pad[0], pad[0], pad[1], pad[1]]
self.method = method
self.pad = pad
self.kernel_size = kernel_size
self.stride = stride
self.pad_corrected = None
def display(self):
super(CaffePooling, self).display()
print " +- pad_corrected: %s" % (self.pad_corrected,)
print " c- method: ", self.method
print " c- pad: %s" % (self.pad,)
print " c- kernel_size: %s" % (self.kernel_size,)
print " c- stride: %s" % (self.stride,)
def reshape(self, model):
shape = model.vars[self.inputs[0]].shape
if not shape: return
# MatConvNet uses a slighly different definition of padding, which we think
# is the correct one (it corresponds to the filters)
self.pad_corrected = copy.deepcopy(self.pad)
for i in [0, 1]:
self.pad_corrected[1 + i*2] = min(
self.pad[1 + i*2] + self.stride[i] - 1,
self.kernel_size[i] - 1)
model.vars[self.outputs[0]].shape = \
getFilterOutputSize(shape[0:2],
self.kernel_size,
self.stride,
self.pad_corrected) + shape[2:5]
def getTransforms(self, model):
return [[getFilterTransform(self.kernel_size, self.stride, self.pad)]]
def transpose(self, model):
self.kernel_size = reorder(self.kernel_size, [1,0])
self.stride = reorder(self.stride, [1,0])
self.pad = reorder(self.pad, [2,3,0,1])
if self.pad_corrected:
self.pad_corrected = reorder(self.pad_corrected, [2,3,0,1])
def toMatlab(self):
mlayer = super(CaffePooling, self).toMatlab()
mlayer['type'][0] = u'dagnn.Pooling'
mlayer['block'][0] = dictToMatlabStruct(
{'method': self.method,
'poolSize': row(self.kernel_size),
'stride': row(self.stride),
'pad': row(self.pad_corrected)})
if not self.pad_corrected:
print "Warning: pad correction for layer %s could not be computed because the layer input shape could not be determined" % (self.name)
return mlayer
def toMatlabSimpleNN(self):
mlayer = super(CaffePooling, self).toMatlabSimpleNN()
mlayer['type'] = u'pool'
mlayer['method'] = self.method
mlayer['pool'] = row(self.kernel_size)
mlayer['stride'] = row(self.stride)
mlayer['pad'] = row(self.pad_corrected)
if not self.pad_corrected:
print "Warning: pad correction for layer %s could not be computed because the layer input shape could not be determined" % (self.name)
return mlayer
# --------------------------------------------------------------------
# ROIPooling
# --------------------------------------------------------------------
class CaffeROIPooling(CaffeLayer):
def __init__(self, name, inputs, outputs,
pooled_w,
pooled_h,
spatial_scale):
super(CaffeROIPooling, self).__init__(name, inputs, outputs)
self.pooled_w = pooled_w
self.pooled_h = pooled_h
self.spatial_scale = spatial_scale
self.flatten = True
def display(self):
super(CaffeROIPooling, self).display()
print " c- pooled_w: %s" % (self.pooled_w,)
print " c- pooled_h: %s" % (self.pooled_h,)
print " c- spatial_scale: %s" % (self.spatial_scale,)
print " c- flatten: %s" % (self.flatten,)
def reshape(self, model):
shape1 = model.vars[self.inputs[0]].shape
shape2 = model.vars[self.inputs[1]].shape
if not shape1 or not shape2: return
numChannels = shape1[2]
numROIs = reduce(mul, shape2, 1) / 5
if self.flatten:
oshape = [1,
1,
self.pooled_w * self.pooled_h * numChannels,
numROIs]
else:
oshape = [self.pooled_w,
self.pooled_h,
numChannels,
numROIs]
model.vars[self.outputs[0]].shape = oshape
def getTransforms(self, model):
# no transform
return [[CaffeTransform([1.,1.], [1.,1.], [1.,1.])]]
def transpose(self, model):
assert(not self.flatten)
tmp = self.pooled_w
self.pooled_w = self.pooled_h
self.pooled_h = tmp
def toMatlab(self):
mlayer = super(CaffeROIPooling, self).toMatlab()
mlayer['type'][0] = u'dagnn.ROIPooling'
mlayer['block'][0] = dictToMatlabStruct(
{'subdivisions':row([self.pooled_w, self.pooled_h]),
'transform':self.spatial_scale,
'flatten':self.flatten})
return mlayer
def toMatlabSimpleNN(self):
mlayer = super(CaffeROIPooling, self).toMatlabSimpleNN()
mlayer['type'] = u'roipool'
mlayer['subdivisions'] = row([self.pooled_w, self.pooled_h])
mlayer['transform'] = self.spatial_scale
mlayer['flatten'] = self.flatten
return mlayer
# --------------------------------------------------------------------
# Scale
# --------------------------------------------------------------------
class CaffeScale(CaffeLayer):
def __init__(self, name, inputs, outputs,
axis,
num_axes,
bias_term):
super(CaffeScale, self).__init__(name, inputs, outputs)
self.axis = axis
self.num_axes = num_axes
self.bias_term = bias_term
if len(self.inputs) == 1:
self.params.append(name + '_mult')
if len(self.inputs) < 2 and self.bias_term:
self.params.append(name + '_bias')
self.mult_size = [0, 0, 0, 0]
def display(self):
super(CaffeScale, self).display()
print " +- mult_size: %s" % (self.mult_size,)
print " c- axis: %s" % (self.axis,)
print " c- num_axes: %s" % (self.num_axes,)
print " c- bias_term: %s" % (self.bias_term,)
def reshape(self, model):
model.vars[self.outputs[0]].shape = model.vars[self.inputs[0]].shape
def setBlob(self, model, i, blob):
assert(i < self.bias_term + 1)
# Caffe *ends* with WIDTH, we start with it, blobs are already swapped here
k = 3 - self.axis
# This means that the MULT dimensions are aligned to the INPUT
# dimensions such that MULT[end] <-> INPUT[k]. For MatConvNet,
# we simply add singletion dimensions at the beginning of MULT
# to achieve this effect. BIAS is the same.
mshape = tuple([1] * (k - len(blob.shape) + 1) + list(blob.shape))
blob = blob.reshape(mshape)
model.params[self.params[i]].value = blob
model.params[self.params[i]].shape = blob.shape
if i == 0: self.mult_size = blob.shape
def getTransforms(self, model):
# The second input can be either a variable or a paramter; in
# both cases, there is no transform for it
return [[CaffeTransform([1.,1.], [1.,1.], [1.,1.])]]
def transpose(self, model):
if len(self.inputs) == 1:
# we only need to transpose if the scale is a parameter, not an input
for i in range(1 + self.bias_term):
param = model.params[self.params[i]]
n = len(param.shape)
if n >= 2:
order = range(n)
order[0] = 1
order[1] = 0
param.value = param.value.transpose(order)
def toMatlab(self):
mlayer = super(CaffeScale, self).toMatlab()
mlayer['type'][0] = u'dagnn.Scale'
mlayer['block'][0] = dictToMatlabStruct(
{'size': row(self.mult_size),
'hasBias': self.bias_term})
return mlayer
def toMatlabSimpleNN(self):
mlayer = super(CaffeScale, self).toMatlabSimpleNN()
# SimpleNN works only if the scaling blob is a parameter (and not a variable)
mlayer['type'] = u'scale'
mlayer['size'] = row(self.mult_size)
mlayer['hasBias'] = self.bias_term
return mlayer
# --------------------------------------------------------------------
# BatchNorm
# --------------------------------------------------------------------
class CaffeBatchNorm(CaffeLayer):
def __init__(self, name, inputs, outputs, use_global_stats, moving_average_fraction, eps):
super(CaffeBatchNorm, self).__init__(name, inputs, outputs)
self.use_global_stats = use_global_stats
self.moving_average_fraction = moving_average_fraction
self.eps = eps
self.params = [name + u'_mean',
name + u'_variance',
name + u'_scale_factor']
def display(self):
super(CaffeBatchNorm, self).display()
print " c- use_global_stats: %s" % (self.use_global_stats,)
print " c- moving_average_fraction: %s" % (self.moving_average_fraction,)
print " c- eps: %s" % (self.eps)
def setBlob(self, model, i, blob):
assert(i < 3)
model.params[self.params[i]].value = blob
model.params[self.params[i]].shape = blob.shape
def reshape(self, model):
shape = model.vars[self.inputs[0]].shape
mean = model.params[self.params[0]].value
variance = model.params[self.params[1]].value
scale_factor = model.params[self.params[2]].value
for i in range(3): del model.params[self.params[i]]
self.params = [self.name + u'_mult',
self.name + u'_bias',
self.name + u'_moments']
model.addParam(self.params[0])
model.addParam(self.params[1])
model.addParam(self.params[2])
if shape:
mult = np.ones((shape[2],),dtype='float32')
bias = np.zeros((shape[2],),dtype='float32')
model.params[self.params[0]].value = mult
model.params[self.params[0]].shape = mult.shape
model.params[self.params[1]].value = bias
model.params[self.params[1]].shape = bias.shape
if mean.size:
moments = np.concatenate(
(mean.reshape(-1,1) / scale_factor,
np.sqrt(variance.reshape(-1,1) / scale_factor + self.eps)),
axis=1)
model.params[self.params[2]].value = moments
model.params[self.params[2]].shape = moments.shape
model.vars[self.outputs[0]].shape = shape
def toMatlab(self):
mlayer = super(CaffeBatchNorm, self).toMatlab()
mlayer['type'][0] = u'dagnn.BatchNorm'
mlayer['block'][0] = dictToMatlabStruct(
{'epsilon': self.eps})
return mlayer
def toMatlabSimpleNN(self):
mlayer = super(CaffeBatchNorm, self).toMatlabSimpleNN()
mlayer['type'] = u'bnorm'
mlayer['epsilon'] = self.eps
return mlayer
# --------------------------------------------------------------------
# Concat
# --------------------------------------------------------------------
class CaffeConcat(CaffeLayer):
def __init__(self, name, inputs, outputs, concatDim):
super(CaffeConcat, self).__init__(name, inputs, outputs)
self.concatDim = concatDim
def transpose(self, model):
self.concatDim = [1, 0, 2, 3][self.concatDim]
def reshape(self, model):
sizes = [model.vars[x].shape for x in self.inputs]
osize = copy.deepcopy(sizes[0])
osize[self.concatDim] = 0
for thisSize in sizes:
for i in range(len(thisSize)):
if self.concatDim == i:
osize[i] = osize[i] + thisSize[i]
else:
if osize[i] != thisSize[i]:
print "Warning: concat layer: inconsistent input dimensions", sizes
model.vars[self.outputs[0]].shape = osize
def display(self):
super(CaffeConcat, self).display()
print " Concat Dim: ", self.concatDim
def toMatlab(self):
mlayer = super(CaffeConcat, self).toMatlab()
mlayer['type'][0] = u'dagnn.Concat'
mlayer['block'][0] = dictToMatlabStruct({'dim': float(self.concatDim) + 1})
return mlayer
def toMatlabSimpleNN(self):
raise ConversionError('Concat layers do not work in a SimpleNN network')
# --------------------------------------------------------------------
# EltWise (Sum, ...)
# --------------------------------------------------------------------
class CaffeEltWise(CaffeElementWise):
def __init__(self, name, inputs, outputs,
operation,
coeff,
stable_prod_grad):
super(CaffeEltWise, self).__init__(name, inputs, outputs)
self.operation = operation
self.coeff = coeff
self.stable_prod_grad = stable_prod_grad
def toMatlab(self):
mlayer = super(CaffeEltWise, self).toMatlab()
if self.operation == 'sum':
mlayer['type'][0] = u'dagnn.Sum'
else:
# not implemented
assert(False)
return mlayer
def display(self):
super(CaffeEltWise, self).display()
print " c- operation: ", self.operation
print " c- coeff: %s" % self.coeff
print " c- stable_prod_grad: %s" % self.stable_prod_grad
def reshape(self, model):
model.vars[self.outputs[0]].shape = \
model.vars[self.inputs[0]].shape
for i in range(1, len(self.inputs)):
assert(model.vars[self.inputs[0]].shape == model.vars[self.inputs[i]].shape)
def toMatlabSimpleNN(self):
raise ConversionError('EltWise (sum, ...) layers do not work in a SimpleNN network')
# --------------------------------------------------------------------
# Crop
# --------------------------------------------------------------------
class CaffeCrop(CaffeLayer):
def __init__(self, name, inputs, outputs):
super(CaffeCrop, self).__init__(name, inputs, outputs)
self.crop = []
def display(self):
super(CaffeCrop, self).display()
print " Crop: %s" % self.crop
def reshape(self, model):
# this is quite complex as we need to compute on the fly
# the geometry
tfs1 = model.getParentTransforms(self.inputs[0], self.name)
tfs2 = model.getParentTransforms(self.inputs[1], self.name)
print
print self.name, self.inputs[0]
for a,x in enumerate(tfs1): print "%10s %s" % (x,tfs1[x])
print self.name, self.inputs[1]
for a,x in enumerate(tfs2): print "%10s %s" % (x,tfs2[x])
# the goal is to crop inputs[0] to make it as big as inputs[1] and
# aligned to it; so now we find the map from inputs[0] to inputs[1]
tf = None
for name, tf2 in tfs2.items():
if tfs1.has_key(name):
tf1 = tfs1[name]
tf = composeTransforms(transposeTransform(tf2), tf1)
break
if tf is None:
print "Error: could not find common ancestor for inputs '%s' and '%s' of the CaffeCrop layer '%s'" % (self.inputs[0], self.inputs[1], self.name)
sys.exit(1)
print " Transformation %s -> %s = %s" % (self.inputs[0],
self.inputs[1], tf)
# for this to make sense it shoudl be tf.stride = 1
assert(tf.stride[0] == 1 and tf.stride[1] == 1)
# finally we can get the crops!
self.crop = [0.,0.]
for i in [0,1]:
# i' = alpha (i - 1) + beta + crop = 1 for i = 1
# crop = 1 - beta
self.crop[i] = round(1 - tf.offset[i])
print " Crop %s" % self.crop
# print
# print "resolved"
# tfs3 = model.getParentTransforms(self.outputs[0])
# for a,x in enumerate(tfs3): print "%10s %s" % (x,tfs3[x])
# now compute output variable size, which will be the size of the second input
model.vars[self.outputs[0]].shape = model.vars[self.inputs[1]].shape
def getTransforms(self, model):
t = CaffeTransform([1.,1.], [1.,1.], [1.+self.crop[0],1.+self.crop[1]])
return [[t],[None]]
def toMatlab(self):
mlayer = super(CaffeCrop, self).toMatlab()
mlayer['type'][0] = u'dagnn.Crop'
mlayer['block'][0] = dictToMatlabStruct({'crop': row(self.crop)})
return mlayer
def toMatlabSimpleNN(self):
# todo: simple 1 input crop layers should be supported though!
raise ConversionError('Crop layers do not work in a SimpleNN network')
# --------------------------------------------------------------------
# Caffe Model
# --------------------------------------------------------------------
class CaffeModel(object):
def __init__(self):
self.layers = OrderedDict()
self.vars = OrderedDict()
self.params = OrderedDict()
def addLayer(self, layer):
ename = layer.name
while self.layers.has_key(ename):
ename = ename + 'x'
if layer.name != ename:
print "Warning: a layer with name %s was already found, using %s instead" % \
(layer.name, ename)
layer.name = ename
for v in layer.inputs: self.addVar(v)
for v in layer.outputs: self.addVar(v)
for p in layer.params: self.addParam(p)
self.layers[layer.name] = layer
def addVar(self, name):
if not self.vars.has_key(name):
self.vars[name] = CaffeBlob(name)
def addParam(self, name):
if not self.params.has_key(name):
self.params[name] = CaffeBlob(name)
def renameLayer(self, old, new):
self.layers[old].name = new
# reinsert layer with new name -- this mess is to preserve the order
layers = OrderedDict([(new,v) if k==old else (k,v)
for k,v in self.layers.items()])
self.layers = layers
def renameVar(self, old, new, afterLayer=None):
self.vars[old].name = new
if afterLayer is not None:
start = self.layers.keys().index(afterLayer) + 1
else:
start = 0
# fix all references to the variable
for layer in self.layers.values()[start:-1]:
layer.inputs = [new if x==old else x for x in layer.inputs]
layer.outputs = [new if x==old else x for x in layer.outputs]
self.vars[new] = copy.deepcopy(self.vars[old])
# check if we can delete the old one (for afterLayet != None)
stillUsed = False
for layer in self.layers.values():
stillUsed = stillUsed or old in layer.inputs or old in layer.outputs
if not stillUsed:
del self.vars[old]
def renameParam(self, old, new):
self.params[old].name = new
# fix all references to the variable
for layer in self.layers.itervalues():
layer.params = [new if x==old else x for x in layer.params]
var = self.params[old]
del self.params[old]
self.params[new] = var
def removeParam(self, name):
del self.params[name]
def removeLayer(self, name):
# todo: fix this stuff for weight sharing
layer = self.layers[name]
for paramName in layer.params:
self.removeParam(paramName)
del self.layers[name]
def getLayersWithOutput(self, varName):
layerNames = []
for layer in self.layers.itervalues():
if varName in layer.outputs:
layerNames.append(layer.name)
return layerNames
def getLayersWithInput(self, varName):
layerNames = []
for layer in self.layers.itervalues():
if varName in layer.inputs:
layerNames.append(layer.name)
return layerNames
def reshape(self):
for layer in self.layers.itervalues():
layer.reshape(self)
def display(self):
for layer in self.layers.itervalues():
layer.display()
for var in self.vars.itervalues():
print 'Variable \'{}\''.format(var.name)
print ' + shape (computed): %s' % (var.shape,)
for par in self.params.itervalues():
print 'Parameter \'{}\''.format(par.name)
print ' + data found: %s' % (par.shape is not None)
print ' + data shape: %s' % (par.shape,)
def transpose(self):
for var in self.vars.itervalues():
if var.transposable: var.transpose()
for layer in self.layers.itervalues():
layer.transpose(self)
def getParentTransforms(self, variableName, topLayerName=None):
layerNames = self.layers.keys()
if topLayerName:
layerIndex = layerNames.index(topLayerName)
else:
layerIndex = len(self.layers) + 1
transforms = OrderedDict()
transforms[variableName] = CaffeTransform([1.,1.], [1.,1.], [1.,1.])
for layerName in reversed(layerNames[0:layerIndex]):
layer = self.layers[layerName]
layerTfs = layer.getTransforms(self)
for i, inputName in enumerate(layer.inputs):
tfs = []
if transforms.has_key(inputName):
tfs.append(transforms[inputName])
for j, outputName in enumerate(layer.outputs):
if layerTfs[i][j] is None: continue
if transforms.has_key(outputName):
composed = composeTransforms(layerTfs[i][j], transforms[outputName])
tfs.append(composed)
if len(tfs) > 0:
# should resolve conflicts, not simply pick the first tf
transforms[inputName] = tfs[0]
return transforms
| 43,791 | 36.493151 | 156 | py |
DRT | DRT-master/external_libs/matconvnet/matconvnet/utils/import-caffe.py | #! /usr/bin/python
# file: import-caffe.py
# brief: Caffe importer for DagNN and SimpleNN
# author: Karel Lenc and Andrea Vedaldi
# Requires Google Protobuf for Python and SciPy
import sys
import os
import argparse
import code
import re
import numpy as np
from math import floor, ceil
import numpy
from numpy import array
import scipy
import scipy.io
import scipy.misc
import google.protobuf.text_format
from ast import literal_eval as make_tuple
from layers import *
# --------------------------------------------------------------------
# Check NumPy version
# --------------------------------------------------------------------
def versiontuple(version):
return tuple(map(int, (version.split("."))))
min_numpy_version = "1.7.0"
if versiontuple(numpy.version.version) < versiontuple(min_numpy_version):
print 'Unsupported numpy version ({}), must be >= {}'.format(numpy.version.version,
min_numpy_version)
sys.exit(0)
# --------------------------------------------------------------------
# Helper functions
# --------------------------------------------------------------------
def find(seq, name):
for item in seq:
if item.name == name:
return item
return None
def blobproto_to_array(blob):
"""Convert a Caffe Blob to a numpy array.
It also reverses the order of all dimensions to [width, height,
channels, instance].
"""
dims = []
if hasattr(blob, 'shape'):
dims = tolist(blob.shape.dim)
if not dims:
dims = [blob.num, blob.channels, blob.height, blob.width]
return np.array(blob.data,dtype='float32').reshape(dims).transpose()
def dict_to_struct_array(d):
if not d:
return np.zeros((0,))
dt=[(x,object) for x in d.keys()]
y = np.empty((1,),dtype=dt)
for x in d.keys():
y[x][0] = d[x]
return y
def tolist(x):
"Convert x to a Python list. x can be a Protobuf container, a list or tuple, or scalar"
if isinstance(x,google.protobuf.internal.containers.RepeatedScalarFieldContainer):
return [z for z in x]
elif isinstance(x, (list,tuple)):
return [z for z in x]
else:
return [x]
def escape(name):
return name.replace('-','_')
# --------------------------------------------------------------------
# Parse options
# --------------------------------------------------------------------
parser = argparse.ArgumentParser(description='Convert a Caffe CNN into a MATLAB structure.')
parser.add_argument('caffe_proto',
type=argparse.FileType('rb'),
help='The Caffe CNN parameter file (ASCII .proto)')
parser.add_argument('--caffe-data',
type=argparse.FileType('rb'),
help='The Caffe CNN data file (binary .proto)')
parser.add_argument('output',
type=argparse.FileType('w'),
help='Output MATLAB file')
parser.add_argument('--full-image-size',
type=str,
nargs='?',
default=None,
help='Size of the full image')
parser.add_argument('--average-image',
type=argparse.FileType('rb'),
nargs='?',
help='Average image')
parser.add_argument('--average-value',
type=str,
nargs='?',
default=None,
help='Average image value')
parser.add_argument('--synsets',
type=argparse.FileType('r'),
nargs='?',
help='Synset file (ASCII)')
parser.add_argument('--class-names',
type=str,
nargs='?',
help='Class names')
parser.add_argument('--caffe-variant',
type=str,
nargs='?',
default='caffe',
help='Variant of Caffe software (use ? to get a list)')
parser.add_argument('--transpose',
dest='transpose',
action='store_true',
help='Transpose CNN in a sane MATLAB format')
parser.add_argument('--no-transpose',
dest='transpose',
action='store_false',
help='Do not transpose CNN')
parser.add_argument('--color-format',
dest='color_format',
default='bgr',
action='store',
help='Set the color format used by the network: ''rgb'' or ''bgr'' (default)')
parser.add_argument('--preproc',
type=str,
nargs='?',
default='caffe',
help='Variant of image preprocessing to use (use ? to get a list)')
parser.add_argument('--simplify',
dest='simplify',
action='store_true',
help='Apply simplifications')
parser.add_argument('--no-simplify',
dest='simplify',
action='store_false',
help='Do not apply simplifications')
parser.add_argument('--remove-dropout',
dest='remove_dropout',
action='store_true',
help='Remove dropout layers')
parser.add_argument('--no-remove-dropout',
dest='remove_dropout',
action='store_false',
help='Do not remove dropout layers')
parser.add_argument('--remove-loss',
dest='remove_loss',
action='store_true',
help='Remove loss layers')
parser.add_argument('--no-remove-loss',
dest='remove_loss',
action='store_false',
help='Do not remove loss layers')
parser.add_argument('--append-softmax',
dest='append_softmax',
action='append',
default=[],
help='Add a softmax layer after the specified layer')
parser.add_argument('--output-format',
dest='output_format',
default='dagnn',
help='Either ''dagnn'' or ''simplenn''')
parser.set_defaults(transpose=True)
parser.set_defaults(remove_dropout=False)
parser.set_defaults(remove_loss=False)
parser.set_defaults(simplify=True)
args = parser.parse_args()
print 'Caffe varaint set to', args.caffe_variant
if args.caffe_variant == 'vgg-caffe':
import proto.vgg_caffe_pb2 as caffe_pb2
elif args.caffe_variant == 'caffe-old':
import proto.caffe_old_pb2 as caffe_pb2
elif args.caffe_variant == 'caffe':
import proto.caffe_pb2 as caffe_pb2
elif args.caffe_variant == 'caffe_0115':
import proto.caffe_0115_pb2 as caffe_pb2
elif args.caffe_variant == 'caffe_6e3916':
import proto.caffe_6e3916_pb2 as caffe_pb2
elif args.caffe_variant == 'caffe_b590f1d':
import proto.caffe_b590f1d_pb2 as caffe_pb2
elif args.caffe_variant == 'caffe_fastrcnn':
import proto.caffe_fastrcnn_pb2 as caffe_pb2
elif args.caffe_variant == '?':
print 'Supported variants: caffe, vgg-caffe, caffe-old, caffe_0115, caffe_6e3916, caffe_b590f1d, caffe_fastrcnn'
sys.exit(0)
else:
print 'Unknown Caffe variant', args.caffe_variant
sys.exit(1)
if args.preproc == '?':
print 'Preprocessing variants: caffe, vgg, fcn'
sys.exit(0)
if args.preproc not in ['caffe', 'vgg-caffe', 'fcn']:
print 'Unknown preprocessing variant', args.preproc
sys.exit(1)
# --------------------------------------------------------------------
# Helper functions
# --------------------------------------------------------------------
def keyboard(banner=None):
''' Function that mimics the matlab keyboard command '''
# use exception trick to pick up the current frame
try:
raise None
except:
frame = sys.exc_info()[2].tb_frame.f_back
print "# Use quit() to exit :) Happy debugging!"
# evaluate commands in current namespace
namespace = frame.f_globals.copy()
namespace.update(frame.f_locals)
try:
code.interact(banner=banner, local=namespace)
except SystemExit:
return
def bilinear_interpolate(im, x, y):
x = np.asarray(x)
y = np.asarray(y)
x0 = np.floor(x).astype(int)
x1 = x0 + 1
y0 = np.floor(y).astype(int)
y1 = y0 + 1
x0 = np.clip(x0, 0, im.shape[1]-1);
x1 = np.clip(x1, 0, im.shape[1]-1);
y0 = np.clip(y0, 0, im.shape[0]-1);
y1 = np.clip(y1, 0, im.shape[0]-1);
Ia = im[ y0, x0 ]
Ib = im[ y1, x0 ]
Ic = im[ y0, x1 ]
Id = im[ y1, x1 ]
wa = (1-x+x0) * (1-y+y0)
wb = (1-x+x0) * (y-y0)
wc = (x-x0) * (1-y+y0)
wd = (x-x0) * (y-y0)
wa = wa.reshape(x.shape[0], x.shape[1], 1)
wb = wb.reshape(x.shape[0], x.shape[1], 1)
wc = wc.reshape(x.shape[0], x.shape[1], 1)
wd = wd.reshape(x.shape[0], x.shape[1], 1)
return wa*Ia + wb*Ib + wc*Ic + wd*Id
# Get the parameters for a layer from Caffe's proto entries
def getopts(layer, name):
if hasattr(layer, name):
return getattr(layer, name)
else:
# Older Caffe proto formats did not have sub-structures for layer
# specific parameters but mixed everything up! This falls back to
# that situation when fetching the parameters.
return layer
# --------------------------------------------------------------------
# Load average image
# --------------------------------------------------------------------
average_image = None
resize_average_image = False
if args.average_image:
print 'Loading average image from {}'.format(args.average_image.name)
resize_average_image = True # in case different from data size
avgim_nm, avgim_ext = os.path.splitext(args.average_image.name)
if avgim_ext == '.binaryproto':
blob=caffe_pb2.BlobProto()
blob.MergeFromString(args.average_image.read())
average_image = blobproto_to_array(blob).astype('float32')
average_image = np.squeeze(average_image,3)
if args.transpose and average_image is not None:
average_image = average_image.transpose([1,0,2])
average_image = average_image[:,:,: : -1] # to RGB
elif avgim_ext == '.mat':
avgim_data = scipy.io.loadmat(args.average_image)
average_image = avgim_data['mean_img']
else:
print 'Unsupported average image format {}'.format(avgim_ext)
if args.average_value:
rgb = make_tuple(args.average_value)
print 'Using average image value', rgb
# this will be resized later to a constant image
average_image = np.array(rgb,dtype=float).reshape(1,1,3,order='F')
resize_average_image = False
# --------------------------------------------------------------------
# Load ImageNet synseths (if any)
# --------------------------------------------------------------------
synsets_wnid=None
synsets_name=None
if args.synsets:
print 'Loading synsets from {}'.format(args.synsets.name)
r=re.compile('(?P<wnid>n[0-9]{8}?) (?P<name>.*)')
synsets_wnid=[]
synsets_name=[]
for line in args.synsets:
match = r.match(line)
synsets_wnid.append(match.group('wnid'))
synsets_name.append(match.group('name'))
if args.class_names:
synsets_wnid=list(make_tuple(args.class_names))
synsets_name=synsets_wnid
# --------------------------------------------------------------------
# Load layers
# --------------------------------------------------------------------
# Caffe stores the network structure and data into two different files
# We load them both and merge them into a single MATLAB structure
net=caffe_pb2.NetParameter()
data=caffe_pb2.NetParameter()
print 'Loading Caffe CNN structure from {}'.format(args.caffe_proto.name)
google.protobuf.text_format.Merge(args.caffe_proto.read(), net)
if args.caffe_data:
print 'Loading Caffe CNN parameters from {}'.format(args.caffe_data.name)
data.MergeFromString(args.caffe_data.read())
# --------------------------------------------------------------------
# Read layers in a CaffeModel object
# --------------------------------------------------------------------
if args.caffe_variant in ['caffe_b590f1d', 'caffe_fastrcnn']:
layers_list = net.layer
data_layers_list = data.layer
else:
layers_list = net.layers
data_layers_list = data.layers
print 'Converting {} layers'.format(len(layers_list))
cmodel = CaffeModel()
for layer in layers_list:
# Depending on how old the proto-buf, the top and bottom parameters
# are found at a different level than the others
top = layer.top
bottom = layer.bottom
if args.caffe_variant in ['vgg-caffe', 'caffe-old']:
layer = layer.layer
# get the type of layer
# depending on the Caffe variant, this is a string or a numeric
# ID, which we convert back to a string
ltype = layer.type
if not isinstance(ltype, basestring): ltype = layers_type[ltype]
print 'Added layer \'{}\' ({})'.format(ltype, layer.name)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
if ltype in ['conv', 'deconvolution', 'Convolution', 'Deconvolution']:
opts = getopts(layer, 'convolution_param')
if hasattr(opts, 'kernelsize'):
kernel_size = opts.kernelsize
else:
kernel_size = opts.kernel_size
if hasattr(opts, 'bias_term'):
bias_term = opts.bias_term
else:
bias_term = True
if hasattr(opts, 'dilation'):
dilation = opts.dilation
else:
dilation = 1
if ltype in ['conv', 'Convolution']:
clayer = CaffeConv(layer.name, bottom, top,
kernel_size = tolist(kernel_size),
bias_term = bias_term,
num_output = opts.num_output,
group = opts.group,
dilation = dilation,
stride = tolist(opts.stride),
pad = tolist(opts.pad))
else:
clayer = CaffeDeconvolution(layer.name, bottom, top,
kernel_size = tolist(kernel_size),
bias_term = bias_term,
num_output = opts.num_output,
group = opts.group,
dilation = dilation,
stride = tolist(opts.stride),
pad = tolist(opts.pad))
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['innerproduct', 'inner_product', 'InnerProduct']:
opts = getopts(layer, 'inner_product_param')
if hasattr(opts, 'bias_term'):
bias_term = opts.bias_term
else:
bias_term = True
if hasattr(opts, 'axis'):
axis = opts.axis
else:
axis = 1
clayer = CaffeInnerProduct(layer.name, bottom, top,
num_output = opts.num_output,
bias_term = bias_term,
axis = axis)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['relu', 'ReLU']:
clayer = CaffeReLU(layer.name, bottom, top)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['crop', 'Crop']:
clayer = CaffeCrop(layer.name, bottom, top)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['lrn', 'LRN']:
opts = getopts(layer, 'lrn_param')
local_size = float(opts.local_size)
alpha = float(opts.alpha)
beta = float(opts.beta)
kappa = opts.k if hasattr(opts,'k') else 1.
regions = ['across_channels', 'within_channel']
if hasattr(opts, 'norm_region'):
norm_region = opts.norm_region
else:
norm_region = 0
clayer = CaffeLRN(layer.name, bottom, top,
local_size = local_size,
alpha = alpha,
beta = beta,
norm_region = regions[norm_region],
kappa = kappa)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['pool', 'Pooling']:
opts = getopts(layer, 'pooling_param')
if hasattr(layer, 'kernelsize'):
kernel_size = opts.kernelsize
else:
kernel_size = opts.kernel_size
clayer = CaffePooling(layer.name, bottom, top,
method = ['max', 'avg'][opts.pool],
pad = tolist(opts.pad),
kernel_size = tolist(kernel_size),
stride = tolist(opts.stride))
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['dropout', 'Dropout']:
opts = getopts(layer, 'dropout_param')
clayer = CaffeDropout(layer.name, bottom, top,
opts.dropout_ratio)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['softmax', 'Softmax']:
clayer = CaffeSoftMax(layer.name, bottom, top)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['softmax_loss', 'SoftmaxLoss']:
clayer = CaffeSoftMaxLoss(layer.name, bottom, top)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['concat', 'Concat']:
opts = getopts(layer, 'concat_param')
clayer = CaffeConcat(layer.name, bottom, top,
3 - opts.concat_dim) # todo: depreceted in recent Caffes
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['Scale']:
opts = getopts(layer, 'scale_param')
clayer = CaffeScale(layer.name, bottom, top,
axis = opts.axis,
num_axes = opts.num_axes,
bias_term = opts.bias_term)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['BatchNorm']:
opts = getopts(layer, 'batch_norm_param')
clayer = CaffeBatchNorm(layer.name, bottom, top,
use_global_stats = opts.use_global_stats,
moving_average_fraction = opts.moving_average_fraction,
eps = opts.eps)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['eltwise', 'Eltwise']:
opts = getopts(layer, 'eltwise_param')
operations = ['prod', 'sum', 'max']
clayer = CaffeEltWise(layer.name, bottom, top,
operation = operations[opts.operation],
coeff = opts.coeff,
stable_prod_grad = opts.stable_prod_grad)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['data', 'Data']:
opts = getopts(layer, 'eltwise_param')
operations = ['prod', 'sum', 'max']
clayer = CaffeData(layer.name, bottom, top,
operation = operations[opts.operation],
coeff = opts.coeff,
stable_prod_grad = opts.stable_prod_grad)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['roipooling', 'ROIPooling']:
opts = getopts(layer, 'roi_pooling_param')
clayer = CaffeROIPooling(layer.name, bottom, top,
pooled_w = opts.pooled_w,
pooled_h = opts.pooled_h,
spatial_scale = opts.spatial_scale)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['accuracy', 'Accuracy']:
continue
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
else:
print 'Warning: unknown layer type', ltype
continue
if clayer is not None:
clayer.model = cmodel
cmodel.addLayer(clayer)
# Fill parameters
for dlayer in data_layers_list:
if args.caffe_variant in ['vgg-caffe', 'caffe-old']:
dlayer = dlayer.layer
if dlayer.name == layer.name:
for i, blob in enumerate(dlayer.blobs):
blob = blobproto_to_array(blob).astype('float32')
print ' + parameter \'%s\' <-- blob%s' % (clayer.params[i], blob.shape)
clayer.setBlob(cmodel, i, blob)
# --------------------------------------------------------------------
# Get the size of the network variables
# --------------------------------------------------------------------
# Get the sizes of the network inputs
for i, inputVarName in enumerate(net.input):
if hasattr(net, 'input_shape') and net.input_shape:
shape = net.input_shape[i].dim._values
# ensure that shape is a list of dimensions
if isinstance(shape, caffe_pb2.BlobShape):
# shape.tolist() may not preserve the order of dimensions
shape = shape.dim._values
shape.reverse()
else:
shape = [net.input_dim[k + 4*i] for k in [3,2,1,0]]
cmodel.vars[inputVarName].shape = shape
print ' c- Input \'{}\' is {}'.format(inputVarName, shape)
# --------------------------------------------------------------------
# Sanitize
# --------------------------------------------------------------------
# Rename layers, parametrs, and variables if they contain symbols that
# are incompatible with MatConvNet.
layerNames = cmodel.layers.keys()
for name in layerNames:
ename = escape(name)
if ename == name: continue
# ensure unique
while cmodel.layers.has_key(ename): ename = ename + 'x'
print "Renaming layer {} to {}".format(name, ename)
cmodel.renameLayer(name, ename)
varNames = cmodel.vars.keys()
for name in varNames:
ename = escape(name)
if ename == name: continue
while cmodel.vars.has_key(ename): ename = ename + 'x'
print "Renaming variable {} to {}".format(name, ename)
cmodel.renameVar(name, ename)
parNames = cmodel.params.keys()
for name in parNames:
ename = escape(name)
if ename == name: continue
while cmodel.params.has_key(ename): ename = ename + 'x'
print "Renaming parameter {} to {}".format(name, ename)
cmodel.renameParam(name, ename)
# Split in-place layers. MatConvNet handles such optimizations
# differently.
for layer in cmodel.layers.itervalues():
if len(layer.inputs[0]) >= 1 and \
len(layer.outputs[0]) >= 1 and \
layer.inputs[0] == layer.outputs[0]:
name = layer.inputs[0]
ename = layer.inputs[0]
while cmodel.vars.has_key(ename): ename = ename + 'x'
print "Splitting in-place layer: renaming variable {} to {}".format(name, ename)
cmodel.addVar(ename)
cmodel.renameVar(name, ename, afterLayer=layer.name)
layer.inputs[0] = name
layer.outputs[0] = ename
# --------------------------------------------------------------------
# Get variable sizes
# --------------------------------------------------------------------
# Get the size of all other variables. This information is required
# for some special layer conversions:
#
# * For Pooling layers, fix incompatibility between padding in
# MatConvNet and Caffe.
#
# * For Crop layers (in FCNs), determine the amount of crop (in Caffe
# this is done at run time).
# Unflatten ROIPooling. ROIPooling will produce a H x W array instead
# of a stacked version of the same. The reshape operation below will
# convert the following InnerProduct layers in corresponding
# convolitions. This works well with transposition later.
layerNames = cmodel.layers.keys()
for name in layerNames:
layer = cmodel.layers[name]
if type(layer) is CaffeROIPooling:
childrenNames = cmodel.getLayersWithInput(layer.outputs[0])
for childName in childrenNames:
child = cmodel.layers[childName]
if type(child) is not CaffeInnerProduct:
print "Error: cannot unflatten ROIPooling if this is not followed only InnerProduct layers"
sys.exit(1)
layer.flatten = False
cmodel.reshape()
# --------------------------------------------------------------------
# Edit
# --------------------------------------------------------------------
# Remove dropout
if args.remove_dropout:
layerNames = cmodel.layers.keys()
for name in layerNames:
layer = cmodel.layers[name]
if type(layer) is CaffeDropout:
print "Removing dropout layer ", name
cmodel.renameVar(layer.outputs[0], layer.inputs[0])
cmodel.removeLayer(name)
# Remove loss
if args.remove_loss:
layerNames = cmodel.layers.keys()
for name in layerNames:
layer = cmodel.layers[name]
if type(layer) is CaffeSoftMaxLoss:
print "Removing loss layer ", name
cmodel.renameVar(layer.outputs[0], layer.inputs[0])
cmodel.removeLayer(name)
# Append softmax
for i, name in enumerate(args.append_softmax):
# search for the layer to append SoftMax to
if not cmodel.layers.has_key(name):
print 'Cannot append softmax to layer {} as no such layer could be found'.format(name)
sys.exit(1)
if len(args.append_softmax) > 1:
layerName = 'softmax' + (l + 1)
outputs= ['prob' + (l + 1)]
else:
layerName = 'softmax'
outputs = ['prob']
cmodel.addLayer(CaffeSoftMax(layerName,
cmodel.layers[name].outputs[0:1],
outputs))
# Simplifications
if args.simplify:
# Merge BatchNorm followed by Scale
layerNames = cmodel.layers.keys()
for name in layerNames:
layer = cmodel.layers[name]
if type(layer) is CaffeScale:
if len(layer.inputs) > 1:
continue # the scaling factor is an input, not a parameter
if len(cmodel.getLayersWithInput(layer.inputs[0])) > 1:
continue # other layers use the same input
parentNames = cmodel.getLayersWithOutput(layer.inputs[0])
if len(parentNames) != 1: continue
parent = cmodel.layers[parentNames[0]]
if type(parent) is not CaffeBatchNorm: continue
smult = cmodel.params[layer.params[0]]
sbias = cmodel.params[layer.params[1]]
mult = cmodel.params[parent.params[0]]
bias = cmodel.params[parent.params[1]]
# simplification can only occur if scale layer is 1x1xC
if smult.shape[0] != 1 or smult.shape[1] != 1: continue
C = smult.shape[2]
mult.value = np.reshape(smult.value, (C,)) * mult.value
bias.value = np.reshape(smult.value, (C,)) * bias.value + \
np.reshape(sbias.value, (C,))
print "Simplifying scale layer \'{}\'".format(name)
cmodel.renameVar(layer.outputs[0], layer.inputs[0])
cmodel.removeLayer(name)
# --------------------------------------------------------------------
# Transposition
# --------------------------------------------------------------------
#
# There are a few different conventions in MATLAB and Caffe:
#
# * In MATLAB, the frist spatial dimension is Y (vertical) followed by
# X (horizontal), whereas in Caffe the opposite is true.
#
# * In MATLAB, images are stored in RGB format, whereas Caffe uses
# BGR.
#
# * In MatConvNet, the first spatial coordinate is Y, whereas in Caffe
# it is X. This affects layers such as ROI pooling.
#
# These conventions means that, if the network is directly saved in
# MCN format, then images and spatial coordinates are transposed as
# just described. While this is not a deal breaker, it is
# inconvenient.
#
# Thus we transpose all X,Y spatial dimensions in the network. For now,
# this is partially heuristic. In the future, we should add adapter layer to
# convert from MCN inputs and outputs to Caffe input and outputs and then
# simplity those away using graph transformations.
# Mark variables:
# - requiring BGR -> RGB conversion
# - requiring XY transposition
for i, inputVarName in enumerate(net.input):
if inputVarName == 'data' or i == 0:
if cmodel.vars[inputVarName].shape[2] == 3:
cmodel.vars[inputVarName].bgrInput = (args.color_format == 'bgr')
if not inputVarName == 'rois':
cmodel.vars[inputVarName].transposable = True
else:
cmodel.vars[inputVarName].transposable = False
# Apply transformations
if args.transpose: cmodel.transpose()
cmodel.display()
# --------------------------------------------------------------------
# Normalization
# --------------------------------------------------------------------
minputs = np.empty(shape=[0,], dtype=minputdt)
# Determine the size of the inputs and input image (dataShape)
for i, inputVarName in enumerate(net.input):
shape = cmodel.vars[inputVarName].shape
# add metadata
minput = np.empty(shape=[1,], dtype=minputdt)
minput['name'][0] = inputVarName
minput['size'][0] = row(shape)
minputs = np.append(minputs, minput, axis=0)
# heuristic: the first input or 'data' is the input image
if i == 0 or inputVarName == 'data':
dataShape = shape
print "Input image data tensor shape:", dataShape
fullImageSize = [256, 256]
if args.full_image_size:
fullImageSize = list(make_tuple(args.full_image_size))
print "Full input image size:", fullImageSize
if average_image is not None:
if resize_average_image:
x = numpy.linspace(0, average_image.shape[1]-1, dataShape[0])
y = numpy.linspace(0, average_image.shape[0]-1, dataShape[1])
x, y = np.meshgrid(x, y, sparse=False, indexing='xy')
average_image = bilinear_interpolate(average_image, x, y)
else:
average_image = np.zeros((0,),dtype='float')
mnormalization = {
'imageSize': row(dataShape),
'averageImage': average_image,
'interpolation': 'bilinear',
'keepAspect': True,
'border': row([0,0]),
'cropSize': 1.0}
if len(fullImageSize) == 1:
fw = max(fullImageSize[0],dataShape[1])
fh = max(fullImageSize[0],dataShape[0])
mnormalization['border'] = max([float(fw - dataShape[1]),
float(fh - dataShape[0])])
mnormalization['cropSize'] = min([float(dataShape[1]) / fw,
float(dataShape[0]) / fh])
else:
fw = max(fullImageSize[0],dataShape[1])
fh = max(fullImageSize[1],dataShape[0])
mnormalization['border'] = row([float(fw - dataShape[1]),
float(fh - dataShape[0])])
mnormalization['cropSize'] = row([float(dataShape[1]) / fw,
float(dataShape[0]) / fh])
if args.caffe_variant == 'caffe_fastrcnn':
mnormalization['interpolation'] = 'bilinear'
if args.preproc == 'caffe':
mnormalization['interpolation'] = 'bicubic'
mnormalization['keepAspect'] = False
print 'Input image border: ', mnormalization['border']
print 'Full input image relative crop size: ', mnormalization['cropSize']
# --------------------------------------------------------------------
# Classes
# --------------------------------------------------------------------
mclassnames = np.empty((0,), dtype=np.object)
mclassdescriptions = np.array((0,), dtype=np.object)
if synsets_wnid:
mclassnames = np.array(synsets_wnid, dtype=np.object).reshape(1,-1)
if synsets_name:
mclassdescriptions = np.array(synsets_name, dtype=np.object).reshape(1,-1)
mclasses = dictToMatlabStruct({'name': mclassnames,
'description': mclassdescriptions})
# --------------------------------------------------------------------
# Convert to MATLAB
# --------------------------------------------------------------------
# net.meta
mmeta = dictToMatlabStruct({'inputs': minputs.reshape(1,-1),
'normalization': mnormalization,
'classes': mclasses})
if args.output_format == 'dagnn':
# This object should stay a dictionary and not a NumPy array due to
# how NumPy saves to MATLAB
mnet = {'layers': np.empty(shape=[0,], dtype=mlayerdt),
'params': np.empty(shape=[0,], dtype=mparamdt),
'meta': mmeta}
for layer in cmodel.layers.itervalues():
mnet['layers'] = np.append(mnet['layers'], layer.toMatlab(), axis=0)
for param in cmodel.params.itervalues():
mnet['params'] = np.append(mnet['params'], param.toMatlab(), axis=0)
# to row
mnet['layers'] = mnet['layers'].reshape(1,-1)
mnet['params'] = mnet['params'].reshape(1,-1)
elif args.output_format == 'simplenn':
# This object should stay a dictionary and not a NumPy array due to
# how NumPy saves to MATLAB
mnet = {'layers': np.empty(shape=[0,], dtype=np.object),
'meta': mmeta}
for layer in cmodel.layers.itervalues():
mnet['layers'] = np.append(mnet['layers'], np.object)
mnet['layers'][-1] = dictToMatlabStruct(layer.toMatlabSimpleNN())
# to row
mnet['layers'] = mnet['layers'].reshape(1,-1)
# --------------------------------------------------------------------
# Save output
# --------------------------------------------------------------------
print 'Saving network to {}'.format(args.output.name)
scipy.io.savemat(args.output, mnet, oned_as='column')
| 33,156 | 36.213244 | 114 | py |
DRT | DRT-master/external_libs/matconvnet/matconvnet/utils/proto/caffe_0115_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='caffe.proto',
package='caffe',
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\x03(\x02\x12\x14\n\trand_skip\x18\x35 \x01(\r:\x01\x30\x12\x1d\n\x10\x64\x65t_fg_threshold\x18\x36 \x01(\x02:\x03\x30.5\x12\x1d\n\x10\x64\x65t_bg_threshold\x18\x37 \x01(\x02:\x03\x30.5\x12\x1d\n\x0f\x64\x65t_fg_fraction\x18\x38 \x01(\x02:\x04\x30.25\x12\x1a\n\x0f\x64\x65t_context_pad\x18: \x01(\r:\x01\x30\x12\x1b\n\rdet_crop_mode\x18; \x01(\t:\x04warp\x12\x12\n\x07new_num\x18< \x01(\x05:\x01\x30\x12\x17\n\x0cnew_channels\x18= \x01(\x05:\x01\x30\x12\x15\n\nnew_height\x18> \x01(\x05:\x01\x30\x12\x14\n\tnew_width\x18? \x01(\x05:\x01\x30\x12\x1d\n\x0eshuffle_images\x18@ \x01(\x08:\x05\x66\x61lse\x12\x15\n\nconcat_dim\x18\x41 \x01(\r:\x01\x31\x12\x36\n\x11hdf5_output_param\x18\xe9\x07 \x01(\x0b\x32\x1a.caffe.HDF5OutputParameter\".\n\nPoolMethod\x12\x07\n\x03MAX\x10\x00\x12\x07\n\x03\x41VE\x10\x01\x12\x0e\n\nSTOCHASTIC\x10\x02*\x1c\n\x05Phase\x12\t\n\x05TRAIN\x10\x00\x12\x08\n\x04TEST\x10\x01')
_PHASE = descriptor.EnumDescriptor(
name='Phase',
full_name='caffe.Phase',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='TRAIN', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='TEST', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=9074,
serialized_end=9102,
)
TRAIN = 0
TEST = 1
_SOLVERPARAMETER_SOLVERMODE = descriptor.EnumDescriptor(
name='SolverMode',
full_name='caffe.SolverParameter.SolverMode',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='CPU', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='GPU', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=1574,
serialized_end=1604,
)
_SOLVERPARAMETER_SOLVERTYPE = descriptor.EnumDescriptor(
name='SolverType',
full_name='caffe.SolverParameter.SolverType',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='SGD', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='NESTEROV', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ADAGRAD', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=1606,
serialized_end=1654,
)
_LAYERPARAMETER_LAYERTYPE = descriptor.EnumDescriptor(
name='LayerType',
full_name='caffe.LayerParameter.LayerType',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='NONE', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ABSVAL', index=1, number=35,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ACCURACY', index=2, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ARGMAX', index=3, number=30,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='BNLL', index=4, number=2,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONCAT', index=5, number=3,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONTRASTIVE_LOSS', index=6, number=37,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONVOLUTION', index=7, number=4,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DATA', index=8, number=5,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DROPOUT', index=9, number=6,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DUMMY_DATA', index=10, number=32,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='EUCLIDEAN_LOSS', index=11, number=7,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ELTWISE', index=12, number=25,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='FLATTEN', index=13, number=8,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HDF5_DATA', index=14, number=9,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HDF5_OUTPUT', index=15, number=10,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HINGE_LOSS', index=16, number=28,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='IM2COL', index=17, number=11,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='IMAGE_DATA', index=18, number=12,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='INFOGAIN_LOSS', index=19, number=13,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='INNER_PRODUCT', index=20, number=14,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='LRN', index=21, number=15,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MEMORY_DATA', index=22, number=29,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MULTINOMIAL_LOGISTIC_LOSS', index=23, number=16,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MVN', index=24, number=34,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='POOLING', index=25, number=17,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='POWER', index=26, number=26,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='RELU', index=27, number=18,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SIGMOID', index=28, number=19,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SIGMOID_CROSS_ENTROPY_LOSS', index=29, number=27,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SILENCE', index=30, number=36,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SOFTMAX', index=31, number=20,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SOFTMAX_LOSS', index=32, number=21,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SPLIT', index=33, number=22,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SLICE', index=34, number=33,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='TANH', index=35, number=23,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='WINDOW_DATA', index=36, number=24,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='THRESHOLD', index=37, number=31,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=3760,
serialized_end=4332,
)
_LAYERPARAMETER_DIMCHECKMODE = descriptor.EnumDescriptor(
name='DimCheckMode',
full_name='caffe.LayerParameter.DimCheckMode',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='STRICT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='PERMISSIVE', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=4334,
serialized_end=4376,
)
_CONVOLUTIONPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.ConvolutionParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5085,
serialized_end=5128,
)
_DATAPARAMETER_DB = descriptor.EnumDescriptor(
name='DB',
full_name='caffe.DataParameter.DB',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='LEVELDB', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='LMDB', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5339,
serialized_end=5366,
)
_ELTWISEPARAMETER_ELTWISEOP = descriptor.EnumDescriptor(
name='EltwiseOp',
full_name='caffe.EltwiseParameter.EltwiseOp',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='PROD', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SUM', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MAX', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5672,
serialized_end=5711,
)
_HINGELOSSPARAMETER_NORM = descriptor.EnumDescriptor(
name='Norm',
full_name='caffe.HingeLossParameter.Norm',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='L1', index=0, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='L2', index=1, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5928,
serialized_end=5950,
)
_LRNPARAMETER_NORMREGION = descriptor.EnumDescriptor(
name='NormRegion',
full_name='caffe.LRNParameter.NormRegion',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='ACROSS_CHANNELS', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='WITHIN_CHANNEL', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6536,
serialized_end=6589,
)
_POOLINGPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.PoolingParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=7062,
serialized_end=7108,
)
_POOLINGPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.PoolingParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5085,
serialized_end=5128,
)
_RELUPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.ReLUParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5085,
serialized_end=5128,
)
_SIGMOIDPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.SigmoidParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5085,
serialized_end=5128,
)
_SOFTMAXPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.SoftmaxParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5085,
serialized_end=5128,
)
_TANHPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.TanHParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5085,
serialized_end=5128,
)
_V0LAYERPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.V0LayerParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=7062,
serialized_end=7108,
)
_BLOBPROTO = descriptor.Descriptor(
name='BlobProto',
full_name='caffe.BlobProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num', full_name='caffe.BlobProto.num', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.BlobProto.channels', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.BlobProto.height', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.BlobProto.width', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.BlobProto.data', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='diff', full_name='caffe.BlobProto.diff', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=22,
serialized_end=143,
)
_BLOBPROTOVECTOR = descriptor.Descriptor(
name='BlobProtoVector',
full_name='caffe.BlobProtoVector',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.BlobProtoVector.blobs', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=145,
serialized_end=195,
)
_DATUM = descriptor.Descriptor(
name='Datum',
full_name='caffe.Datum',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='channels', full_name='caffe.Datum.channels', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.Datum.height', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.Datum.width', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.Datum.data', index=3,
number=4, type=12, cpp_type=9, label=1,
has_default_value=False, default_value="",
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='label', full_name='caffe.Datum.label', index=4,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='float_data', full_name='caffe.Datum.float_data', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=197,
serialized_end=302,
)
_FILLERPARAMETER = descriptor.Descriptor(
name='FillerParameter',
full_name='caffe.FillerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='type', full_name='caffe.FillerParameter.type', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("constant", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='value', full_name='caffe.FillerParameter.value', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='min', full_name='caffe.FillerParameter.min', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max', full_name='caffe.FillerParameter.max', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean', full_name='caffe.FillerParameter.mean', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='std', full_name='caffe.FillerParameter.std', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='sparse', full_name='caffe.FillerParameter.sparse', index=6,
number=7, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=305,
serialized_end=449,
)
_NETPARAMETER = descriptor.Descriptor(
name='NetParameter',
full_name='caffe.NetParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.NetParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layers', full_name='caffe.NetParameter.layers', index=1,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input', full_name='caffe.NetParameter.input', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input_dim', full_name='caffe.NetParameter.input_dim', index=3,
number=4, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_backward', full_name='caffe.NetParameter.force_backward', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='state', full_name='caffe.NetParameter.state', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=452,
serialized_end=616,
)
_SOLVERPARAMETER = descriptor.Descriptor(
name='SolverParameter',
full_name='caffe.SolverParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='net', full_name='caffe.SolverParameter.net', index=0,
number=24, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='net_param', full_name='caffe.SolverParameter.net_param', index=1,
number=25, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='train_net', full_name='caffe.SolverParameter.train_net', index=2,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_net', full_name='caffe.SolverParameter.test_net', index=3,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='train_net_param', full_name='caffe.SolverParameter.train_net_param', index=4,
number=21, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_net_param', full_name='caffe.SolverParameter.test_net_param', index=5,
number=22, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='train_state', full_name='caffe.SolverParameter.train_state', index=6,
number=26, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_state', full_name='caffe.SolverParameter.test_state', index=7,
number=27, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_iter', full_name='caffe.SolverParameter.test_iter', index=8,
number=3, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_interval', full_name='caffe.SolverParameter.test_interval', index=9,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_compute_loss', full_name='caffe.SolverParameter.test_compute_loss', index=10,
number=19, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_initialization', full_name='caffe.SolverParameter.test_initialization', index=11,
number=32, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='base_lr', full_name='caffe.SolverParameter.base_lr', index=12,
number=5, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='display', full_name='caffe.SolverParameter.display', index=13,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='average_loss', full_name='caffe.SolverParameter.average_loss', index=14,
number=33, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max_iter', full_name='caffe.SolverParameter.max_iter', index=15,
number=7, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lr_policy', full_name='caffe.SolverParameter.lr_policy', index=16,
number=8, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='gamma', full_name='caffe.SolverParameter.gamma', index=17,
number=9, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power', full_name='caffe.SolverParameter.power', index=18,
number=10, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='momentum', full_name='caffe.SolverParameter.momentum', index=19,
number=11, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.SolverParameter.weight_decay', index=20,
number=12, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='regularization_type', full_name='caffe.SolverParameter.regularization_type', index=21,
number=29, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("L2", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stepsize', full_name='caffe.SolverParameter.stepsize', index=22,
number=13, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stepvalue', full_name='caffe.SolverParameter.stepvalue', index=23,
number=34, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot', full_name='caffe.SolverParameter.snapshot', index=24,
number=14, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_prefix', full_name='caffe.SolverParameter.snapshot_prefix', index=25,
number=15, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_diff', full_name='caffe.SolverParameter.snapshot_diff', index=26,
number=16, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='solver_mode', full_name='caffe.SolverParameter.solver_mode', index=27,
number=17, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='device_id', full_name='caffe.SolverParameter.device_id', index=28,
number=18, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='random_seed', full_name='caffe.SolverParameter.random_seed', index=29,
number=20, type=3, cpp_type=2, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='solver_type', full_name='caffe.SolverParameter.solver_type', index=30,
number=30, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='delta', full_name='caffe.SolverParameter.delta', index=31,
number=31, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1e-08,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='debug_info', full_name='caffe.SolverParameter.debug_info', index=32,
number=23, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_after_train', full_name='caffe.SolverParameter.snapshot_after_train', index=33,
number=28, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SOLVERPARAMETER_SOLVERMODE,
_SOLVERPARAMETER_SOLVERTYPE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=619,
serialized_end=1654,
)
_SOLVERSTATE = descriptor.Descriptor(
name='SolverState',
full_name='caffe.SolverState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='iter', full_name='caffe.SolverState.iter', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='learned_net', full_name='caffe.SolverState.learned_net', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='history', full_name='caffe.SolverState.history', index=2,
number=3, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='current_step', full_name='caffe.SolverState.current_step', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1656,
serialized_end=1764,
)
_NETSTATE = descriptor.Descriptor(
name='NetState',
full_name='caffe.NetState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='phase', full_name='caffe.NetState.phase', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='level', full_name='caffe.NetState.level', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stage', full_name='caffe.NetState.stage', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1766,
serialized_end=1844,
)
_NETSTATERULE = descriptor.Descriptor(
name='NetStateRule',
full_name='caffe.NetStateRule',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='phase', full_name='caffe.NetStateRule.phase', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='min_level', full_name='caffe.NetStateRule.min_level', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max_level', full_name='caffe.NetStateRule.max_level', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stage', full_name='caffe.NetStateRule.stage', index=3,
number=4, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='not_stage', full_name='caffe.NetStateRule.not_stage', index=4,
number=5, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1846,
serialized_end=1961,
)
_LAYERPARAMETER = descriptor.Descriptor(
name='LayerParameter',
full_name='caffe.LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='bottom', full_name='caffe.LayerParameter.bottom', index=0,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top', full_name='caffe.LayerParameter.top', index=1,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='name', full_name='caffe.LayerParameter.name', index=2,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='include', full_name='caffe.LayerParameter.include', index=3,
number=32, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='exclude', full_name='caffe.LayerParameter.exclude', index=4,
number=33, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.LayerParameter.type', index=5,
number=5, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.LayerParameter.blobs', index=6,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='param', full_name='caffe.LayerParameter.param', index=7,
number=1001, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blob_share_mode', full_name='caffe.LayerParameter.blob_share_mode', index=8,
number=1002, type=14, cpp_type=8, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.LayerParameter.blobs_lr', index=9,
number=7, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.LayerParameter.weight_decay', index=10,
number=8, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='loss_weight', full_name='caffe.LayerParameter.loss_weight', index=11,
number=35, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='accuracy_param', full_name='caffe.LayerParameter.accuracy_param', index=12,
number=27, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='argmax_param', full_name='caffe.LayerParameter.argmax_param', index=13,
number=23, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_param', full_name='caffe.LayerParameter.concat_param', index=14,
number=9, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='contrastive_loss_param', full_name='caffe.LayerParameter.contrastive_loss_param', index=15,
number=40, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='convolution_param', full_name='caffe.LayerParameter.convolution_param', index=16,
number=10, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data_param', full_name='caffe.LayerParameter.data_param', index=17,
number=11, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_param', full_name='caffe.LayerParameter.dropout_param', index=18,
number=12, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dummy_data_param', full_name='caffe.LayerParameter.dummy_data_param', index=19,
number=26, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='eltwise_param', full_name='caffe.LayerParameter.eltwise_param', index=20,
number=24, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_data_param', full_name='caffe.LayerParameter.hdf5_data_param', index=21,
number=13, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.LayerParameter.hdf5_output_param', index=22,
number=14, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hinge_loss_param', full_name='caffe.LayerParameter.hinge_loss_param', index=23,
number=29, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='image_data_param', full_name='caffe.LayerParameter.image_data_param', index=24,
number=15, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='infogain_loss_param', full_name='caffe.LayerParameter.infogain_loss_param', index=25,
number=16, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='inner_product_param', full_name='caffe.LayerParameter.inner_product_param', index=26,
number=17, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lrn_param', full_name='caffe.LayerParameter.lrn_param', index=27,
number=18, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='memory_data_param', full_name='caffe.LayerParameter.memory_data_param', index=28,
number=22, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mvn_param', full_name='caffe.LayerParameter.mvn_param', index=29,
number=34, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pooling_param', full_name='caffe.LayerParameter.pooling_param', index=30,
number=19, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power_param', full_name='caffe.LayerParameter.power_param', index=31,
number=21, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='relu_param', full_name='caffe.LayerParameter.relu_param', index=32,
number=30, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='sigmoid_param', full_name='caffe.LayerParameter.sigmoid_param', index=33,
number=38, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='softmax_param', full_name='caffe.LayerParameter.softmax_param', index=34,
number=39, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='slice_param', full_name='caffe.LayerParameter.slice_param', index=35,
number=31, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='tanh_param', full_name='caffe.LayerParameter.tanh_param', index=36,
number=37, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='threshold_param', full_name='caffe.LayerParameter.threshold_param', index=37,
number=25, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='window_data_param', full_name='caffe.LayerParameter.window_data_param', index=38,
number=20, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='transform_param', full_name='caffe.LayerParameter.transform_param', index=39,
number=36, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layer', full_name='caffe.LayerParameter.layer', index=40,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_LAYERPARAMETER_LAYERTYPE,
_LAYERPARAMETER_DIMCHECKMODE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1964,
serialized_end=4376,
)
_TRANSFORMATIONPARAMETER = descriptor.Descriptor(
name='TransformationParameter',
full_name='caffe.TransformationParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='scale', full_name='caffe.TransformationParameter.scale', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.TransformationParameter.mirror', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.TransformationParameter.crop_size', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.TransformationParameter.mean_file', index=3,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4378,
serialized_end=4485,
)
_ACCURACYPARAMETER = descriptor.Descriptor(
name='AccuracyParameter',
full_name='caffe.AccuracyParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='top_k', full_name='caffe.AccuracyParameter.top_k', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4487,
serialized_end=4524,
)
_ARGMAXPARAMETER = descriptor.Descriptor(
name='ArgMaxParameter',
full_name='caffe.ArgMaxParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='out_max_val', full_name='caffe.ArgMaxParameter.out_max_val', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top_k', full_name='caffe.ArgMaxParameter.top_k', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4526,
serialized_end=4589,
)
_CONCATPARAMETER = descriptor.Descriptor(
name='ConcatParameter',
full_name='caffe.ConcatParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.ConcatParameter.concat_dim', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4591,
serialized_end=4631,
)
_CONTRASTIVELOSSPARAMETER = descriptor.Descriptor(
name='ContrastiveLossParameter',
full_name='caffe.ContrastiveLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='margin', full_name='caffe.ContrastiveLossParameter.margin', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4633,
serialized_end=4678,
)
_CONVOLUTIONPARAMETER = descriptor.Descriptor(
name='ConvolutionParameter',
full_name='caffe.ConvolutionParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.ConvolutionParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.ConvolutionParameter.bias_term', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.ConvolutionParameter.pad', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_h', full_name='caffe.ConvolutionParameter.pad_h', index=3,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_w', full_name='caffe.ConvolutionParameter.pad_w', index=4,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_size', full_name='caffe.ConvolutionParameter.kernel_size', index=5,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_h', full_name='caffe.ConvolutionParameter.kernel_h', index=6,
number=11, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_w', full_name='caffe.ConvolutionParameter.kernel_w', index=7,
number=12, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='group', full_name='caffe.ConvolutionParameter.group', index=8,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.ConvolutionParameter.stride', index=9,
number=6, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_h', full_name='caffe.ConvolutionParameter.stride_h', index=10,
number=13, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_w', full_name='caffe.ConvolutionParameter.stride_w', index=11,
number=14, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.ConvolutionParameter.weight_filler', index=12,
number=7, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.ConvolutionParameter.bias_filler', index=13,
number=8, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.ConvolutionParameter.engine', index=14,
number=15, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_CONVOLUTIONPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4681,
serialized_end=5128,
)
_DATAPARAMETER = descriptor.Descriptor(
name='DataParameter',
full_name='caffe.DataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.DataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.DataParameter.batch_size', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.DataParameter.rand_skip', index=2,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='backend', full_name='caffe.DataParameter.backend', index=3,
number=8, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.DataParameter.scale', index=4,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.DataParameter.mean_file', index=5,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.DataParameter.crop_size', index=6,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.DataParameter.mirror', index=7,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_DATAPARAMETER_DB,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5131,
serialized_end=5366,
)
_DROPOUTPARAMETER = descriptor.Descriptor(
name='DropoutParameter',
full_name='caffe.DropoutParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.DropoutParameter.dropout_ratio', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5368,
serialized_end=5414,
)
_DUMMYDATAPARAMETER = descriptor.Descriptor(
name='DummyDataParameter',
full_name='caffe.DummyDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='data_filler', full_name='caffe.DummyDataParameter.data_filler', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num', full_name='caffe.DummyDataParameter.num', index=1,
number=2, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.DummyDataParameter.channels', index=2,
number=3, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.DummyDataParameter.height', index=3,
number=4, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.DummyDataParameter.width', index=4,
number=5, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5416,
serialized_end=5543,
)
_ELTWISEPARAMETER = descriptor.Descriptor(
name='EltwiseParameter',
full_name='caffe.EltwiseParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='operation', full_name='caffe.EltwiseParameter.operation', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='coeff', full_name='caffe.EltwiseParameter.coeff', index=1,
number=2, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stable_prod_grad', full_name='caffe.EltwiseParameter.stable_prod_grad', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_ELTWISEPARAMETER_ELTWISEOP,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5546,
serialized_end=5711,
)
_THRESHOLDPARAMETER = descriptor.Descriptor(
name='ThresholdParameter',
full_name='caffe.ThresholdParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='threshold', full_name='caffe.ThresholdParameter.threshold', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5713,
serialized_end=5755,
)
_HDF5DATAPARAMETER = descriptor.Descriptor(
name='HDF5DataParameter',
full_name='caffe.HDF5DataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.HDF5DataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.HDF5DataParameter.batch_size', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5757,
serialized_end=5812,
)
_HDF5OUTPUTPARAMETER = descriptor.Descriptor(
name='HDF5OutputParameter',
full_name='caffe.HDF5OutputParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='file_name', full_name='caffe.HDF5OutputParameter.file_name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5814,
serialized_end=5854,
)
_HINGELOSSPARAMETER = descriptor.Descriptor(
name='HingeLossParameter',
full_name='caffe.HingeLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='norm', full_name='caffe.HingeLossParameter.norm', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_HINGELOSSPARAMETER_NORM,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5856,
serialized_end=5950,
)
_IMAGEDATAPARAMETER = descriptor.Descriptor(
name='ImageDataParameter',
full_name='caffe.ImageDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.ImageDataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.ImageDataParameter.batch_size', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.ImageDataParameter.rand_skip', index=2,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle', full_name='caffe.ImageDataParameter.shuffle', index=3,
number=8, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_height', full_name='caffe.ImageDataParameter.new_height', index=4,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_width', full_name='caffe.ImageDataParameter.new_width', index=5,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.ImageDataParameter.scale', index=6,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.ImageDataParameter.mean_file', index=7,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.ImageDataParameter.crop_size', index=8,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.ImageDataParameter.mirror', index=9,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5953,
serialized_end=6182,
)
_INFOGAINLOSSPARAMETER = descriptor.Descriptor(
name='InfogainLossParameter',
full_name='caffe.InfogainLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.InfogainLossParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6184,
serialized_end=6223,
)
_INNERPRODUCTPARAMETER = descriptor.Descriptor(
name='InnerProductParameter',
full_name='caffe.InnerProductParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.InnerProductParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.InnerProductParameter.bias_term', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.InnerProductParameter.weight_filler', index=2,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.InnerProductParameter.bias_filler', index=3,
number=4, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6226,
serialized_end=6386,
)
_LRNPARAMETER = descriptor.Descriptor(
name='LRNParameter',
full_name='caffe.LRNParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='local_size', full_name='caffe.LRNParameter.local_size', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.LRNParameter.alpha', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='beta', full_name='caffe.LRNParameter.beta', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='norm_region', full_name='caffe.LRNParameter.norm_region', index=3,
number=4, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_LRNPARAMETER_NORMREGION,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6389,
serialized_end=6589,
)
_MEMORYDATAPARAMETER = descriptor.Descriptor(
name='MemoryDataParameter',
full_name='caffe.MemoryDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.MemoryDataParameter.batch_size', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.MemoryDataParameter.channels', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.MemoryDataParameter.height', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.MemoryDataParameter.width', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6591,
serialized_end=6681,
)
_MVNPARAMETER = descriptor.Descriptor(
name='MVNParameter',
full_name='caffe.MVNParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='normalize_variance', full_name='caffe.MVNParameter.normalize_variance', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='across_channels', full_name='caffe.MVNParameter.across_channels', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6683,
serialized_end=6763,
)
_POOLINGPARAMETER = descriptor.Descriptor(
name='PoolingParameter',
full_name='caffe.PoolingParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='pool', full_name='caffe.PoolingParameter.pool', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.PoolingParameter.pad', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_h', full_name='caffe.PoolingParameter.pad_h', index=2,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_w', full_name='caffe.PoolingParameter.pad_w', index=3,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_size', full_name='caffe.PoolingParameter.kernel_size', index=4,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_h', full_name='caffe.PoolingParameter.kernel_h', index=5,
number=5, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_w', full_name='caffe.PoolingParameter.kernel_w', index=6,
number=6, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.PoolingParameter.stride', index=7,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_h', full_name='caffe.PoolingParameter.stride_h', index=8,
number=7, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_w', full_name='caffe.PoolingParameter.stride_w', index=9,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.PoolingParameter.engine', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_POOLINGPARAMETER_POOLMETHOD,
_POOLINGPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6766,
serialized_end=7153,
)
_POWERPARAMETER = descriptor.Descriptor(
name='PowerParameter',
full_name='caffe.PowerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='power', full_name='caffe.PowerParameter.power', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.PowerParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shift', full_name='caffe.PowerParameter.shift', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7155,
serialized_end=7225,
)
_RELUPARAMETER = descriptor.Descriptor(
name='ReLUParameter',
full_name='caffe.ReLUParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='negative_slope', full_name='caffe.ReLUParameter.negative_slope', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.ReLUParameter.engine', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_RELUPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7228,
serialized_end=7369,
)
_SIGMOIDPARAMETER = descriptor.Descriptor(
name='SigmoidParameter',
full_name='caffe.SigmoidParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='engine', full_name='caffe.SigmoidParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SIGMOIDPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7371,
serialized_end=7491,
)
_SLICEPARAMETER = descriptor.Descriptor(
name='SliceParameter',
full_name='caffe.SliceParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='slice_dim', full_name='caffe.SliceParameter.slice_dim', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='slice_point', full_name='caffe.SliceParameter.slice_point', index=1,
number=2, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7493,
serialized_end=7552,
)
_SOFTMAXPARAMETER = descriptor.Descriptor(
name='SoftmaxParameter',
full_name='caffe.SoftmaxParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='engine', full_name='caffe.SoftmaxParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SOFTMAXPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7554,
serialized_end=7674,
)
_TANHPARAMETER = descriptor.Descriptor(
name='TanHParameter',
full_name='caffe.TanHParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='engine', full_name='caffe.TanHParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_TANHPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7676,
serialized_end=7790,
)
_WINDOWDATAPARAMETER = descriptor.Descriptor(
name='WindowDataParameter',
full_name='caffe.WindowDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.WindowDataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.WindowDataParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.WindowDataParameter.mean_file', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.WindowDataParameter.batch_size', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.WindowDataParameter.crop_size', index=4,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.WindowDataParameter.mirror', index=5,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='fg_threshold', full_name='caffe.WindowDataParameter.fg_threshold', index=6,
number=7, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bg_threshold', full_name='caffe.WindowDataParameter.bg_threshold', index=7,
number=8, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='fg_fraction', full_name='caffe.WindowDataParameter.fg_fraction', index=8,
number=9, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='context_pad', full_name='caffe.WindowDataParameter.context_pad', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_mode', full_name='caffe.WindowDataParameter.crop_mode', index=10,
number=11, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7793,
serialized_end=8062,
)
_V0LAYERPARAMETER = descriptor.Descriptor(
name='V0LayerParameter',
full_name='caffe.V0LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.V0LayerParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.V0LayerParameter.type', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.V0LayerParameter.num_output', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='biasterm', full_name='caffe.V0LayerParameter.biasterm', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.V0LayerParameter.weight_filler', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.V0LayerParameter.bias_filler', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.V0LayerParameter.pad', index=6,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernelsize', full_name='caffe.V0LayerParameter.kernelsize', index=7,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='group', full_name='caffe.V0LayerParameter.group', index=8,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.V0LayerParameter.stride', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pool', full_name='caffe.V0LayerParameter.pool', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.V0LayerParameter.dropout_ratio', index=11,
number=12, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='local_size', full_name='caffe.V0LayerParameter.local_size', index=12,
number=13, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.V0LayerParameter.alpha', index=13,
number=14, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='beta', full_name='caffe.V0LayerParameter.beta', index=14,
number=15, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='source', full_name='caffe.V0LayerParameter.source', index=15,
number=16, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.V0LayerParameter.scale', index=16,
number=17, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='meanfile', full_name='caffe.V0LayerParameter.meanfile', index=17,
number=18, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batchsize', full_name='caffe.V0LayerParameter.batchsize', index=18,
number=19, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='cropsize', full_name='caffe.V0LayerParameter.cropsize', index=19,
number=20, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.V0LayerParameter.mirror', index=20,
number=21, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.V0LayerParameter.blobs', index=21,
number=50, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.V0LayerParameter.blobs_lr', index=22,
number=51, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.V0LayerParameter.weight_decay', index=23,
number=52, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.V0LayerParameter.rand_skip', index=24,
number=53, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_fg_threshold', full_name='caffe.V0LayerParameter.det_fg_threshold', index=25,
number=54, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_bg_threshold', full_name='caffe.V0LayerParameter.det_bg_threshold', index=26,
number=55, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_fg_fraction', full_name='caffe.V0LayerParameter.det_fg_fraction', index=27,
number=56, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_context_pad', full_name='caffe.V0LayerParameter.det_context_pad', index=28,
number=58, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_crop_mode', full_name='caffe.V0LayerParameter.det_crop_mode', index=29,
number=59, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_num', full_name='caffe.V0LayerParameter.new_num', index=30,
number=60, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_channels', full_name='caffe.V0LayerParameter.new_channels', index=31,
number=61, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_height', full_name='caffe.V0LayerParameter.new_height', index=32,
number=62, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_width', full_name='caffe.V0LayerParameter.new_width', index=33,
number=63, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle_images', full_name='caffe.V0LayerParameter.shuffle_images', index=34,
number=64, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.V0LayerParameter.concat_dim', index=35,
number=65, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.V0LayerParameter.hdf5_output_param', index=36,
number=1001, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_V0LAYERPARAMETER_POOLMETHOD,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8065,
serialized_end=9072,
)
_BLOBPROTOVECTOR.fields_by_name['blobs'].message_type = _BLOBPROTO
_NETPARAMETER.fields_by_name['layers'].message_type = _LAYERPARAMETER
_NETPARAMETER.fields_by_name['state'].message_type = _NETSTATE
_SOLVERPARAMETER.fields_by_name['net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['train_net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['test_net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['train_state'].message_type = _NETSTATE
_SOLVERPARAMETER.fields_by_name['test_state'].message_type = _NETSTATE
_SOLVERPARAMETER.fields_by_name['solver_mode'].enum_type = _SOLVERPARAMETER_SOLVERMODE
_SOLVERPARAMETER.fields_by_name['solver_type'].enum_type = _SOLVERPARAMETER_SOLVERTYPE
_SOLVERPARAMETER_SOLVERMODE.containing_type = _SOLVERPARAMETER;
_SOLVERPARAMETER_SOLVERTYPE.containing_type = _SOLVERPARAMETER;
_SOLVERSTATE.fields_by_name['history'].message_type = _BLOBPROTO
_NETSTATE.fields_by_name['phase'].enum_type = _PHASE
_NETSTATERULE.fields_by_name['phase'].enum_type = _PHASE
_LAYERPARAMETER.fields_by_name['include'].message_type = _NETSTATERULE
_LAYERPARAMETER.fields_by_name['exclude'].message_type = _NETSTATERULE
_LAYERPARAMETER.fields_by_name['type'].enum_type = _LAYERPARAMETER_LAYERTYPE
_LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_LAYERPARAMETER.fields_by_name['blob_share_mode'].enum_type = _LAYERPARAMETER_DIMCHECKMODE
_LAYERPARAMETER.fields_by_name['accuracy_param'].message_type = _ACCURACYPARAMETER
_LAYERPARAMETER.fields_by_name['argmax_param'].message_type = _ARGMAXPARAMETER
_LAYERPARAMETER.fields_by_name['concat_param'].message_type = _CONCATPARAMETER
_LAYERPARAMETER.fields_by_name['contrastive_loss_param'].message_type = _CONTRASTIVELOSSPARAMETER
_LAYERPARAMETER.fields_by_name['convolution_param'].message_type = _CONVOLUTIONPARAMETER
_LAYERPARAMETER.fields_by_name['data_param'].message_type = _DATAPARAMETER
_LAYERPARAMETER.fields_by_name['dropout_param'].message_type = _DROPOUTPARAMETER
_LAYERPARAMETER.fields_by_name['dummy_data_param'].message_type = _DUMMYDATAPARAMETER
_LAYERPARAMETER.fields_by_name['eltwise_param'].message_type = _ELTWISEPARAMETER
_LAYERPARAMETER.fields_by_name['hdf5_data_param'].message_type = _HDF5DATAPARAMETER
_LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_LAYERPARAMETER.fields_by_name['hinge_loss_param'].message_type = _HINGELOSSPARAMETER
_LAYERPARAMETER.fields_by_name['image_data_param'].message_type = _IMAGEDATAPARAMETER
_LAYERPARAMETER.fields_by_name['infogain_loss_param'].message_type = _INFOGAINLOSSPARAMETER
_LAYERPARAMETER.fields_by_name['inner_product_param'].message_type = _INNERPRODUCTPARAMETER
_LAYERPARAMETER.fields_by_name['lrn_param'].message_type = _LRNPARAMETER
_LAYERPARAMETER.fields_by_name['memory_data_param'].message_type = _MEMORYDATAPARAMETER
_LAYERPARAMETER.fields_by_name['mvn_param'].message_type = _MVNPARAMETER
_LAYERPARAMETER.fields_by_name['pooling_param'].message_type = _POOLINGPARAMETER
_LAYERPARAMETER.fields_by_name['power_param'].message_type = _POWERPARAMETER
_LAYERPARAMETER.fields_by_name['relu_param'].message_type = _RELUPARAMETER
_LAYERPARAMETER.fields_by_name['sigmoid_param'].message_type = _SIGMOIDPARAMETER
_LAYERPARAMETER.fields_by_name['softmax_param'].message_type = _SOFTMAXPARAMETER
_LAYERPARAMETER.fields_by_name['slice_param'].message_type = _SLICEPARAMETER
_LAYERPARAMETER.fields_by_name['tanh_param'].message_type = _TANHPARAMETER
_LAYERPARAMETER.fields_by_name['threshold_param'].message_type = _THRESHOLDPARAMETER
_LAYERPARAMETER.fields_by_name['window_data_param'].message_type = _WINDOWDATAPARAMETER
_LAYERPARAMETER.fields_by_name['transform_param'].message_type = _TRANSFORMATIONPARAMETER
_LAYERPARAMETER.fields_by_name['layer'].message_type = _V0LAYERPARAMETER
_LAYERPARAMETER_LAYERTYPE.containing_type = _LAYERPARAMETER;
_LAYERPARAMETER_DIMCHECKMODE.containing_type = _LAYERPARAMETER;
_CONVOLUTIONPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['engine'].enum_type = _CONVOLUTIONPARAMETER_ENGINE
_CONVOLUTIONPARAMETER_ENGINE.containing_type = _CONVOLUTIONPARAMETER;
_DATAPARAMETER.fields_by_name['backend'].enum_type = _DATAPARAMETER_DB
_DATAPARAMETER_DB.containing_type = _DATAPARAMETER;
_DUMMYDATAPARAMETER.fields_by_name['data_filler'].message_type = _FILLERPARAMETER
_ELTWISEPARAMETER.fields_by_name['operation'].enum_type = _ELTWISEPARAMETER_ELTWISEOP
_ELTWISEPARAMETER_ELTWISEOP.containing_type = _ELTWISEPARAMETER;
_HINGELOSSPARAMETER.fields_by_name['norm'].enum_type = _HINGELOSSPARAMETER_NORM
_HINGELOSSPARAMETER_NORM.containing_type = _HINGELOSSPARAMETER;
_INNERPRODUCTPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_INNERPRODUCTPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_LRNPARAMETER.fields_by_name['norm_region'].enum_type = _LRNPARAMETER_NORMREGION
_LRNPARAMETER_NORMREGION.containing_type = _LRNPARAMETER;
_POOLINGPARAMETER.fields_by_name['pool'].enum_type = _POOLINGPARAMETER_POOLMETHOD
_POOLINGPARAMETER.fields_by_name['engine'].enum_type = _POOLINGPARAMETER_ENGINE
_POOLINGPARAMETER_POOLMETHOD.containing_type = _POOLINGPARAMETER;
_POOLINGPARAMETER_ENGINE.containing_type = _POOLINGPARAMETER;
_RELUPARAMETER.fields_by_name['engine'].enum_type = _RELUPARAMETER_ENGINE
_RELUPARAMETER_ENGINE.containing_type = _RELUPARAMETER;
_SIGMOIDPARAMETER.fields_by_name['engine'].enum_type = _SIGMOIDPARAMETER_ENGINE
_SIGMOIDPARAMETER_ENGINE.containing_type = _SIGMOIDPARAMETER;
_SOFTMAXPARAMETER.fields_by_name['engine'].enum_type = _SOFTMAXPARAMETER_ENGINE
_SOFTMAXPARAMETER_ENGINE.containing_type = _SOFTMAXPARAMETER;
_TANHPARAMETER.fields_by_name['engine'].enum_type = _TANHPARAMETER_ENGINE
_TANHPARAMETER_ENGINE.containing_type = _TANHPARAMETER;
_V0LAYERPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_V0LAYERPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_V0LAYERPARAMETER.fields_by_name['pool'].enum_type = _V0LAYERPARAMETER_POOLMETHOD
_V0LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_V0LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_V0LAYERPARAMETER_POOLMETHOD.containing_type = _V0LAYERPARAMETER;
DESCRIPTOR.message_types_by_name['BlobProto'] = _BLOBPROTO
DESCRIPTOR.message_types_by_name['BlobProtoVector'] = _BLOBPROTOVECTOR
DESCRIPTOR.message_types_by_name['Datum'] = _DATUM
DESCRIPTOR.message_types_by_name['FillerParameter'] = _FILLERPARAMETER
DESCRIPTOR.message_types_by_name['NetParameter'] = _NETPARAMETER
DESCRIPTOR.message_types_by_name['SolverParameter'] = _SOLVERPARAMETER
DESCRIPTOR.message_types_by_name['SolverState'] = _SOLVERSTATE
DESCRIPTOR.message_types_by_name['NetState'] = _NETSTATE
DESCRIPTOR.message_types_by_name['NetStateRule'] = _NETSTATERULE
DESCRIPTOR.message_types_by_name['LayerParameter'] = _LAYERPARAMETER
DESCRIPTOR.message_types_by_name['TransformationParameter'] = _TRANSFORMATIONPARAMETER
DESCRIPTOR.message_types_by_name['AccuracyParameter'] = _ACCURACYPARAMETER
DESCRIPTOR.message_types_by_name['ArgMaxParameter'] = _ARGMAXPARAMETER
DESCRIPTOR.message_types_by_name['ConcatParameter'] = _CONCATPARAMETER
DESCRIPTOR.message_types_by_name['ContrastiveLossParameter'] = _CONTRASTIVELOSSPARAMETER
DESCRIPTOR.message_types_by_name['ConvolutionParameter'] = _CONVOLUTIONPARAMETER
DESCRIPTOR.message_types_by_name['DataParameter'] = _DATAPARAMETER
DESCRIPTOR.message_types_by_name['DropoutParameter'] = _DROPOUTPARAMETER
DESCRIPTOR.message_types_by_name['DummyDataParameter'] = _DUMMYDATAPARAMETER
DESCRIPTOR.message_types_by_name['EltwiseParameter'] = _ELTWISEPARAMETER
DESCRIPTOR.message_types_by_name['ThresholdParameter'] = _THRESHOLDPARAMETER
DESCRIPTOR.message_types_by_name['HDF5DataParameter'] = _HDF5DATAPARAMETER
DESCRIPTOR.message_types_by_name['HDF5OutputParameter'] = _HDF5OUTPUTPARAMETER
DESCRIPTOR.message_types_by_name['HingeLossParameter'] = _HINGELOSSPARAMETER
DESCRIPTOR.message_types_by_name['ImageDataParameter'] = _IMAGEDATAPARAMETER
DESCRIPTOR.message_types_by_name['InfogainLossParameter'] = _INFOGAINLOSSPARAMETER
DESCRIPTOR.message_types_by_name['InnerProductParameter'] = _INNERPRODUCTPARAMETER
DESCRIPTOR.message_types_by_name['LRNParameter'] = _LRNPARAMETER
DESCRIPTOR.message_types_by_name['MemoryDataParameter'] = _MEMORYDATAPARAMETER
DESCRIPTOR.message_types_by_name['MVNParameter'] = _MVNPARAMETER
DESCRIPTOR.message_types_by_name['PoolingParameter'] = _POOLINGPARAMETER
DESCRIPTOR.message_types_by_name['PowerParameter'] = _POWERPARAMETER
DESCRIPTOR.message_types_by_name['ReLUParameter'] = _RELUPARAMETER
DESCRIPTOR.message_types_by_name['SigmoidParameter'] = _SIGMOIDPARAMETER
DESCRIPTOR.message_types_by_name['SliceParameter'] = _SLICEPARAMETER
DESCRIPTOR.message_types_by_name['SoftmaxParameter'] = _SOFTMAXPARAMETER
DESCRIPTOR.message_types_by_name['TanHParameter'] = _TANHPARAMETER
DESCRIPTOR.message_types_by_name['WindowDataParameter'] = _WINDOWDATAPARAMETER
DESCRIPTOR.message_types_by_name['V0LayerParameter'] = _V0LAYERPARAMETER
class BlobProto(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTO
# @@protoc_insertion_point(class_scope:caffe.BlobProto)
class BlobProtoVector(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTOVECTOR
# @@protoc_insertion_point(class_scope:caffe.BlobProtoVector)
class Datum(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATUM
# @@protoc_insertion_point(class_scope:caffe.Datum)
class FillerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FILLERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.FillerParameter)
class NetParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETPARAMETER
# @@protoc_insertion_point(class_scope:caffe.NetParameter)
class SolverParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SolverParameter)
class SolverState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERSTATE
# @@protoc_insertion_point(class_scope:caffe.SolverState)
class NetState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATE
# @@protoc_insertion_point(class_scope:caffe.NetState)
class NetStateRule(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATERULE
# @@protoc_insertion_point(class_scope:caffe.NetStateRule)
class LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LayerParameter)
class TransformationParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TRANSFORMATIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.TransformationParameter)
class AccuracyParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ACCURACYPARAMETER
# @@protoc_insertion_point(class_scope:caffe.AccuracyParameter)
class ArgMaxParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ARGMAXPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ArgMaxParameter)
class ConcatParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONCATPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ConcatParameter)
class ContrastiveLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONTRASTIVELOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ContrastiveLossParameter)
class ConvolutionParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONVOLUTIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ConvolutionParameter)
class DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DataParameter)
class DropoutParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DROPOUTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DropoutParameter)
class DummyDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DUMMYDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DummyDataParameter)
class EltwiseParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ELTWISEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.EltwiseParameter)
class ThresholdParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _THRESHOLDPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ThresholdParameter)
class HDF5DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5DATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HDF5DataParameter)
class HDF5OutputParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5OUTPUTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HDF5OutputParameter)
class HingeLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HINGELOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HingeLossParameter)
class ImageDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _IMAGEDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ImageDataParameter)
class InfogainLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INFOGAINLOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.InfogainLossParameter)
class InnerProductParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INNERPRODUCTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.InnerProductParameter)
class LRNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LRNPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LRNParameter)
class MemoryDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MEMORYDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.MemoryDataParameter)
class MVNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MVNPARAMETER
# @@protoc_insertion_point(class_scope:caffe.MVNParameter)
class PoolingParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POOLINGPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PoolingParameter)
class PowerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POWERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PowerParameter)
class ReLUParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _RELUPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ReLUParameter)
class SigmoidParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SIGMOIDPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SigmoidParameter)
class SliceParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SLICEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SliceParameter)
class SoftmaxParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOFTMAXPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SoftmaxParameter)
class TanHParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TANHPARAMETER
# @@protoc_insertion_point(class_scope:caffe.TanHParameter)
class WindowDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _WINDOWDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.WindowDataParameter)
class V0LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _V0LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.V0LayerParameter)
# @@protoc_insertion_point(module_scope)
| 148,708 | 41.163028 | 17,413 | py |
DRT | DRT-master/external_libs/matconvnet/matconvnet/utils/proto/caffe_fastrcnn_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
# source: caffe_fastrcnn.proto
from google.protobuf.internal import enum_type_wrapper
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = _descriptor.FileDescriptor(
name='caffe_fastrcnn.proto',
package='caffe',
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_PHASE = _descriptor.EnumDescriptor(
name='Phase',
full_name='caffe.Phase',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='TRAIN', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='TEST', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=12254,
serialized_end=12282,
)
Phase = enum_type_wrapper.EnumTypeWrapper(_PHASE)
TRAIN = 0
TEST = 1
_SOLVERPARAMETER_SOLVERMODE = _descriptor.EnumDescriptor(
name='SolverMode',
full_name='caffe.SolverParameter.SolverMode',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='CPU', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='GPU', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=1806,
serialized_end=1836,
)
_SOLVERPARAMETER_SOLVERTYPE = _descriptor.EnumDescriptor(
name='SolverType',
full_name='caffe.SolverParameter.SolverType',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='SGD', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='NESTEROV', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='ADAGRAD', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=1838,
serialized_end=1886,
)
_PARAMSPEC_DIMCHECKMODE = _descriptor.EnumDescriptor(
name='DimCheckMode',
full_name='caffe.ParamSpec.DimCheckMode',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='STRICT', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='PERMISSIVE', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2317,
serialized_end=2359,
)
_CONVOLUTIONPARAMETER_ENGINE = _descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.ConvolutionParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5109,
serialized_end=5152,
)
_DATAPARAMETER_DB = _descriptor.EnumDescriptor(
name='DB',
full_name='caffe.DataParameter.DB',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='LEVELDB', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='LMDB', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5399,
serialized_end=5426,
)
_ELTWISEPARAMETER_ELTWISEOP = _descriptor.EnumDescriptor(
name='EltwiseOp',
full_name='caffe.EltwiseParameter.EltwiseOp',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='PROD', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='SUM', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='MAX', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5766,
serialized_end=5805,
)
_HINGELOSSPARAMETER_NORM = _descriptor.EnumDescriptor(
name='Norm',
full_name='caffe.HingeLossParameter.Norm',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='L1', index=0, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='L2', index=1, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6072,
serialized_end=6094,
)
_LRNPARAMETER_NORMREGION = _descriptor.EnumDescriptor(
name='NormRegion',
full_name='caffe.LRNParameter.NormRegion',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='ACROSS_CHANNELS', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='WITHIN_CHANNEL', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6758,
serialized_end=6811,
)
_POOLINGPARAMETER_POOLMETHOD = _descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.PoolingParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=7315,
serialized_end=7361,
)
_POOLINGPARAMETER_ENGINE = _descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.PoolingParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5109,
serialized_end=5152,
)
_RELUPARAMETER_ENGINE = _descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.ReLUParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5109,
serialized_end=5152,
)
_SIGMOIDPARAMETER_ENGINE = _descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.SigmoidParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5109,
serialized_end=5152,
)
_SOFTMAXPARAMETER_ENGINE = _descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.SoftmaxParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5109,
serialized_end=5152,
)
_TANHPARAMETER_ENGINE = _descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.TanHParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5109,
serialized_end=5152,
)
_V1LAYERPARAMETER_LAYERTYPE = _descriptor.EnumDescriptor(
name='LayerType',
full_name='caffe.V1LayerParameter.LayerType',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='NONE', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='ABSVAL', index=1, number=35,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='ACCURACY', index=2, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='ARGMAX', index=3, number=30,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='BNLL', index=4, number=2,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CONCAT', index=5, number=3,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CONTRASTIVE_LOSS', index=6, number=37,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CONVOLUTION', index=7, number=4,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='DATA', index=8, number=5,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='DECONVOLUTION', index=9, number=39,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='DROPOUT', index=10, number=6,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='DUMMY_DATA', index=11, number=32,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='EUCLIDEAN_LOSS', index=12, number=7,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='ELTWISE', index=13, number=25,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='EXP', index=14, number=38,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='FLATTEN', index=15, number=8,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='HDF5_DATA', index=16, number=9,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='HDF5_OUTPUT', index=17, number=10,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='HINGE_LOSS', index=18, number=28,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='IM2COL', index=19, number=11,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='IMAGE_DATA', index=20, number=12,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='INFOGAIN_LOSS', index=21, number=13,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='INNER_PRODUCT', index=22, number=14,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='LRN', index=23, number=15,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='MEMORY_DATA', index=24, number=29,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='MULTINOMIAL_LOGISTIC_LOSS', index=25, number=16,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='MVN', index=26, number=34,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='POOLING', index=27, number=17,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='POWER', index=28, number=26,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='RELU', index=29, number=18,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='SIGMOID', index=30, number=19,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='SIGMOID_CROSS_ENTROPY_LOSS', index=31, number=27,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='SILENCE', index=32, number=36,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='SOFTMAX', index=33, number=20,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='SOFTMAX_LOSS', index=34, number=21,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='SPLIT', index=35, number=22,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='SLICE', index=36, number=33,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='TANH', index=37, number=23,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='WINDOW_DATA', index=38, number=24,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='THRESHOLD', index=39, number=31,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=10495,
serialized_end=11095,
)
_V1LAYERPARAMETER_DIMCHECKMODE = _descriptor.EnumDescriptor(
name='DimCheckMode',
full_name='caffe.V1LayerParameter.DimCheckMode',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='STRICT', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='PERMISSIVE', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2317,
serialized_end=2359,
)
_V0LAYERPARAMETER_POOLMETHOD = _descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.V0LayerParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=7315,
serialized_end=7361,
)
_BLOBSHAPE = _descriptor.Descriptor(
name='BlobShape',
full_name='caffe.BlobShape',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='dim', full_name='caffe.BlobShape.dim', index=0,
number=1, type=3, cpp_type=2, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=31,
serialized_end=59,
)
_BLOBPROTO = _descriptor.Descriptor(
name='BlobProto',
full_name='caffe.BlobProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='shape', full_name='caffe.BlobProto.shape', index=0,
number=7, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='data', full_name='caffe.BlobProto.data', index=1,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
_descriptor.FieldDescriptor(
name='diff', full_name='caffe.BlobProto.diff', index=2,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
_descriptor.FieldDescriptor(
name='num', full_name='caffe.BlobProto.num', index=3,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='channels', full_name='caffe.BlobProto.channels', index=4,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='height', full_name='caffe.BlobProto.height', index=5,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='width', full_name='caffe.BlobProto.width', index=6,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=62,
serialized_end=216,
)
_BLOBPROTOVECTOR = _descriptor.Descriptor(
name='BlobProtoVector',
full_name='caffe.BlobProtoVector',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='blobs', full_name='caffe.BlobProtoVector.blobs', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=218,
serialized_end=268,
)
_DATUM = _descriptor.Descriptor(
name='Datum',
full_name='caffe.Datum',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='channels', full_name='caffe.Datum.channels', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='height', full_name='caffe.Datum.height', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='width', full_name='caffe.Datum.width', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='data', full_name='caffe.Datum.data', index=3,
number=4, type=12, cpp_type=9, label=1,
has_default_value=False, default_value="",
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='label', full_name='caffe.Datum.label', index=4,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='float_data', full_name='caffe.Datum.float_data', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='encoded', full_name='caffe.Datum.encoded', index=6,
number=7, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=271,
serialized_end=400,
)
_FILLERPARAMETER = _descriptor.Descriptor(
name='FillerParameter',
full_name='caffe.FillerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='type', full_name='caffe.FillerParameter.type', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("constant", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='value', full_name='caffe.FillerParameter.value', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='min', full_name='caffe.FillerParameter.min', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='max', full_name='caffe.FillerParameter.max', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mean', full_name='caffe.FillerParameter.mean', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='std', full_name='caffe.FillerParameter.std', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='sparse', full_name='caffe.FillerParameter.sparse', index=6,
number=7, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=403,
serialized_end=547,
)
_NETPARAMETER = _descriptor.Descriptor(
name='NetParameter',
full_name='caffe.NetParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='name', full_name='caffe.NetParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='input', full_name='caffe.NetParameter.input', index=1,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='input_shape', full_name='caffe.NetParameter.input_shape', index=2,
number=8, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='input_dim', full_name='caffe.NetParameter.input_dim', index=3,
number=4, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='force_backward', full_name='caffe.NetParameter.force_backward', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='state', full_name='caffe.NetParameter.state', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='debug_info', full_name='caffe.NetParameter.debug_info', index=6,
number=7, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='layer', full_name='caffe.NetParameter.layer', index=7,
number=100, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='layers', full_name='caffe.NetParameter.layers', index=8,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=550,
serialized_end=820,
)
_SOLVERPARAMETER = _descriptor.Descriptor(
name='SolverParameter',
full_name='caffe.SolverParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='net', full_name='caffe.SolverParameter.net', index=0,
number=24, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='net_param', full_name='caffe.SolverParameter.net_param', index=1,
number=25, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='train_net', full_name='caffe.SolverParameter.train_net', index=2,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='test_net', full_name='caffe.SolverParameter.test_net', index=3,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='train_net_param', full_name='caffe.SolverParameter.train_net_param', index=4,
number=21, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='test_net_param', full_name='caffe.SolverParameter.test_net_param', index=5,
number=22, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='train_state', full_name='caffe.SolverParameter.train_state', index=6,
number=26, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='test_state', full_name='caffe.SolverParameter.test_state', index=7,
number=27, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='test_iter', full_name='caffe.SolverParameter.test_iter', index=8,
number=3, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='test_interval', full_name='caffe.SolverParameter.test_interval', index=9,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='test_compute_loss', full_name='caffe.SolverParameter.test_compute_loss', index=10,
number=19, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='test_initialization', full_name='caffe.SolverParameter.test_initialization', index=11,
number=32, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='base_lr', full_name='caffe.SolverParameter.base_lr', index=12,
number=5, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='display', full_name='caffe.SolverParameter.display', index=13,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='average_loss', full_name='caffe.SolverParameter.average_loss', index=14,
number=33, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='max_iter', full_name='caffe.SolverParameter.max_iter', index=15,
number=7, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='lr_policy', full_name='caffe.SolverParameter.lr_policy', index=16,
number=8, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='gamma', full_name='caffe.SolverParameter.gamma', index=17,
number=9, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='power', full_name='caffe.SolverParameter.power', index=18,
number=10, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='momentum', full_name='caffe.SolverParameter.momentum', index=19,
number=11, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.SolverParameter.weight_decay', index=20,
number=12, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='regularization_type', full_name='caffe.SolverParameter.regularization_type', index=21,
number=29, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("L2", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stepsize', full_name='caffe.SolverParameter.stepsize', index=22,
number=13, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stepvalue', full_name='caffe.SolverParameter.stepvalue', index=23,
number=34, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='clip_gradients', full_name='caffe.SolverParameter.clip_gradients', index=24,
number=35, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='snapshot', full_name='caffe.SolverParameter.snapshot', index=25,
number=14, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='snapshot_prefix', full_name='caffe.SolverParameter.snapshot_prefix', index=26,
number=15, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='snapshot_diff', full_name='caffe.SolverParameter.snapshot_diff', index=27,
number=16, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='solver_mode', full_name='caffe.SolverParameter.solver_mode', index=28,
number=17, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='device_id', full_name='caffe.SolverParameter.device_id', index=29,
number=18, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='random_seed', full_name='caffe.SolverParameter.random_seed', index=30,
number=20, type=3, cpp_type=2, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='solver_type', full_name='caffe.SolverParameter.solver_type', index=31,
number=30, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='delta', full_name='caffe.SolverParameter.delta', index=32,
number=31, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1e-08,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='debug_info', full_name='caffe.SolverParameter.debug_info', index=33,
number=23, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='snapshot_after_train', full_name='caffe.SolverParameter.snapshot_after_train', index=34,
number=28, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SOLVERPARAMETER_SOLVERMODE,
_SOLVERPARAMETER_SOLVERTYPE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=823,
serialized_end=1886,
)
_SOLVERSTATE = _descriptor.Descriptor(
name='SolverState',
full_name='caffe.SolverState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='iter', full_name='caffe.SolverState.iter', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='learned_net', full_name='caffe.SolverState.learned_net', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='history', full_name='caffe.SolverState.history', index=2,
number=3, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='current_step', full_name='caffe.SolverState.current_step', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1888,
serialized_end=1996,
)
_NETSTATE = _descriptor.Descriptor(
name='NetState',
full_name='caffe.NetState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='phase', full_name='caffe.NetState.phase', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='level', full_name='caffe.NetState.level', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stage', full_name='caffe.NetState.stage', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1998,
serialized_end=2076,
)
_NETSTATERULE = _descriptor.Descriptor(
name='NetStateRule',
full_name='caffe.NetStateRule',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='phase', full_name='caffe.NetStateRule.phase', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='min_level', full_name='caffe.NetStateRule.min_level', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='max_level', full_name='caffe.NetStateRule.max_level', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stage', full_name='caffe.NetStateRule.stage', index=3,
number=4, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='not_stage', full_name='caffe.NetStateRule.not_stage', index=4,
number=5, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2078,
serialized_end=2193,
)
_PARAMSPEC = _descriptor.Descriptor(
name='ParamSpec',
full_name='caffe.ParamSpec',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='name', full_name='caffe.ParamSpec.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='share_mode', full_name='caffe.ParamSpec.share_mode', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='lr_mult', full_name='caffe.ParamSpec.lr_mult', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='decay_mult', full_name='caffe.ParamSpec.decay_mult', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_PARAMSPEC_DIMCHECKMODE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2196,
serialized_end=2359,
)
_LAYERPARAMETER = _descriptor.Descriptor(
name='LayerParameter',
full_name='caffe.LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='name', full_name='caffe.LayerParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='type', full_name='caffe.LayerParameter.type', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='bottom', full_name='caffe.LayerParameter.bottom', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='top', full_name='caffe.LayerParameter.top', index=3,
number=4, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='phase', full_name='caffe.LayerParameter.phase', index=4,
number=10, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='loss_weight', full_name='caffe.LayerParameter.loss_weight', index=5,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='param', full_name='caffe.LayerParameter.param', index=6,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='blobs', full_name='caffe.LayerParameter.blobs', index=7,
number=7, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='include', full_name='caffe.LayerParameter.include', index=8,
number=8, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='exclude', full_name='caffe.LayerParameter.exclude', index=9,
number=9, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='transform_param', full_name='caffe.LayerParameter.transform_param', index=10,
number=100, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='loss_param', full_name='caffe.LayerParameter.loss_param', index=11,
number=101, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='accuracy_param', full_name='caffe.LayerParameter.accuracy_param', index=12,
number=102, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='argmax_param', full_name='caffe.LayerParameter.argmax_param', index=13,
number=103, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='concat_param', full_name='caffe.LayerParameter.concat_param', index=14,
number=104, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='contrastive_loss_param', full_name='caffe.LayerParameter.contrastive_loss_param', index=15,
number=105, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='convolution_param', full_name='caffe.LayerParameter.convolution_param', index=16,
number=106, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='data_param', full_name='caffe.LayerParameter.data_param', index=17,
number=107, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='dropout_param', full_name='caffe.LayerParameter.dropout_param', index=18,
number=108, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='dummy_data_param', full_name='caffe.LayerParameter.dummy_data_param', index=19,
number=109, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='eltwise_param', full_name='caffe.LayerParameter.eltwise_param', index=20,
number=110, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='exp_param', full_name='caffe.LayerParameter.exp_param', index=21,
number=111, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='hdf5_data_param', full_name='caffe.LayerParameter.hdf5_data_param', index=22,
number=112, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.LayerParameter.hdf5_output_param', index=23,
number=113, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='hinge_loss_param', full_name='caffe.LayerParameter.hinge_loss_param', index=24,
number=114, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='image_data_param', full_name='caffe.LayerParameter.image_data_param', index=25,
number=115, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='infogain_loss_param', full_name='caffe.LayerParameter.infogain_loss_param', index=26,
number=116, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='inner_product_param', full_name='caffe.LayerParameter.inner_product_param', index=27,
number=117, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='lrn_param', full_name='caffe.LayerParameter.lrn_param', index=28,
number=118, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='memory_data_param', full_name='caffe.LayerParameter.memory_data_param', index=29,
number=119, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mvn_param', full_name='caffe.LayerParameter.mvn_param', index=30,
number=120, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pooling_param', full_name='caffe.LayerParameter.pooling_param', index=31,
number=121, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='power_param', full_name='caffe.LayerParameter.power_param', index=32,
number=122, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='prelu_param', full_name='caffe.LayerParameter.prelu_param', index=33,
number=131, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='python_param', full_name='caffe.LayerParameter.python_param', index=34,
number=130, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='relu_param', full_name='caffe.LayerParameter.relu_param', index=35,
number=123, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='roi_pooling_param', full_name='caffe.LayerParameter.roi_pooling_param', index=36,
number=8266711, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='sigmoid_param', full_name='caffe.LayerParameter.sigmoid_param', index=37,
number=124, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='softmax_param', full_name='caffe.LayerParameter.softmax_param', index=38,
number=125, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='slice_param', full_name='caffe.LayerParameter.slice_param', index=39,
number=126, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='tanh_param', full_name='caffe.LayerParameter.tanh_param', index=40,
number=127, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='threshold_param', full_name='caffe.LayerParameter.threshold_param', index=41,
number=128, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='window_data_param', full_name='caffe.LayerParameter.window_data_param', index=42,
number=129, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2362,
serialized_end=4260,
)
_TRANSFORMATIONPARAMETER = _descriptor.Descriptor(
name='TransformationParameter',
full_name='caffe.TransformationParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='scale', full_name='caffe.TransformationParameter.scale', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mirror', full_name='caffe.TransformationParameter.mirror', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.TransformationParameter.crop_size', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.TransformationParameter.mean_file', index=3,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mean_value', full_name='caffe.TransformationParameter.mean_value', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4262,
serialized_end=4389,
)
_LOSSPARAMETER = _descriptor.Descriptor(
name='LossParameter',
full_name='caffe.LossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='ignore_label', full_name='caffe.LossParameter.ignore_label', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='normalize', full_name='caffe.LossParameter.normalize', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4391,
serialized_end=4453,
)
_ACCURACYPARAMETER = _descriptor.Descriptor(
name='AccuracyParameter',
full_name='caffe.AccuracyParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='top_k', full_name='caffe.AccuracyParameter.top_k', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='axis', full_name='caffe.AccuracyParameter.axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='ignore_label', full_name='caffe.AccuracyParameter.ignore_label', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4455,
serialized_end=4531,
)
_ARGMAXPARAMETER = _descriptor.Descriptor(
name='ArgMaxParameter',
full_name='caffe.ArgMaxParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='out_max_val', full_name='caffe.ArgMaxParameter.out_max_val', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='top_k', full_name='caffe.ArgMaxParameter.top_k', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4533,
serialized_end=4596,
)
_CONCATPARAMETER = _descriptor.Descriptor(
name='ConcatParameter',
full_name='caffe.ConcatParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='axis', full_name='caffe.ConcatParameter.axis', index=0,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.ConcatParameter.concat_dim', index=1,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4598,
serialized_end=4655,
)
_CONTRASTIVELOSSPARAMETER = _descriptor.Descriptor(
name='ContrastiveLossParameter',
full_name='caffe.ContrastiveLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='margin', full_name='caffe.ContrastiveLossParameter.margin', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4657,
serialized_end=4702,
)
_CONVOLUTIONPARAMETER = _descriptor.Descriptor(
name='ConvolutionParameter',
full_name='caffe.ConvolutionParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='num_output', full_name='caffe.ConvolutionParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.ConvolutionParameter.bias_term', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pad', full_name='caffe.ConvolutionParameter.pad', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pad_h', full_name='caffe.ConvolutionParameter.pad_h', index=3,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pad_w', full_name='caffe.ConvolutionParameter.pad_w', index=4,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='kernel_size', full_name='caffe.ConvolutionParameter.kernel_size', index=5,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='kernel_h', full_name='caffe.ConvolutionParameter.kernel_h', index=6,
number=11, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='kernel_w', full_name='caffe.ConvolutionParameter.kernel_w', index=7,
number=12, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='group', full_name='caffe.ConvolutionParameter.group', index=8,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stride', full_name='caffe.ConvolutionParameter.stride', index=9,
number=6, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stride_h', full_name='caffe.ConvolutionParameter.stride_h', index=10,
number=13, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stride_w', full_name='caffe.ConvolutionParameter.stride_w', index=11,
number=14, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.ConvolutionParameter.weight_filler', index=12,
number=7, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.ConvolutionParameter.bias_filler', index=13,
number=8, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='engine', full_name='caffe.ConvolutionParameter.engine', index=14,
number=15, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_CONVOLUTIONPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4705,
serialized_end=5152,
)
_DATAPARAMETER = _descriptor.Descriptor(
name='DataParameter',
full_name='caffe.DataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='source', full_name='caffe.DataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.DataParameter.batch_size', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.DataParameter.rand_skip', index=2,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='backend', full_name='caffe.DataParameter.backend', index=3,
number=8, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='scale', full_name='caffe.DataParameter.scale', index=4,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.DataParameter.mean_file', index=5,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.DataParameter.crop_size', index=6,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mirror', full_name='caffe.DataParameter.mirror', index=7,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='force_encoded_color', full_name='caffe.DataParameter.force_encoded_color', index=8,
number=9, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_DATAPARAMETER_DB,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5155,
serialized_end=5426,
)
_DROPOUTPARAMETER = _descriptor.Descriptor(
name='DropoutParameter',
full_name='caffe.DropoutParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.DropoutParameter.dropout_ratio', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5428,
serialized_end=5474,
)
_DUMMYDATAPARAMETER = _descriptor.Descriptor(
name='DummyDataParameter',
full_name='caffe.DummyDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='data_filler', full_name='caffe.DummyDataParameter.data_filler', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='shape', full_name='caffe.DummyDataParameter.shape', index=1,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='num', full_name='caffe.DummyDataParameter.num', index=2,
number=2, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='channels', full_name='caffe.DummyDataParameter.channels', index=3,
number=3, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='height', full_name='caffe.DummyDataParameter.height', index=4,
number=4, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='width', full_name='caffe.DummyDataParameter.width', index=5,
number=5, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5477,
serialized_end=5637,
)
_ELTWISEPARAMETER = _descriptor.Descriptor(
name='EltwiseParameter',
full_name='caffe.EltwiseParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='operation', full_name='caffe.EltwiseParameter.operation', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='coeff', full_name='caffe.EltwiseParameter.coeff', index=1,
number=2, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stable_prod_grad', full_name='caffe.EltwiseParameter.stable_prod_grad', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_ELTWISEPARAMETER_ELTWISEOP,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5640,
serialized_end=5805,
)
_EXPPARAMETER = _descriptor.Descriptor(
name='ExpParameter',
full_name='caffe.ExpParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='base', full_name='caffe.ExpParameter.base', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='scale', full_name='caffe.ExpParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='shift', full_name='caffe.ExpParameter.shift', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5807,
serialized_end=5875,
)
_HDF5DATAPARAMETER = _descriptor.Descriptor(
name='HDF5DataParameter',
full_name='caffe.HDF5DataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='source', full_name='caffe.HDF5DataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.HDF5DataParameter.batch_size', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='shuffle', full_name='caffe.HDF5DataParameter.shuffle', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5877,
serialized_end=5956,
)
_HDF5OUTPUTPARAMETER = _descriptor.Descriptor(
name='HDF5OutputParameter',
full_name='caffe.HDF5OutputParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='file_name', full_name='caffe.HDF5OutputParameter.file_name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5958,
serialized_end=5998,
)
_HINGELOSSPARAMETER = _descriptor.Descriptor(
name='HingeLossParameter',
full_name='caffe.HingeLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='norm', full_name='caffe.HingeLossParameter.norm', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_HINGELOSSPARAMETER_NORM,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6000,
serialized_end=6094,
)
_IMAGEDATAPARAMETER = _descriptor.Descriptor(
name='ImageDataParameter',
full_name='caffe.ImageDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='source', full_name='caffe.ImageDataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.ImageDataParameter.batch_size', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.ImageDataParameter.rand_skip', index=2,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='shuffle', full_name='caffe.ImageDataParameter.shuffle', index=3,
number=8, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='new_height', full_name='caffe.ImageDataParameter.new_height', index=4,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='new_width', full_name='caffe.ImageDataParameter.new_width', index=5,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='is_color', full_name='caffe.ImageDataParameter.is_color', index=6,
number=11, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='scale', full_name='caffe.ImageDataParameter.scale', index=7,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.ImageDataParameter.mean_file', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.ImageDataParameter.crop_size', index=9,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mirror', full_name='caffe.ImageDataParameter.mirror', index=10,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='root_folder', full_name='caffe.ImageDataParameter.root_folder', index=11,
number=12, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6097,
serialized_end=6373,
)
_INFOGAINLOSSPARAMETER = _descriptor.Descriptor(
name='InfogainLossParameter',
full_name='caffe.InfogainLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='source', full_name='caffe.InfogainLossParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6375,
serialized_end=6414,
)
_INNERPRODUCTPARAMETER = _descriptor.Descriptor(
name='InnerProductParameter',
full_name='caffe.InnerProductParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='num_output', full_name='caffe.InnerProductParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.InnerProductParameter.bias_term', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.InnerProductParameter.weight_filler', index=2,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.InnerProductParameter.bias_filler', index=3,
number=4, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='axis', full_name='caffe.InnerProductParameter.axis', index=4,
number=5, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6417,
serialized_end=6594,
)
_LRNPARAMETER = _descriptor.Descriptor(
name='LRNParameter',
full_name='caffe.LRNParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='local_size', full_name='caffe.LRNParameter.local_size', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='alpha', full_name='caffe.LRNParameter.alpha', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='beta', full_name='caffe.LRNParameter.beta', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='norm_region', full_name='caffe.LRNParameter.norm_region', index=3,
number=4, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='k', full_name='caffe.LRNParameter.k', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_LRNPARAMETER_NORMREGION,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6597,
serialized_end=6811,
)
_MEMORYDATAPARAMETER = _descriptor.Descriptor(
name='MemoryDataParameter',
full_name='caffe.MemoryDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.MemoryDataParameter.batch_size', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='channels', full_name='caffe.MemoryDataParameter.channels', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='height', full_name='caffe.MemoryDataParameter.height', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='width', full_name='caffe.MemoryDataParameter.width', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6813,
serialized_end=6903,
)
_MVNPARAMETER = _descriptor.Descriptor(
name='MVNParameter',
full_name='caffe.MVNParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='normalize_variance', full_name='caffe.MVNParameter.normalize_variance', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='across_channels', full_name='caffe.MVNParameter.across_channels', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6905,
serialized_end=6985,
)
_POOLINGPARAMETER = _descriptor.Descriptor(
name='PoolingParameter',
full_name='caffe.PoolingParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='pool', full_name='caffe.PoolingParameter.pool', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pad', full_name='caffe.PoolingParameter.pad', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pad_h', full_name='caffe.PoolingParameter.pad_h', index=2,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pad_w', full_name='caffe.PoolingParameter.pad_w', index=3,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='kernel_size', full_name='caffe.PoolingParameter.kernel_size', index=4,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='kernel_h', full_name='caffe.PoolingParameter.kernel_h', index=5,
number=5, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='kernel_w', full_name='caffe.PoolingParameter.kernel_w', index=6,
number=6, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stride', full_name='caffe.PoolingParameter.stride', index=7,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stride_h', full_name='caffe.PoolingParameter.stride_h', index=8,
number=7, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stride_w', full_name='caffe.PoolingParameter.stride_w', index=9,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='engine', full_name='caffe.PoolingParameter.engine', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='global_pooling', full_name='caffe.PoolingParameter.global_pooling', index=11,
number=12, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_POOLINGPARAMETER_POOLMETHOD,
_POOLINGPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6988,
serialized_end=7406,
)
_POWERPARAMETER = _descriptor.Descriptor(
name='PowerParameter',
full_name='caffe.PowerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='power', full_name='caffe.PowerParameter.power', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='scale', full_name='caffe.PowerParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='shift', full_name='caffe.PowerParameter.shift', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7408,
serialized_end=7478,
)
_PYTHONPARAMETER = _descriptor.Descriptor(
name='PythonParameter',
full_name='caffe.PythonParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='module', full_name='caffe.PythonParameter.module', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='layer', full_name='caffe.PythonParameter.layer', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='param_str', full_name='caffe.PythonParameter.param_str', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7480,
serialized_end=7549,
)
_RELUPARAMETER = _descriptor.Descriptor(
name='ReLUParameter',
full_name='caffe.ReLUParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='negative_slope', full_name='caffe.ReLUParameter.negative_slope', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='engine', full_name='caffe.ReLUParameter.engine', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_RELUPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7552,
serialized_end=7693,
)
_ROIPOOLINGPARAMETER = _descriptor.Descriptor(
name='ROIPoolingParameter',
full_name='caffe.ROIPoolingParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='pooled_h', full_name='caffe.ROIPoolingParameter.pooled_h', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pooled_w', full_name='caffe.ROIPoolingParameter.pooled_w', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='spatial_scale', full_name='caffe.ROIPoolingParameter.spatial_scale', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7695,
serialized_end=7784,
)
_SIGMOIDPARAMETER = _descriptor.Descriptor(
name='SigmoidParameter',
full_name='caffe.SigmoidParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='engine', full_name='caffe.SigmoidParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SIGMOIDPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7786,
serialized_end=7906,
)
_SLICEPARAMETER = _descriptor.Descriptor(
name='SliceParameter',
full_name='caffe.SliceParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='axis', full_name='caffe.SliceParameter.axis', index=0,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='slice_point', full_name='caffe.SliceParameter.slice_point', index=1,
number=2, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='slice_dim', full_name='caffe.SliceParameter.slice_dim', index=2,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7908,
serialized_end=7984,
)
_SOFTMAXPARAMETER = _descriptor.Descriptor(
name='SoftmaxParameter',
full_name='caffe.SoftmaxParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='engine', full_name='caffe.SoftmaxParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='axis', full_name='caffe.SoftmaxParameter.axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SOFTMAXPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7987,
serialized_end=8124,
)
_TANHPARAMETER = _descriptor.Descriptor(
name='TanHParameter',
full_name='caffe.TanHParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='engine', full_name='caffe.TanHParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_TANHPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8126,
serialized_end=8240,
)
_THRESHOLDPARAMETER = _descriptor.Descriptor(
name='ThresholdParameter',
full_name='caffe.ThresholdParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='threshold', full_name='caffe.ThresholdParameter.threshold', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8242,
serialized_end=8284,
)
_WINDOWDATAPARAMETER = _descriptor.Descriptor(
name='WindowDataParameter',
full_name='caffe.WindowDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='source', full_name='caffe.WindowDataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='scale', full_name='caffe.WindowDataParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.WindowDataParameter.mean_file', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.WindowDataParameter.batch_size', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.WindowDataParameter.crop_size', index=4,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mirror', full_name='caffe.WindowDataParameter.mirror', index=5,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='fg_threshold', full_name='caffe.WindowDataParameter.fg_threshold', index=6,
number=7, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='bg_threshold', full_name='caffe.WindowDataParameter.bg_threshold', index=7,
number=8, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='fg_fraction', full_name='caffe.WindowDataParameter.fg_fraction', index=8,
number=9, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='context_pad', full_name='caffe.WindowDataParameter.context_pad', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='crop_mode', full_name='caffe.WindowDataParameter.crop_mode', index=10,
number=11, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='cache_images', full_name='caffe.WindowDataParameter.cache_images', index=11,
number=12, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='root_folder', full_name='caffe.WindowDataParameter.root_folder', index=12,
number=13, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8287,
serialized_end=8608,
)
_V1LAYERPARAMETER = _descriptor.Descriptor(
name='V1LayerParameter',
full_name='caffe.V1LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='bottom', full_name='caffe.V1LayerParameter.bottom', index=0,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='top', full_name='caffe.V1LayerParameter.top', index=1,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='name', full_name='caffe.V1LayerParameter.name', index=2,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='include', full_name='caffe.V1LayerParameter.include', index=3,
number=32, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='exclude', full_name='caffe.V1LayerParameter.exclude', index=4,
number=33, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='type', full_name='caffe.V1LayerParameter.type', index=5,
number=5, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='blobs', full_name='caffe.V1LayerParameter.blobs', index=6,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='param', full_name='caffe.V1LayerParameter.param', index=7,
number=1001, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='blob_share_mode', full_name='caffe.V1LayerParameter.blob_share_mode', index=8,
number=1002, type=14, cpp_type=8, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.V1LayerParameter.blobs_lr', index=9,
number=7, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.V1LayerParameter.weight_decay', index=10,
number=8, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='loss_weight', full_name='caffe.V1LayerParameter.loss_weight', index=11,
number=35, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='accuracy_param', full_name='caffe.V1LayerParameter.accuracy_param', index=12,
number=27, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='argmax_param', full_name='caffe.V1LayerParameter.argmax_param', index=13,
number=23, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='concat_param', full_name='caffe.V1LayerParameter.concat_param', index=14,
number=9, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='contrastive_loss_param', full_name='caffe.V1LayerParameter.contrastive_loss_param', index=15,
number=40, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='convolution_param', full_name='caffe.V1LayerParameter.convolution_param', index=16,
number=10, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='data_param', full_name='caffe.V1LayerParameter.data_param', index=17,
number=11, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='dropout_param', full_name='caffe.V1LayerParameter.dropout_param', index=18,
number=12, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='dummy_data_param', full_name='caffe.V1LayerParameter.dummy_data_param', index=19,
number=26, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='eltwise_param', full_name='caffe.V1LayerParameter.eltwise_param', index=20,
number=24, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='exp_param', full_name='caffe.V1LayerParameter.exp_param', index=21,
number=41, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='hdf5_data_param', full_name='caffe.V1LayerParameter.hdf5_data_param', index=22,
number=13, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.V1LayerParameter.hdf5_output_param', index=23,
number=14, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='hinge_loss_param', full_name='caffe.V1LayerParameter.hinge_loss_param', index=24,
number=29, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='image_data_param', full_name='caffe.V1LayerParameter.image_data_param', index=25,
number=15, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='infogain_loss_param', full_name='caffe.V1LayerParameter.infogain_loss_param', index=26,
number=16, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='inner_product_param', full_name='caffe.V1LayerParameter.inner_product_param', index=27,
number=17, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='lrn_param', full_name='caffe.V1LayerParameter.lrn_param', index=28,
number=18, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='memory_data_param', full_name='caffe.V1LayerParameter.memory_data_param', index=29,
number=22, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mvn_param', full_name='caffe.V1LayerParameter.mvn_param', index=30,
number=34, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pooling_param', full_name='caffe.V1LayerParameter.pooling_param', index=31,
number=19, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='power_param', full_name='caffe.V1LayerParameter.power_param', index=32,
number=21, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='relu_param', full_name='caffe.V1LayerParameter.relu_param', index=33,
number=30, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='sigmoid_param', full_name='caffe.V1LayerParameter.sigmoid_param', index=34,
number=38, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='softmax_param', full_name='caffe.V1LayerParameter.softmax_param', index=35,
number=39, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='slice_param', full_name='caffe.V1LayerParameter.slice_param', index=36,
number=31, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='tanh_param', full_name='caffe.V1LayerParameter.tanh_param', index=37,
number=37, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='threshold_param', full_name='caffe.V1LayerParameter.threshold_param', index=38,
number=25, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='window_data_param', full_name='caffe.V1LayerParameter.window_data_param', index=39,
number=20, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='transform_param', full_name='caffe.V1LayerParameter.transform_param', index=40,
number=36, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='loss_param', full_name='caffe.V1LayerParameter.loss_param', index=41,
number=42, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='layer', full_name='caffe.V1LayerParameter.layer', index=42,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_V1LAYERPARAMETER_LAYERTYPE,
_V1LAYERPARAMETER_DIMCHECKMODE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8611,
serialized_end=11139,
)
_V0LAYERPARAMETER = _descriptor.Descriptor(
name='V0LayerParameter',
full_name='caffe.V0LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='name', full_name='caffe.V0LayerParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='type', full_name='caffe.V0LayerParameter.type', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='num_output', full_name='caffe.V0LayerParameter.num_output', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='biasterm', full_name='caffe.V0LayerParameter.biasterm', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.V0LayerParameter.weight_filler', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.V0LayerParameter.bias_filler', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pad', full_name='caffe.V0LayerParameter.pad', index=6,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='kernelsize', full_name='caffe.V0LayerParameter.kernelsize', index=7,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='group', full_name='caffe.V0LayerParameter.group', index=8,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stride', full_name='caffe.V0LayerParameter.stride', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pool', full_name='caffe.V0LayerParameter.pool', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.V0LayerParameter.dropout_ratio', index=11,
number=12, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='local_size', full_name='caffe.V0LayerParameter.local_size', index=12,
number=13, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='alpha', full_name='caffe.V0LayerParameter.alpha', index=13,
number=14, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='beta', full_name='caffe.V0LayerParameter.beta', index=14,
number=15, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='k', full_name='caffe.V0LayerParameter.k', index=15,
number=22, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='source', full_name='caffe.V0LayerParameter.source', index=16,
number=16, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='scale', full_name='caffe.V0LayerParameter.scale', index=17,
number=17, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='meanfile', full_name='caffe.V0LayerParameter.meanfile', index=18,
number=18, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='batchsize', full_name='caffe.V0LayerParameter.batchsize', index=19,
number=19, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='cropsize', full_name='caffe.V0LayerParameter.cropsize', index=20,
number=20, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mirror', full_name='caffe.V0LayerParameter.mirror', index=21,
number=21, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='blobs', full_name='caffe.V0LayerParameter.blobs', index=22,
number=50, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.V0LayerParameter.blobs_lr', index=23,
number=51, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.V0LayerParameter.weight_decay', index=24,
number=52, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.V0LayerParameter.rand_skip', index=25,
number=53, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='det_fg_threshold', full_name='caffe.V0LayerParameter.det_fg_threshold', index=26,
number=54, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='det_bg_threshold', full_name='caffe.V0LayerParameter.det_bg_threshold', index=27,
number=55, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='det_fg_fraction', full_name='caffe.V0LayerParameter.det_fg_fraction', index=28,
number=56, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='det_context_pad', full_name='caffe.V0LayerParameter.det_context_pad', index=29,
number=58, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='det_crop_mode', full_name='caffe.V0LayerParameter.det_crop_mode', index=30,
number=59, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='new_num', full_name='caffe.V0LayerParameter.new_num', index=31,
number=60, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='new_channels', full_name='caffe.V0LayerParameter.new_channels', index=32,
number=61, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='new_height', full_name='caffe.V0LayerParameter.new_height', index=33,
number=62, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='new_width', full_name='caffe.V0LayerParameter.new_width', index=34,
number=63, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='shuffle_images', full_name='caffe.V0LayerParameter.shuffle_images', index=35,
number=64, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.V0LayerParameter.concat_dim', index=36,
number=65, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.V0LayerParameter.hdf5_output_param', index=37,
number=1001, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_V0LAYERPARAMETER_POOLMETHOD,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=11142,
serialized_end=12163,
)
_PRELUPARAMETER = _descriptor.Descriptor(
name='PReLUParameter',
full_name='caffe.PReLUParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='filler', full_name='caffe.PReLUParameter.filler', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='channel_shared', full_name='caffe.PReLUParameter.channel_shared', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=12165,
serialized_end=12252,
)
_BLOBPROTO.fields_by_name['shape'].message_type = _BLOBSHAPE
_BLOBPROTOVECTOR.fields_by_name['blobs'].message_type = _BLOBPROTO
_NETPARAMETER.fields_by_name['input_shape'].message_type = _BLOBSHAPE
_NETPARAMETER.fields_by_name['state'].message_type = _NETSTATE
_NETPARAMETER.fields_by_name['layer'].message_type = _LAYERPARAMETER
_NETPARAMETER.fields_by_name['layers'].message_type = _V1LAYERPARAMETER
_SOLVERPARAMETER.fields_by_name['net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['train_net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['test_net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['train_state'].message_type = _NETSTATE
_SOLVERPARAMETER.fields_by_name['test_state'].message_type = _NETSTATE
_SOLVERPARAMETER.fields_by_name['solver_mode'].enum_type = _SOLVERPARAMETER_SOLVERMODE
_SOLVERPARAMETER.fields_by_name['solver_type'].enum_type = _SOLVERPARAMETER_SOLVERTYPE
_SOLVERPARAMETER_SOLVERMODE.containing_type = _SOLVERPARAMETER;
_SOLVERPARAMETER_SOLVERTYPE.containing_type = _SOLVERPARAMETER;
_SOLVERSTATE.fields_by_name['history'].message_type = _BLOBPROTO
_NETSTATE.fields_by_name['phase'].enum_type = _PHASE
_NETSTATERULE.fields_by_name['phase'].enum_type = _PHASE
_PARAMSPEC.fields_by_name['share_mode'].enum_type = _PARAMSPEC_DIMCHECKMODE
_PARAMSPEC_DIMCHECKMODE.containing_type = _PARAMSPEC;
_LAYERPARAMETER.fields_by_name['phase'].enum_type = _PHASE
_LAYERPARAMETER.fields_by_name['param'].message_type = _PARAMSPEC
_LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_LAYERPARAMETER.fields_by_name['include'].message_type = _NETSTATERULE
_LAYERPARAMETER.fields_by_name['exclude'].message_type = _NETSTATERULE
_LAYERPARAMETER.fields_by_name['transform_param'].message_type = _TRANSFORMATIONPARAMETER
_LAYERPARAMETER.fields_by_name['loss_param'].message_type = _LOSSPARAMETER
_LAYERPARAMETER.fields_by_name['accuracy_param'].message_type = _ACCURACYPARAMETER
_LAYERPARAMETER.fields_by_name['argmax_param'].message_type = _ARGMAXPARAMETER
_LAYERPARAMETER.fields_by_name['concat_param'].message_type = _CONCATPARAMETER
_LAYERPARAMETER.fields_by_name['contrastive_loss_param'].message_type = _CONTRASTIVELOSSPARAMETER
_LAYERPARAMETER.fields_by_name['convolution_param'].message_type = _CONVOLUTIONPARAMETER
_LAYERPARAMETER.fields_by_name['data_param'].message_type = _DATAPARAMETER
_LAYERPARAMETER.fields_by_name['dropout_param'].message_type = _DROPOUTPARAMETER
_LAYERPARAMETER.fields_by_name['dummy_data_param'].message_type = _DUMMYDATAPARAMETER
_LAYERPARAMETER.fields_by_name['eltwise_param'].message_type = _ELTWISEPARAMETER
_LAYERPARAMETER.fields_by_name['exp_param'].message_type = _EXPPARAMETER
_LAYERPARAMETER.fields_by_name['hdf5_data_param'].message_type = _HDF5DATAPARAMETER
_LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_LAYERPARAMETER.fields_by_name['hinge_loss_param'].message_type = _HINGELOSSPARAMETER
_LAYERPARAMETER.fields_by_name['image_data_param'].message_type = _IMAGEDATAPARAMETER
_LAYERPARAMETER.fields_by_name['infogain_loss_param'].message_type = _INFOGAINLOSSPARAMETER
_LAYERPARAMETER.fields_by_name['inner_product_param'].message_type = _INNERPRODUCTPARAMETER
_LAYERPARAMETER.fields_by_name['lrn_param'].message_type = _LRNPARAMETER
_LAYERPARAMETER.fields_by_name['memory_data_param'].message_type = _MEMORYDATAPARAMETER
_LAYERPARAMETER.fields_by_name['mvn_param'].message_type = _MVNPARAMETER
_LAYERPARAMETER.fields_by_name['pooling_param'].message_type = _POOLINGPARAMETER
_LAYERPARAMETER.fields_by_name['power_param'].message_type = _POWERPARAMETER
_LAYERPARAMETER.fields_by_name['prelu_param'].message_type = _PRELUPARAMETER
_LAYERPARAMETER.fields_by_name['python_param'].message_type = _PYTHONPARAMETER
_LAYERPARAMETER.fields_by_name['relu_param'].message_type = _RELUPARAMETER
_LAYERPARAMETER.fields_by_name['roi_pooling_param'].message_type = _ROIPOOLINGPARAMETER
_LAYERPARAMETER.fields_by_name['sigmoid_param'].message_type = _SIGMOIDPARAMETER
_LAYERPARAMETER.fields_by_name['softmax_param'].message_type = _SOFTMAXPARAMETER
_LAYERPARAMETER.fields_by_name['slice_param'].message_type = _SLICEPARAMETER
_LAYERPARAMETER.fields_by_name['tanh_param'].message_type = _TANHPARAMETER
_LAYERPARAMETER.fields_by_name['threshold_param'].message_type = _THRESHOLDPARAMETER
_LAYERPARAMETER.fields_by_name['window_data_param'].message_type = _WINDOWDATAPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['engine'].enum_type = _CONVOLUTIONPARAMETER_ENGINE
_CONVOLUTIONPARAMETER_ENGINE.containing_type = _CONVOLUTIONPARAMETER;
_DATAPARAMETER.fields_by_name['backend'].enum_type = _DATAPARAMETER_DB
_DATAPARAMETER_DB.containing_type = _DATAPARAMETER;
_DUMMYDATAPARAMETER.fields_by_name['data_filler'].message_type = _FILLERPARAMETER
_DUMMYDATAPARAMETER.fields_by_name['shape'].message_type = _BLOBSHAPE
_ELTWISEPARAMETER.fields_by_name['operation'].enum_type = _ELTWISEPARAMETER_ELTWISEOP
_ELTWISEPARAMETER_ELTWISEOP.containing_type = _ELTWISEPARAMETER;
_HINGELOSSPARAMETER.fields_by_name['norm'].enum_type = _HINGELOSSPARAMETER_NORM
_HINGELOSSPARAMETER_NORM.containing_type = _HINGELOSSPARAMETER;
_INNERPRODUCTPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_INNERPRODUCTPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_LRNPARAMETER.fields_by_name['norm_region'].enum_type = _LRNPARAMETER_NORMREGION
_LRNPARAMETER_NORMREGION.containing_type = _LRNPARAMETER;
_POOLINGPARAMETER.fields_by_name['pool'].enum_type = _POOLINGPARAMETER_POOLMETHOD
_POOLINGPARAMETER.fields_by_name['engine'].enum_type = _POOLINGPARAMETER_ENGINE
_POOLINGPARAMETER_POOLMETHOD.containing_type = _POOLINGPARAMETER;
_POOLINGPARAMETER_ENGINE.containing_type = _POOLINGPARAMETER;
_RELUPARAMETER.fields_by_name['engine'].enum_type = _RELUPARAMETER_ENGINE
_RELUPARAMETER_ENGINE.containing_type = _RELUPARAMETER;
_SIGMOIDPARAMETER.fields_by_name['engine'].enum_type = _SIGMOIDPARAMETER_ENGINE
_SIGMOIDPARAMETER_ENGINE.containing_type = _SIGMOIDPARAMETER;
_SOFTMAXPARAMETER.fields_by_name['engine'].enum_type = _SOFTMAXPARAMETER_ENGINE
_SOFTMAXPARAMETER_ENGINE.containing_type = _SOFTMAXPARAMETER;
_TANHPARAMETER.fields_by_name['engine'].enum_type = _TANHPARAMETER_ENGINE
_TANHPARAMETER_ENGINE.containing_type = _TANHPARAMETER;
_V1LAYERPARAMETER.fields_by_name['include'].message_type = _NETSTATERULE
_V1LAYERPARAMETER.fields_by_name['exclude'].message_type = _NETSTATERULE
_V1LAYERPARAMETER.fields_by_name['type'].enum_type = _V1LAYERPARAMETER_LAYERTYPE
_V1LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_V1LAYERPARAMETER.fields_by_name['blob_share_mode'].enum_type = _V1LAYERPARAMETER_DIMCHECKMODE
_V1LAYERPARAMETER.fields_by_name['accuracy_param'].message_type = _ACCURACYPARAMETER
_V1LAYERPARAMETER.fields_by_name['argmax_param'].message_type = _ARGMAXPARAMETER
_V1LAYERPARAMETER.fields_by_name['concat_param'].message_type = _CONCATPARAMETER
_V1LAYERPARAMETER.fields_by_name['contrastive_loss_param'].message_type = _CONTRASTIVELOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['convolution_param'].message_type = _CONVOLUTIONPARAMETER
_V1LAYERPARAMETER.fields_by_name['data_param'].message_type = _DATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['dropout_param'].message_type = _DROPOUTPARAMETER
_V1LAYERPARAMETER.fields_by_name['dummy_data_param'].message_type = _DUMMYDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['eltwise_param'].message_type = _ELTWISEPARAMETER
_V1LAYERPARAMETER.fields_by_name['exp_param'].message_type = _EXPPARAMETER
_V1LAYERPARAMETER.fields_by_name['hdf5_data_param'].message_type = _HDF5DATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_V1LAYERPARAMETER.fields_by_name['hinge_loss_param'].message_type = _HINGELOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['image_data_param'].message_type = _IMAGEDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['infogain_loss_param'].message_type = _INFOGAINLOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['inner_product_param'].message_type = _INNERPRODUCTPARAMETER
_V1LAYERPARAMETER.fields_by_name['lrn_param'].message_type = _LRNPARAMETER
_V1LAYERPARAMETER.fields_by_name['memory_data_param'].message_type = _MEMORYDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['mvn_param'].message_type = _MVNPARAMETER
_V1LAYERPARAMETER.fields_by_name['pooling_param'].message_type = _POOLINGPARAMETER
_V1LAYERPARAMETER.fields_by_name['power_param'].message_type = _POWERPARAMETER
_V1LAYERPARAMETER.fields_by_name['relu_param'].message_type = _RELUPARAMETER
_V1LAYERPARAMETER.fields_by_name['sigmoid_param'].message_type = _SIGMOIDPARAMETER
_V1LAYERPARAMETER.fields_by_name['softmax_param'].message_type = _SOFTMAXPARAMETER
_V1LAYERPARAMETER.fields_by_name['slice_param'].message_type = _SLICEPARAMETER
_V1LAYERPARAMETER.fields_by_name['tanh_param'].message_type = _TANHPARAMETER
_V1LAYERPARAMETER.fields_by_name['threshold_param'].message_type = _THRESHOLDPARAMETER
_V1LAYERPARAMETER.fields_by_name['window_data_param'].message_type = _WINDOWDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['transform_param'].message_type = _TRANSFORMATIONPARAMETER
_V1LAYERPARAMETER.fields_by_name['loss_param'].message_type = _LOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['layer'].message_type = _V0LAYERPARAMETER
_V1LAYERPARAMETER_LAYERTYPE.containing_type = _V1LAYERPARAMETER;
_V1LAYERPARAMETER_DIMCHECKMODE.containing_type = _V1LAYERPARAMETER;
_V0LAYERPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_V0LAYERPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_V0LAYERPARAMETER.fields_by_name['pool'].enum_type = _V0LAYERPARAMETER_POOLMETHOD
_V0LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_V0LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_V0LAYERPARAMETER_POOLMETHOD.containing_type = _V0LAYERPARAMETER;
_PRELUPARAMETER.fields_by_name['filler'].message_type = _FILLERPARAMETER
DESCRIPTOR.message_types_by_name['BlobShape'] = _BLOBSHAPE
DESCRIPTOR.message_types_by_name['BlobProto'] = _BLOBPROTO
DESCRIPTOR.message_types_by_name['BlobProtoVector'] = _BLOBPROTOVECTOR
DESCRIPTOR.message_types_by_name['Datum'] = _DATUM
DESCRIPTOR.message_types_by_name['FillerParameter'] = _FILLERPARAMETER
DESCRIPTOR.message_types_by_name['NetParameter'] = _NETPARAMETER
DESCRIPTOR.message_types_by_name['SolverParameter'] = _SOLVERPARAMETER
DESCRIPTOR.message_types_by_name['SolverState'] = _SOLVERSTATE
DESCRIPTOR.message_types_by_name['NetState'] = _NETSTATE
DESCRIPTOR.message_types_by_name['NetStateRule'] = _NETSTATERULE
DESCRIPTOR.message_types_by_name['ParamSpec'] = _PARAMSPEC
DESCRIPTOR.message_types_by_name['LayerParameter'] = _LAYERPARAMETER
DESCRIPTOR.message_types_by_name['TransformationParameter'] = _TRANSFORMATIONPARAMETER
DESCRIPTOR.message_types_by_name['LossParameter'] = _LOSSPARAMETER
DESCRIPTOR.message_types_by_name['AccuracyParameter'] = _ACCURACYPARAMETER
DESCRIPTOR.message_types_by_name['ArgMaxParameter'] = _ARGMAXPARAMETER
DESCRIPTOR.message_types_by_name['ConcatParameter'] = _CONCATPARAMETER
DESCRIPTOR.message_types_by_name['ContrastiveLossParameter'] = _CONTRASTIVELOSSPARAMETER
DESCRIPTOR.message_types_by_name['ConvolutionParameter'] = _CONVOLUTIONPARAMETER
DESCRIPTOR.message_types_by_name['DataParameter'] = _DATAPARAMETER
DESCRIPTOR.message_types_by_name['DropoutParameter'] = _DROPOUTPARAMETER
DESCRIPTOR.message_types_by_name['DummyDataParameter'] = _DUMMYDATAPARAMETER
DESCRIPTOR.message_types_by_name['EltwiseParameter'] = _ELTWISEPARAMETER
DESCRIPTOR.message_types_by_name['ExpParameter'] = _EXPPARAMETER
DESCRIPTOR.message_types_by_name['HDF5DataParameter'] = _HDF5DATAPARAMETER
DESCRIPTOR.message_types_by_name['HDF5OutputParameter'] = _HDF5OUTPUTPARAMETER
DESCRIPTOR.message_types_by_name['HingeLossParameter'] = _HINGELOSSPARAMETER
DESCRIPTOR.message_types_by_name['ImageDataParameter'] = _IMAGEDATAPARAMETER
DESCRIPTOR.message_types_by_name['InfogainLossParameter'] = _INFOGAINLOSSPARAMETER
DESCRIPTOR.message_types_by_name['InnerProductParameter'] = _INNERPRODUCTPARAMETER
DESCRIPTOR.message_types_by_name['LRNParameter'] = _LRNPARAMETER
DESCRIPTOR.message_types_by_name['MemoryDataParameter'] = _MEMORYDATAPARAMETER
DESCRIPTOR.message_types_by_name['MVNParameter'] = _MVNPARAMETER
DESCRIPTOR.message_types_by_name['PoolingParameter'] = _POOLINGPARAMETER
DESCRIPTOR.message_types_by_name['PowerParameter'] = _POWERPARAMETER
DESCRIPTOR.message_types_by_name['PythonParameter'] = _PYTHONPARAMETER
DESCRIPTOR.message_types_by_name['ReLUParameter'] = _RELUPARAMETER
DESCRIPTOR.message_types_by_name['ROIPoolingParameter'] = _ROIPOOLINGPARAMETER
DESCRIPTOR.message_types_by_name['SigmoidParameter'] = _SIGMOIDPARAMETER
DESCRIPTOR.message_types_by_name['SliceParameter'] = _SLICEPARAMETER
DESCRIPTOR.message_types_by_name['SoftmaxParameter'] = _SOFTMAXPARAMETER
DESCRIPTOR.message_types_by_name['TanHParameter'] = _TANHPARAMETER
DESCRIPTOR.message_types_by_name['ThresholdParameter'] = _THRESHOLDPARAMETER
DESCRIPTOR.message_types_by_name['WindowDataParameter'] = _WINDOWDATAPARAMETER
DESCRIPTOR.message_types_by_name['V1LayerParameter'] = _V1LAYERPARAMETER
DESCRIPTOR.message_types_by_name['V0LayerParameter'] = _V0LAYERPARAMETER
DESCRIPTOR.message_types_by_name['PReLUParameter'] = _PRELUPARAMETER
class BlobShape(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBSHAPE
# @@protoc_insertion_point(class_scope:caffe.BlobShape)
class BlobProto(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTO
# @@protoc_insertion_point(class_scope:caffe.BlobProto)
class BlobProtoVector(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTOVECTOR
# @@protoc_insertion_point(class_scope:caffe.BlobProtoVector)
class Datum(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATUM
# @@protoc_insertion_point(class_scope:caffe.Datum)
class FillerParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FILLERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.FillerParameter)
class NetParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETPARAMETER
# @@protoc_insertion_point(class_scope:caffe.NetParameter)
class SolverParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SolverParameter)
class SolverState(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERSTATE
# @@protoc_insertion_point(class_scope:caffe.SolverState)
class NetState(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATE
# @@protoc_insertion_point(class_scope:caffe.NetState)
class NetStateRule(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATERULE
# @@protoc_insertion_point(class_scope:caffe.NetStateRule)
class ParamSpec(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PARAMSPEC
# @@protoc_insertion_point(class_scope:caffe.ParamSpec)
class LayerParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LayerParameter)
class TransformationParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TRANSFORMATIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.TransformationParameter)
class LossParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LossParameter)
class AccuracyParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ACCURACYPARAMETER
# @@protoc_insertion_point(class_scope:caffe.AccuracyParameter)
class ArgMaxParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ARGMAXPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ArgMaxParameter)
class ConcatParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONCATPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ConcatParameter)
class ContrastiveLossParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONTRASTIVELOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ContrastiveLossParameter)
class ConvolutionParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONVOLUTIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ConvolutionParameter)
class DataParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DataParameter)
class DropoutParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DROPOUTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DropoutParameter)
class DummyDataParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DUMMYDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DummyDataParameter)
class EltwiseParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ELTWISEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.EltwiseParameter)
class ExpParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _EXPPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ExpParameter)
class HDF5DataParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5DATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HDF5DataParameter)
class HDF5OutputParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5OUTPUTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HDF5OutputParameter)
class HingeLossParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HINGELOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HingeLossParameter)
class ImageDataParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _IMAGEDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ImageDataParameter)
class InfogainLossParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INFOGAINLOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.InfogainLossParameter)
class InnerProductParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INNERPRODUCTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.InnerProductParameter)
class LRNParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LRNPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LRNParameter)
class MemoryDataParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MEMORYDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.MemoryDataParameter)
class MVNParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MVNPARAMETER
# @@protoc_insertion_point(class_scope:caffe.MVNParameter)
class PoolingParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POOLINGPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PoolingParameter)
class PowerParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POWERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PowerParameter)
class PythonParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PYTHONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PythonParameter)
class ReLUParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _RELUPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ReLUParameter)
class ROIPoolingParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ROIPOOLINGPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ROIPoolingParameter)
class SigmoidParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SIGMOIDPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SigmoidParameter)
class SliceParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SLICEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SliceParameter)
class SoftmaxParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOFTMAXPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SoftmaxParameter)
class TanHParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TANHPARAMETER
# @@protoc_insertion_point(class_scope:caffe.TanHParameter)
class ThresholdParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _THRESHOLDPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ThresholdParameter)
class WindowDataParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _WINDOWDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.WindowDataParameter)
class V1LayerParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _V1LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.V1LayerParameter)
class V0LayerParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _V0LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.V0LayerParameter)
class PReLUParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PRELUPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PReLUParameter)
_BLOBSHAPE.fields_by_name['dim'].has_options = True
_BLOBSHAPE.fields_by_name['dim']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')
_BLOBPROTO.fields_by_name['data'].has_options = True
_BLOBPROTO.fields_by_name['data']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')
_BLOBPROTO.fields_by_name['diff'].has_options = True
_BLOBPROTO.fields_by_name['diff']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')
# @@protoc_insertion_point(module_scope)
| 194,370 | 42.777252 | 22,943 | py |
DRT | DRT-master/external_libs/matconvnet/matconvnet/utils/proto/caffe_6e3916_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='caffe_6e3916.proto',
package='caffe',
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_PHASE = descriptor.EnumDescriptor(
name='Phase',
full_name='caffe.Phase',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='TRAIN', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='TEST', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=13880,
serialized_end=13908,
)
TRAIN = 0
TEST = 1
_FILLERPARAMETER_VARIANCENORM = descriptor.EnumDescriptor(
name='VarianceNorm',
full_name='caffe.FillerParameter.VarianceNorm',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='FAN_IN', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='FAN_OUT', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVERAGE', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=665,
serialized_end=717,
)
_SOLVERPARAMETER_SNAPSHOTFORMAT = descriptor.EnumDescriptor(
name='SnapshotFormat',
full_name='caffe.SolverParameter.SnapshotFormat',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='HDF5', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='BINARYPROTO', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2120,
serialized_end=2163,
)
_SOLVERPARAMETER_SOLVERMODE = descriptor.EnumDescriptor(
name='SolverMode',
full_name='caffe.SolverParameter.SolverMode',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='CPU', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='GPU', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2165,
serialized_end=2195,
)
_SOLVERPARAMETER_SOLVERTYPE = descriptor.EnumDescriptor(
name='SolverType',
full_name='caffe.SolverParameter.SolverType',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='SGD', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='NESTEROV', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ADAGRAD', index=2, number=2,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='RMSPROP', index=3, number=3,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ADADELTA', index=4, number=4,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ADAM', index=5, number=5,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2197,
serialized_end=2282,
)
_PARAMSPEC_DIMCHECKMODE = descriptor.EnumDescriptor(
name='DimCheckMode',
full_name='caffe.ParamSpec.DimCheckMode',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='STRICT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='PERMISSIVE', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2713,
serialized_end=2755,
)
_CONVOLUTIONPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.ConvolutionParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5879,
serialized_end=5922,
)
_DATAPARAMETER_DB = descriptor.EnumDescriptor(
name='DB',
full_name='caffe.DataParameter.DB',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='LEVELDB', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='LMDB', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6190,
serialized_end=6217,
)
_ELTWISEPARAMETER_ELTWISEOP = descriptor.EnumDescriptor(
name='EltwiseOp',
full_name='caffe.EltwiseParameter.EltwiseOp',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='PROD', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SUM', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MAX', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6557,
serialized_end=6596,
)
_HINGELOSSPARAMETER_NORM = descriptor.EnumDescriptor(
name='Norm',
full_name='caffe.HingeLossParameter.Norm',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='L1', index=0, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='L2', index=1, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=7097,
serialized_end=7119,
)
_LRNPARAMETER_NORMREGION = descriptor.EnumDescriptor(
name='NormRegion',
full_name='caffe.LRNParameter.NormRegion',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='ACROSS_CHANNELS', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='WITHIN_CHANNEL', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=7856,
serialized_end=7909,
)
_POOLINGPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.PoolingParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=8433,
serialized_end=8479,
)
_POOLINGPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.PoolingParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5879,
serialized_end=5922,
)
_REDUCTIONPARAMETER_REDUCTIONOP = descriptor.EnumDescriptor(
name='ReductionOp',
full_name='caffe.ReductionParameter.ReductionOp',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='SUM', index=0, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ASUM', index=1, number=2,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SUMSQ', index=2, number=3,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MEAN', index=3, number=4,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=8824,
serialized_end=8877,
)
_RELUPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.ReLUParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5879,
serialized_end=5922,
)
_SIGMOIDPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.SigmoidParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5879,
serialized_end=5922,
)
_SOFTMAXPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.SoftmaxParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5879,
serialized_end=5922,
)
_TANHPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.TanHParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5879,
serialized_end=5922,
)
_SPPPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.SPPParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=8433,
serialized_end=8479,
)
_SPPPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.SPPParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5879,
serialized_end=5922,
)
_V1LAYERPARAMETER_LAYERTYPE = descriptor.EnumDescriptor(
name='LayerType',
full_name='caffe.V1LayerParameter.LayerType',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='NONE', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ABSVAL', index=1, number=35,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ACCURACY', index=2, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ARGMAX', index=3, number=30,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='BNLL', index=4, number=2,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONCAT', index=5, number=3,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONTRASTIVE_LOSS', index=6, number=37,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONVOLUTION', index=7, number=4,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CROP', index=8, number=40,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DATA', index=9, number=5,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DECONVOLUTION', index=10, number=39,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DROPOUT', index=11, number=6,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DUMMY_DATA', index=12, number=32,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='EUCLIDEAN_LOSS', index=13, number=7,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ELTWISE', index=14, number=25,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='EXP', index=15, number=38,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='FLATTEN', index=16, number=8,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HDF5_DATA', index=17, number=9,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HDF5_OUTPUT', index=18, number=10,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HINGE_LOSS', index=19, number=28,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='IM2COL', index=20, number=11,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='IMAGE_DATA', index=21, number=12,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='INFOGAIN_LOSS', index=22, number=13,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='INNER_PRODUCT', index=23, number=14,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='LRN', index=24, number=15,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MEMORY_DATA', index=25, number=29,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MULTINOMIAL_LOGISTIC_LOSS', index=26, number=16,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MVN', index=27, number=34,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='POOLING', index=28, number=17,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='POWER', index=29, number=26,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='RELU', index=30, number=18,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SIGMOID', index=31, number=19,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SIGMOID_CROSS_ENTROPY_LOSS', index=32, number=27,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SILENCE', index=33, number=36,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SOFTMAX', index=34, number=20,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SOFTMAX_LOSS', index=35, number=21,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SPLIT', index=36, number=22,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SLICE', index=37, number=33,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='TANH', index=38, number=23,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='WINDOW_DATA', index=39, number=24,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='THRESHOLD', index=40, number=31,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=12111,
serialized_end=12721,
)
_V1LAYERPARAMETER_DIMCHECKMODE = descriptor.EnumDescriptor(
name='DimCheckMode',
full_name='caffe.V1LayerParameter.DimCheckMode',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='STRICT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='PERMISSIVE', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2713,
serialized_end=2755,
)
_V0LAYERPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.V0LayerParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=8433,
serialized_end=8479,
)
_BLOBSHAPE = descriptor.Descriptor(
name='BlobShape',
full_name='caffe.BlobShape',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='dim', full_name='caffe.BlobShape.dim', index=0,
number=1, type=3, cpp_type=2, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=29,
serialized_end=57,
)
_BLOBPROTO = descriptor.Descriptor(
name='BlobProto',
full_name='caffe.BlobProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='shape', full_name='caffe.BlobProto.shape', index=0,
number=7, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.BlobProto.data', index=1,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='diff', full_name='caffe.BlobProto.diff', index=2,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='double_data', full_name='caffe.BlobProto.double_data', index=3,
number=8, type=1, cpp_type=5, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='double_diff', full_name='caffe.BlobProto.double_diff', index=4,
number=9, type=1, cpp_type=5, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='num', full_name='caffe.BlobProto.num', index=5,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.BlobProto.channels', index=6,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.BlobProto.height', index=7,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.BlobProto.width', index=8,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=60,
serialized_end=264,
)
_BLOBPROTOVECTOR = descriptor.Descriptor(
name='BlobProtoVector',
full_name='caffe.BlobProtoVector',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.BlobProtoVector.blobs', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=266,
serialized_end=316,
)
_DATUM = descriptor.Descriptor(
name='Datum',
full_name='caffe.Datum',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='channels', full_name='caffe.Datum.channels', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.Datum.height', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.Datum.width', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.Datum.data', index=3,
number=4, type=12, cpp_type=9, label=1,
has_default_value=False, default_value="",
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='label', full_name='caffe.Datum.label', index=4,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='float_data', full_name='caffe.Datum.float_data', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='encoded', full_name='caffe.Datum.encoded', index=6,
number=7, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=319,
serialized_end=448,
)
_FILLERPARAMETER = descriptor.Descriptor(
name='FillerParameter',
full_name='caffe.FillerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='type', full_name='caffe.FillerParameter.type', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("constant", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='value', full_name='caffe.FillerParameter.value', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='min', full_name='caffe.FillerParameter.min', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max', full_name='caffe.FillerParameter.max', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean', full_name='caffe.FillerParameter.mean', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='std', full_name='caffe.FillerParameter.std', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='sparse', full_name='caffe.FillerParameter.sparse', index=6,
number=7, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='variance_norm', full_name='caffe.FillerParameter.variance_norm', index=7,
number=8, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_FILLERPARAMETER_VARIANCENORM,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=451,
serialized_end=717,
)
_NETPARAMETER = descriptor.Descriptor(
name='NetParameter',
full_name='caffe.NetParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.NetParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input', full_name='caffe.NetParameter.input', index=1,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input_shape', full_name='caffe.NetParameter.input_shape', index=2,
number=8, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input_dim', full_name='caffe.NetParameter.input_dim', index=3,
number=4, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_backward', full_name='caffe.NetParameter.force_backward', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='state', full_name='caffe.NetParameter.state', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='debug_info', full_name='caffe.NetParameter.debug_info', index=6,
number=7, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layer', full_name='caffe.NetParameter.layer', index=7,
number=100, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layers', full_name='caffe.NetParameter.layers', index=8,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=720,
serialized_end=990,
)
_SOLVERPARAMETER = descriptor.Descriptor(
name='SolverParameter',
full_name='caffe.SolverParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='net', full_name='caffe.SolverParameter.net', index=0,
number=24, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='net_param', full_name='caffe.SolverParameter.net_param', index=1,
number=25, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='train_net', full_name='caffe.SolverParameter.train_net', index=2,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_net', full_name='caffe.SolverParameter.test_net', index=3,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='train_net_param', full_name='caffe.SolverParameter.train_net_param', index=4,
number=21, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_net_param', full_name='caffe.SolverParameter.test_net_param', index=5,
number=22, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='train_state', full_name='caffe.SolverParameter.train_state', index=6,
number=26, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_state', full_name='caffe.SolverParameter.test_state', index=7,
number=27, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_iter', full_name='caffe.SolverParameter.test_iter', index=8,
number=3, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_interval', full_name='caffe.SolverParameter.test_interval', index=9,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_compute_loss', full_name='caffe.SolverParameter.test_compute_loss', index=10,
number=19, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_initialization', full_name='caffe.SolverParameter.test_initialization', index=11,
number=32, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='base_lr', full_name='caffe.SolverParameter.base_lr', index=12,
number=5, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='display', full_name='caffe.SolverParameter.display', index=13,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='average_loss', full_name='caffe.SolverParameter.average_loss', index=14,
number=33, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max_iter', full_name='caffe.SolverParameter.max_iter', index=15,
number=7, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='iter_size', full_name='caffe.SolverParameter.iter_size', index=16,
number=36, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lr_policy', full_name='caffe.SolverParameter.lr_policy', index=17,
number=8, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='gamma', full_name='caffe.SolverParameter.gamma', index=18,
number=9, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power', full_name='caffe.SolverParameter.power', index=19,
number=10, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='momentum', full_name='caffe.SolverParameter.momentum', index=20,
number=11, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.SolverParameter.weight_decay', index=21,
number=12, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='regularization_type', full_name='caffe.SolverParameter.regularization_type', index=22,
number=29, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("L2", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stepsize', full_name='caffe.SolverParameter.stepsize', index=23,
number=13, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stepvalue', full_name='caffe.SolverParameter.stepvalue', index=24,
number=34, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='clip_gradients', full_name='caffe.SolverParameter.clip_gradients', index=25,
number=35, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot', full_name='caffe.SolverParameter.snapshot', index=26,
number=14, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_prefix', full_name='caffe.SolverParameter.snapshot_prefix', index=27,
number=15, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_diff', full_name='caffe.SolverParameter.snapshot_diff', index=28,
number=16, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_format', full_name='caffe.SolverParameter.snapshot_format', index=29,
number=37, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='solver_mode', full_name='caffe.SolverParameter.solver_mode', index=30,
number=17, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='device_id', full_name='caffe.SolverParameter.device_id', index=31,
number=18, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='random_seed', full_name='caffe.SolverParameter.random_seed', index=32,
number=20, type=3, cpp_type=2, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='solver_type', full_name='caffe.SolverParameter.solver_type', index=33,
number=30, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='delta', full_name='caffe.SolverParameter.delta', index=34,
number=31, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1e-08,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='momentum2', full_name='caffe.SolverParameter.momentum2', index=35,
number=39, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.999,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rms_decay', full_name='caffe.SolverParameter.rms_decay', index=36,
number=38, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='debug_info', full_name='caffe.SolverParameter.debug_info', index=37,
number=23, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_after_train', full_name='caffe.SolverParameter.snapshot_after_train', index=38,
number=28, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SOLVERPARAMETER_SNAPSHOTFORMAT,
_SOLVERPARAMETER_SOLVERMODE,
_SOLVERPARAMETER_SOLVERTYPE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=993,
serialized_end=2282,
)
_SOLVERSTATE = descriptor.Descriptor(
name='SolverState',
full_name='caffe.SolverState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='iter', full_name='caffe.SolverState.iter', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='learned_net', full_name='caffe.SolverState.learned_net', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='history', full_name='caffe.SolverState.history', index=2,
number=3, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='current_step', full_name='caffe.SolverState.current_step', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2284,
serialized_end=2392,
)
_NETSTATE = descriptor.Descriptor(
name='NetState',
full_name='caffe.NetState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='phase', full_name='caffe.NetState.phase', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='level', full_name='caffe.NetState.level', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stage', full_name='caffe.NetState.stage', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2394,
serialized_end=2472,
)
_NETSTATERULE = descriptor.Descriptor(
name='NetStateRule',
full_name='caffe.NetStateRule',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='phase', full_name='caffe.NetStateRule.phase', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='min_level', full_name='caffe.NetStateRule.min_level', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max_level', full_name='caffe.NetStateRule.max_level', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stage', full_name='caffe.NetStateRule.stage', index=3,
number=4, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='not_stage', full_name='caffe.NetStateRule.not_stage', index=4,
number=5, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2474,
serialized_end=2589,
)
_PARAMSPEC = descriptor.Descriptor(
name='ParamSpec',
full_name='caffe.ParamSpec',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.ParamSpec.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='share_mode', full_name='caffe.ParamSpec.share_mode', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lr_mult', full_name='caffe.ParamSpec.lr_mult', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='decay_mult', full_name='caffe.ParamSpec.decay_mult', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_PARAMSPEC_DIMCHECKMODE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2592,
serialized_end=2755,
)
_LAYERPARAMETER = descriptor.Descriptor(
name='LayerParameter',
full_name='caffe.LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.LayerParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.LayerParameter.type', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bottom', full_name='caffe.LayerParameter.bottom', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top', full_name='caffe.LayerParameter.top', index=3,
number=4, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='phase', full_name='caffe.LayerParameter.phase', index=4,
number=10, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='loss_weight', full_name='caffe.LayerParameter.loss_weight', index=5,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='param', full_name='caffe.LayerParameter.param', index=6,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.LayerParameter.blobs', index=7,
number=7, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='propagate_down', full_name='caffe.LayerParameter.propagate_down', index=8,
number=11, type=8, cpp_type=7, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='include', full_name='caffe.LayerParameter.include', index=9,
number=8, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='exclude', full_name='caffe.LayerParameter.exclude', index=10,
number=9, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='transform_param', full_name='caffe.LayerParameter.transform_param', index=11,
number=100, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='loss_param', full_name='caffe.LayerParameter.loss_param', index=12,
number=101, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='accuracy_param', full_name='caffe.LayerParameter.accuracy_param', index=13,
number=102, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='argmax_param', full_name='caffe.LayerParameter.argmax_param', index=14,
number=103, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_param', full_name='caffe.LayerParameter.concat_param', index=15,
number=104, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='contrastive_loss_param', full_name='caffe.LayerParameter.contrastive_loss_param', index=16,
number=105, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='convolution_param', full_name='caffe.LayerParameter.convolution_param', index=17,
number=106, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data_param', full_name='caffe.LayerParameter.data_param', index=18,
number=107, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_param', full_name='caffe.LayerParameter.dropout_param', index=19,
number=108, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dummy_data_param', full_name='caffe.LayerParameter.dummy_data_param', index=20,
number=109, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='eltwise_param', full_name='caffe.LayerParameter.eltwise_param', index=21,
number=110, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='embed_param', full_name='caffe.LayerParameter.embed_param', index=22,
number=137, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='exp_param', full_name='caffe.LayerParameter.exp_param', index=23,
number=111, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='flatten_param', full_name='caffe.LayerParameter.flatten_param', index=24,
number=135, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_data_param', full_name='caffe.LayerParameter.hdf5_data_param', index=25,
number=112, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.LayerParameter.hdf5_output_param', index=26,
number=113, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hinge_loss_param', full_name='caffe.LayerParameter.hinge_loss_param', index=27,
number=114, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='image_data_param', full_name='caffe.LayerParameter.image_data_param', index=28,
number=115, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='infogain_loss_param', full_name='caffe.LayerParameter.infogain_loss_param', index=29,
number=116, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='inner_product_param', full_name='caffe.LayerParameter.inner_product_param', index=30,
number=117, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='log_param', full_name='caffe.LayerParameter.log_param', index=31,
number=134, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lrn_param', full_name='caffe.LayerParameter.lrn_param', index=32,
number=118, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='memory_data_param', full_name='caffe.LayerParameter.memory_data_param', index=33,
number=119, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mvn_param', full_name='caffe.LayerParameter.mvn_param', index=34,
number=120, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pooling_param', full_name='caffe.LayerParameter.pooling_param', index=35,
number=121, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power_param', full_name='caffe.LayerParameter.power_param', index=36,
number=122, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='prelu_param', full_name='caffe.LayerParameter.prelu_param', index=37,
number=131, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='python_param', full_name='caffe.LayerParameter.python_param', index=38,
number=130, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='reduction_param', full_name='caffe.LayerParameter.reduction_param', index=39,
number=136, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='relu_param', full_name='caffe.LayerParameter.relu_param', index=40,
number=123, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='reshape_param', full_name='caffe.LayerParameter.reshape_param', index=41,
number=133, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='sigmoid_param', full_name='caffe.LayerParameter.sigmoid_param', index=42,
number=124, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='softmax_param', full_name='caffe.LayerParameter.softmax_param', index=43,
number=125, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='spp_param', full_name='caffe.LayerParameter.spp_param', index=44,
number=132, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='slice_param', full_name='caffe.LayerParameter.slice_param', index=45,
number=126, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='tanh_param', full_name='caffe.LayerParameter.tanh_param', index=46,
number=127, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='threshold_param', full_name='caffe.LayerParameter.threshold_param', index=47,
number=128, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='tile_param', full_name='caffe.LayerParameter.tile_param', index=48,
number=138, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='window_data_param', full_name='caffe.LayerParameter.window_data_param', index=49,
number=129, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2758,
serialized_end=4943,
)
_TRANSFORMATIONPARAMETER = descriptor.Descriptor(
name='TransformationParameter',
full_name='caffe.TransformationParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='scale', full_name='caffe.TransformationParameter.scale', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.TransformationParameter.mirror', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.TransformationParameter.crop_size', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.TransformationParameter.mean_file', index=3,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_value', full_name='caffe.TransformationParameter.mean_value', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_color', full_name='caffe.TransformationParameter.force_color', index=5,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_gray', full_name='caffe.TransformationParameter.force_gray', index=6,
number=7, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4946,
serialized_end=5128,
)
_LOSSPARAMETER = descriptor.Descriptor(
name='LossParameter',
full_name='caffe.LossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='ignore_label', full_name='caffe.LossParameter.ignore_label', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='normalize', full_name='caffe.LossParameter.normalize', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5130,
serialized_end=5192,
)
_ACCURACYPARAMETER = descriptor.Descriptor(
name='AccuracyParameter',
full_name='caffe.AccuracyParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='top_k', full_name='caffe.AccuracyParameter.top_k', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.AccuracyParameter.axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='ignore_label', full_name='caffe.AccuracyParameter.ignore_label', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5194,
serialized_end=5270,
)
_ARGMAXPARAMETER = descriptor.Descriptor(
name='ArgMaxParameter',
full_name='caffe.ArgMaxParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='out_max_val', full_name='caffe.ArgMaxParameter.out_max_val', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top_k', full_name='caffe.ArgMaxParameter.top_k', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5272,
serialized_end=5335,
)
_CONCATPARAMETER = descriptor.Descriptor(
name='ConcatParameter',
full_name='caffe.ConcatParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='axis', full_name='caffe.ConcatParameter.axis', index=0,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.ConcatParameter.concat_dim', index=1,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5337,
serialized_end=5394,
)
_CONTRASTIVELOSSPARAMETER = descriptor.Descriptor(
name='ContrastiveLossParameter',
full_name='caffe.ContrastiveLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='margin', full_name='caffe.ContrastiveLossParameter.margin', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='legacy_version', full_name='caffe.ContrastiveLossParameter.legacy_version', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5396,
serialized_end=5472,
)
_CONVOLUTIONPARAMETER = descriptor.Descriptor(
name='ConvolutionParameter',
full_name='caffe.ConvolutionParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.ConvolutionParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.ConvolutionParameter.bias_term', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.ConvolutionParameter.pad', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_h', full_name='caffe.ConvolutionParameter.pad_h', index=3,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_w', full_name='caffe.ConvolutionParameter.pad_w', index=4,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_size', full_name='caffe.ConvolutionParameter.kernel_size', index=5,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_h', full_name='caffe.ConvolutionParameter.kernel_h', index=6,
number=11, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_w', full_name='caffe.ConvolutionParameter.kernel_w', index=7,
number=12, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='group', full_name='caffe.ConvolutionParameter.group', index=8,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.ConvolutionParameter.stride', index=9,
number=6, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_h', full_name='caffe.ConvolutionParameter.stride_h', index=10,
number=13, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_w', full_name='caffe.ConvolutionParameter.stride_w', index=11,
number=14, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.ConvolutionParameter.weight_filler', index=12,
number=7, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.ConvolutionParameter.bias_filler', index=13,
number=8, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.ConvolutionParameter.engine', index=14,
number=15, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_CONVOLUTIONPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5475,
serialized_end=5922,
)
_DATAPARAMETER = descriptor.Descriptor(
name='DataParameter',
full_name='caffe.DataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.DataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.DataParameter.batch_size', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.DataParameter.rand_skip', index=2,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='backend', full_name='caffe.DataParameter.backend', index=3,
number=8, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.DataParameter.scale', index=4,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.DataParameter.mean_file', index=5,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.DataParameter.crop_size', index=6,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.DataParameter.mirror', index=7,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_encoded_color', full_name='caffe.DataParameter.force_encoded_color', index=8,
number=9, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='prefetch', full_name='caffe.DataParameter.prefetch', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=4,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_DATAPARAMETER_DB,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5925,
serialized_end=6217,
)
_DROPOUTPARAMETER = descriptor.Descriptor(
name='DropoutParameter',
full_name='caffe.DropoutParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.DropoutParameter.dropout_ratio', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6219,
serialized_end=6265,
)
_DUMMYDATAPARAMETER = descriptor.Descriptor(
name='DummyDataParameter',
full_name='caffe.DummyDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='data_filler', full_name='caffe.DummyDataParameter.data_filler', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shape', full_name='caffe.DummyDataParameter.shape', index=1,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num', full_name='caffe.DummyDataParameter.num', index=2,
number=2, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.DummyDataParameter.channels', index=3,
number=3, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.DummyDataParameter.height', index=4,
number=4, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.DummyDataParameter.width', index=5,
number=5, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6268,
serialized_end=6428,
)
_ELTWISEPARAMETER = descriptor.Descriptor(
name='EltwiseParameter',
full_name='caffe.EltwiseParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='operation', full_name='caffe.EltwiseParameter.operation', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='coeff', full_name='caffe.EltwiseParameter.coeff', index=1,
number=2, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stable_prod_grad', full_name='caffe.EltwiseParameter.stable_prod_grad', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_ELTWISEPARAMETER_ELTWISEOP,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6431,
serialized_end=6596,
)
_EMBEDPARAMETER = descriptor.Descriptor(
name='EmbedParameter',
full_name='caffe.EmbedParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.EmbedParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input_dim', full_name='caffe.EmbedParameter.input_dim', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.EmbedParameter.bias_term', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.EmbedParameter.weight_filler', index=3,
number=4, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.EmbedParameter.bias_filler', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6599,
serialized_end=6771,
)
_EXPPARAMETER = descriptor.Descriptor(
name='ExpParameter',
full_name='caffe.ExpParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='base', full_name='caffe.ExpParameter.base', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.ExpParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shift', full_name='caffe.ExpParameter.shift', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6773,
serialized_end=6841,
)
_FLATTENPARAMETER = descriptor.Descriptor(
name='FlattenParameter',
full_name='caffe.FlattenParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='axis', full_name='caffe.FlattenParameter.axis', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='end_axis', full_name='caffe.FlattenParameter.end_axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6843,
serialized_end=6900,
)
_HDF5DATAPARAMETER = descriptor.Descriptor(
name='HDF5DataParameter',
full_name='caffe.HDF5DataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.HDF5DataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.HDF5DataParameter.batch_size', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle', full_name='caffe.HDF5DataParameter.shuffle', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6902,
serialized_end=6981,
)
_HDF5OUTPUTPARAMETER = descriptor.Descriptor(
name='HDF5OutputParameter',
full_name='caffe.HDF5OutputParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='file_name', full_name='caffe.HDF5OutputParameter.file_name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6983,
serialized_end=7023,
)
_HINGELOSSPARAMETER = descriptor.Descriptor(
name='HingeLossParameter',
full_name='caffe.HingeLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='norm', full_name='caffe.HingeLossParameter.norm', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_HINGELOSSPARAMETER_NORM,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7025,
serialized_end=7119,
)
_IMAGEDATAPARAMETER = descriptor.Descriptor(
name='ImageDataParameter',
full_name='caffe.ImageDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.ImageDataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.ImageDataParameter.batch_size', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.ImageDataParameter.rand_skip', index=2,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle', full_name='caffe.ImageDataParameter.shuffle', index=3,
number=8, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_height', full_name='caffe.ImageDataParameter.new_height', index=4,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_width', full_name='caffe.ImageDataParameter.new_width', index=5,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='is_color', full_name='caffe.ImageDataParameter.is_color', index=6,
number=11, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.ImageDataParameter.scale', index=7,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.ImageDataParameter.mean_file', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.ImageDataParameter.crop_size', index=9,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.ImageDataParameter.mirror', index=10,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='root_folder', full_name='caffe.ImageDataParameter.root_folder', index=11,
number=12, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7122,
serialized_end=7401,
)
_INFOGAINLOSSPARAMETER = descriptor.Descriptor(
name='InfogainLossParameter',
full_name='caffe.InfogainLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.InfogainLossParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7403,
serialized_end=7442,
)
_INNERPRODUCTPARAMETER = descriptor.Descriptor(
name='InnerProductParameter',
full_name='caffe.InnerProductParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.InnerProductParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.InnerProductParameter.bias_term', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.InnerProductParameter.weight_filler', index=2,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.InnerProductParameter.bias_filler', index=3,
number=4, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.InnerProductParameter.axis', index=4,
number=5, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7445,
serialized_end=7622,
)
_LOGPARAMETER = descriptor.Descriptor(
name='LogParameter',
full_name='caffe.LogParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='base', full_name='caffe.LogParameter.base', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.LogParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shift', full_name='caffe.LogParameter.shift', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7624,
serialized_end=7692,
)
_LRNPARAMETER = descriptor.Descriptor(
name='LRNParameter',
full_name='caffe.LRNParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='local_size', full_name='caffe.LRNParameter.local_size', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.LRNParameter.alpha', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='beta', full_name='caffe.LRNParameter.beta', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='norm_region', full_name='caffe.LRNParameter.norm_region', index=3,
number=4, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='k', full_name='caffe.LRNParameter.k', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_LRNPARAMETER_NORMREGION,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7695,
serialized_end=7909,
)
_MEMORYDATAPARAMETER = descriptor.Descriptor(
name='MemoryDataParameter',
full_name='caffe.MemoryDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.MemoryDataParameter.batch_size', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.MemoryDataParameter.channels', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.MemoryDataParameter.height', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.MemoryDataParameter.width', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7911,
serialized_end=8001,
)
_MVNPARAMETER = descriptor.Descriptor(
name='MVNParameter',
full_name='caffe.MVNParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='normalize_variance', full_name='caffe.MVNParameter.normalize_variance', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='across_channels', full_name='caffe.MVNParameter.across_channels', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='eps', full_name='caffe.MVNParameter.eps', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1e-09,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8003,
serialized_end=8103,
)
_POOLINGPARAMETER = descriptor.Descriptor(
name='PoolingParameter',
full_name='caffe.PoolingParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='pool', full_name='caffe.PoolingParameter.pool', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.PoolingParameter.pad', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_h', full_name='caffe.PoolingParameter.pad_h', index=2,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_w', full_name='caffe.PoolingParameter.pad_w', index=3,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_size', full_name='caffe.PoolingParameter.kernel_size', index=4,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_h', full_name='caffe.PoolingParameter.kernel_h', index=5,
number=5, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_w', full_name='caffe.PoolingParameter.kernel_w', index=6,
number=6, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.PoolingParameter.stride', index=7,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_h', full_name='caffe.PoolingParameter.stride_h', index=8,
number=7, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_w', full_name='caffe.PoolingParameter.stride_w', index=9,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.PoolingParameter.engine', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='global_pooling', full_name='caffe.PoolingParameter.global_pooling', index=11,
number=12, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_POOLINGPARAMETER_POOLMETHOD,
_POOLINGPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8106,
serialized_end=8524,
)
_POWERPARAMETER = descriptor.Descriptor(
name='PowerParameter',
full_name='caffe.PowerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='power', full_name='caffe.PowerParameter.power', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.PowerParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shift', full_name='caffe.PowerParameter.shift', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8526,
serialized_end=8596,
)
_PYTHONPARAMETER = descriptor.Descriptor(
name='PythonParameter',
full_name='caffe.PythonParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='module', full_name='caffe.PythonParameter.module', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layer', full_name='caffe.PythonParameter.layer', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='param_str', full_name='caffe.PythonParameter.param_str', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='share_in_parallel', full_name='caffe.PythonParameter.share_in_parallel', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8598,
serialized_end=8701,
)
_REDUCTIONPARAMETER = descriptor.Descriptor(
name='ReductionParameter',
full_name='caffe.ReductionParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='operation', full_name='caffe.ReductionParameter.operation', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.ReductionParameter.axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='coeff', full_name='caffe.ReductionParameter.coeff', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_REDUCTIONPARAMETER_REDUCTIONOP,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8704,
serialized_end=8877,
)
_RELUPARAMETER = descriptor.Descriptor(
name='ReLUParameter',
full_name='caffe.ReLUParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='negative_slope', full_name='caffe.ReLUParameter.negative_slope', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.ReLUParameter.engine', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_RELUPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8880,
serialized_end=9021,
)
_RESHAPEPARAMETER = descriptor.Descriptor(
name='ReshapeParameter',
full_name='caffe.ReshapeParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='shape', full_name='caffe.ReshapeParameter.shape', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.ReshapeParameter.axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num_axes', full_name='caffe.ReshapeParameter.num_axes', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9023,
serialized_end=9113,
)
_SIGMOIDPARAMETER = descriptor.Descriptor(
name='SigmoidParameter',
full_name='caffe.SigmoidParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='engine', full_name='caffe.SigmoidParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SIGMOIDPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9115,
serialized_end=9235,
)
_SLICEPARAMETER = descriptor.Descriptor(
name='SliceParameter',
full_name='caffe.SliceParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='axis', full_name='caffe.SliceParameter.axis', index=0,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='slice_point', full_name='caffe.SliceParameter.slice_point', index=1,
number=2, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='slice_dim', full_name='caffe.SliceParameter.slice_dim', index=2,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9237,
serialized_end=9313,
)
_SOFTMAXPARAMETER = descriptor.Descriptor(
name='SoftmaxParameter',
full_name='caffe.SoftmaxParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='engine', full_name='caffe.SoftmaxParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.SoftmaxParameter.axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SOFTMAXPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9316,
serialized_end=9453,
)
_TANHPARAMETER = descriptor.Descriptor(
name='TanHParameter',
full_name='caffe.TanHParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='engine', full_name='caffe.TanHParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_TANHPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9455,
serialized_end=9569,
)
_TILEPARAMETER = descriptor.Descriptor(
name='TileParameter',
full_name='caffe.TileParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='axis', full_name='caffe.TileParameter.axis', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='tiles', full_name='caffe.TileParameter.tiles', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9571,
serialized_end=9618,
)
_THRESHOLDPARAMETER = descriptor.Descriptor(
name='ThresholdParameter',
full_name='caffe.ThresholdParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='threshold', full_name='caffe.ThresholdParameter.threshold', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9620,
serialized_end=9662,
)
_WINDOWDATAPARAMETER = descriptor.Descriptor(
name='WindowDataParameter',
full_name='caffe.WindowDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.WindowDataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.WindowDataParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.WindowDataParameter.mean_file', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.WindowDataParameter.batch_size', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.WindowDataParameter.crop_size', index=4,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.WindowDataParameter.mirror', index=5,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='fg_threshold', full_name='caffe.WindowDataParameter.fg_threshold', index=6,
number=7, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bg_threshold', full_name='caffe.WindowDataParameter.bg_threshold', index=7,
number=8, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='fg_fraction', full_name='caffe.WindowDataParameter.fg_fraction', index=8,
number=9, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='context_pad', full_name='caffe.WindowDataParameter.context_pad', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_mode', full_name='caffe.WindowDataParameter.crop_mode', index=10,
number=11, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='cache_images', full_name='caffe.WindowDataParameter.cache_images', index=11,
number=12, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='root_folder', full_name='caffe.WindowDataParameter.root_folder', index=12,
number=13, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9665,
serialized_end=9986,
)
_SPPPARAMETER = descriptor.Descriptor(
name='SPPParameter',
full_name='caffe.SPPParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='pyramid_height', full_name='caffe.SPPParameter.pyramid_height', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pool', full_name='caffe.SPPParameter.pool', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.SPPParameter.engine', index=2,
number=6, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SPPPARAMETER_POOLMETHOD,
_SPPPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9989,
serialized_end=10224,
)
_V1LAYERPARAMETER = descriptor.Descriptor(
name='V1LayerParameter',
full_name='caffe.V1LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='bottom', full_name='caffe.V1LayerParameter.bottom', index=0,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top', full_name='caffe.V1LayerParameter.top', index=1,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='name', full_name='caffe.V1LayerParameter.name', index=2,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='include', full_name='caffe.V1LayerParameter.include', index=3,
number=32, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='exclude', full_name='caffe.V1LayerParameter.exclude', index=4,
number=33, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.V1LayerParameter.type', index=5,
number=5, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.V1LayerParameter.blobs', index=6,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='param', full_name='caffe.V1LayerParameter.param', index=7,
number=1001, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blob_share_mode', full_name='caffe.V1LayerParameter.blob_share_mode', index=8,
number=1002, type=14, cpp_type=8, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.V1LayerParameter.blobs_lr', index=9,
number=7, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.V1LayerParameter.weight_decay', index=10,
number=8, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='loss_weight', full_name='caffe.V1LayerParameter.loss_weight', index=11,
number=35, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='accuracy_param', full_name='caffe.V1LayerParameter.accuracy_param', index=12,
number=27, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='argmax_param', full_name='caffe.V1LayerParameter.argmax_param', index=13,
number=23, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_param', full_name='caffe.V1LayerParameter.concat_param', index=14,
number=9, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='contrastive_loss_param', full_name='caffe.V1LayerParameter.contrastive_loss_param', index=15,
number=40, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='convolution_param', full_name='caffe.V1LayerParameter.convolution_param', index=16,
number=10, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data_param', full_name='caffe.V1LayerParameter.data_param', index=17,
number=11, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_param', full_name='caffe.V1LayerParameter.dropout_param', index=18,
number=12, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dummy_data_param', full_name='caffe.V1LayerParameter.dummy_data_param', index=19,
number=26, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='eltwise_param', full_name='caffe.V1LayerParameter.eltwise_param', index=20,
number=24, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='exp_param', full_name='caffe.V1LayerParameter.exp_param', index=21,
number=41, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_data_param', full_name='caffe.V1LayerParameter.hdf5_data_param', index=22,
number=13, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.V1LayerParameter.hdf5_output_param', index=23,
number=14, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hinge_loss_param', full_name='caffe.V1LayerParameter.hinge_loss_param', index=24,
number=29, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='image_data_param', full_name='caffe.V1LayerParameter.image_data_param', index=25,
number=15, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='infogain_loss_param', full_name='caffe.V1LayerParameter.infogain_loss_param', index=26,
number=16, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='inner_product_param', full_name='caffe.V1LayerParameter.inner_product_param', index=27,
number=17, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lrn_param', full_name='caffe.V1LayerParameter.lrn_param', index=28,
number=18, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='memory_data_param', full_name='caffe.V1LayerParameter.memory_data_param', index=29,
number=22, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mvn_param', full_name='caffe.V1LayerParameter.mvn_param', index=30,
number=34, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pooling_param', full_name='caffe.V1LayerParameter.pooling_param', index=31,
number=19, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power_param', full_name='caffe.V1LayerParameter.power_param', index=32,
number=21, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='relu_param', full_name='caffe.V1LayerParameter.relu_param', index=33,
number=30, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='sigmoid_param', full_name='caffe.V1LayerParameter.sigmoid_param', index=34,
number=38, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='softmax_param', full_name='caffe.V1LayerParameter.softmax_param', index=35,
number=39, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='slice_param', full_name='caffe.V1LayerParameter.slice_param', index=36,
number=31, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='tanh_param', full_name='caffe.V1LayerParameter.tanh_param', index=37,
number=37, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='threshold_param', full_name='caffe.V1LayerParameter.threshold_param', index=38,
number=25, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='window_data_param', full_name='caffe.V1LayerParameter.window_data_param', index=39,
number=20, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='transform_param', full_name='caffe.V1LayerParameter.transform_param', index=40,
number=36, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='loss_param', full_name='caffe.V1LayerParameter.loss_param', index=41,
number=42, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layer', full_name='caffe.V1LayerParameter.layer', index=42,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_V1LAYERPARAMETER_LAYERTYPE,
_V1LAYERPARAMETER_DIMCHECKMODE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=10227,
serialized_end=12765,
)
_V0LAYERPARAMETER = descriptor.Descriptor(
name='V0LayerParameter',
full_name='caffe.V0LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.V0LayerParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.V0LayerParameter.type', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.V0LayerParameter.num_output', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='biasterm', full_name='caffe.V0LayerParameter.biasterm', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.V0LayerParameter.weight_filler', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.V0LayerParameter.bias_filler', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.V0LayerParameter.pad', index=6,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernelsize', full_name='caffe.V0LayerParameter.kernelsize', index=7,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='group', full_name='caffe.V0LayerParameter.group', index=8,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.V0LayerParameter.stride', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pool', full_name='caffe.V0LayerParameter.pool', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.V0LayerParameter.dropout_ratio', index=11,
number=12, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='local_size', full_name='caffe.V0LayerParameter.local_size', index=12,
number=13, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.V0LayerParameter.alpha', index=13,
number=14, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='beta', full_name='caffe.V0LayerParameter.beta', index=14,
number=15, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='k', full_name='caffe.V0LayerParameter.k', index=15,
number=22, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='source', full_name='caffe.V0LayerParameter.source', index=16,
number=16, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.V0LayerParameter.scale', index=17,
number=17, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='meanfile', full_name='caffe.V0LayerParameter.meanfile', index=18,
number=18, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batchsize', full_name='caffe.V0LayerParameter.batchsize', index=19,
number=19, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='cropsize', full_name='caffe.V0LayerParameter.cropsize', index=20,
number=20, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.V0LayerParameter.mirror', index=21,
number=21, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.V0LayerParameter.blobs', index=22,
number=50, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.V0LayerParameter.blobs_lr', index=23,
number=51, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.V0LayerParameter.weight_decay', index=24,
number=52, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.V0LayerParameter.rand_skip', index=25,
number=53, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_fg_threshold', full_name='caffe.V0LayerParameter.det_fg_threshold', index=26,
number=54, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_bg_threshold', full_name='caffe.V0LayerParameter.det_bg_threshold', index=27,
number=55, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_fg_fraction', full_name='caffe.V0LayerParameter.det_fg_fraction', index=28,
number=56, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_context_pad', full_name='caffe.V0LayerParameter.det_context_pad', index=29,
number=58, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_crop_mode', full_name='caffe.V0LayerParameter.det_crop_mode', index=30,
number=59, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_num', full_name='caffe.V0LayerParameter.new_num', index=31,
number=60, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_channels', full_name='caffe.V0LayerParameter.new_channels', index=32,
number=61, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_height', full_name='caffe.V0LayerParameter.new_height', index=33,
number=62, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_width', full_name='caffe.V0LayerParameter.new_width', index=34,
number=63, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle_images', full_name='caffe.V0LayerParameter.shuffle_images', index=35,
number=64, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.V0LayerParameter.concat_dim', index=36,
number=65, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.V0LayerParameter.hdf5_output_param', index=37,
number=1001, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_V0LAYERPARAMETER_POOLMETHOD,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=12768,
serialized_end=13789,
)
_PRELUPARAMETER = descriptor.Descriptor(
name='PReLUParameter',
full_name='caffe.PReLUParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='filler', full_name='caffe.PReLUParameter.filler', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channel_shared', full_name='caffe.PReLUParameter.channel_shared', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=13791,
serialized_end=13878,
)
_BLOBPROTO.fields_by_name['shape'].message_type = _BLOBSHAPE
_BLOBPROTOVECTOR.fields_by_name['blobs'].message_type = _BLOBPROTO
_FILLERPARAMETER.fields_by_name['variance_norm'].enum_type = _FILLERPARAMETER_VARIANCENORM
_FILLERPARAMETER_VARIANCENORM.containing_type = _FILLERPARAMETER;
_NETPARAMETER.fields_by_name['input_shape'].message_type = _BLOBSHAPE
_NETPARAMETER.fields_by_name['state'].message_type = _NETSTATE
_NETPARAMETER.fields_by_name['layer'].message_type = _LAYERPARAMETER
_NETPARAMETER.fields_by_name['layers'].message_type = _V1LAYERPARAMETER
_SOLVERPARAMETER.fields_by_name['net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['train_net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['test_net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['train_state'].message_type = _NETSTATE
_SOLVERPARAMETER.fields_by_name['test_state'].message_type = _NETSTATE
_SOLVERPARAMETER.fields_by_name['snapshot_format'].enum_type = _SOLVERPARAMETER_SNAPSHOTFORMAT
_SOLVERPARAMETER.fields_by_name['solver_mode'].enum_type = _SOLVERPARAMETER_SOLVERMODE
_SOLVERPARAMETER.fields_by_name['solver_type'].enum_type = _SOLVERPARAMETER_SOLVERTYPE
_SOLVERPARAMETER_SNAPSHOTFORMAT.containing_type = _SOLVERPARAMETER;
_SOLVERPARAMETER_SOLVERMODE.containing_type = _SOLVERPARAMETER;
_SOLVERPARAMETER_SOLVERTYPE.containing_type = _SOLVERPARAMETER;
_SOLVERSTATE.fields_by_name['history'].message_type = _BLOBPROTO
_NETSTATE.fields_by_name['phase'].enum_type = _PHASE
_NETSTATERULE.fields_by_name['phase'].enum_type = _PHASE
_PARAMSPEC.fields_by_name['share_mode'].enum_type = _PARAMSPEC_DIMCHECKMODE
_PARAMSPEC_DIMCHECKMODE.containing_type = _PARAMSPEC;
_LAYERPARAMETER.fields_by_name['phase'].enum_type = _PHASE
_LAYERPARAMETER.fields_by_name['param'].message_type = _PARAMSPEC
_LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_LAYERPARAMETER.fields_by_name['include'].message_type = _NETSTATERULE
_LAYERPARAMETER.fields_by_name['exclude'].message_type = _NETSTATERULE
_LAYERPARAMETER.fields_by_name['transform_param'].message_type = _TRANSFORMATIONPARAMETER
_LAYERPARAMETER.fields_by_name['loss_param'].message_type = _LOSSPARAMETER
_LAYERPARAMETER.fields_by_name['accuracy_param'].message_type = _ACCURACYPARAMETER
_LAYERPARAMETER.fields_by_name['argmax_param'].message_type = _ARGMAXPARAMETER
_LAYERPARAMETER.fields_by_name['concat_param'].message_type = _CONCATPARAMETER
_LAYERPARAMETER.fields_by_name['contrastive_loss_param'].message_type = _CONTRASTIVELOSSPARAMETER
_LAYERPARAMETER.fields_by_name['convolution_param'].message_type = _CONVOLUTIONPARAMETER
_LAYERPARAMETER.fields_by_name['data_param'].message_type = _DATAPARAMETER
_LAYERPARAMETER.fields_by_name['dropout_param'].message_type = _DROPOUTPARAMETER
_LAYERPARAMETER.fields_by_name['dummy_data_param'].message_type = _DUMMYDATAPARAMETER
_LAYERPARAMETER.fields_by_name['eltwise_param'].message_type = _ELTWISEPARAMETER
_LAYERPARAMETER.fields_by_name['embed_param'].message_type = _EMBEDPARAMETER
_LAYERPARAMETER.fields_by_name['exp_param'].message_type = _EXPPARAMETER
_LAYERPARAMETER.fields_by_name['flatten_param'].message_type = _FLATTENPARAMETER
_LAYERPARAMETER.fields_by_name['hdf5_data_param'].message_type = _HDF5DATAPARAMETER
_LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_LAYERPARAMETER.fields_by_name['hinge_loss_param'].message_type = _HINGELOSSPARAMETER
_LAYERPARAMETER.fields_by_name['image_data_param'].message_type = _IMAGEDATAPARAMETER
_LAYERPARAMETER.fields_by_name['infogain_loss_param'].message_type = _INFOGAINLOSSPARAMETER
_LAYERPARAMETER.fields_by_name['inner_product_param'].message_type = _INNERPRODUCTPARAMETER
_LAYERPARAMETER.fields_by_name['log_param'].message_type = _LOGPARAMETER
_LAYERPARAMETER.fields_by_name['lrn_param'].message_type = _LRNPARAMETER
_LAYERPARAMETER.fields_by_name['memory_data_param'].message_type = _MEMORYDATAPARAMETER
_LAYERPARAMETER.fields_by_name['mvn_param'].message_type = _MVNPARAMETER
_LAYERPARAMETER.fields_by_name['pooling_param'].message_type = _POOLINGPARAMETER
_LAYERPARAMETER.fields_by_name['power_param'].message_type = _POWERPARAMETER
_LAYERPARAMETER.fields_by_name['prelu_param'].message_type = _PRELUPARAMETER
_LAYERPARAMETER.fields_by_name['python_param'].message_type = _PYTHONPARAMETER
_LAYERPARAMETER.fields_by_name['reduction_param'].message_type = _REDUCTIONPARAMETER
_LAYERPARAMETER.fields_by_name['relu_param'].message_type = _RELUPARAMETER
_LAYERPARAMETER.fields_by_name['reshape_param'].message_type = _RESHAPEPARAMETER
_LAYERPARAMETER.fields_by_name['sigmoid_param'].message_type = _SIGMOIDPARAMETER
_LAYERPARAMETER.fields_by_name['softmax_param'].message_type = _SOFTMAXPARAMETER
_LAYERPARAMETER.fields_by_name['spp_param'].message_type = _SPPPARAMETER
_LAYERPARAMETER.fields_by_name['slice_param'].message_type = _SLICEPARAMETER
_LAYERPARAMETER.fields_by_name['tanh_param'].message_type = _TANHPARAMETER
_LAYERPARAMETER.fields_by_name['threshold_param'].message_type = _THRESHOLDPARAMETER
_LAYERPARAMETER.fields_by_name['tile_param'].message_type = _TILEPARAMETER
_LAYERPARAMETER.fields_by_name['window_data_param'].message_type = _WINDOWDATAPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['engine'].enum_type = _CONVOLUTIONPARAMETER_ENGINE
_CONVOLUTIONPARAMETER_ENGINE.containing_type = _CONVOLUTIONPARAMETER;
_DATAPARAMETER.fields_by_name['backend'].enum_type = _DATAPARAMETER_DB
_DATAPARAMETER_DB.containing_type = _DATAPARAMETER;
_DUMMYDATAPARAMETER.fields_by_name['data_filler'].message_type = _FILLERPARAMETER
_DUMMYDATAPARAMETER.fields_by_name['shape'].message_type = _BLOBSHAPE
_ELTWISEPARAMETER.fields_by_name['operation'].enum_type = _ELTWISEPARAMETER_ELTWISEOP
_ELTWISEPARAMETER_ELTWISEOP.containing_type = _ELTWISEPARAMETER;
_EMBEDPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_EMBEDPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_HINGELOSSPARAMETER.fields_by_name['norm'].enum_type = _HINGELOSSPARAMETER_NORM
_HINGELOSSPARAMETER_NORM.containing_type = _HINGELOSSPARAMETER;
_INNERPRODUCTPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_INNERPRODUCTPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_LRNPARAMETER.fields_by_name['norm_region'].enum_type = _LRNPARAMETER_NORMREGION
_LRNPARAMETER_NORMREGION.containing_type = _LRNPARAMETER;
_POOLINGPARAMETER.fields_by_name['pool'].enum_type = _POOLINGPARAMETER_POOLMETHOD
_POOLINGPARAMETER.fields_by_name['engine'].enum_type = _POOLINGPARAMETER_ENGINE
_POOLINGPARAMETER_POOLMETHOD.containing_type = _POOLINGPARAMETER;
_POOLINGPARAMETER_ENGINE.containing_type = _POOLINGPARAMETER;
_REDUCTIONPARAMETER.fields_by_name['operation'].enum_type = _REDUCTIONPARAMETER_REDUCTIONOP
_REDUCTIONPARAMETER_REDUCTIONOP.containing_type = _REDUCTIONPARAMETER;
_RELUPARAMETER.fields_by_name['engine'].enum_type = _RELUPARAMETER_ENGINE
_RELUPARAMETER_ENGINE.containing_type = _RELUPARAMETER;
_RESHAPEPARAMETER.fields_by_name['shape'].message_type = _BLOBSHAPE
_SIGMOIDPARAMETER.fields_by_name['engine'].enum_type = _SIGMOIDPARAMETER_ENGINE
_SIGMOIDPARAMETER_ENGINE.containing_type = _SIGMOIDPARAMETER;
_SOFTMAXPARAMETER.fields_by_name['engine'].enum_type = _SOFTMAXPARAMETER_ENGINE
_SOFTMAXPARAMETER_ENGINE.containing_type = _SOFTMAXPARAMETER;
_TANHPARAMETER.fields_by_name['engine'].enum_type = _TANHPARAMETER_ENGINE
_TANHPARAMETER_ENGINE.containing_type = _TANHPARAMETER;
_SPPPARAMETER.fields_by_name['pool'].enum_type = _SPPPARAMETER_POOLMETHOD
_SPPPARAMETER.fields_by_name['engine'].enum_type = _SPPPARAMETER_ENGINE
_SPPPARAMETER_POOLMETHOD.containing_type = _SPPPARAMETER;
_SPPPARAMETER_ENGINE.containing_type = _SPPPARAMETER;
_V1LAYERPARAMETER.fields_by_name['include'].message_type = _NETSTATERULE
_V1LAYERPARAMETER.fields_by_name['exclude'].message_type = _NETSTATERULE
_V1LAYERPARAMETER.fields_by_name['type'].enum_type = _V1LAYERPARAMETER_LAYERTYPE
_V1LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_V1LAYERPARAMETER.fields_by_name['blob_share_mode'].enum_type = _V1LAYERPARAMETER_DIMCHECKMODE
_V1LAYERPARAMETER.fields_by_name['accuracy_param'].message_type = _ACCURACYPARAMETER
_V1LAYERPARAMETER.fields_by_name['argmax_param'].message_type = _ARGMAXPARAMETER
_V1LAYERPARAMETER.fields_by_name['concat_param'].message_type = _CONCATPARAMETER
_V1LAYERPARAMETER.fields_by_name['contrastive_loss_param'].message_type = _CONTRASTIVELOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['convolution_param'].message_type = _CONVOLUTIONPARAMETER
_V1LAYERPARAMETER.fields_by_name['data_param'].message_type = _DATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['dropout_param'].message_type = _DROPOUTPARAMETER
_V1LAYERPARAMETER.fields_by_name['dummy_data_param'].message_type = _DUMMYDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['eltwise_param'].message_type = _ELTWISEPARAMETER
_V1LAYERPARAMETER.fields_by_name['exp_param'].message_type = _EXPPARAMETER
_V1LAYERPARAMETER.fields_by_name['hdf5_data_param'].message_type = _HDF5DATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_V1LAYERPARAMETER.fields_by_name['hinge_loss_param'].message_type = _HINGELOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['image_data_param'].message_type = _IMAGEDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['infogain_loss_param'].message_type = _INFOGAINLOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['inner_product_param'].message_type = _INNERPRODUCTPARAMETER
_V1LAYERPARAMETER.fields_by_name['lrn_param'].message_type = _LRNPARAMETER
_V1LAYERPARAMETER.fields_by_name['memory_data_param'].message_type = _MEMORYDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['mvn_param'].message_type = _MVNPARAMETER
_V1LAYERPARAMETER.fields_by_name['pooling_param'].message_type = _POOLINGPARAMETER
_V1LAYERPARAMETER.fields_by_name['power_param'].message_type = _POWERPARAMETER
_V1LAYERPARAMETER.fields_by_name['relu_param'].message_type = _RELUPARAMETER
_V1LAYERPARAMETER.fields_by_name['sigmoid_param'].message_type = _SIGMOIDPARAMETER
_V1LAYERPARAMETER.fields_by_name['softmax_param'].message_type = _SOFTMAXPARAMETER
_V1LAYERPARAMETER.fields_by_name['slice_param'].message_type = _SLICEPARAMETER
_V1LAYERPARAMETER.fields_by_name['tanh_param'].message_type = _TANHPARAMETER
_V1LAYERPARAMETER.fields_by_name['threshold_param'].message_type = _THRESHOLDPARAMETER
_V1LAYERPARAMETER.fields_by_name['window_data_param'].message_type = _WINDOWDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['transform_param'].message_type = _TRANSFORMATIONPARAMETER
_V1LAYERPARAMETER.fields_by_name['loss_param'].message_type = _LOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['layer'].message_type = _V0LAYERPARAMETER
_V1LAYERPARAMETER_LAYERTYPE.containing_type = _V1LAYERPARAMETER;
_V1LAYERPARAMETER_DIMCHECKMODE.containing_type = _V1LAYERPARAMETER;
_V0LAYERPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_V0LAYERPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_V0LAYERPARAMETER.fields_by_name['pool'].enum_type = _V0LAYERPARAMETER_POOLMETHOD
_V0LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_V0LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_V0LAYERPARAMETER_POOLMETHOD.containing_type = _V0LAYERPARAMETER;
_PRELUPARAMETER.fields_by_name['filler'].message_type = _FILLERPARAMETER
DESCRIPTOR.message_types_by_name['BlobShape'] = _BLOBSHAPE
DESCRIPTOR.message_types_by_name['BlobProto'] = _BLOBPROTO
DESCRIPTOR.message_types_by_name['BlobProtoVector'] = _BLOBPROTOVECTOR
DESCRIPTOR.message_types_by_name['Datum'] = _DATUM
DESCRIPTOR.message_types_by_name['FillerParameter'] = _FILLERPARAMETER
DESCRIPTOR.message_types_by_name['NetParameter'] = _NETPARAMETER
DESCRIPTOR.message_types_by_name['SolverParameter'] = _SOLVERPARAMETER
DESCRIPTOR.message_types_by_name['SolverState'] = _SOLVERSTATE
DESCRIPTOR.message_types_by_name['NetState'] = _NETSTATE
DESCRIPTOR.message_types_by_name['NetStateRule'] = _NETSTATERULE
DESCRIPTOR.message_types_by_name['ParamSpec'] = _PARAMSPEC
DESCRIPTOR.message_types_by_name['LayerParameter'] = _LAYERPARAMETER
DESCRIPTOR.message_types_by_name['TransformationParameter'] = _TRANSFORMATIONPARAMETER
DESCRIPTOR.message_types_by_name['LossParameter'] = _LOSSPARAMETER
DESCRIPTOR.message_types_by_name['AccuracyParameter'] = _ACCURACYPARAMETER
DESCRIPTOR.message_types_by_name['ArgMaxParameter'] = _ARGMAXPARAMETER
DESCRIPTOR.message_types_by_name['ConcatParameter'] = _CONCATPARAMETER
DESCRIPTOR.message_types_by_name['ContrastiveLossParameter'] = _CONTRASTIVELOSSPARAMETER
DESCRIPTOR.message_types_by_name['ConvolutionParameter'] = _CONVOLUTIONPARAMETER
DESCRIPTOR.message_types_by_name['DataParameter'] = _DATAPARAMETER
DESCRIPTOR.message_types_by_name['DropoutParameter'] = _DROPOUTPARAMETER
DESCRIPTOR.message_types_by_name['DummyDataParameter'] = _DUMMYDATAPARAMETER
DESCRIPTOR.message_types_by_name['EltwiseParameter'] = _ELTWISEPARAMETER
DESCRIPTOR.message_types_by_name['EmbedParameter'] = _EMBEDPARAMETER
DESCRIPTOR.message_types_by_name['ExpParameter'] = _EXPPARAMETER
DESCRIPTOR.message_types_by_name['FlattenParameter'] = _FLATTENPARAMETER
DESCRIPTOR.message_types_by_name['HDF5DataParameter'] = _HDF5DATAPARAMETER
DESCRIPTOR.message_types_by_name['HDF5OutputParameter'] = _HDF5OUTPUTPARAMETER
DESCRIPTOR.message_types_by_name['HingeLossParameter'] = _HINGELOSSPARAMETER
DESCRIPTOR.message_types_by_name['ImageDataParameter'] = _IMAGEDATAPARAMETER
DESCRIPTOR.message_types_by_name['InfogainLossParameter'] = _INFOGAINLOSSPARAMETER
DESCRIPTOR.message_types_by_name['InnerProductParameter'] = _INNERPRODUCTPARAMETER
DESCRIPTOR.message_types_by_name['LogParameter'] = _LOGPARAMETER
DESCRIPTOR.message_types_by_name['LRNParameter'] = _LRNPARAMETER
DESCRIPTOR.message_types_by_name['MemoryDataParameter'] = _MEMORYDATAPARAMETER
DESCRIPTOR.message_types_by_name['MVNParameter'] = _MVNPARAMETER
DESCRIPTOR.message_types_by_name['PoolingParameter'] = _POOLINGPARAMETER
DESCRIPTOR.message_types_by_name['PowerParameter'] = _POWERPARAMETER
DESCRIPTOR.message_types_by_name['PythonParameter'] = _PYTHONPARAMETER
DESCRIPTOR.message_types_by_name['ReductionParameter'] = _REDUCTIONPARAMETER
DESCRIPTOR.message_types_by_name['ReLUParameter'] = _RELUPARAMETER
DESCRIPTOR.message_types_by_name['ReshapeParameter'] = _RESHAPEPARAMETER
DESCRIPTOR.message_types_by_name['SigmoidParameter'] = _SIGMOIDPARAMETER
DESCRIPTOR.message_types_by_name['SliceParameter'] = _SLICEPARAMETER
DESCRIPTOR.message_types_by_name['SoftmaxParameter'] = _SOFTMAXPARAMETER
DESCRIPTOR.message_types_by_name['TanHParameter'] = _TANHPARAMETER
DESCRIPTOR.message_types_by_name['TileParameter'] = _TILEPARAMETER
DESCRIPTOR.message_types_by_name['ThresholdParameter'] = _THRESHOLDPARAMETER
DESCRIPTOR.message_types_by_name['WindowDataParameter'] = _WINDOWDATAPARAMETER
DESCRIPTOR.message_types_by_name['SPPParameter'] = _SPPPARAMETER
DESCRIPTOR.message_types_by_name['V1LayerParameter'] = _V1LAYERPARAMETER
DESCRIPTOR.message_types_by_name['V0LayerParameter'] = _V0LAYERPARAMETER
DESCRIPTOR.message_types_by_name['PReLUParameter'] = _PRELUPARAMETER
class BlobShape(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBSHAPE
# @@protoc_insertion_point(class_scope:caffe.BlobShape)
class BlobProto(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTO
# @@protoc_insertion_point(class_scope:caffe.BlobProto)
class BlobProtoVector(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTOVECTOR
# @@protoc_insertion_point(class_scope:caffe.BlobProtoVector)
class Datum(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATUM
# @@protoc_insertion_point(class_scope:caffe.Datum)
class FillerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FILLERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.FillerParameter)
class NetParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETPARAMETER
# @@protoc_insertion_point(class_scope:caffe.NetParameter)
class SolverParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SolverParameter)
class SolverState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERSTATE
# @@protoc_insertion_point(class_scope:caffe.SolverState)
class NetState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATE
# @@protoc_insertion_point(class_scope:caffe.NetState)
class NetStateRule(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATERULE
# @@protoc_insertion_point(class_scope:caffe.NetStateRule)
class ParamSpec(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PARAMSPEC
# @@protoc_insertion_point(class_scope:caffe.ParamSpec)
class LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LayerParameter)
class TransformationParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TRANSFORMATIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.TransformationParameter)
class LossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LossParameter)
class AccuracyParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ACCURACYPARAMETER
# @@protoc_insertion_point(class_scope:caffe.AccuracyParameter)
class ArgMaxParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ARGMAXPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ArgMaxParameter)
class ConcatParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONCATPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ConcatParameter)
class ContrastiveLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONTRASTIVELOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ContrastiveLossParameter)
class ConvolutionParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONVOLUTIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ConvolutionParameter)
class DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DataParameter)
class DropoutParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DROPOUTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DropoutParameter)
class DummyDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DUMMYDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DummyDataParameter)
class EltwiseParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ELTWISEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.EltwiseParameter)
class EmbedParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _EMBEDPARAMETER
# @@protoc_insertion_point(class_scope:caffe.EmbedParameter)
class ExpParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _EXPPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ExpParameter)
class FlattenParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FLATTENPARAMETER
# @@protoc_insertion_point(class_scope:caffe.FlattenParameter)
class HDF5DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5DATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HDF5DataParameter)
class HDF5OutputParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5OUTPUTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HDF5OutputParameter)
class HingeLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HINGELOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HingeLossParameter)
class ImageDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _IMAGEDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ImageDataParameter)
class InfogainLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INFOGAINLOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.InfogainLossParameter)
class InnerProductParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INNERPRODUCTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.InnerProductParameter)
class LogParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LOGPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LogParameter)
class LRNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LRNPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LRNParameter)
class MemoryDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MEMORYDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.MemoryDataParameter)
class MVNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MVNPARAMETER
# @@protoc_insertion_point(class_scope:caffe.MVNParameter)
class PoolingParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POOLINGPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PoolingParameter)
class PowerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POWERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PowerParameter)
class PythonParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PYTHONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PythonParameter)
class ReductionParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _REDUCTIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ReductionParameter)
class ReLUParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _RELUPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ReLUParameter)
class ReshapeParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _RESHAPEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ReshapeParameter)
class SigmoidParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SIGMOIDPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SigmoidParameter)
class SliceParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SLICEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SliceParameter)
class SoftmaxParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOFTMAXPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SoftmaxParameter)
class TanHParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TANHPARAMETER
# @@protoc_insertion_point(class_scope:caffe.TanHParameter)
class TileParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TILEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.TileParameter)
class ThresholdParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _THRESHOLDPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ThresholdParameter)
class WindowDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _WINDOWDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.WindowDataParameter)
class SPPParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SPPPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SPPParameter)
class V1LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _V1LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.V1LayerParameter)
class V0LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _V0LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.V0LayerParameter)
class PReLUParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PRELUPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PReLUParameter)
# @@protoc_insertion_point(module_scope)
| 218,004 | 42.349572 | 26,073 | py |
DRT | DRT-master/external_libs/matconvnet/matconvnet/utils/proto/caffe_old_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='caffe-old.proto',
package='caffe',
serialized_pb='\n\x0f\x63\x61\x66\x66\x65-old.proto\x12\x05\x63\x61\x66\x66\x65\"y\n\tBlobProto\x12\x0e\n\x03num\x18\x01 \x01(\x05:\x01\x30\x12\x13\n\x08\x63hannels\x18\x02 \x01(\x05:\x01\x30\x12\x11\n\x06height\x18\x03 \x01(\x05:\x01\x30\x12\x10\n\x05width\x18\x04 \x01(\x05:\x01\x30\x12\x10\n\x04\x64\x61ta\x18\x05 \x03(\x02\x42\x02\x10\x01\x12\x10\n\x04\x64iff\x18\x06 \x03(\x02\x42\x02\x10\x01\"2\n\x0f\x42lobProtoVector\x12\x1f\n\x05\x62lobs\x18\x01 \x03(\x0b\x32\x10.caffe.BlobProto\"i\n\x05\x44\x61tum\x12\x10\n\x08\x63hannels\x18\x01 \x01(\x05\x12\x0e\n\x06height\x18\x02 \x01(\x05\x12\r\n\x05width\x18\x03 \x01(\x05\x12\x0c\n\x04\x64\x61ta\x18\x04 \x01(\x0c\x12\r\n\x05label\x18\x05 \x01(\x05\x12\x12\n\nfloat_data\x18\x06 \x03(\x02\"|\n\x0f\x46illerParameter\x12\x16\n\x04type\x18\x01 \x01(\t:\x08\x63onstant\x12\x10\n\x05value\x18\x02 \x01(\x02:\x01\x30\x12\x0e\n\x03min\x18\x03 \x01(\x02:\x01\x30\x12\x0e\n\x03max\x18\x04 \x01(\x02:\x01\x31\x12\x0f\n\x04mean\x18\x05 \x01(\x02:\x01\x30\x12\x0e\n\x03std\x18\x06 \x01(\x02:\x01\x31\"\xb3\x07\n\x0eLayerParameter\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0c\n\x04type\x18\x02 \x01(\t\x12\x12\n\nnum_output\x18\x03 \x01(\r\x12\x16\n\x08\x62iasterm\x18\x04 \x01(\x08:\x04true\x12-\n\rweight_filler\x18\x05 \x01(\x0b\x32\x16.caffe.FillerParameter\x12+\n\x0b\x62ias_filler\x18\x06 \x01(\x0b\x32\x16.caffe.FillerParameter\x12\x0e\n\x03pad\x18\x07 \x01(\r:\x01\x30\x12\x12\n\nkernelsize\x18\x08 \x01(\r\x12\x10\n\x05group\x18\t \x01(\r:\x01\x31\x12\x11\n\x06stride\x18\n \x01(\r:\x01\x31\x12\x33\n\x04pool\x18\x0b \x01(\x0e\x32 .caffe.LayerParameter.PoolMethod:\x03MAX\x12\x1a\n\rdropout_ratio\x18\x0c \x01(\x02:\x03\x30.5\x12\x15\n\nlocal_size\x18\r \x01(\r:\x01\x35\x12\x10\n\x05\x61lpha\x18\x0e \x01(\x02:\x01\x31\x12\x12\n\x04\x62\x65ta\x18\x0f \x01(\x02:\x04\x30.75\x12\x0e\n\x06source\x18\x10 \x01(\t\x12\x10\n\x05scale\x18\x11 \x01(\x02:\x01\x31\x12\x10\n\x08meanfile\x18\x12 \x01(\t\x12\x11\n\tbatchsize\x18\x13 \x01(\r\x12\x13\n\x08\x63ropsize\x18\x14 \x01(\r:\x01\x30\x12\x15\n\x06mirror\x18\x15 \x01(\x08:\x05\x66\x61lse\x12\x1f\n\x05\x62lobs\x18\x32 \x03(\x0b\x32\x10.caffe.BlobProto\x12\x10\n\x08\x62lobs_lr\x18\x33 \x03(\x02\x12\x14\n\x0cweight_decay\x18\x34 \x03(\x02\x12\x14\n\trand_skip\x18\x35 \x01(\r:\x01\x30\x12\x1d\n\x10\x64\x65t_fg_threshold\x18\x36 \x01(\x02:\x03\x30.5\x12\x1d\n\x10\x64\x65t_bg_threshold\x18\x37 \x01(\x02:\x03\x30.5\x12\x1d\n\x0f\x64\x65t_fg_fraction\x18\x38 \x01(\x02:\x04\x30.25\x12\x1a\n\x0f\x64\x65t_context_pad\x18: \x01(\r:\x01\x30\x12\x1b\n\rdet_crop_mode\x18; \x01(\t:\x04warp\x12\x12\n\x07new_num\x18< \x01(\x05:\x01\x30\x12\x17\n\x0cnew_channels\x18= \x01(\x05:\x01\x30\x12\x15\n\nnew_height\x18> \x01(\x05:\x01\x30\x12\x14\n\tnew_width\x18? \x01(\x05:\x01\x30\x12\x1d\n\x0eshuffle_images\x18@ \x01(\x08:\x05\x66\x61lse\x12\x15\n\nconcat_dim\x18\x41 \x01(\r:\x01\x31\".\n\nPoolMethod\x12\x07\n\x03MAX\x10\x00\x12\x07\n\x03\x41VE\x10\x01\x12\x0e\n\nSTOCHASTIC\x10\x02\"T\n\x0fLayerConnection\x12$\n\x05layer\x18\x01 \x01(\x0b\x32\x15.caffe.LayerParameter\x12\x0e\n\x06\x62ottom\x18\x02 \x03(\t\x12\x0b\n\x03top\x18\x03 \x03(\t\"\x85\x01\n\x0cNetParameter\x12\x0c\n\x04name\x18\x01 \x01(\t\x12&\n\x06layers\x18\x02 \x03(\x0b\x32\x16.caffe.LayerConnection\x12\r\n\x05input\x18\x03 \x03(\t\x12\x11\n\tinput_dim\x18\x04 \x03(\x05\x12\x1d\n\x0e\x66orce_backward\x18\x05 \x01(\x08:\x05\x66\x61lse\"\xff\x02\n\x0fSolverParameter\x12\x11\n\ttrain_net\x18\x01 \x01(\t\x12\x10\n\x08test_net\x18\x02 \x01(\t\x12\x14\n\ttest_iter\x18\x03 \x01(\x05:\x01\x30\x12\x18\n\rtest_interval\x18\x04 \x01(\x05:\x01\x30\x12\x0f\n\x07\x62\x61se_lr\x18\x05 \x01(\x02\x12\x0f\n\x07\x64isplay\x18\x06 \x01(\x05\x12\x10\n\x08max_iter\x18\x07 \x01(\x05\x12\x11\n\tlr_policy\x18\x08 \x01(\t\x12\r\n\x05gamma\x18\t \x01(\x02\x12\r\n\x05power\x18\n \x01(\x02\x12\x10\n\x08momentum\x18\x0b \x01(\x02\x12\x14\n\x0cweight_decay\x18\x0c \x01(\x02\x12\x10\n\x08stepsize\x18\r \x01(\x05\x12\x13\n\x08snapshot\x18\x0e \x01(\x05:\x01\x30\x12\x17\n\x0fsnapshot_prefix\x18\x0f \x01(\t\x12\x1c\n\rsnapshot_diff\x18\x10 \x01(\x08:\x05\x66\x61lse\x12\x16\n\x0bsolver_mode\x18\x11 \x01(\x05:\x01\x31\x12\x14\n\tdevice_id\x18\x12 \x01(\x05:\x01\x30\"S\n\x0bSolverState\x12\x0c\n\x04iter\x18\x01 \x01(\x05\x12\x13\n\x0blearned_net\x18\x02 \x01(\t\x12!\n\x07history\x18\x03 \x03(\x0b\x32\x10.caffe.BlobProto')
_LAYERPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.LayerParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=1336,
serialized_end=1382,
)
_BLOBPROTO = descriptor.Descriptor(
name='BlobProto',
full_name='caffe.BlobProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num', full_name='caffe.BlobProto.num', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.BlobProto.channels', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.BlobProto.height', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.BlobProto.width', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.BlobProto.data', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='diff', full_name='caffe.BlobProto.diff', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=26,
serialized_end=147,
)
_BLOBPROTOVECTOR = descriptor.Descriptor(
name='BlobProtoVector',
full_name='caffe.BlobProtoVector',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.BlobProtoVector.blobs', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=149,
serialized_end=199,
)
_DATUM = descriptor.Descriptor(
name='Datum',
full_name='caffe.Datum',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='channels', full_name='caffe.Datum.channels', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.Datum.height', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.Datum.width', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.Datum.data', index=3,
number=4, type=12, cpp_type=9, label=1,
has_default_value=False, default_value="",
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='label', full_name='caffe.Datum.label', index=4,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='float_data', full_name='caffe.Datum.float_data', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=201,
serialized_end=306,
)
_FILLERPARAMETER = descriptor.Descriptor(
name='FillerParameter',
full_name='caffe.FillerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='type', full_name='caffe.FillerParameter.type', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("constant", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='value', full_name='caffe.FillerParameter.value', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='min', full_name='caffe.FillerParameter.min', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max', full_name='caffe.FillerParameter.max', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean', full_name='caffe.FillerParameter.mean', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='std', full_name='caffe.FillerParameter.std', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=308,
serialized_end=432,
)
_LAYERPARAMETER = descriptor.Descriptor(
name='LayerParameter',
full_name='caffe.LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.LayerParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.LayerParameter.type', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.LayerParameter.num_output', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='biasterm', full_name='caffe.LayerParameter.biasterm', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.LayerParameter.weight_filler', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.LayerParameter.bias_filler', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.LayerParameter.pad', index=6,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernelsize', full_name='caffe.LayerParameter.kernelsize', index=7,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='group', full_name='caffe.LayerParameter.group', index=8,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.LayerParameter.stride', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pool', full_name='caffe.LayerParameter.pool', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.LayerParameter.dropout_ratio', index=11,
number=12, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='local_size', full_name='caffe.LayerParameter.local_size', index=12,
number=13, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.LayerParameter.alpha', index=13,
number=14, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='beta', full_name='caffe.LayerParameter.beta', index=14,
number=15, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='source', full_name='caffe.LayerParameter.source', index=15,
number=16, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.LayerParameter.scale', index=16,
number=17, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='meanfile', full_name='caffe.LayerParameter.meanfile', index=17,
number=18, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batchsize', full_name='caffe.LayerParameter.batchsize', index=18,
number=19, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='cropsize', full_name='caffe.LayerParameter.cropsize', index=19,
number=20, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.LayerParameter.mirror', index=20,
number=21, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.LayerParameter.blobs', index=21,
number=50, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.LayerParameter.blobs_lr', index=22,
number=51, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.LayerParameter.weight_decay', index=23,
number=52, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.LayerParameter.rand_skip', index=24,
number=53, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_fg_threshold', full_name='caffe.LayerParameter.det_fg_threshold', index=25,
number=54, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_bg_threshold', full_name='caffe.LayerParameter.det_bg_threshold', index=26,
number=55, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_fg_fraction', full_name='caffe.LayerParameter.det_fg_fraction', index=27,
number=56, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_context_pad', full_name='caffe.LayerParameter.det_context_pad', index=28,
number=58, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_crop_mode', full_name='caffe.LayerParameter.det_crop_mode', index=29,
number=59, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_num', full_name='caffe.LayerParameter.new_num', index=30,
number=60, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_channels', full_name='caffe.LayerParameter.new_channels', index=31,
number=61, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_height', full_name='caffe.LayerParameter.new_height', index=32,
number=62, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_width', full_name='caffe.LayerParameter.new_width', index=33,
number=63, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle_images', full_name='caffe.LayerParameter.shuffle_images', index=34,
number=64, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.LayerParameter.concat_dim', index=35,
number=65, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_LAYERPARAMETER_POOLMETHOD,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=435,
serialized_end=1382,
)
_LAYERCONNECTION = descriptor.Descriptor(
name='LayerConnection',
full_name='caffe.LayerConnection',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='layer', full_name='caffe.LayerConnection.layer', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bottom', full_name='caffe.LayerConnection.bottom', index=1,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top', full_name='caffe.LayerConnection.top', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1384,
serialized_end=1468,
)
_NETPARAMETER = descriptor.Descriptor(
name='NetParameter',
full_name='caffe.NetParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.NetParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layers', full_name='caffe.NetParameter.layers', index=1,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input', full_name='caffe.NetParameter.input', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input_dim', full_name='caffe.NetParameter.input_dim', index=3,
number=4, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_backward', full_name='caffe.NetParameter.force_backward', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1471,
serialized_end=1604,
)
_SOLVERPARAMETER = descriptor.Descriptor(
name='SolverParameter',
full_name='caffe.SolverParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='train_net', full_name='caffe.SolverParameter.train_net', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_net', full_name='caffe.SolverParameter.test_net', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_iter', full_name='caffe.SolverParameter.test_iter', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_interval', full_name='caffe.SolverParameter.test_interval', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='base_lr', full_name='caffe.SolverParameter.base_lr', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='display', full_name='caffe.SolverParameter.display', index=5,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max_iter', full_name='caffe.SolverParameter.max_iter', index=6,
number=7, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lr_policy', full_name='caffe.SolverParameter.lr_policy', index=7,
number=8, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='gamma', full_name='caffe.SolverParameter.gamma', index=8,
number=9, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power', full_name='caffe.SolverParameter.power', index=9,
number=10, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='momentum', full_name='caffe.SolverParameter.momentum', index=10,
number=11, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.SolverParameter.weight_decay', index=11,
number=12, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stepsize', full_name='caffe.SolverParameter.stepsize', index=12,
number=13, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot', full_name='caffe.SolverParameter.snapshot', index=13,
number=14, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_prefix', full_name='caffe.SolverParameter.snapshot_prefix', index=14,
number=15, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_diff', full_name='caffe.SolverParameter.snapshot_diff', index=15,
number=16, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='solver_mode', full_name='caffe.SolverParameter.solver_mode', index=16,
number=17, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='device_id', full_name='caffe.SolverParameter.device_id', index=17,
number=18, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1607,
serialized_end=1990,
)
_SOLVERSTATE = descriptor.Descriptor(
name='SolverState',
full_name='caffe.SolverState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='iter', full_name='caffe.SolverState.iter', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='learned_net', full_name='caffe.SolverState.learned_net', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='history', full_name='caffe.SolverState.history', index=2,
number=3, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1992,
serialized_end=2075,
)
_BLOBPROTOVECTOR.fields_by_name['blobs'].message_type = _BLOBPROTO
_LAYERPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_LAYERPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_LAYERPARAMETER.fields_by_name['pool'].enum_type = _LAYERPARAMETER_POOLMETHOD
_LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_LAYERPARAMETER_POOLMETHOD.containing_type = _LAYERPARAMETER;
_LAYERCONNECTION.fields_by_name['layer'].message_type = _LAYERPARAMETER
_NETPARAMETER.fields_by_name['layers'].message_type = _LAYERCONNECTION
_SOLVERSTATE.fields_by_name['history'].message_type = _BLOBPROTO
DESCRIPTOR.message_types_by_name['BlobProto'] = _BLOBPROTO
DESCRIPTOR.message_types_by_name['BlobProtoVector'] = _BLOBPROTOVECTOR
DESCRIPTOR.message_types_by_name['Datum'] = _DATUM
DESCRIPTOR.message_types_by_name['FillerParameter'] = _FILLERPARAMETER
DESCRIPTOR.message_types_by_name['LayerParameter'] = _LAYERPARAMETER
DESCRIPTOR.message_types_by_name['LayerConnection'] = _LAYERCONNECTION
DESCRIPTOR.message_types_by_name['NetParameter'] = _NETPARAMETER
DESCRIPTOR.message_types_by_name['SolverParameter'] = _SOLVERPARAMETER
DESCRIPTOR.message_types_by_name['SolverState'] = _SOLVERSTATE
class BlobProto(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTO
# @@protoc_insertion_point(class_scope:caffe.BlobProto)
class BlobProtoVector(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTOVECTOR
# @@protoc_insertion_point(class_scope:caffe.BlobProtoVector)
class Datum(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATUM
# @@protoc_insertion_point(class_scope:caffe.Datum)
class FillerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FILLERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.FillerParameter)
class LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LayerParameter)
class LayerConnection(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERCONNECTION
# @@protoc_insertion_point(class_scope:caffe.LayerConnection)
class NetParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETPARAMETER
# @@protoc_insertion_point(class_scope:caffe.NetParameter)
class SolverParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SolverParameter)
class SolverState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERSTATE
# @@protoc_insertion_point(class_scope:caffe.SolverState)
# @@protoc_insertion_point(module_scope)
| 39,691 | 43.348603 | 4,364 | py |
DRT | DRT-master/external_libs/matconvnet/matconvnet/utils/proto/caffe_b590f1d_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='caffe_b590f1d.proto',
package='caffe',
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_PHASE = descriptor.EnumDescriptor(
name='Phase',
full_name='caffe.Phase',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='TRAIN', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='TEST', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=14784,
serialized_end=14812,
)
TRAIN = 0
TEST = 1
_FILLERPARAMETER_VARIANCENORM = descriptor.EnumDescriptor(
name='VarianceNorm',
full_name='caffe.FillerParameter.VarianceNorm',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='FAN_IN', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='FAN_OUT', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVERAGE', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=666,
serialized_end=718,
)
_SOLVERPARAMETER_SNAPSHOTFORMAT = descriptor.EnumDescriptor(
name='SnapshotFormat',
full_name='caffe.SolverParameter.SnapshotFormat',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='HDF5', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='BINARYPROTO', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2140,
serialized_end=2183,
)
_SOLVERPARAMETER_SOLVERMODE = descriptor.EnumDescriptor(
name='SolverMode',
full_name='caffe.SolverParameter.SolverMode',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='CPU', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='GPU', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2185,
serialized_end=2215,
)
_SOLVERPARAMETER_SOLVERTYPE = descriptor.EnumDescriptor(
name='SolverType',
full_name='caffe.SolverParameter.SolverType',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='SGD', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='NESTEROV', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ADAGRAD', index=2, number=2,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='RMSPROP', index=3, number=3,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ADADELTA', index=4, number=4,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ADAM', index=5, number=5,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2217,
serialized_end=2302,
)
_PARAMSPEC_DIMCHECKMODE = descriptor.EnumDescriptor(
name='DimCheckMode',
full_name='caffe.ParamSpec.DimCheckMode',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='STRICT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='PERMISSIVE', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2733,
serialized_end=2775,
)
_LOSSPARAMETER_NORMALIZATIONMODE = descriptor.EnumDescriptor(
name='NormalizationMode',
full_name='caffe.LossParameter.NormalizationMode',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='FULL', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='VALID', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='BATCH_SIZE', index=2, number=2,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='NONE', index=3, number=3,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5462,
serialized_end=5528,
)
_CONVOLUTIONPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.ConvolutionParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6493,
serialized_end=6536,
)
_DATAPARAMETER_DB = descriptor.EnumDescriptor(
name='DB',
full_name='caffe.DataParameter.DB',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='LEVELDB', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='LMDB', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6804,
serialized_end=6831,
)
_ELTWISEPARAMETER_ELTWISEOP = descriptor.EnumDescriptor(
name='EltwiseOp',
full_name='caffe.EltwiseParameter.EltwiseOp',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='PROD', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SUM', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MAX', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=7171,
serialized_end=7210,
)
_HINGELOSSPARAMETER_NORM = descriptor.EnumDescriptor(
name='Norm',
full_name='caffe.HingeLossParameter.Norm',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='L1', index=0, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='L2', index=1, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=7745,
serialized_end=7767,
)
_LRNPARAMETER_NORMREGION = descriptor.EnumDescriptor(
name='NormRegion',
full_name='caffe.LRNParameter.NormRegion',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='ACROSS_CHANNELS', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='WITHIN_CHANNEL', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=8557,
serialized_end=8610,
)
_LRNPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.LRNParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6493,
serialized_end=6536,
)
_POOLINGPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.PoolingParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=9179,
serialized_end=9225,
)
_POOLINGPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.PoolingParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6493,
serialized_end=6536,
)
_REDUCTIONPARAMETER_REDUCTIONOP = descriptor.EnumDescriptor(
name='ReductionOp',
full_name='caffe.ReductionParameter.ReductionOp',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='SUM', index=0, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ASUM', index=1, number=2,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SUMSQ', index=2, number=3,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MEAN', index=3, number=4,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=9570,
serialized_end=9623,
)
_RELUPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.ReLUParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6493,
serialized_end=6536,
)
_SIGMOIDPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.SigmoidParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6493,
serialized_end=6536,
)
_SOFTMAXPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.SoftmaxParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6493,
serialized_end=6536,
)
_TANHPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.TanHParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6493,
serialized_end=6536,
)
_SPPPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.SPPParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=9179,
serialized_end=9225,
)
_SPPPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.SPPParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6493,
serialized_end=6536,
)
_V1LAYERPARAMETER_LAYERTYPE = descriptor.EnumDescriptor(
name='LayerType',
full_name='caffe.V1LayerParameter.LayerType',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='NONE', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ABSVAL', index=1, number=35,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ACCURACY', index=2, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ARGMAX', index=3, number=30,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='BNLL', index=4, number=2,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONCAT', index=5, number=3,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONTRASTIVE_LOSS', index=6, number=37,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONVOLUTION', index=7, number=4,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DATA', index=8, number=5,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DECONVOLUTION', index=9, number=39,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DROPOUT', index=10, number=6,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DUMMY_DATA', index=11, number=32,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='EUCLIDEAN_LOSS', index=12, number=7,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ELTWISE', index=13, number=25,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='EXP', index=14, number=38,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='FLATTEN', index=15, number=8,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HDF5_DATA', index=16, number=9,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HDF5_OUTPUT', index=17, number=10,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HINGE_LOSS', index=18, number=28,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='IM2COL', index=19, number=11,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='IMAGE_DATA', index=20, number=12,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='INFOGAIN_LOSS', index=21, number=13,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='INNER_PRODUCT', index=22, number=14,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='LRN', index=23, number=15,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MEMORY_DATA', index=24, number=29,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MULTINOMIAL_LOGISTIC_LOSS', index=25, number=16,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MVN', index=26, number=34,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='POOLING', index=27, number=17,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='POWER', index=28, number=26,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='RELU', index=29, number=18,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SIGMOID', index=30, number=19,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SIGMOID_CROSS_ENTROPY_LOSS', index=31, number=27,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SILENCE', index=32, number=36,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SOFTMAX', index=33, number=20,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SOFTMAX_LOSS', index=34, number=21,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SPLIT', index=35, number=22,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SLICE', index=36, number=33,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='TANH', index=37, number=23,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='WINDOW_DATA', index=38, number=24,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='THRESHOLD', index=39, number=31,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=13025,
serialized_end=13625,
)
_V1LAYERPARAMETER_DIMCHECKMODE = descriptor.EnumDescriptor(
name='DimCheckMode',
full_name='caffe.V1LayerParameter.DimCheckMode',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='STRICT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='PERMISSIVE', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2733,
serialized_end=2775,
)
_V0LAYERPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.V0LayerParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=9179,
serialized_end=9225,
)
_BLOBSHAPE = descriptor.Descriptor(
name='BlobShape',
full_name='caffe.BlobShape',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='dim', full_name='caffe.BlobShape.dim', index=0,
number=1, type=3, cpp_type=2, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=30,
serialized_end=58,
)
_BLOBPROTO = descriptor.Descriptor(
name='BlobProto',
full_name='caffe.BlobProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='shape', full_name='caffe.BlobProto.shape', index=0,
number=7, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.BlobProto.data', index=1,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='diff', full_name='caffe.BlobProto.diff', index=2,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='double_data', full_name='caffe.BlobProto.double_data', index=3,
number=8, type=1, cpp_type=5, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='double_diff', full_name='caffe.BlobProto.double_diff', index=4,
number=9, type=1, cpp_type=5, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='num', full_name='caffe.BlobProto.num', index=5,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.BlobProto.channels', index=6,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.BlobProto.height', index=7,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.BlobProto.width', index=8,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=61,
serialized_end=265,
)
_BLOBPROTOVECTOR = descriptor.Descriptor(
name='BlobProtoVector',
full_name='caffe.BlobProtoVector',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.BlobProtoVector.blobs', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=267,
serialized_end=317,
)
_DATUM = descriptor.Descriptor(
name='Datum',
full_name='caffe.Datum',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='channels', full_name='caffe.Datum.channels', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.Datum.height', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.Datum.width', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.Datum.data', index=3,
number=4, type=12, cpp_type=9, label=1,
has_default_value=False, default_value="",
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='label', full_name='caffe.Datum.label', index=4,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='float_data', full_name='caffe.Datum.float_data', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='encoded', full_name='caffe.Datum.encoded', index=6,
number=7, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=320,
serialized_end=449,
)
_FILLERPARAMETER = descriptor.Descriptor(
name='FillerParameter',
full_name='caffe.FillerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='type', full_name='caffe.FillerParameter.type', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("constant", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='value', full_name='caffe.FillerParameter.value', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='min', full_name='caffe.FillerParameter.min', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max', full_name='caffe.FillerParameter.max', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean', full_name='caffe.FillerParameter.mean', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='std', full_name='caffe.FillerParameter.std', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='sparse', full_name='caffe.FillerParameter.sparse', index=6,
number=7, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='variance_norm', full_name='caffe.FillerParameter.variance_norm', index=7,
number=8, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_FILLERPARAMETER_VARIANCENORM,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=452,
serialized_end=718,
)
_NETPARAMETER = descriptor.Descriptor(
name='NetParameter',
full_name='caffe.NetParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.NetParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input', full_name='caffe.NetParameter.input', index=1,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input_shape', full_name='caffe.NetParameter.input_shape', index=2,
number=8, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input_dim', full_name='caffe.NetParameter.input_dim', index=3,
number=4, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_backward', full_name='caffe.NetParameter.force_backward', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='state', full_name='caffe.NetParameter.state', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='debug_info', full_name='caffe.NetParameter.debug_info', index=6,
number=7, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layer', full_name='caffe.NetParameter.layer', index=7,
number=100, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layers', full_name='caffe.NetParameter.layers', index=8,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=721,
serialized_end=991,
)
_SOLVERPARAMETER = descriptor.Descriptor(
name='SolverParameter',
full_name='caffe.SolverParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='net', full_name='caffe.SolverParameter.net', index=0,
number=24, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='net_param', full_name='caffe.SolverParameter.net_param', index=1,
number=25, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='train_net', full_name='caffe.SolverParameter.train_net', index=2,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_net', full_name='caffe.SolverParameter.test_net', index=3,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='train_net_param', full_name='caffe.SolverParameter.train_net_param', index=4,
number=21, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_net_param', full_name='caffe.SolverParameter.test_net_param', index=5,
number=22, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='train_state', full_name='caffe.SolverParameter.train_state', index=6,
number=26, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_state', full_name='caffe.SolverParameter.test_state', index=7,
number=27, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_iter', full_name='caffe.SolverParameter.test_iter', index=8,
number=3, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_interval', full_name='caffe.SolverParameter.test_interval', index=9,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_compute_loss', full_name='caffe.SolverParameter.test_compute_loss', index=10,
number=19, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_initialization', full_name='caffe.SolverParameter.test_initialization', index=11,
number=32, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='base_lr', full_name='caffe.SolverParameter.base_lr', index=12,
number=5, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='display', full_name='caffe.SolverParameter.display', index=13,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='average_loss', full_name='caffe.SolverParameter.average_loss', index=14,
number=33, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max_iter', full_name='caffe.SolverParameter.max_iter', index=15,
number=7, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='iter_size', full_name='caffe.SolverParameter.iter_size', index=16,
number=36, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lr_policy', full_name='caffe.SolverParameter.lr_policy', index=17,
number=8, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='gamma', full_name='caffe.SolverParameter.gamma', index=18,
number=9, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power', full_name='caffe.SolverParameter.power', index=19,
number=10, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='momentum', full_name='caffe.SolverParameter.momentum', index=20,
number=11, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.SolverParameter.weight_decay', index=21,
number=12, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='regularization_type', full_name='caffe.SolverParameter.regularization_type', index=22,
number=29, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("L2", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stepsize', full_name='caffe.SolverParameter.stepsize', index=23,
number=13, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stepvalue', full_name='caffe.SolverParameter.stepvalue', index=24,
number=34, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='clip_gradients', full_name='caffe.SolverParameter.clip_gradients', index=25,
number=35, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot', full_name='caffe.SolverParameter.snapshot', index=26,
number=14, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_prefix', full_name='caffe.SolverParameter.snapshot_prefix', index=27,
number=15, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_diff', full_name='caffe.SolverParameter.snapshot_diff', index=28,
number=16, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_format', full_name='caffe.SolverParameter.snapshot_format', index=29,
number=37, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='solver_mode', full_name='caffe.SolverParameter.solver_mode', index=30,
number=17, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='device_id', full_name='caffe.SolverParameter.device_id', index=31,
number=18, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='random_seed', full_name='caffe.SolverParameter.random_seed', index=32,
number=20, type=3, cpp_type=2, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.SolverParameter.type', index=33,
number=40, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("SGD", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='delta', full_name='caffe.SolverParameter.delta', index=34,
number=31, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1e-08,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='momentum2', full_name='caffe.SolverParameter.momentum2', index=35,
number=39, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.999,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rms_decay', full_name='caffe.SolverParameter.rms_decay', index=36,
number=38, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='debug_info', full_name='caffe.SolverParameter.debug_info', index=37,
number=23, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_after_train', full_name='caffe.SolverParameter.snapshot_after_train', index=38,
number=28, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='solver_type', full_name='caffe.SolverParameter.solver_type', index=39,
number=30, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SOLVERPARAMETER_SNAPSHOTFORMAT,
_SOLVERPARAMETER_SOLVERMODE,
_SOLVERPARAMETER_SOLVERTYPE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=994,
serialized_end=2302,
)
_SOLVERSTATE = descriptor.Descriptor(
name='SolverState',
full_name='caffe.SolverState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='iter', full_name='caffe.SolverState.iter', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='learned_net', full_name='caffe.SolverState.learned_net', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='history', full_name='caffe.SolverState.history', index=2,
number=3, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='current_step', full_name='caffe.SolverState.current_step', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2304,
serialized_end=2412,
)
_NETSTATE = descriptor.Descriptor(
name='NetState',
full_name='caffe.NetState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='phase', full_name='caffe.NetState.phase', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='level', full_name='caffe.NetState.level', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stage', full_name='caffe.NetState.stage', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2414,
serialized_end=2492,
)
_NETSTATERULE = descriptor.Descriptor(
name='NetStateRule',
full_name='caffe.NetStateRule',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='phase', full_name='caffe.NetStateRule.phase', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='min_level', full_name='caffe.NetStateRule.min_level', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max_level', full_name='caffe.NetStateRule.max_level', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stage', full_name='caffe.NetStateRule.stage', index=3,
number=4, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='not_stage', full_name='caffe.NetStateRule.not_stage', index=4,
number=5, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2494,
serialized_end=2609,
)
_PARAMSPEC = descriptor.Descriptor(
name='ParamSpec',
full_name='caffe.ParamSpec',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.ParamSpec.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='share_mode', full_name='caffe.ParamSpec.share_mode', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lr_mult', full_name='caffe.ParamSpec.lr_mult', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='decay_mult', full_name='caffe.ParamSpec.decay_mult', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_PARAMSPEC_DIMCHECKMODE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2612,
serialized_end=2775,
)
_LAYERPARAMETER = descriptor.Descriptor(
name='LayerParameter',
full_name='caffe.LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.LayerParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.LayerParameter.type', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bottom', full_name='caffe.LayerParameter.bottom', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top', full_name='caffe.LayerParameter.top', index=3,
number=4, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='phase', full_name='caffe.LayerParameter.phase', index=4,
number=10, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='loss_weight', full_name='caffe.LayerParameter.loss_weight', index=5,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='param', full_name='caffe.LayerParameter.param', index=6,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.LayerParameter.blobs', index=7,
number=7, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='propagate_down', full_name='caffe.LayerParameter.propagate_down', index=8,
number=11, type=8, cpp_type=7, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='include', full_name='caffe.LayerParameter.include', index=9,
number=8, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='exclude', full_name='caffe.LayerParameter.exclude', index=10,
number=9, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='transform_param', full_name='caffe.LayerParameter.transform_param', index=11,
number=100, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='loss_param', full_name='caffe.LayerParameter.loss_param', index=12,
number=101, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='accuracy_param', full_name='caffe.LayerParameter.accuracy_param', index=13,
number=102, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='argmax_param', full_name='caffe.LayerParameter.argmax_param', index=14,
number=103, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_norm_param', full_name='caffe.LayerParameter.batch_norm_param', index=15,
number=139, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_param', full_name='caffe.LayerParameter.bias_param', index=16,
number=141, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_param', full_name='caffe.LayerParameter.concat_param', index=17,
number=104, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='contrastive_loss_param', full_name='caffe.LayerParameter.contrastive_loss_param', index=18,
number=105, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='convolution_param', full_name='caffe.LayerParameter.convolution_param', index=19,
number=106, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data_param', full_name='caffe.LayerParameter.data_param', index=20,
number=107, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_param', full_name='caffe.LayerParameter.dropout_param', index=21,
number=108, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dummy_data_param', full_name='caffe.LayerParameter.dummy_data_param', index=22,
number=109, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='eltwise_param', full_name='caffe.LayerParameter.eltwise_param', index=23,
number=110, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='elu_param', full_name='caffe.LayerParameter.elu_param', index=24,
number=140, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='embed_param', full_name='caffe.LayerParameter.embed_param', index=25,
number=137, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='exp_param', full_name='caffe.LayerParameter.exp_param', index=26,
number=111, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='flatten_param', full_name='caffe.LayerParameter.flatten_param', index=27,
number=135, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_data_param', full_name='caffe.LayerParameter.hdf5_data_param', index=28,
number=112, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.LayerParameter.hdf5_output_param', index=29,
number=113, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hinge_loss_param', full_name='caffe.LayerParameter.hinge_loss_param', index=30,
number=114, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='image_data_param', full_name='caffe.LayerParameter.image_data_param', index=31,
number=115, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='infogain_loss_param', full_name='caffe.LayerParameter.infogain_loss_param', index=32,
number=116, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='inner_product_param', full_name='caffe.LayerParameter.inner_product_param', index=33,
number=117, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='log_param', full_name='caffe.LayerParameter.log_param', index=34,
number=134, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lrn_param', full_name='caffe.LayerParameter.lrn_param', index=35,
number=118, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='memory_data_param', full_name='caffe.LayerParameter.memory_data_param', index=36,
number=119, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mvn_param', full_name='caffe.LayerParameter.mvn_param', index=37,
number=120, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pooling_param', full_name='caffe.LayerParameter.pooling_param', index=38,
number=121, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power_param', full_name='caffe.LayerParameter.power_param', index=39,
number=122, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='prelu_param', full_name='caffe.LayerParameter.prelu_param', index=40,
number=131, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='python_param', full_name='caffe.LayerParameter.python_param', index=41,
number=130, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='reduction_param', full_name='caffe.LayerParameter.reduction_param', index=42,
number=136, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='relu_param', full_name='caffe.LayerParameter.relu_param', index=43,
number=123, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='reshape_param', full_name='caffe.LayerParameter.reshape_param', index=44,
number=133, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale_param', full_name='caffe.LayerParameter.scale_param', index=45,
number=142, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='sigmoid_param', full_name='caffe.LayerParameter.sigmoid_param', index=46,
number=124, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='softmax_param', full_name='caffe.LayerParameter.softmax_param', index=47,
number=125, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='spp_param', full_name='caffe.LayerParameter.spp_param', index=48,
number=132, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='slice_param', full_name='caffe.LayerParameter.slice_param', index=49,
number=126, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='tanh_param', full_name='caffe.LayerParameter.tanh_param', index=50,
number=127, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='threshold_param', full_name='caffe.LayerParameter.threshold_param', index=51,
number=128, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='tile_param', full_name='caffe.LayerParameter.tile_param', index=52,
number=138, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='window_data_param', full_name='caffe.LayerParameter.window_data_param', index=53,
number=129, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2778,
serialized_end=5146,
)
_TRANSFORMATIONPARAMETER = descriptor.Descriptor(
name='TransformationParameter',
full_name='caffe.TransformationParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='scale', full_name='caffe.TransformationParameter.scale', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.TransformationParameter.mirror', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.TransformationParameter.crop_size', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.TransformationParameter.mean_file', index=3,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_value', full_name='caffe.TransformationParameter.mean_value', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_color', full_name='caffe.TransformationParameter.force_color', index=5,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_gray', full_name='caffe.TransformationParameter.force_gray', index=6,
number=7, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5149,
serialized_end=5331,
)
_LOSSPARAMETER = descriptor.Descriptor(
name='LossParameter',
full_name='caffe.LossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='ignore_label', full_name='caffe.LossParameter.ignore_label', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='normalization', full_name='caffe.LossParameter.normalization', index=1,
number=3, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='normalize', full_name='caffe.LossParameter.normalize', index=2,
number=2, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_LOSSPARAMETER_NORMALIZATIONMODE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5334,
serialized_end=5528,
)
_ACCURACYPARAMETER = descriptor.Descriptor(
name='AccuracyParameter',
full_name='caffe.AccuracyParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='top_k', full_name='caffe.AccuracyParameter.top_k', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.AccuracyParameter.axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='ignore_label', full_name='caffe.AccuracyParameter.ignore_label', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5530,
serialized_end=5606,
)
_ARGMAXPARAMETER = descriptor.Descriptor(
name='ArgMaxParameter',
full_name='caffe.ArgMaxParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='out_max_val', full_name='caffe.ArgMaxParameter.out_max_val', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top_k', full_name='caffe.ArgMaxParameter.top_k', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.ArgMaxParameter.axis', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5608,
serialized_end=5685,
)
_CONCATPARAMETER = descriptor.Descriptor(
name='ConcatParameter',
full_name='caffe.ConcatParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='axis', full_name='caffe.ConcatParameter.axis', index=0,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.ConcatParameter.concat_dim', index=1,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5687,
serialized_end=5744,
)
_BATCHNORMPARAMETER = descriptor.Descriptor(
name='BatchNormParameter',
full_name='caffe.BatchNormParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='use_global_stats', full_name='caffe.BatchNormParameter.use_global_stats', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='moving_average_fraction', full_name='caffe.BatchNormParameter.moving_average_fraction', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.999,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='eps', full_name='caffe.BatchNormParameter.eps', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1e-05,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5746,
serialized_end=5852,
)
_BIASPARAMETER = descriptor.Descriptor(
name='BiasParameter',
full_name='caffe.BiasParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='axis', full_name='caffe.BiasParameter.axis', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num_axes', full_name='caffe.BiasParameter.num_axes', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='filler', full_name='caffe.BiasParameter.filler', index=2,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5854,
serialized_end=5947,
)
_CONTRASTIVELOSSPARAMETER = descriptor.Descriptor(
name='ContrastiveLossParameter',
full_name='caffe.ContrastiveLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='margin', full_name='caffe.ContrastiveLossParameter.margin', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='legacy_version', full_name='caffe.ContrastiveLossParameter.legacy_version', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5949,
serialized_end=6025,
)
_CONVOLUTIONPARAMETER = descriptor.Descriptor(
name='ConvolutionParameter',
full_name='caffe.ConvolutionParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.ConvolutionParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.ConvolutionParameter.bias_term', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.ConvolutionParameter.pad', index=2,
number=3, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_size', full_name='caffe.ConvolutionParameter.kernel_size', index=3,
number=4, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.ConvolutionParameter.stride', index=4,
number=6, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dilation', full_name='caffe.ConvolutionParameter.dilation', index=5,
number=18, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_h', full_name='caffe.ConvolutionParameter.pad_h', index=6,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_w', full_name='caffe.ConvolutionParameter.pad_w', index=7,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_h', full_name='caffe.ConvolutionParameter.kernel_h', index=8,
number=11, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_w', full_name='caffe.ConvolutionParameter.kernel_w', index=9,
number=12, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_h', full_name='caffe.ConvolutionParameter.stride_h', index=10,
number=13, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_w', full_name='caffe.ConvolutionParameter.stride_w', index=11,
number=14, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='group', full_name='caffe.ConvolutionParameter.group', index=12,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.ConvolutionParameter.weight_filler', index=13,
number=7, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.ConvolutionParameter.bias_filler', index=14,
number=8, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.ConvolutionParameter.engine', index=15,
number=15, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.ConvolutionParameter.axis', index=16,
number=16, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_nd_im2col', full_name='caffe.ConvolutionParameter.force_nd_im2col', index=17,
number=17, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_CONVOLUTIONPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6028,
serialized_end=6536,
)
_DATAPARAMETER = descriptor.Descriptor(
name='DataParameter',
full_name='caffe.DataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.DataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.DataParameter.batch_size', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.DataParameter.rand_skip', index=2,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='backend', full_name='caffe.DataParameter.backend', index=3,
number=8, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.DataParameter.scale', index=4,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.DataParameter.mean_file', index=5,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.DataParameter.crop_size', index=6,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.DataParameter.mirror', index=7,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_encoded_color', full_name='caffe.DataParameter.force_encoded_color', index=8,
number=9, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='prefetch', full_name='caffe.DataParameter.prefetch', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=4,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_DATAPARAMETER_DB,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6539,
serialized_end=6831,
)
_DROPOUTPARAMETER = descriptor.Descriptor(
name='DropoutParameter',
full_name='caffe.DropoutParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.DropoutParameter.dropout_ratio', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6833,
serialized_end=6879,
)
_DUMMYDATAPARAMETER = descriptor.Descriptor(
name='DummyDataParameter',
full_name='caffe.DummyDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='data_filler', full_name='caffe.DummyDataParameter.data_filler', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shape', full_name='caffe.DummyDataParameter.shape', index=1,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num', full_name='caffe.DummyDataParameter.num', index=2,
number=2, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.DummyDataParameter.channels', index=3,
number=3, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.DummyDataParameter.height', index=4,
number=4, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.DummyDataParameter.width', index=5,
number=5, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6882,
serialized_end=7042,
)
_ELTWISEPARAMETER = descriptor.Descriptor(
name='EltwiseParameter',
full_name='caffe.EltwiseParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='operation', full_name='caffe.EltwiseParameter.operation', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='coeff', full_name='caffe.EltwiseParameter.coeff', index=1,
number=2, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stable_prod_grad', full_name='caffe.EltwiseParameter.stable_prod_grad', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_ELTWISEPARAMETER_ELTWISEOP,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7045,
serialized_end=7210,
)
_ELUPARAMETER = descriptor.Descriptor(
name='ELUParameter',
full_name='caffe.ELUParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.ELUParameter.alpha', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7212,
serialized_end=7244,
)
_EMBEDPARAMETER = descriptor.Descriptor(
name='EmbedParameter',
full_name='caffe.EmbedParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.EmbedParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input_dim', full_name='caffe.EmbedParameter.input_dim', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.EmbedParameter.bias_term', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.EmbedParameter.weight_filler', index=3,
number=4, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.EmbedParameter.bias_filler', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7247,
serialized_end=7419,
)
_EXPPARAMETER = descriptor.Descriptor(
name='ExpParameter',
full_name='caffe.ExpParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='base', full_name='caffe.ExpParameter.base', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.ExpParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shift', full_name='caffe.ExpParameter.shift', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7421,
serialized_end=7489,
)
_FLATTENPARAMETER = descriptor.Descriptor(
name='FlattenParameter',
full_name='caffe.FlattenParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='axis', full_name='caffe.FlattenParameter.axis', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='end_axis', full_name='caffe.FlattenParameter.end_axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7491,
serialized_end=7548,
)
_HDF5DATAPARAMETER = descriptor.Descriptor(
name='HDF5DataParameter',
full_name='caffe.HDF5DataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.HDF5DataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.HDF5DataParameter.batch_size', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle', full_name='caffe.HDF5DataParameter.shuffle', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7550,
serialized_end=7629,
)
_HDF5OUTPUTPARAMETER = descriptor.Descriptor(
name='HDF5OutputParameter',
full_name='caffe.HDF5OutputParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='file_name', full_name='caffe.HDF5OutputParameter.file_name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7631,
serialized_end=7671,
)
_HINGELOSSPARAMETER = descriptor.Descriptor(
name='HingeLossParameter',
full_name='caffe.HingeLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='norm', full_name='caffe.HingeLossParameter.norm', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_HINGELOSSPARAMETER_NORM,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7673,
serialized_end=7767,
)
_IMAGEDATAPARAMETER = descriptor.Descriptor(
name='ImageDataParameter',
full_name='caffe.ImageDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.ImageDataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.ImageDataParameter.batch_size', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.ImageDataParameter.rand_skip', index=2,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle', full_name='caffe.ImageDataParameter.shuffle', index=3,
number=8, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_height', full_name='caffe.ImageDataParameter.new_height', index=4,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_width', full_name='caffe.ImageDataParameter.new_width', index=5,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='is_color', full_name='caffe.ImageDataParameter.is_color', index=6,
number=11, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.ImageDataParameter.scale', index=7,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.ImageDataParameter.mean_file', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.ImageDataParameter.crop_size', index=9,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.ImageDataParameter.mirror', index=10,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='root_folder', full_name='caffe.ImageDataParameter.root_folder', index=11,
number=12, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7770,
serialized_end=8049,
)
_INFOGAINLOSSPARAMETER = descriptor.Descriptor(
name='InfogainLossParameter',
full_name='caffe.InfogainLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.InfogainLossParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8051,
serialized_end=8090,
)
_INNERPRODUCTPARAMETER = descriptor.Descriptor(
name='InnerProductParameter',
full_name='caffe.InnerProductParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.InnerProductParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.InnerProductParameter.bias_term', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.InnerProductParameter.weight_filler', index=2,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.InnerProductParameter.bias_filler', index=3,
number=4, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.InnerProductParameter.axis', index=4,
number=5, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8093,
serialized_end=8270,
)
_LOGPARAMETER = descriptor.Descriptor(
name='LogParameter',
full_name='caffe.LogParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='base', full_name='caffe.LogParameter.base', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.LogParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shift', full_name='caffe.LogParameter.shift', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8272,
serialized_end=8340,
)
_LRNPARAMETER = descriptor.Descriptor(
name='LRNParameter',
full_name='caffe.LRNParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='local_size', full_name='caffe.LRNParameter.local_size', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.LRNParameter.alpha', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='beta', full_name='caffe.LRNParameter.beta', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='norm_region', full_name='caffe.LRNParameter.norm_region', index=3,
number=4, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='k', full_name='caffe.LRNParameter.k', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.LRNParameter.engine', index=5,
number=6, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_LRNPARAMETER_NORMREGION,
_LRNPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8343,
serialized_end=8655,
)
_MEMORYDATAPARAMETER = descriptor.Descriptor(
name='MemoryDataParameter',
full_name='caffe.MemoryDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.MemoryDataParameter.batch_size', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.MemoryDataParameter.channels', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.MemoryDataParameter.height', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.MemoryDataParameter.width', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8657,
serialized_end=8747,
)
_MVNPARAMETER = descriptor.Descriptor(
name='MVNParameter',
full_name='caffe.MVNParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='normalize_variance', full_name='caffe.MVNParameter.normalize_variance', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='across_channels', full_name='caffe.MVNParameter.across_channels', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='eps', full_name='caffe.MVNParameter.eps', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1e-09,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8749,
serialized_end=8849,
)
_POOLINGPARAMETER = descriptor.Descriptor(
name='PoolingParameter',
full_name='caffe.PoolingParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='pool', full_name='caffe.PoolingParameter.pool', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.PoolingParameter.pad', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_h', full_name='caffe.PoolingParameter.pad_h', index=2,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_w', full_name='caffe.PoolingParameter.pad_w', index=3,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_size', full_name='caffe.PoolingParameter.kernel_size', index=4,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_h', full_name='caffe.PoolingParameter.kernel_h', index=5,
number=5, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_w', full_name='caffe.PoolingParameter.kernel_w', index=6,
number=6, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.PoolingParameter.stride', index=7,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_h', full_name='caffe.PoolingParameter.stride_h', index=8,
number=7, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_w', full_name='caffe.PoolingParameter.stride_w', index=9,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.PoolingParameter.engine', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='global_pooling', full_name='caffe.PoolingParameter.global_pooling', index=11,
number=12, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_POOLINGPARAMETER_POOLMETHOD,
_POOLINGPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8852,
serialized_end=9270,
)
_POWERPARAMETER = descriptor.Descriptor(
name='PowerParameter',
full_name='caffe.PowerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='power', full_name='caffe.PowerParameter.power', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.PowerParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shift', full_name='caffe.PowerParameter.shift', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9272,
serialized_end=9342,
)
_PYTHONPARAMETER = descriptor.Descriptor(
name='PythonParameter',
full_name='caffe.PythonParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='module', full_name='caffe.PythonParameter.module', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layer', full_name='caffe.PythonParameter.layer', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='param_str', full_name='caffe.PythonParameter.param_str', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='share_in_parallel', full_name='caffe.PythonParameter.share_in_parallel', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9344,
serialized_end=9447,
)
_REDUCTIONPARAMETER = descriptor.Descriptor(
name='ReductionParameter',
full_name='caffe.ReductionParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='operation', full_name='caffe.ReductionParameter.operation', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.ReductionParameter.axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='coeff', full_name='caffe.ReductionParameter.coeff', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_REDUCTIONPARAMETER_REDUCTIONOP,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9450,
serialized_end=9623,
)
_RELUPARAMETER = descriptor.Descriptor(
name='ReLUParameter',
full_name='caffe.ReLUParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='negative_slope', full_name='caffe.ReLUParameter.negative_slope', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.ReLUParameter.engine', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_RELUPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9626,
serialized_end=9767,
)
_RESHAPEPARAMETER = descriptor.Descriptor(
name='ReshapeParameter',
full_name='caffe.ReshapeParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='shape', full_name='caffe.ReshapeParameter.shape', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.ReshapeParameter.axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num_axes', full_name='caffe.ReshapeParameter.num_axes', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9769,
serialized_end=9859,
)
_SCALEPARAMETER = descriptor.Descriptor(
name='ScaleParameter',
full_name='caffe.ScaleParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='axis', full_name='caffe.ScaleParameter.axis', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num_axes', full_name='caffe.ScaleParameter.num_axes', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='filler', full_name='caffe.ScaleParameter.filler', index=2,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.ScaleParameter.bias_term', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.ScaleParameter.bias_filler', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9862,
serialized_end=10027,
)
_SIGMOIDPARAMETER = descriptor.Descriptor(
name='SigmoidParameter',
full_name='caffe.SigmoidParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='engine', full_name='caffe.SigmoidParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SIGMOIDPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=10029,
serialized_end=10149,
)
_SLICEPARAMETER = descriptor.Descriptor(
name='SliceParameter',
full_name='caffe.SliceParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='axis', full_name='caffe.SliceParameter.axis', index=0,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='slice_point', full_name='caffe.SliceParameter.slice_point', index=1,
number=2, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='slice_dim', full_name='caffe.SliceParameter.slice_dim', index=2,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=10151,
serialized_end=10227,
)
_SOFTMAXPARAMETER = descriptor.Descriptor(
name='SoftmaxParameter',
full_name='caffe.SoftmaxParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='engine', full_name='caffe.SoftmaxParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.SoftmaxParameter.axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SOFTMAXPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=10230,
serialized_end=10367,
)
_TANHPARAMETER = descriptor.Descriptor(
name='TanHParameter',
full_name='caffe.TanHParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='engine', full_name='caffe.TanHParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_TANHPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=10369,
serialized_end=10483,
)
_TILEPARAMETER = descriptor.Descriptor(
name='TileParameter',
full_name='caffe.TileParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='axis', full_name='caffe.TileParameter.axis', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='tiles', full_name='caffe.TileParameter.tiles', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=10485,
serialized_end=10532,
)
_THRESHOLDPARAMETER = descriptor.Descriptor(
name='ThresholdParameter',
full_name='caffe.ThresholdParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='threshold', full_name='caffe.ThresholdParameter.threshold', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=10534,
serialized_end=10576,
)
_WINDOWDATAPARAMETER = descriptor.Descriptor(
name='WindowDataParameter',
full_name='caffe.WindowDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.WindowDataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.WindowDataParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.WindowDataParameter.mean_file', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.WindowDataParameter.batch_size', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.WindowDataParameter.crop_size', index=4,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.WindowDataParameter.mirror', index=5,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='fg_threshold', full_name='caffe.WindowDataParameter.fg_threshold', index=6,
number=7, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bg_threshold', full_name='caffe.WindowDataParameter.bg_threshold', index=7,
number=8, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='fg_fraction', full_name='caffe.WindowDataParameter.fg_fraction', index=8,
number=9, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='context_pad', full_name='caffe.WindowDataParameter.context_pad', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_mode', full_name='caffe.WindowDataParameter.crop_mode', index=10,
number=11, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='cache_images', full_name='caffe.WindowDataParameter.cache_images', index=11,
number=12, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='root_folder', full_name='caffe.WindowDataParameter.root_folder', index=12,
number=13, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=10579,
serialized_end=10900,
)
_SPPPARAMETER = descriptor.Descriptor(
name='SPPParameter',
full_name='caffe.SPPParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='pyramid_height', full_name='caffe.SPPParameter.pyramid_height', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pool', full_name='caffe.SPPParameter.pool', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.SPPParameter.engine', index=2,
number=6, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SPPPARAMETER_POOLMETHOD,
_SPPPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=10903,
serialized_end=11138,
)
_V1LAYERPARAMETER = descriptor.Descriptor(
name='V1LayerParameter',
full_name='caffe.V1LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='bottom', full_name='caffe.V1LayerParameter.bottom', index=0,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top', full_name='caffe.V1LayerParameter.top', index=1,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='name', full_name='caffe.V1LayerParameter.name', index=2,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='include', full_name='caffe.V1LayerParameter.include', index=3,
number=32, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='exclude', full_name='caffe.V1LayerParameter.exclude', index=4,
number=33, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.V1LayerParameter.type', index=5,
number=5, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.V1LayerParameter.blobs', index=6,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='param', full_name='caffe.V1LayerParameter.param', index=7,
number=1001, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blob_share_mode', full_name='caffe.V1LayerParameter.blob_share_mode', index=8,
number=1002, type=14, cpp_type=8, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.V1LayerParameter.blobs_lr', index=9,
number=7, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.V1LayerParameter.weight_decay', index=10,
number=8, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='loss_weight', full_name='caffe.V1LayerParameter.loss_weight', index=11,
number=35, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='accuracy_param', full_name='caffe.V1LayerParameter.accuracy_param', index=12,
number=27, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='argmax_param', full_name='caffe.V1LayerParameter.argmax_param', index=13,
number=23, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_param', full_name='caffe.V1LayerParameter.concat_param', index=14,
number=9, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='contrastive_loss_param', full_name='caffe.V1LayerParameter.contrastive_loss_param', index=15,
number=40, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='convolution_param', full_name='caffe.V1LayerParameter.convolution_param', index=16,
number=10, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data_param', full_name='caffe.V1LayerParameter.data_param', index=17,
number=11, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_param', full_name='caffe.V1LayerParameter.dropout_param', index=18,
number=12, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dummy_data_param', full_name='caffe.V1LayerParameter.dummy_data_param', index=19,
number=26, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='eltwise_param', full_name='caffe.V1LayerParameter.eltwise_param', index=20,
number=24, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='exp_param', full_name='caffe.V1LayerParameter.exp_param', index=21,
number=41, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_data_param', full_name='caffe.V1LayerParameter.hdf5_data_param', index=22,
number=13, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.V1LayerParameter.hdf5_output_param', index=23,
number=14, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hinge_loss_param', full_name='caffe.V1LayerParameter.hinge_loss_param', index=24,
number=29, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='image_data_param', full_name='caffe.V1LayerParameter.image_data_param', index=25,
number=15, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='infogain_loss_param', full_name='caffe.V1LayerParameter.infogain_loss_param', index=26,
number=16, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='inner_product_param', full_name='caffe.V1LayerParameter.inner_product_param', index=27,
number=17, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lrn_param', full_name='caffe.V1LayerParameter.lrn_param', index=28,
number=18, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='memory_data_param', full_name='caffe.V1LayerParameter.memory_data_param', index=29,
number=22, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mvn_param', full_name='caffe.V1LayerParameter.mvn_param', index=30,
number=34, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pooling_param', full_name='caffe.V1LayerParameter.pooling_param', index=31,
number=19, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power_param', full_name='caffe.V1LayerParameter.power_param', index=32,
number=21, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='relu_param', full_name='caffe.V1LayerParameter.relu_param', index=33,
number=30, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='sigmoid_param', full_name='caffe.V1LayerParameter.sigmoid_param', index=34,
number=38, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='softmax_param', full_name='caffe.V1LayerParameter.softmax_param', index=35,
number=39, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='slice_param', full_name='caffe.V1LayerParameter.slice_param', index=36,
number=31, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='tanh_param', full_name='caffe.V1LayerParameter.tanh_param', index=37,
number=37, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='threshold_param', full_name='caffe.V1LayerParameter.threshold_param', index=38,
number=25, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='window_data_param', full_name='caffe.V1LayerParameter.window_data_param', index=39,
number=20, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='transform_param', full_name='caffe.V1LayerParameter.transform_param', index=40,
number=36, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='loss_param', full_name='caffe.V1LayerParameter.loss_param', index=41,
number=42, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layer', full_name='caffe.V1LayerParameter.layer', index=42,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_V1LAYERPARAMETER_LAYERTYPE,
_V1LAYERPARAMETER_DIMCHECKMODE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=11141,
serialized_end=13669,
)
_V0LAYERPARAMETER = descriptor.Descriptor(
name='V0LayerParameter',
full_name='caffe.V0LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.V0LayerParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.V0LayerParameter.type', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.V0LayerParameter.num_output', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='biasterm', full_name='caffe.V0LayerParameter.biasterm', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.V0LayerParameter.weight_filler', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.V0LayerParameter.bias_filler', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.V0LayerParameter.pad', index=6,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernelsize', full_name='caffe.V0LayerParameter.kernelsize', index=7,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='group', full_name='caffe.V0LayerParameter.group', index=8,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.V0LayerParameter.stride', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pool', full_name='caffe.V0LayerParameter.pool', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.V0LayerParameter.dropout_ratio', index=11,
number=12, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='local_size', full_name='caffe.V0LayerParameter.local_size', index=12,
number=13, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.V0LayerParameter.alpha', index=13,
number=14, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='beta', full_name='caffe.V0LayerParameter.beta', index=14,
number=15, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='k', full_name='caffe.V0LayerParameter.k', index=15,
number=22, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='source', full_name='caffe.V0LayerParameter.source', index=16,
number=16, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.V0LayerParameter.scale', index=17,
number=17, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='meanfile', full_name='caffe.V0LayerParameter.meanfile', index=18,
number=18, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batchsize', full_name='caffe.V0LayerParameter.batchsize', index=19,
number=19, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='cropsize', full_name='caffe.V0LayerParameter.cropsize', index=20,
number=20, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.V0LayerParameter.mirror', index=21,
number=21, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.V0LayerParameter.blobs', index=22,
number=50, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.V0LayerParameter.blobs_lr', index=23,
number=51, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.V0LayerParameter.weight_decay', index=24,
number=52, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.V0LayerParameter.rand_skip', index=25,
number=53, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_fg_threshold', full_name='caffe.V0LayerParameter.det_fg_threshold', index=26,
number=54, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_bg_threshold', full_name='caffe.V0LayerParameter.det_bg_threshold', index=27,
number=55, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_fg_fraction', full_name='caffe.V0LayerParameter.det_fg_fraction', index=28,
number=56, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_context_pad', full_name='caffe.V0LayerParameter.det_context_pad', index=29,
number=58, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_crop_mode', full_name='caffe.V0LayerParameter.det_crop_mode', index=30,
number=59, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_num', full_name='caffe.V0LayerParameter.new_num', index=31,
number=60, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_channels', full_name='caffe.V0LayerParameter.new_channels', index=32,
number=61, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_height', full_name='caffe.V0LayerParameter.new_height', index=33,
number=62, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_width', full_name='caffe.V0LayerParameter.new_width', index=34,
number=63, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle_images', full_name='caffe.V0LayerParameter.shuffle_images', index=35,
number=64, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.V0LayerParameter.concat_dim', index=36,
number=65, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.V0LayerParameter.hdf5_output_param', index=37,
number=1001, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_V0LAYERPARAMETER_POOLMETHOD,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=13672,
serialized_end=14693,
)
_PRELUPARAMETER = descriptor.Descriptor(
name='PReLUParameter',
full_name='caffe.PReLUParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='filler', full_name='caffe.PReLUParameter.filler', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channel_shared', full_name='caffe.PReLUParameter.channel_shared', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=14695,
serialized_end=14782,
)
_BLOBPROTO.fields_by_name['shape'].message_type = _BLOBSHAPE
_BLOBPROTOVECTOR.fields_by_name['blobs'].message_type = _BLOBPROTO
_FILLERPARAMETER.fields_by_name['variance_norm'].enum_type = _FILLERPARAMETER_VARIANCENORM
_FILLERPARAMETER_VARIANCENORM.containing_type = _FILLERPARAMETER;
_NETPARAMETER.fields_by_name['input_shape'].message_type = _BLOBSHAPE
_NETPARAMETER.fields_by_name['state'].message_type = _NETSTATE
_NETPARAMETER.fields_by_name['layer'].message_type = _LAYERPARAMETER
_NETPARAMETER.fields_by_name['layers'].message_type = _V1LAYERPARAMETER
_SOLVERPARAMETER.fields_by_name['net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['train_net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['test_net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['train_state'].message_type = _NETSTATE
_SOLVERPARAMETER.fields_by_name['test_state'].message_type = _NETSTATE
_SOLVERPARAMETER.fields_by_name['snapshot_format'].enum_type = _SOLVERPARAMETER_SNAPSHOTFORMAT
_SOLVERPARAMETER.fields_by_name['solver_mode'].enum_type = _SOLVERPARAMETER_SOLVERMODE
_SOLVERPARAMETER.fields_by_name['solver_type'].enum_type = _SOLVERPARAMETER_SOLVERTYPE
_SOLVERPARAMETER_SNAPSHOTFORMAT.containing_type = _SOLVERPARAMETER;
_SOLVERPARAMETER_SOLVERMODE.containing_type = _SOLVERPARAMETER;
_SOLVERPARAMETER_SOLVERTYPE.containing_type = _SOLVERPARAMETER;
_SOLVERSTATE.fields_by_name['history'].message_type = _BLOBPROTO
_NETSTATE.fields_by_name['phase'].enum_type = _PHASE
_NETSTATERULE.fields_by_name['phase'].enum_type = _PHASE
_PARAMSPEC.fields_by_name['share_mode'].enum_type = _PARAMSPEC_DIMCHECKMODE
_PARAMSPEC_DIMCHECKMODE.containing_type = _PARAMSPEC;
_LAYERPARAMETER.fields_by_name['phase'].enum_type = _PHASE
_LAYERPARAMETER.fields_by_name['param'].message_type = _PARAMSPEC
_LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_LAYERPARAMETER.fields_by_name['include'].message_type = _NETSTATERULE
_LAYERPARAMETER.fields_by_name['exclude'].message_type = _NETSTATERULE
_LAYERPARAMETER.fields_by_name['transform_param'].message_type = _TRANSFORMATIONPARAMETER
_LAYERPARAMETER.fields_by_name['loss_param'].message_type = _LOSSPARAMETER
_LAYERPARAMETER.fields_by_name['accuracy_param'].message_type = _ACCURACYPARAMETER
_LAYERPARAMETER.fields_by_name['argmax_param'].message_type = _ARGMAXPARAMETER
_LAYERPARAMETER.fields_by_name['batch_norm_param'].message_type = _BATCHNORMPARAMETER
_LAYERPARAMETER.fields_by_name['bias_param'].message_type = _BIASPARAMETER
_LAYERPARAMETER.fields_by_name['concat_param'].message_type = _CONCATPARAMETER
_LAYERPARAMETER.fields_by_name['contrastive_loss_param'].message_type = _CONTRASTIVELOSSPARAMETER
_LAYERPARAMETER.fields_by_name['convolution_param'].message_type = _CONVOLUTIONPARAMETER
_LAYERPARAMETER.fields_by_name['data_param'].message_type = _DATAPARAMETER
_LAYERPARAMETER.fields_by_name['dropout_param'].message_type = _DROPOUTPARAMETER
_LAYERPARAMETER.fields_by_name['dummy_data_param'].message_type = _DUMMYDATAPARAMETER
_LAYERPARAMETER.fields_by_name['eltwise_param'].message_type = _ELTWISEPARAMETER
_LAYERPARAMETER.fields_by_name['elu_param'].message_type = _ELUPARAMETER
_LAYERPARAMETER.fields_by_name['embed_param'].message_type = _EMBEDPARAMETER
_LAYERPARAMETER.fields_by_name['exp_param'].message_type = _EXPPARAMETER
_LAYERPARAMETER.fields_by_name['flatten_param'].message_type = _FLATTENPARAMETER
_LAYERPARAMETER.fields_by_name['hdf5_data_param'].message_type = _HDF5DATAPARAMETER
_LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_LAYERPARAMETER.fields_by_name['hinge_loss_param'].message_type = _HINGELOSSPARAMETER
_LAYERPARAMETER.fields_by_name['image_data_param'].message_type = _IMAGEDATAPARAMETER
_LAYERPARAMETER.fields_by_name['infogain_loss_param'].message_type = _INFOGAINLOSSPARAMETER
_LAYERPARAMETER.fields_by_name['inner_product_param'].message_type = _INNERPRODUCTPARAMETER
_LAYERPARAMETER.fields_by_name['log_param'].message_type = _LOGPARAMETER
_LAYERPARAMETER.fields_by_name['lrn_param'].message_type = _LRNPARAMETER
_LAYERPARAMETER.fields_by_name['memory_data_param'].message_type = _MEMORYDATAPARAMETER
_LAYERPARAMETER.fields_by_name['mvn_param'].message_type = _MVNPARAMETER
_LAYERPARAMETER.fields_by_name['pooling_param'].message_type = _POOLINGPARAMETER
_LAYERPARAMETER.fields_by_name['power_param'].message_type = _POWERPARAMETER
_LAYERPARAMETER.fields_by_name['prelu_param'].message_type = _PRELUPARAMETER
_LAYERPARAMETER.fields_by_name['python_param'].message_type = _PYTHONPARAMETER
_LAYERPARAMETER.fields_by_name['reduction_param'].message_type = _REDUCTIONPARAMETER
_LAYERPARAMETER.fields_by_name['relu_param'].message_type = _RELUPARAMETER
_LAYERPARAMETER.fields_by_name['reshape_param'].message_type = _RESHAPEPARAMETER
_LAYERPARAMETER.fields_by_name['scale_param'].message_type = _SCALEPARAMETER
_LAYERPARAMETER.fields_by_name['sigmoid_param'].message_type = _SIGMOIDPARAMETER
_LAYERPARAMETER.fields_by_name['softmax_param'].message_type = _SOFTMAXPARAMETER
_LAYERPARAMETER.fields_by_name['spp_param'].message_type = _SPPPARAMETER
_LAYERPARAMETER.fields_by_name['slice_param'].message_type = _SLICEPARAMETER
_LAYERPARAMETER.fields_by_name['tanh_param'].message_type = _TANHPARAMETER
_LAYERPARAMETER.fields_by_name['threshold_param'].message_type = _THRESHOLDPARAMETER
_LAYERPARAMETER.fields_by_name['tile_param'].message_type = _TILEPARAMETER
_LAYERPARAMETER.fields_by_name['window_data_param'].message_type = _WINDOWDATAPARAMETER
_LOSSPARAMETER.fields_by_name['normalization'].enum_type = _LOSSPARAMETER_NORMALIZATIONMODE
_LOSSPARAMETER_NORMALIZATIONMODE.containing_type = _LOSSPARAMETER;
_BIASPARAMETER.fields_by_name['filler'].message_type = _FILLERPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['engine'].enum_type = _CONVOLUTIONPARAMETER_ENGINE
_CONVOLUTIONPARAMETER_ENGINE.containing_type = _CONVOLUTIONPARAMETER;
_DATAPARAMETER.fields_by_name['backend'].enum_type = _DATAPARAMETER_DB
_DATAPARAMETER_DB.containing_type = _DATAPARAMETER;
_DUMMYDATAPARAMETER.fields_by_name['data_filler'].message_type = _FILLERPARAMETER
_DUMMYDATAPARAMETER.fields_by_name['shape'].message_type = _BLOBSHAPE
_ELTWISEPARAMETER.fields_by_name['operation'].enum_type = _ELTWISEPARAMETER_ELTWISEOP
_ELTWISEPARAMETER_ELTWISEOP.containing_type = _ELTWISEPARAMETER;
_EMBEDPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_EMBEDPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_HINGELOSSPARAMETER.fields_by_name['norm'].enum_type = _HINGELOSSPARAMETER_NORM
_HINGELOSSPARAMETER_NORM.containing_type = _HINGELOSSPARAMETER;
_INNERPRODUCTPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_INNERPRODUCTPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_LRNPARAMETER.fields_by_name['norm_region'].enum_type = _LRNPARAMETER_NORMREGION
_LRNPARAMETER.fields_by_name['engine'].enum_type = _LRNPARAMETER_ENGINE
_LRNPARAMETER_NORMREGION.containing_type = _LRNPARAMETER;
_LRNPARAMETER_ENGINE.containing_type = _LRNPARAMETER;
_POOLINGPARAMETER.fields_by_name['pool'].enum_type = _POOLINGPARAMETER_POOLMETHOD
_POOLINGPARAMETER.fields_by_name['engine'].enum_type = _POOLINGPARAMETER_ENGINE
_POOLINGPARAMETER_POOLMETHOD.containing_type = _POOLINGPARAMETER;
_POOLINGPARAMETER_ENGINE.containing_type = _POOLINGPARAMETER;
_REDUCTIONPARAMETER.fields_by_name['operation'].enum_type = _REDUCTIONPARAMETER_REDUCTIONOP
_REDUCTIONPARAMETER_REDUCTIONOP.containing_type = _REDUCTIONPARAMETER;
_RELUPARAMETER.fields_by_name['engine'].enum_type = _RELUPARAMETER_ENGINE
_RELUPARAMETER_ENGINE.containing_type = _RELUPARAMETER;
_RESHAPEPARAMETER.fields_by_name['shape'].message_type = _BLOBSHAPE
_SCALEPARAMETER.fields_by_name['filler'].message_type = _FILLERPARAMETER
_SCALEPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_SIGMOIDPARAMETER.fields_by_name['engine'].enum_type = _SIGMOIDPARAMETER_ENGINE
_SIGMOIDPARAMETER_ENGINE.containing_type = _SIGMOIDPARAMETER;
_SOFTMAXPARAMETER.fields_by_name['engine'].enum_type = _SOFTMAXPARAMETER_ENGINE
_SOFTMAXPARAMETER_ENGINE.containing_type = _SOFTMAXPARAMETER;
_TANHPARAMETER.fields_by_name['engine'].enum_type = _TANHPARAMETER_ENGINE
_TANHPARAMETER_ENGINE.containing_type = _TANHPARAMETER;
_SPPPARAMETER.fields_by_name['pool'].enum_type = _SPPPARAMETER_POOLMETHOD
_SPPPARAMETER.fields_by_name['engine'].enum_type = _SPPPARAMETER_ENGINE
_SPPPARAMETER_POOLMETHOD.containing_type = _SPPPARAMETER;
_SPPPARAMETER_ENGINE.containing_type = _SPPPARAMETER;
_V1LAYERPARAMETER.fields_by_name['include'].message_type = _NETSTATERULE
_V1LAYERPARAMETER.fields_by_name['exclude'].message_type = _NETSTATERULE
_V1LAYERPARAMETER.fields_by_name['type'].enum_type = _V1LAYERPARAMETER_LAYERTYPE
_V1LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_V1LAYERPARAMETER.fields_by_name['blob_share_mode'].enum_type = _V1LAYERPARAMETER_DIMCHECKMODE
_V1LAYERPARAMETER.fields_by_name['accuracy_param'].message_type = _ACCURACYPARAMETER
_V1LAYERPARAMETER.fields_by_name['argmax_param'].message_type = _ARGMAXPARAMETER
_V1LAYERPARAMETER.fields_by_name['concat_param'].message_type = _CONCATPARAMETER
_V1LAYERPARAMETER.fields_by_name['contrastive_loss_param'].message_type = _CONTRASTIVELOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['convolution_param'].message_type = _CONVOLUTIONPARAMETER
_V1LAYERPARAMETER.fields_by_name['data_param'].message_type = _DATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['dropout_param'].message_type = _DROPOUTPARAMETER
_V1LAYERPARAMETER.fields_by_name['dummy_data_param'].message_type = _DUMMYDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['eltwise_param'].message_type = _ELTWISEPARAMETER
_V1LAYERPARAMETER.fields_by_name['exp_param'].message_type = _EXPPARAMETER
_V1LAYERPARAMETER.fields_by_name['hdf5_data_param'].message_type = _HDF5DATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_V1LAYERPARAMETER.fields_by_name['hinge_loss_param'].message_type = _HINGELOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['image_data_param'].message_type = _IMAGEDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['infogain_loss_param'].message_type = _INFOGAINLOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['inner_product_param'].message_type = _INNERPRODUCTPARAMETER
_V1LAYERPARAMETER.fields_by_name['lrn_param'].message_type = _LRNPARAMETER
_V1LAYERPARAMETER.fields_by_name['memory_data_param'].message_type = _MEMORYDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['mvn_param'].message_type = _MVNPARAMETER
_V1LAYERPARAMETER.fields_by_name['pooling_param'].message_type = _POOLINGPARAMETER
_V1LAYERPARAMETER.fields_by_name['power_param'].message_type = _POWERPARAMETER
_V1LAYERPARAMETER.fields_by_name['relu_param'].message_type = _RELUPARAMETER
_V1LAYERPARAMETER.fields_by_name['sigmoid_param'].message_type = _SIGMOIDPARAMETER
_V1LAYERPARAMETER.fields_by_name['softmax_param'].message_type = _SOFTMAXPARAMETER
_V1LAYERPARAMETER.fields_by_name['slice_param'].message_type = _SLICEPARAMETER
_V1LAYERPARAMETER.fields_by_name['tanh_param'].message_type = _TANHPARAMETER
_V1LAYERPARAMETER.fields_by_name['threshold_param'].message_type = _THRESHOLDPARAMETER
_V1LAYERPARAMETER.fields_by_name['window_data_param'].message_type = _WINDOWDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['transform_param'].message_type = _TRANSFORMATIONPARAMETER
_V1LAYERPARAMETER.fields_by_name['loss_param'].message_type = _LOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['layer'].message_type = _V0LAYERPARAMETER
_V1LAYERPARAMETER_LAYERTYPE.containing_type = _V1LAYERPARAMETER;
_V1LAYERPARAMETER_DIMCHECKMODE.containing_type = _V1LAYERPARAMETER;
_V0LAYERPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_V0LAYERPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_V0LAYERPARAMETER.fields_by_name['pool'].enum_type = _V0LAYERPARAMETER_POOLMETHOD
_V0LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_V0LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_V0LAYERPARAMETER_POOLMETHOD.containing_type = _V0LAYERPARAMETER;
_PRELUPARAMETER.fields_by_name['filler'].message_type = _FILLERPARAMETER
DESCRIPTOR.message_types_by_name['BlobShape'] = _BLOBSHAPE
DESCRIPTOR.message_types_by_name['BlobProto'] = _BLOBPROTO
DESCRIPTOR.message_types_by_name['BlobProtoVector'] = _BLOBPROTOVECTOR
DESCRIPTOR.message_types_by_name['Datum'] = _DATUM
DESCRIPTOR.message_types_by_name['FillerParameter'] = _FILLERPARAMETER
DESCRIPTOR.message_types_by_name['NetParameter'] = _NETPARAMETER
DESCRIPTOR.message_types_by_name['SolverParameter'] = _SOLVERPARAMETER
DESCRIPTOR.message_types_by_name['SolverState'] = _SOLVERSTATE
DESCRIPTOR.message_types_by_name['NetState'] = _NETSTATE
DESCRIPTOR.message_types_by_name['NetStateRule'] = _NETSTATERULE
DESCRIPTOR.message_types_by_name['ParamSpec'] = _PARAMSPEC
DESCRIPTOR.message_types_by_name['LayerParameter'] = _LAYERPARAMETER
DESCRIPTOR.message_types_by_name['TransformationParameter'] = _TRANSFORMATIONPARAMETER
DESCRIPTOR.message_types_by_name['LossParameter'] = _LOSSPARAMETER
DESCRIPTOR.message_types_by_name['AccuracyParameter'] = _ACCURACYPARAMETER
DESCRIPTOR.message_types_by_name['ArgMaxParameter'] = _ARGMAXPARAMETER
DESCRIPTOR.message_types_by_name['ConcatParameter'] = _CONCATPARAMETER
DESCRIPTOR.message_types_by_name['BatchNormParameter'] = _BATCHNORMPARAMETER
DESCRIPTOR.message_types_by_name['BiasParameter'] = _BIASPARAMETER
DESCRIPTOR.message_types_by_name['ContrastiveLossParameter'] = _CONTRASTIVELOSSPARAMETER
DESCRIPTOR.message_types_by_name['ConvolutionParameter'] = _CONVOLUTIONPARAMETER
DESCRIPTOR.message_types_by_name['DataParameter'] = _DATAPARAMETER
DESCRIPTOR.message_types_by_name['DropoutParameter'] = _DROPOUTPARAMETER
DESCRIPTOR.message_types_by_name['DummyDataParameter'] = _DUMMYDATAPARAMETER
DESCRIPTOR.message_types_by_name['EltwiseParameter'] = _ELTWISEPARAMETER
DESCRIPTOR.message_types_by_name['ELUParameter'] = _ELUPARAMETER
DESCRIPTOR.message_types_by_name['EmbedParameter'] = _EMBEDPARAMETER
DESCRIPTOR.message_types_by_name['ExpParameter'] = _EXPPARAMETER
DESCRIPTOR.message_types_by_name['FlattenParameter'] = _FLATTENPARAMETER
DESCRIPTOR.message_types_by_name['HDF5DataParameter'] = _HDF5DATAPARAMETER
DESCRIPTOR.message_types_by_name['HDF5OutputParameter'] = _HDF5OUTPUTPARAMETER
DESCRIPTOR.message_types_by_name['HingeLossParameter'] = _HINGELOSSPARAMETER
DESCRIPTOR.message_types_by_name['ImageDataParameter'] = _IMAGEDATAPARAMETER
DESCRIPTOR.message_types_by_name['InfogainLossParameter'] = _INFOGAINLOSSPARAMETER
DESCRIPTOR.message_types_by_name['InnerProductParameter'] = _INNERPRODUCTPARAMETER
DESCRIPTOR.message_types_by_name['LogParameter'] = _LOGPARAMETER
DESCRIPTOR.message_types_by_name['LRNParameter'] = _LRNPARAMETER
DESCRIPTOR.message_types_by_name['MemoryDataParameter'] = _MEMORYDATAPARAMETER
DESCRIPTOR.message_types_by_name['MVNParameter'] = _MVNPARAMETER
DESCRIPTOR.message_types_by_name['PoolingParameter'] = _POOLINGPARAMETER
DESCRIPTOR.message_types_by_name['PowerParameter'] = _POWERPARAMETER
DESCRIPTOR.message_types_by_name['PythonParameter'] = _PYTHONPARAMETER
DESCRIPTOR.message_types_by_name['ReductionParameter'] = _REDUCTIONPARAMETER
DESCRIPTOR.message_types_by_name['ReLUParameter'] = _RELUPARAMETER
DESCRIPTOR.message_types_by_name['ReshapeParameter'] = _RESHAPEPARAMETER
DESCRIPTOR.message_types_by_name['ScaleParameter'] = _SCALEPARAMETER
DESCRIPTOR.message_types_by_name['SigmoidParameter'] = _SIGMOIDPARAMETER
DESCRIPTOR.message_types_by_name['SliceParameter'] = _SLICEPARAMETER
DESCRIPTOR.message_types_by_name['SoftmaxParameter'] = _SOFTMAXPARAMETER
DESCRIPTOR.message_types_by_name['TanHParameter'] = _TANHPARAMETER
DESCRIPTOR.message_types_by_name['TileParameter'] = _TILEPARAMETER
DESCRIPTOR.message_types_by_name['ThresholdParameter'] = _THRESHOLDPARAMETER
DESCRIPTOR.message_types_by_name['WindowDataParameter'] = _WINDOWDATAPARAMETER
DESCRIPTOR.message_types_by_name['SPPParameter'] = _SPPPARAMETER
DESCRIPTOR.message_types_by_name['V1LayerParameter'] = _V1LAYERPARAMETER
DESCRIPTOR.message_types_by_name['V0LayerParameter'] = _V0LAYERPARAMETER
DESCRIPTOR.message_types_by_name['PReLUParameter'] = _PRELUPARAMETER
class BlobShape(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBSHAPE
# @@protoc_insertion_point(class_scope:caffe.BlobShape)
class BlobProto(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTO
# @@protoc_insertion_point(class_scope:caffe.BlobProto)
class BlobProtoVector(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTOVECTOR
# @@protoc_insertion_point(class_scope:caffe.BlobProtoVector)
class Datum(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATUM
# @@protoc_insertion_point(class_scope:caffe.Datum)
class FillerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FILLERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.FillerParameter)
class NetParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETPARAMETER
# @@protoc_insertion_point(class_scope:caffe.NetParameter)
class SolverParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SolverParameter)
class SolverState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERSTATE
# @@protoc_insertion_point(class_scope:caffe.SolverState)
class NetState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATE
# @@protoc_insertion_point(class_scope:caffe.NetState)
class NetStateRule(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATERULE
# @@protoc_insertion_point(class_scope:caffe.NetStateRule)
class ParamSpec(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PARAMSPEC
# @@protoc_insertion_point(class_scope:caffe.ParamSpec)
class LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LayerParameter)
class TransformationParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TRANSFORMATIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.TransformationParameter)
class LossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LossParameter)
class AccuracyParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ACCURACYPARAMETER
# @@protoc_insertion_point(class_scope:caffe.AccuracyParameter)
class ArgMaxParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ARGMAXPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ArgMaxParameter)
class ConcatParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONCATPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ConcatParameter)
class BatchNormParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BATCHNORMPARAMETER
# @@protoc_insertion_point(class_scope:caffe.BatchNormParameter)
class BiasParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BIASPARAMETER
# @@protoc_insertion_point(class_scope:caffe.BiasParameter)
class ContrastiveLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONTRASTIVELOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ContrastiveLossParameter)
class ConvolutionParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONVOLUTIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ConvolutionParameter)
class DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DataParameter)
class DropoutParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DROPOUTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DropoutParameter)
class DummyDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DUMMYDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DummyDataParameter)
class EltwiseParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ELTWISEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.EltwiseParameter)
class ELUParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ELUPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ELUParameter)
class EmbedParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _EMBEDPARAMETER
# @@protoc_insertion_point(class_scope:caffe.EmbedParameter)
class ExpParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _EXPPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ExpParameter)
class FlattenParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FLATTENPARAMETER
# @@protoc_insertion_point(class_scope:caffe.FlattenParameter)
class HDF5DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5DATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HDF5DataParameter)
class HDF5OutputParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5OUTPUTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HDF5OutputParameter)
class HingeLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HINGELOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HingeLossParameter)
class ImageDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _IMAGEDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ImageDataParameter)
class InfogainLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INFOGAINLOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.InfogainLossParameter)
class InnerProductParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INNERPRODUCTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.InnerProductParameter)
class LogParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LOGPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LogParameter)
class LRNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LRNPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LRNParameter)
class MemoryDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MEMORYDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.MemoryDataParameter)
class MVNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MVNPARAMETER
# @@protoc_insertion_point(class_scope:caffe.MVNParameter)
class PoolingParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POOLINGPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PoolingParameter)
class PowerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POWERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PowerParameter)
class PythonParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PYTHONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PythonParameter)
class ReductionParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _REDUCTIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ReductionParameter)
class ReLUParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _RELUPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ReLUParameter)
class ReshapeParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _RESHAPEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ReshapeParameter)
class ScaleParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SCALEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ScaleParameter)
class SigmoidParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SIGMOIDPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SigmoidParameter)
class SliceParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SLICEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SliceParameter)
class SoftmaxParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOFTMAXPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SoftmaxParameter)
class TanHParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TANHPARAMETER
# @@protoc_insertion_point(class_scope:caffe.TanHParameter)
class TileParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TILEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.TileParameter)
class ThresholdParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _THRESHOLDPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ThresholdParameter)
class WindowDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _WINDOWDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.WindowDataParameter)
class SPPParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SPPPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SPPParameter)
class V1LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _V1LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.V1LayerParameter)
class V0LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _V0LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.V0LayerParameter)
class PReLUParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PRELUPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PReLUParameter)
# @@protoc_insertion_point(module_scope)
| 232,112 | 42.264306 | 27,801 | py |
DRT | DRT-master/external_libs/matconvnet/matconvnet/utils/proto/caffe_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='caffe.proto',
package='caffe',
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_SOLVERPARAMETER_SOLVERMODE = descriptor.EnumDescriptor(
name='SolverMode',
full_name='caffe.SolverParameter.SolverMode',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='CPU', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='GPU', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=1068,
serialized_end=1098,
)
_LAYERPARAMETER_LAYERTYPE = descriptor.EnumDescriptor(
name='LayerType',
full_name='caffe.LayerParameter.LayerType',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='NONE', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ACCURACY', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='BNLL', index=2, number=2,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONCAT', index=3, number=3,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONVOLUTION', index=4, number=4,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DATA', index=5, number=5,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DROPOUT', index=6, number=6,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='EUCLIDEAN_LOSS', index=7, number=7,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ELTWISE_PRODUCT', index=8, number=25,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='FLATTEN', index=9, number=8,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HDF5_DATA', index=10, number=9,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HDF5_OUTPUT', index=11, number=10,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HINGE_LOSS', index=12, number=28,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='IM2COL', index=13, number=11,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='IMAGE_DATA', index=14, number=12,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='INFOGAIN_LOSS', index=15, number=13,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='INNER_PRODUCT', index=16, number=14,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='LRN', index=17, number=15,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MEMORY_DATA', index=18, number=29,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MULTINOMIAL_LOGISTIC_LOSS', index=19, number=16,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='POOLING', index=20, number=17,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='POWER', index=21, number=26,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='RELU', index=22, number=18,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SIGMOID', index=23, number=19,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SIGMOID_CROSS_ENTROPY_LOSS', index=24, number=27,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SOFTMAX', index=25, number=20,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SOFTMAX_LOSS', index=26, number=21,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SPLIT', index=27, number=22,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='TANH', index=28, number=23,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='WINDOW_DATA', index=29, number=24,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2119,
serialized_end=2589,
)
_LRNPARAMETER_NORMREGION = descriptor.EnumDescriptor(
name='NormRegion',
full_name='caffe.LRNParameter.NormRegion',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='ACROSS_CHANNELS', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='WITHIN_CHANNEL', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=3758,
serialized_end=3811,
)
_POOLINGPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.PoolingParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=4037,
serialized_end=4083,
)
_V0LAYERPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.V0LayerParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=4037,
serialized_end=4083,
)
_BLOBPROTO = descriptor.Descriptor(
name='BlobProto',
full_name='caffe.BlobProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num', full_name='caffe.BlobProto.num', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.BlobProto.channels', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.BlobProto.height', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.BlobProto.width', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.BlobProto.data', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='diff', full_name='caffe.BlobProto.diff', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=22,
serialized_end=143,
)
_BLOBPROTOVECTOR = descriptor.Descriptor(
name='BlobProtoVector',
full_name='caffe.BlobProtoVector',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.BlobProtoVector.blobs', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=145,
serialized_end=195,
)
_DATUM = descriptor.Descriptor(
name='Datum',
full_name='caffe.Datum',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='channels', full_name='caffe.Datum.channels', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.Datum.height', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.Datum.width', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.Datum.data', index=3,
number=4, type=12, cpp_type=9, label=1,
has_default_value=False, default_value="",
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='label', full_name='caffe.Datum.label', index=4,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='float_data', full_name='caffe.Datum.float_data', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=197,
serialized_end=302,
)
_FILLERPARAMETER = descriptor.Descriptor(
name='FillerParameter',
full_name='caffe.FillerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='type', full_name='caffe.FillerParameter.type', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("constant", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='value', full_name='caffe.FillerParameter.value', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='min', full_name='caffe.FillerParameter.min', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max', full_name='caffe.FillerParameter.max', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean', full_name='caffe.FillerParameter.mean', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='std', full_name='caffe.FillerParameter.std', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='sparse', full_name='caffe.FillerParameter.sparse', index=6,
number=7, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=305,
serialized_end=449,
)
_NETPARAMETER = descriptor.Descriptor(
name='NetParameter',
full_name='caffe.NetParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.NetParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layers', full_name='caffe.NetParameter.layers', index=1,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input', full_name='caffe.NetParameter.input', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input_dim', full_name='caffe.NetParameter.input_dim', index=3,
number=4, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_backward', full_name='caffe.NetParameter.force_backward', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=452,
serialized_end=584,
)
_SOLVERPARAMETER = descriptor.Descriptor(
name='SolverParameter',
full_name='caffe.SolverParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='train_net', full_name='caffe.SolverParameter.train_net', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_net', full_name='caffe.SolverParameter.test_net', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_iter', full_name='caffe.SolverParameter.test_iter', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_interval', full_name='caffe.SolverParameter.test_interval', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_compute_loss', full_name='caffe.SolverParameter.test_compute_loss', index=4,
number=19, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='base_lr', full_name='caffe.SolverParameter.base_lr', index=5,
number=5, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='display', full_name='caffe.SolverParameter.display', index=6,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max_iter', full_name='caffe.SolverParameter.max_iter', index=7,
number=7, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lr_policy', full_name='caffe.SolverParameter.lr_policy', index=8,
number=8, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='gamma', full_name='caffe.SolverParameter.gamma', index=9,
number=9, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power', full_name='caffe.SolverParameter.power', index=10,
number=10, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='momentum', full_name='caffe.SolverParameter.momentum', index=11,
number=11, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.SolverParameter.weight_decay', index=12,
number=12, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stepsize', full_name='caffe.SolverParameter.stepsize', index=13,
number=13, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot', full_name='caffe.SolverParameter.snapshot', index=14,
number=14, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_prefix', full_name='caffe.SolverParameter.snapshot_prefix', index=15,
number=15, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_diff', full_name='caffe.SolverParameter.snapshot_diff', index=16,
number=16, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='solver_mode', full_name='caffe.SolverParameter.solver_mode', index=17,
number=17, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='device_id', full_name='caffe.SolverParameter.device_id', index=18,
number=18, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='random_seed', full_name='caffe.SolverParameter.random_seed', index=19,
number=20, type=3, cpp_type=2, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SOLVERPARAMETER_SOLVERMODE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=587,
serialized_end=1098,
)
_SOLVERSTATE = descriptor.Descriptor(
name='SolverState',
full_name='caffe.SolverState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='iter', full_name='caffe.SolverState.iter', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='learned_net', full_name='caffe.SolverState.learned_net', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='history', full_name='caffe.SolverState.history', index=2,
number=3, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1100,
serialized_end=1183,
)
_LAYERPARAMETER = descriptor.Descriptor(
name='LayerParameter',
full_name='caffe.LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='bottom', full_name='caffe.LayerParameter.bottom', index=0,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top', full_name='caffe.LayerParameter.top', index=1,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='name', full_name='caffe.LayerParameter.name', index=2,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.LayerParameter.type', index=3,
number=5, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.LayerParameter.blobs', index=4,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.LayerParameter.blobs_lr', index=5,
number=7, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.LayerParameter.weight_decay', index=6,
number=8, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_param', full_name='caffe.LayerParameter.concat_param', index=7,
number=9, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='convolution_param', full_name='caffe.LayerParameter.convolution_param', index=8,
number=10, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data_param', full_name='caffe.LayerParameter.data_param', index=9,
number=11, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_param', full_name='caffe.LayerParameter.dropout_param', index=10,
number=12, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_data_param', full_name='caffe.LayerParameter.hdf5_data_param', index=11,
number=13, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.LayerParameter.hdf5_output_param', index=12,
number=14, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='image_data_param', full_name='caffe.LayerParameter.image_data_param', index=13,
number=15, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='infogain_loss_param', full_name='caffe.LayerParameter.infogain_loss_param', index=14,
number=16, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='inner_product_param', full_name='caffe.LayerParameter.inner_product_param', index=15,
number=17, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lrn_param', full_name='caffe.LayerParameter.lrn_param', index=16,
number=18, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='memory_data_param', full_name='caffe.LayerParameter.memory_data_param', index=17,
number=22, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pooling_param', full_name='caffe.LayerParameter.pooling_param', index=18,
number=19, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power_param', full_name='caffe.LayerParameter.power_param', index=19,
number=21, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='window_data_param', full_name='caffe.LayerParameter.window_data_param', index=20,
number=20, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layer', full_name='caffe.LayerParameter.layer', index=21,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_LAYERPARAMETER_LAYERTYPE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1186,
serialized_end=2589,
)
_CONCATPARAMETER = descriptor.Descriptor(
name='ConcatParameter',
full_name='caffe.ConcatParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.ConcatParameter.concat_dim', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2591,
serialized_end=2631,
)
_CONVOLUTIONPARAMETER = descriptor.Descriptor(
name='ConvolutionParameter',
full_name='caffe.ConvolutionParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.ConvolutionParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.ConvolutionParameter.bias_term', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.ConvolutionParameter.pad', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_size', full_name='caffe.ConvolutionParameter.kernel_size', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='group', full_name='caffe.ConvolutionParameter.group', index=4,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.ConvolutionParameter.stride', index=5,
number=6, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.ConvolutionParameter.weight_filler', index=6,
number=7, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.ConvolutionParameter.bias_filler', index=7,
number=8, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2634,
serialized_end=2867,
)
_DATAPARAMETER = descriptor.Descriptor(
name='DataParameter',
full_name='caffe.DataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.DataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.DataParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.DataParameter.mean_file', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.DataParameter.batch_size', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.DataParameter.crop_size', index=4,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.DataParameter.mirror', index=5,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.DataParameter.rand_skip', index=6,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2870,
serialized_end=3025,
)
_DROPOUTPARAMETER = descriptor.Descriptor(
name='DropoutParameter',
full_name='caffe.DropoutParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.DropoutParameter.dropout_ratio', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=3027,
serialized_end=3073,
)
_HDF5DATAPARAMETER = descriptor.Descriptor(
name='HDF5DataParameter',
full_name='caffe.HDF5DataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.HDF5DataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.HDF5DataParameter.batch_size', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=3075,
serialized_end=3130,
)
_HDF5OUTPUTPARAMETER = descriptor.Descriptor(
name='HDF5OutputParameter',
full_name='caffe.HDF5OutputParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='file_name', full_name='caffe.HDF5OutputParameter.file_name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=3132,
serialized_end=3172,
)
_IMAGEDATAPARAMETER = descriptor.Descriptor(
name='ImageDataParameter',
full_name='caffe.ImageDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.ImageDataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.ImageDataParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.ImageDataParameter.mean_file', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.ImageDataParameter.batch_size', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.ImageDataParameter.crop_size', index=4,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.ImageDataParameter.mirror', index=5,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.ImageDataParameter.rand_skip', index=6,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle', full_name='caffe.ImageDataParameter.shuffle', index=7,
number=8, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_height', full_name='caffe.ImageDataParameter.new_height', index=8,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_width', full_name='caffe.ImageDataParameter.new_width', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=3175,
serialized_end=3404,
)
_INFOGAINLOSSPARAMETER = descriptor.Descriptor(
name='InfogainLossParameter',
full_name='caffe.InfogainLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.InfogainLossParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=3406,
serialized_end=3445,
)
_INNERPRODUCTPARAMETER = descriptor.Descriptor(
name='InnerProductParameter',
full_name='caffe.InnerProductParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.InnerProductParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.InnerProductParameter.bias_term', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.InnerProductParameter.weight_filler', index=2,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.InnerProductParameter.bias_filler', index=3,
number=4, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=3448,
serialized_end=3608,
)
_LRNPARAMETER = descriptor.Descriptor(
name='LRNParameter',
full_name='caffe.LRNParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='local_size', full_name='caffe.LRNParameter.local_size', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.LRNParameter.alpha', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='beta', full_name='caffe.LRNParameter.beta', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='norm_region', full_name='caffe.LRNParameter.norm_region', index=3,
number=4, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_LRNPARAMETER_NORMREGION,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=3611,
serialized_end=3811,
)
_MEMORYDATAPARAMETER = descriptor.Descriptor(
name='MemoryDataParameter',
full_name='caffe.MemoryDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.MemoryDataParameter.batch_size', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.MemoryDataParameter.channels', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.MemoryDataParameter.height', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.MemoryDataParameter.width', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=3813,
serialized_end=3903,
)
_POOLINGPARAMETER = descriptor.Descriptor(
name='PoolingParameter',
full_name='caffe.PoolingParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='pool', full_name='caffe.PoolingParameter.pool', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_size', full_name='caffe.PoolingParameter.kernel_size', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.PoolingParameter.stride', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.PoolingParameter.pad', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_POOLINGPARAMETER_POOLMETHOD,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=3906,
serialized_end=4083,
)
_POWERPARAMETER = descriptor.Descriptor(
name='PowerParameter',
full_name='caffe.PowerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='power', full_name='caffe.PowerParameter.power', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.PowerParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shift', full_name='caffe.PowerParameter.shift', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4085,
serialized_end=4155,
)
_WINDOWDATAPARAMETER = descriptor.Descriptor(
name='WindowDataParameter',
full_name='caffe.WindowDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.WindowDataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.WindowDataParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.WindowDataParameter.mean_file', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.WindowDataParameter.batch_size', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.WindowDataParameter.crop_size', index=4,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.WindowDataParameter.mirror', index=5,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='fg_threshold', full_name='caffe.WindowDataParameter.fg_threshold', index=6,
number=7, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bg_threshold', full_name='caffe.WindowDataParameter.bg_threshold', index=7,
number=8, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='fg_fraction', full_name='caffe.WindowDataParameter.fg_fraction', index=8,
number=9, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='context_pad', full_name='caffe.WindowDataParameter.context_pad', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_mode', full_name='caffe.WindowDataParameter.crop_mode', index=10,
number=11, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4158,
serialized_end=4427,
)
_V0LAYERPARAMETER = descriptor.Descriptor(
name='V0LayerParameter',
full_name='caffe.V0LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.V0LayerParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.V0LayerParameter.type', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.V0LayerParameter.num_output', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='biasterm', full_name='caffe.V0LayerParameter.biasterm', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.V0LayerParameter.weight_filler', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.V0LayerParameter.bias_filler', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.V0LayerParameter.pad', index=6,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernelsize', full_name='caffe.V0LayerParameter.kernelsize', index=7,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='group', full_name='caffe.V0LayerParameter.group', index=8,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.V0LayerParameter.stride', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pool', full_name='caffe.V0LayerParameter.pool', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.V0LayerParameter.dropout_ratio', index=11,
number=12, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='local_size', full_name='caffe.V0LayerParameter.local_size', index=12,
number=13, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.V0LayerParameter.alpha', index=13,
number=14, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='beta', full_name='caffe.V0LayerParameter.beta', index=14,
number=15, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='source', full_name='caffe.V0LayerParameter.source', index=15,
number=16, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.V0LayerParameter.scale', index=16,
number=17, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='meanfile', full_name='caffe.V0LayerParameter.meanfile', index=17,
number=18, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batchsize', full_name='caffe.V0LayerParameter.batchsize', index=18,
number=19, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='cropsize', full_name='caffe.V0LayerParameter.cropsize', index=19,
number=20, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.V0LayerParameter.mirror', index=20,
number=21, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.V0LayerParameter.blobs', index=21,
number=50, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.V0LayerParameter.blobs_lr', index=22,
number=51, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.V0LayerParameter.weight_decay', index=23,
number=52, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.V0LayerParameter.rand_skip', index=24,
number=53, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_fg_threshold', full_name='caffe.V0LayerParameter.det_fg_threshold', index=25,
number=54, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_bg_threshold', full_name='caffe.V0LayerParameter.det_bg_threshold', index=26,
number=55, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_fg_fraction', full_name='caffe.V0LayerParameter.det_fg_fraction', index=27,
number=56, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_context_pad', full_name='caffe.V0LayerParameter.det_context_pad', index=28,
number=58, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_crop_mode', full_name='caffe.V0LayerParameter.det_crop_mode', index=29,
number=59, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_num', full_name='caffe.V0LayerParameter.new_num', index=30,
number=60, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_channels', full_name='caffe.V0LayerParameter.new_channels', index=31,
number=61, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_height', full_name='caffe.V0LayerParameter.new_height', index=32,
number=62, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_width', full_name='caffe.V0LayerParameter.new_width', index=33,
number=63, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle_images', full_name='caffe.V0LayerParameter.shuffle_images', index=34,
number=64, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.V0LayerParameter.concat_dim', index=35,
number=65, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.V0LayerParameter.hdf5_output_param', index=36,
number=1001, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_V0LAYERPARAMETER_POOLMETHOD,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4430,
serialized_end=5437,
)
_BLOBPROTOVECTOR.fields_by_name['blobs'].message_type = _BLOBPROTO
_NETPARAMETER.fields_by_name['layers'].message_type = _LAYERPARAMETER
_SOLVERPARAMETER.fields_by_name['solver_mode'].enum_type = _SOLVERPARAMETER_SOLVERMODE
_SOLVERPARAMETER_SOLVERMODE.containing_type = _SOLVERPARAMETER;
_SOLVERSTATE.fields_by_name['history'].message_type = _BLOBPROTO
_LAYERPARAMETER.fields_by_name['type'].enum_type = _LAYERPARAMETER_LAYERTYPE
_LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_LAYERPARAMETER.fields_by_name['concat_param'].message_type = _CONCATPARAMETER
_LAYERPARAMETER.fields_by_name['convolution_param'].message_type = _CONVOLUTIONPARAMETER
_LAYERPARAMETER.fields_by_name['data_param'].message_type = _DATAPARAMETER
_LAYERPARAMETER.fields_by_name['dropout_param'].message_type = _DROPOUTPARAMETER
_LAYERPARAMETER.fields_by_name['hdf5_data_param'].message_type = _HDF5DATAPARAMETER
_LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_LAYERPARAMETER.fields_by_name['image_data_param'].message_type = _IMAGEDATAPARAMETER
_LAYERPARAMETER.fields_by_name['infogain_loss_param'].message_type = _INFOGAINLOSSPARAMETER
_LAYERPARAMETER.fields_by_name['inner_product_param'].message_type = _INNERPRODUCTPARAMETER
_LAYERPARAMETER.fields_by_name['lrn_param'].message_type = _LRNPARAMETER
_LAYERPARAMETER.fields_by_name['memory_data_param'].message_type = _MEMORYDATAPARAMETER
_LAYERPARAMETER.fields_by_name['pooling_param'].message_type = _POOLINGPARAMETER
_LAYERPARAMETER.fields_by_name['power_param'].message_type = _POWERPARAMETER
_LAYERPARAMETER.fields_by_name['window_data_param'].message_type = _WINDOWDATAPARAMETER
_LAYERPARAMETER.fields_by_name['layer'].message_type = _V0LAYERPARAMETER
_LAYERPARAMETER_LAYERTYPE.containing_type = _LAYERPARAMETER;
_CONVOLUTIONPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_INNERPRODUCTPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_INNERPRODUCTPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_LRNPARAMETER.fields_by_name['norm_region'].enum_type = _LRNPARAMETER_NORMREGION
_LRNPARAMETER_NORMREGION.containing_type = _LRNPARAMETER;
_POOLINGPARAMETER.fields_by_name['pool'].enum_type = _POOLINGPARAMETER_POOLMETHOD
_POOLINGPARAMETER_POOLMETHOD.containing_type = _POOLINGPARAMETER;
_V0LAYERPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_V0LAYERPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_V0LAYERPARAMETER.fields_by_name['pool'].enum_type = _V0LAYERPARAMETER_POOLMETHOD
_V0LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_V0LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_V0LAYERPARAMETER_POOLMETHOD.containing_type = _V0LAYERPARAMETER;
DESCRIPTOR.message_types_by_name['BlobProto'] = _BLOBPROTO
DESCRIPTOR.message_types_by_name['BlobProtoVector'] = _BLOBPROTOVECTOR
DESCRIPTOR.message_types_by_name['Datum'] = _DATUM
DESCRIPTOR.message_types_by_name['FillerParameter'] = _FILLERPARAMETER
DESCRIPTOR.message_types_by_name['NetParameter'] = _NETPARAMETER
DESCRIPTOR.message_types_by_name['SolverParameter'] = _SOLVERPARAMETER
DESCRIPTOR.message_types_by_name['SolverState'] = _SOLVERSTATE
DESCRIPTOR.message_types_by_name['LayerParameter'] = _LAYERPARAMETER
DESCRIPTOR.message_types_by_name['ConcatParameter'] = _CONCATPARAMETER
DESCRIPTOR.message_types_by_name['ConvolutionParameter'] = _CONVOLUTIONPARAMETER
DESCRIPTOR.message_types_by_name['DataParameter'] = _DATAPARAMETER
DESCRIPTOR.message_types_by_name['DropoutParameter'] = _DROPOUTPARAMETER
DESCRIPTOR.message_types_by_name['HDF5DataParameter'] = _HDF5DATAPARAMETER
DESCRIPTOR.message_types_by_name['HDF5OutputParameter'] = _HDF5OUTPUTPARAMETER
DESCRIPTOR.message_types_by_name['ImageDataParameter'] = _IMAGEDATAPARAMETER
DESCRIPTOR.message_types_by_name['InfogainLossParameter'] = _INFOGAINLOSSPARAMETER
DESCRIPTOR.message_types_by_name['InnerProductParameter'] = _INNERPRODUCTPARAMETER
DESCRIPTOR.message_types_by_name['LRNParameter'] = _LRNPARAMETER
DESCRIPTOR.message_types_by_name['MemoryDataParameter'] = _MEMORYDATAPARAMETER
DESCRIPTOR.message_types_by_name['PoolingParameter'] = _POOLINGPARAMETER
DESCRIPTOR.message_types_by_name['PowerParameter'] = _POWERPARAMETER
DESCRIPTOR.message_types_by_name['WindowDataParameter'] = _WINDOWDATAPARAMETER
DESCRIPTOR.message_types_by_name['V0LayerParameter'] = _V0LAYERPARAMETER
class BlobProto(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTO
# @@protoc_insertion_point(class_scope:caffe.BlobProto)
class BlobProtoVector(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTOVECTOR
# @@protoc_insertion_point(class_scope:caffe.BlobProtoVector)
class Datum(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATUM
# @@protoc_insertion_point(class_scope:caffe.Datum)
class FillerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FILLERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.FillerParameter)
class NetParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETPARAMETER
# @@protoc_insertion_point(class_scope:caffe.NetParameter)
class SolverParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SolverParameter)
class SolverState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERSTATE
# @@protoc_insertion_point(class_scope:caffe.SolverState)
class LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LayerParameter)
class ConcatParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONCATPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ConcatParameter)
class ConvolutionParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONVOLUTIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ConvolutionParameter)
class DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DataParameter)
class DropoutParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DROPOUTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DropoutParameter)
class HDF5DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5DATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HDF5DataParameter)
class HDF5OutputParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5OUTPUTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HDF5OutputParameter)
class ImageDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _IMAGEDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ImageDataParameter)
class InfogainLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INFOGAINLOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.InfogainLossParameter)
class InnerProductParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INNERPRODUCTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.InnerProductParameter)
class LRNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LRNPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LRNParameter)
class MemoryDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MEMORYDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.MemoryDataParameter)
class PoolingParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POOLINGPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PoolingParameter)
class PowerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POWERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PowerParameter)
class WindowDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _WINDOWDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.WindowDataParameter)
class V0LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _V0LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.V0LayerParameter)
# @@protoc_insertion_point(module_scope)
| 91,458 | 42.407214 | 10,562 | py |
DRT | DRT-master/external_libs/matconvnet/matconvnet/utils/proto/vgg_caffe_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='vgg_caffe.proto',
package='caffe',
serialized_pb='\n\x0fvgg_caffe.proto\x12\x05\x63\x61\x66\x66\x65\"y\n\tBlobProto\x12\x0e\n\x03num\x18\x01 \x01(\x05:\x01\x30\x12\x13\n\x08\x63hannels\x18\x02 \x01(\x05:\x01\x30\x12\x11\n\x06height\x18\x03 \x01(\x05:\x01\x30\x12\x10\n\x05width\x18\x04 \x01(\x05:\x01\x30\x12\x10\n\x04\x64\x61ta\x18\x05 \x03(\x02\x42\x02\x10\x01\x12\x10\n\x04\x64iff\x18\x06 \x03(\x02\x42\x02\x10\x01\"2\n\x0f\x42lobProtoVector\x12\x1f\n\x05\x62lobs\x18\x01 \x03(\x0b\x32\x10.caffe.BlobProto\"i\n\x05\x44\x61tum\x12\x10\n\x08\x63hannels\x18\x01 \x01(\x05\x12\x0e\n\x06height\x18\x02 \x01(\x05\x12\r\n\x05width\x18\x03 \x01(\x05\x12\x0c\n\x04\x64\x61ta\x18\x04 \x01(\x0c\x12\r\n\x05label\x18\x05 \x01(\x05\x12\x12\n\nfloat_data\x18\x06 \x03(\x02\"\xaa\x01\n\x0f\x46illerParameter\x12\x16\n\x04type\x18\x01 \x01(\t:\x08\x63onstant\x12\x10\n\x05value\x18\x02 \x01(\x02:\x01\x30\x12\x0e\n\x03min\x18\x03 \x01(\x02:\x01\x30\x12\x0e\n\x03max\x18\x04 \x01(\x02:\x01\x31\x12\x0f\n\x04mean\x18\x05 \x01(\x02:\x01\x30\x12\x0e\n\x03std\x18\x06 \x01(\x02:\x01\x31\x12\x12\n\nmodel_path\x18\x07 \x01(\t\x12\x18\n\x10model_layer_name\x18\x08 \x01(\t\"\xe9\x06\n\x0eLayerParameter\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0c\n\x04type\x18\x02 \x01(\t\x12\x12\n\nnum_output\x18\x03 \x01(\r\x12\x16\n\x08\x62iasterm\x18\x04 \x01(\x08:\x04true\x12-\n\rweight_filler\x18\x05 \x01(\x0b\x32\x16.caffe.FillerParameter\x12+\n\x0b\x62ias_filler\x18\x06 \x01(\x0b\x32\x16.caffe.FillerParameter\x12\x0e\n\x03pad\x18\x07 \x01(\r:\x01\x30\x12\x12\n\nkernelsize\x18\x08 \x01(\r\x12\x10\n\x05group\x18\t \x01(\r:\x01\x31\x12\x11\n\x06stride\x18\n \x01(\r:\x01\x31\x12\x33\n\x04pool\x18\x0b \x01(\x0e\x32 .caffe.LayerParameter.PoolMethod:\x03MAX\x12\x1a\n\rdropout_ratio\x18\x0c \x01(\x02:\x03\x30.5\x12\x15\n\nlocal_size\x18\r \x01(\r:\x01\x35\x12\x10\n\x05\x61lpha\x18\x0e \x01(\x02:\x01\x31\x12\x12\n\x04\x62\x65ta\x18\x0f \x01(\x02:\x04\x30.75\x12\x0c\n\x01k\x18t \x01(\x02:\x01\x31\x12\x0e\n\x06source\x18\x10 \x01(\t\x12\x14\n\x0croot_img_dir\x18u \x01(\t\x12\x10\n\x05scale\x18\x11 \x01(\x02:\x01\x31\x12\x10\n\x08meanfile\x18\x12 \x01(\t\x12\x15\n\rcrop_meanfile\x18w \x01(\t\x12\x11\n\tbatchsize\x18\x13 \x01(\r\x12\x13\n\x08\x63ropsize\x18\x14 \x01(\r:\x01\x30\x12\x15\n\x06mirror\x18\x15 \x01(\x08:\x05\x66\x61lse\x12\x14\n\x0cimg_aug_type\x18\x16 \x01(\r\x12\x19\n\x11img_sampling_type\x18\x17 \x01(\r\x12\r\n\x05top_k\x18\x1f \x03(\r\x12\x11\n\tvis_label\x18\x18 \x01(\x05\x12\x10\n\x08\x63hannels\x18\x19 \x01(\x05\x12\x10\n\x08save_dir\x18\x1a \x01(\t\x12\x15\n\nlabel_rank\x18\x1e \x01(\r:\x01\x30\x12\x11\n\x06margin\x18 \x01(\x02:\x01\x31\x12\x1f\n\x05\x62lobs\x18\x32 \x03(\x0b\x32\x10.caffe.BlobProto\x12\x10\n\x08\x62lobs_lr\x18\x33 \x03(\x02\x12\x14\n\x0cweight_decay\x18\x34 \x03(\x02\x12\x14\n\trand_skip\x18\x35 \x01(\r:\x01\x30\".\n\nPoolMethod\x12\x07\n\x03MAX\x10\x00\x12\x07\n\x03\x41VE\x10\x01\x12\x0e\n\nSTOCHASTIC\x10\x02\"T\n\x0fLayerConnection\x12$\n\x05layer\x18\x01 \x01(\x0b\x32\x15.caffe.LayerParameter\x12\x0e\n\x06\x62ottom\x18\x02 \x03(\t\x12\x0b\n\x03top\x18\x03 \x03(\t\"\x85\x01\n\x0cNetParameter\x12\x0c\n\x04name\x18\x01 \x01(\t\x12&\n\x06layers\x18\x02 \x03(\x0b\x32\x16.caffe.LayerConnection\x12\r\n\x05input\x18\x03 \x03(\t\x12\x11\n\tinput_dim\x18\x04 \x03(\x05\x12\x1d\n\x0e\x66orce_backward\x18\x05 \x01(\x08:\x05\x66\x61lse\"\xef\x03\n\x0fSolverParameter\x12\x11\n\ttrain_net\x18\x01 \x01(\t\x12\x10\n\x08test_net\x18\x02 \x01(\t\x12\x14\n\ttest_iter\x18\x03 \x01(\x05:\x01\x30\x12\x18\n\rtest_interval\x18\x04 \x01(\x05:\x01\x30\x12\x0f\n\x07\x62\x61se_lr\x18\x05 \x01(\x02\x12\x0f\n\x07\x64isplay\x18\x06 \x01(\x05\x12\x10\n\x08max_iter\x18\x07 \x01(\x05\x12\x11\n\tlr_policy\x18\x08 \x01(\t\x12\r\n\x05gamma\x18\t \x01(\x02\x12\r\n\x05power\x18\n \x01(\x02\x12\x10\n\x08momentum\x18\x0b \x01(\x02\x12\x14\n\x0cweight_decay\x18\x0c \x01(\x02\x12\x10\n\x08stepsize\x18\r \x01(\x05\x12\x12\n\nbreakpoint\x18\x16 \x03(\x05\x12\x13\n\x08snapshot\x18\x0e \x01(\x05:\x01\x30\x12\x17\n\x0fsnapshot_prefix\x18\x0f \x01(\t\x12\"\n\x17snapshot_history_length\x18\x12 \x01(\x05:\x01\x30\x12\x1c\n\rsnapshot_diff\x18\x10 \x01(\x08:\x05\x66\x61lse\x12\x16\n\x0bsolver_mode\x18\x11 \x01(\x05:\x01\x31\x12\x11\n\tbatchsize\x18\x13 \x01(\x05\x12\x18\n\rdisplay_debug\x18\x14 \x01(\x05:\x01\x30\x12\x1f\n\x11load_solver_state\x18\x15 \x01(\x08:\x04true\"-\n\x0f\x45valHistoryIter\x12\x0c\n\x04iter\x18\x01 \x01(\x05\x12\x0c\n\x04perf\x18\x02 \x03(\x02\";\n\x0b\x45valHistory\x12,\n\x0cmeasurements\x18\x01 \x03(\x0b\x32\x16.caffe.EvalHistoryIter\"|\n\x0bSolverState\x12\x0c\n\x04iter\x18\x01 \x01(\x05\x12\x13\n\x0blearned_net\x18\x02 \x01(\t\x12!\n\x07history\x18\x03 \x03(\x0b\x32\x10.caffe.BlobProto\x12\'\n\x0bval_history\x18\x04 \x01(\x0b\x32\x12.caffe.EvalHistory')
_LAYERPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.LayerParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=1309,
serialized_end=1355,
)
_BLOBPROTO = descriptor.Descriptor(
name='BlobProto',
full_name='caffe.BlobProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num', full_name='caffe.BlobProto.num', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.BlobProto.channels', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.BlobProto.height', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.BlobProto.width', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.BlobProto.data', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='diff', full_name='caffe.BlobProto.diff', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=26,
serialized_end=147,
)
_BLOBPROTOVECTOR = descriptor.Descriptor(
name='BlobProtoVector',
full_name='caffe.BlobProtoVector',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.BlobProtoVector.blobs', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=149,
serialized_end=199,
)
_DATUM = descriptor.Descriptor(
name='Datum',
full_name='caffe.Datum',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='channels', full_name='caffe.Datum.channels', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.Datum.height', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.Datum.width', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.Datum.data', index=3,
number=4, type=12, cpp_type=9, label=1,
has_default_value=False, default_value="",
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='label', full_name='caffe.Datum.label', index=4,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='float_data', full_name='caffe.Datum.float_data', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=201,
serialized_end=306,
)
_FILLERPARAMETER = descriptor.Descriptor(
name='FillerParameter',
full_name='caffe.FillerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='type', full_name='caffe.FillerParameter.type', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("constant", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='value', full_name='caffe.FillerParameter.value', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='min', full_name='caffe.FillerParameter.min', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max', full_name='caffe.FillerParameter.max', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean', full_name='caffe.FillerParameter.mean', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='std', full_name='caffe.FillerParameter.std', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='model_path', full_name='caffe.FillerParameter.model_path', index=6,
number=7, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='model_layer_name', full_name='caffe.FillerParameter.model_layer_name', index=7,
number=8, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=309,
serialized_end=479,
)
_LAYERPARAMETER = descriptor.Descriptor(
name='LayerParameter',
full_name='caffe.LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.LayerParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.LayerParameter.type', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.LayerParameter.num_output', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='biasterm', full_name='caffe.LayerParameter.biasterm', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.LayerParameter.weight_filler', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.LayerParameter.bias_filler', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.LayerParameter.pad', index=6,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernelsize', full_name='caffe.LayerParameter.kernelsize', index=7,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='group', full_name='caffe.LayerParameter.group', index=8,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.LayerParameter.stride', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pool', full_name='caffe.LayerParameter.pool', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.LayerParameter.dropout_ratio', index=11,
number=12, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='local_size', full_name='caffe.LayerParameter.local_size', index=12,
number=13, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.LayerParameter.alpha', index=13,
number=14, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='beta', full_name='caffe.LayerParameter.beta', index=14,
number=15, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='k', full_name='caffe.LayerParameter.k', index=15,
number=116, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='source', full_name='caffe.LayerParameter.source', index=16,
number=16, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='root_img_dir', full_name='caffe.LayerParameter.root_img_dir', index=17,
number=117, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.LayerParameter.scale', index=18,
number=17, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='meanfile', full_name='caffe.LayerParameter.meanfile', index=19,
number=18, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_meanfile', full_name='caffe.LayerParameter.crop_meanfile', index=20,
number=119, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batchsize', full_name='caffe.LayerParameter.batchsize', index=21,
number=19, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='cropsize', full_name='caffe.LayerParameter.cropsize', index=22,
number=20, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.LayerParameter.mirror', index=23,
number=21, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='img_aug_type', full_name='caffe.LayerParameter.img_aug_type', index=24,
number=22, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='img_sampling_type', full_name='caffe.LayerParameter.img_sampling_type', index=25,
number=23, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top_k', full_name='caffe.LayerParameter.top_k', index=26,
number=31, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='vis_label', full_name='caffe.LayerParameter.vis_label', index=27,
number=24, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.LayerParameter.channels', index=28,
number=25, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='save_dir', full_name='caffe.LayerParameter.save_dir', index=29,
number=26, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='label_rank', full_name='caffe.LayerParameter.label_rank', index=30,
number=30, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='margin', full_name='caffe.LayerParameter.margin', index=31,
number=32, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.LayerParameter.blobs', index=32,
number=50, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.LayerParameter.blobs_lr', index=33,
number=51, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.LayerParameter.weight_decay', index=34,
number=52, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.LayerParameter.rand_skip', index=35,
number=53, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_LAYERPARAMETER_POOLMETHOD,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=482,
serialized_end=1355,
)
_LAYERCONNECTION = descriptor.Descriptor(
name='LayerConnection',
full_name='caffe.LayerConnection',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='layer', full_name='caffe.LayerConnection.layer', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bottom', full_name='caffe.LayerConnection.bottom', index=1,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top', full_name='caffe.LayerConnection.top', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1357,
serialized_end=1441,
)
_NETPARAMETER = descriptor.Descriptor(
name='NetParameter',
full_name='caffe.NetParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.NetParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layers', full_name='caffe.NetParameter.layers', index=1,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input', full_name='caffe.NetParameter.input', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input_dim', full_name='caffe.NetParameter.input_dim', index=3,
number=4, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_backward', full_name='caffe.NetParameter.force_backward', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1444,
serialized_end=1577,
)
_SOLVERPARAMETER = descriptor.Descriptor(
name='SolverParameter',
full_name='caffe.SolverParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='train_net', full_name='caffe.SolverParameter.train_net', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_net', full_name='caffe.SolverParameter.test_net', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_iter', full_name='caffe.SolverParameter.test_iter', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_interval', full_name='caffe.SolverParameter.test_interval', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='base_lr', full_name='caffe.SolverParameter.base_lr', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='display', full_name='caffe.SolverParameter.display', index=5,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max_iter', full_name='caffe.SolverParameter.max_iter', index=6,
number=7, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lr_policy', full_name='caffe.SolverParameter.lr_policy', index=7,
number=8, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='gamma', full_name='caffe.SolverParameter.gamma', index=8,
number=9, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power', full_name='caffe.SolverParameter.power', index=9,
number=10, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='momentum', full_name='caffe.SolverParameter.momentum', index=10,
number=11, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.SolverParameter.weight_decay', index=11,
number=12, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stepsize', full_name='caffe.SolverParameter.stepsize', index=12,
number=13, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='breakpoint', full_name='caffe.SolverParameter.breakpoint', index=13,
number=22, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot', full_name='caffe.SolverParameter.snapshot', index=14,
number=14, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_prefix', full_name='caffe.SolverParameter.snapshot_prefix', index=15,
number=15, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_history_length', full_name='caffe.SolverParameter.snapshot_history_length', index=16,
number=18, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_diff', full_name='caffe.SolverParameter.snapshot_diff', index=17,
number=16, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='solver_mode', full_name='caffe.SolverParameter.solver_mode', index=18,
number=17, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batchsize', full_name='caffe.SolverParameter.batchsize', index=19,
number=19, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='display_debug', full_name='caffe.SolverParameter.display_debug', index=20,
number=20, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='load_solver_state', full_name='caffe.SolverParameter.load_solver_state', index=21,
number=21, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1580,
serialized_end=2075,
)
_EVALHISTORYITER = descriptor.Descriptor(
name='EvalHistoryIter',
full_name='caffe.EvalHistoryIter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='iter', full_name='caffe.EvalHistoryIter.iter', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='perf', full_name='caffe.EvalHistoryIter.perf', index=1,
number=2, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2077,
serialized_end=2122,
)
_EVALHISTORY = descriptor.Descriptor(
name='EvalHistory',
full_name='caffe.EvalHistory',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='measurements', full_name='caffe.EvalHistory.measurements', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2124,
serialized_end=2183,
)
_SOLVERSTATE = descriptor.Descriptor(
name='SolverState',
full_name='caffe.SolverState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='iter', full_name='caffe.SolverState.iter', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='learned_net', full_name='caffe.SolverState.learned_net', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='history', full_name='caffe.SolverState.history', index=2,
number=3, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='val_history', full_name='caffe.SolverState.val_history', index=3,
number=4, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2185,
serialized_end=2309,
)
_BLOBPROTOVECTOR.fields_by_name['blobs'].message_type = _BLOBPROTO
_LAYERPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_LAYERPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_LAYERPARAMETER.fields_by_name['pool'].enum_type = _LAYERPARAMETER_POOLMETHOD
_LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_LAYERPARAMETER_POOLMETHOD.containing_type = _LAYERPARAMETER;
_LAYERCONNECTION.fields_by_name['layer'].message_type = _LAYERPARAMETER
_NETPARAMETER.fields_by_name['layers'].message_type = _LAYERCONNECTION
_EVALHISTORY.fields_by_name['measurements'].message_type = _EVALHISTORYITER
_SOLVERSTATE.fields_by_name['history'].message_type = _BLOBPROTO
_SOLVERSTATE.fields_by_name['val_history'].message_type = _EVALHISTORY
DESCRIPTOR.message_types_by_name['BlobProto'] = _BLOBPROTO
DESCRIPTOR.message_types_by_name['BlobProtoVector'] = _BLOBPROTOVECTOR
DESCRIPTOR.message_types_by_name['Datum'] = _DATUM
DESCRIPTOR.message_types_by_name['FillerParameter'] = _FILLERPARAMETER
DESCRIPTOR.message_types_by_name['LayerParameter'] = _LAYERPARAMETER
DESCRIPTOR.message_types_by_name['LayerConnection'] = _LAYERCONNECTION
DESCRIPTOR.message_types_by_name['NetParameter'] = _NETPARAMETER
DESCRIPTOR.message_types_by_name['SolverParameter'] = _SOLVERPARAMETER
DESCRIPTOR.message_types_by_name['EvalHistoryIter'] = _EVALHISTORYITER
DESCRIPTOR.message_types_by_name['EvalHistory'] = _EVALHISTORY
DESCRIPTOR.message_types_by_name['SolverState'] = _SOLVERSTATE
class BlobProto(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTO
# @@protoc_insertion_point(class_scope:caffe.BlobProto)
class BlobProtoVector(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTOVECTOR
# @@protoc_insertion_point(class_scope:caffe.BlobProtoVector)
class Datum(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATUM
# @@protoc_insertion_point(class_scope:caffe.Datum)
class FillerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FILLERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.FillerParameter)
class LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LayerParameter)
class LayerConnection(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERCONNECTION
# @@protoc_insertion_point(class_scope:caffe.LayerConnection)
class NetParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETPARAMETER
# @@protoc_insertion_point(class_scope:caffe.NetParameter)
class SolverParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SolverParameter)
class EvalHistoryIter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _EVALHISTORYITER
# @@protoc_insertion_point(class_scope:caffe.EvalHistoryIter)
class EvalHistory(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _EVALHISTORY
# @@protoc_insertion_point(class_scope:caffe.EvalHistory)
class SolverState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERSTATE
# @@protoc_insertion_point(class_scope:caffe.SolverState)
# @@protoc_insertion_point(module_scope)
| 44,873 | 42.865103 | 4,761 | py |
DRT | DRT-master/external_libs/matconvnet/utils/layers.py | # file: layers.py
# brief: A number of objects to wrap caffe layers for conversion
# author: Andrea Vedaldi
from collections import OrderedDict
from math import floor, ceil
from operator import mul
import numpy as np
from numpy import array
import scipy
import scipy.io
import scipy.misc
import copy
import collections
# Recent Caffes just pass a string as a type; this is used for legacy support
layers_type = {}
layers_type[0] = 'none'
layers_type[1] = 'accuracy'
layers_type[2] = 'bnll'
layers_type[3] = 'concat'
layers_type[4] = 'conv'
layers_type[5] = 'data'
layers_type[6] = 'dropout'
layers_type[7] = 'euclidean_loss'
layers_type[8] = 'flatten'
layers_type[9] = 'hdf5_data'
layers_type[10] = 'hdf5_output'
layers_type[28] = 'hinge_loss'
layers_type[11] = 'im2col'
layers_type[12] = 'image_data'
layers_type[13] = 'infogain_loss'
layers_type[14] = 'inner_product'
layers_type[15] = 'lrn'
layers_type[25] = 'eltwise'
layers_type[29] = 'memory_data'
layers_type[16] = 'multinomial_logistic_loss'
layers_type[17] = 'pool'
layers_type[26] = 'power'
layers_type[18] = 'relu'
layers_type[19] = 'sigmoid'
layers_type[27] = 'sigmoid_cross_entropy_loss'
layers_type[20] = 'softmax'
layers_type[21] = 'softmax_loss'
layers_type[22] = 'split'
layers_type[23] = 'tanh'
layers_type[24] = 'window_data'
layers_type[39] = 'deconvolution'
layers_type[40] = 'crop'
def getFilterOutputSize(size, kernelSize, stride, pad):
return [floor((size[0] + pad[0]+pad[1] - kernelSize[0]) / stride[0]) + 1., \
floor((size[1] + pad[2]+pad[3] - kernelSize[1]) / stride[1]) + 1.]
def getFilterTransform(ks, stride, pad):
y1 = 1. - pad[0] ;
y2 = 1. - pad[0] + ks[0] - 1 ;
x1 = 1. - pad[2] ;
x2 = 1. - pad[2] + ks[1] - 1 ;
h = y2 - y1 + 1. ;
w = x2 - x1 + 1. ;
return CaffeTransform([h, w], stride, [(y1+y2)/2, (x1+x2)/2])
def reorder(aList, order):
return [aList[i] for i in order]
def row(x):
return np.array(x,dtype=float).reshape(1,-1)
def rowarray(x):
return x.reshape(1,-1)
def rowcell(x):
return np.array(x,dtype=object).reshape(1,-1)
def dictToMatlabStruct(d):
if not d:
return np.zeros((0,))
dt = []
for x in d.keys():
pair = (x,object)
if isinstance(d[x], np.ndarray): pair = (x,type(d[x]))
dt.append(pair)
y = np.empty((1,),dtype=dt)
for x in d.keys():
y[x][0] = d[x]
return y
# --------------------------------------------------------------------
# MatConvNet in NumPy
# --------------------------------------------------------------------
mlayerdt = [('name',object),
('type',object),
('inputs',object),
('outputs',object),
('params',object),
('block',object)]
mparamdt = [('name',object),
('value',object)]
minputdt = [('name',object),
('size',object)]
# --------------------------------------------------------------------
# Vars and params
# --------------------------------------------------------------------
class CaffeBlob(object):
def __init__(self, name):
self.name = name
self.shape = None
self.value = np.zeros(shape=(0,0), dtype='float32')
self.bgrInput = False
self.transposable = True # first two dimensions are spatial
def transpose(self):
if self.shape: self.shape = [self.shape[k] for k in [1,0,2,3]]
def toMatlab(self):
mparam = np.empty(shape=[1,], dtype=mparamdt)
mparam['name'][0] = self.name
mparam['value'][0] = self.value
return mparam
def toMatlabSimpleNN(self):
return self.value
def hasValue(self):
return reduce(mul, self.value.shape, 1) > 0
class CaffeTransform(object):
def __init__(self, size, stride, offset):
self.shape = size
self.stride = stride
self.offset = offset
def __str__(self):
return "<%s %s %s>" % (self.shape, self.stride, self.offset)
def composeTransforms(a, b):
size = [0.,0.]
stride = [0.,0.]
offset = [0.,0.]
for i in [0,1]:
size[i] = a.stride[i] * (b.shape[i] - 1) + a.shape[i]
stride[i] = a.stride[i] * b.stride[i]
offset[i] = a.stride[i] * (b.offset[i] - 1) + a.offset[i]
c = CaffeTransform(size, stride, offset)
return c
def transposeTransform(a):
size = [0.,0.]
stride = [0.,0.]
offset = [0.,0.]
for i in [0,1]:
size[i] = (a.shape[i] + a.stride[i] - 1.0) / a.stride[i]
stride[i] = 1.0/a.stride[i]
offset[i] = (1.0 + a.stride[i] - a.offset[i]) / a.stride[i]
c = CaffeTransform(size, stride, offset)
return c
# --------------------------------------------------------------------
# Errors
# --------------------------------------------------------------------
class ConversionError(Exception):
def __init__(self, value):
self.value = value
def __str__(self):
return repr(self.value)
# --------------------------------------------------------------------
# Basic Layers
# --------------------------------------------------------------------
class CaffeLayer(object):
def __init__(self, name, inputs, outputs):
self.name = name
self.inputs = inputs
self.outputs = outputs
self.params = []
self.model = None
def reshape(self, model):
pass
def display(self):
print "Layer \'{}\'".format(self.name)
print " +- type: %s" % (self.__class__.__name__)
print " +- inputs: %s" % (self.inputs,)
print " +- outputs: %s" % (self.outputs,)
print " +- params: %s" % (self.params,)
def getTransforms(self, model):
transforms = []
for i in enumerate(self.inputs):
row = []
for j in enumerate(self.outputs):
row.append(CaffeTransform([1.,1.], [1.,1.], [1.,1.]))
transforms.append(row)
return transforms
def transpose(self, model):
pass
def setBlob(self, model, i, blob):
assert(False)
def toMatlab(self):
mlayer = np.empty(shape=[1,],dtype=mlayerdt)
mlayer['name'][0] = self.name
mlayer['type'][0] = None
mlayer['inputs'][0] = rowcell(self.inputs)
mlayer['outputs'][0] = rowcell(self.outputs)
mlayer['params'][0] = rowcell(self.params)
mlayer['block'][0] = dictToMatlabStruct({})
return mlayer
def toMatlabSimpleNN(self):
mparam = collections.OrderedDict() ;
mparam['name'] = self.name
mparam['type'] = None
return mparam
class CaffeElementWise(CaffeLayer):
def reshape(self, model):
for i in range(len(self.inputs)):
model.vars[self.outputs[i]].shape = \
model.vars[self.inputs[i]].shape
class CaffeReLU(CaffeElementWise):
def __init__(self, name, inputs, outputs):
super(CaffeReLU, self).__init__(name, inputs, outputs)
def toMatlab(self):
mlayer = super(CaffeReLU, self).toMatlab()
mlayer['type'][0] = u'dagnn.ReLU'
mlayer['block'][0] = dictToMatlabStruct(
{'leak': float(0.0) })
# todo: leak factor
return mlayer
def toMatlabSimpleNN(self):
mlayer = super(CaffeReLU, self).toMatlabSimpleNN()
mlayer['type'] = u'relu'
mlayer['leak'] = float(0.0)
return mlayer
class CaffeLRN(CaffeElementWise):
def __init__(self, name, inputs, outputs,
local_size,
alpha,
beta,
norm_region,
kappa):
super(CaffeLRN, self).__init__(name, inputs, outputs)
self.local_size = local_size
self.alpha = alpha
self.beta = beta
self.norm_region = norm_region
self.kappa = kappa
assert(norm_region == 'across_channels')
def toMatlab(self):
mlayer = super(CaffeLRN, self).toMatlab()
mlayer['type'][0] = u'dagnn.LRN'
mlayer['block'][0] = dictToMatlabStruct(
{'param': row([self.local_size,
self.kappa,
self.alpha / self.local_size,
self.beta])})
return mlayer
def toMatlabSimpleNN(self):
mlayer = super(CaffeLRN, self).toMatlabSimpleNN()
mlayer['type'] = u'lrn'
mlayer['param'] = row([self.local_size,
self.kappa,
self.alpha / self.local_size,
self.beta])
return mlayer
class CaffeSoftMax(CaffeElementWise):
def __init__(self, name, inputs, outputs):
super(CaffeSoftMax, self).__init__(name, inputs, outputs)
def toMatlab(self):
mlayer = super(CaffeSoftMax, self).toMatlab()
mlayer['type'][0] = u'dagnn.SoftMax'
return mlayer
def toMatlabSimpleNN(self):
mlayer = super(CaffeSoftMax, self).toMatlabSimpleNN()
mlayer['type'] = u'softmax'
return mlayer
class CaffeSoftMaxLoss(CaffeElementWise):
def __init__(self, name, inputs, outputs):
super(CaffeSoftMaxLoss, self).__init__(name, inputs, outputs)
def toMatlab(self):
mlayer = super(CaffeSoftMaxLoss, self).toMatlab()
mlayer['type'][0] = u'dagnn.SoftMaxLoss'
return mlayer
def toMatlabSimpleNN(self):
mlayer = super(CaffeSoftMaxLoss, self).toMatlabSimpleNN()
mlayer['type'] = u'softmax'
return mlayer
class CaffeDropout(CaffeElementWise):
def __init__(self, name, inputs, outputs, ratio):
super(CaffeDropout, self).__init__(name, inputs, outputs)
self.ratio = ratio
def toMatlab(self):
mlayer = super(CaffeDropout, self).toMatlab()
mlayer['type'][0] = u'dagnn.DropOut'
mlayer['block'][0] = dictToMatlabStruct({'rate': float(self.ratio)})
return mlayer
def toMatlabSimpleNN(self):
mlayer = super(CaffeDropout, self).toMatlabSimpleNN()
mlayer['type'] = u'dropout'
mlayer['rate'] = float(self.ratio)
return mlayer
def display(self):
super(CaffeDropout, self).display()
print " c- ratio (dropout rate):", self.ratio
class CaffeData(CaffeLayer):
def __init__(self, name, inputs, outputs):
super(CaffeData, self).__init__(name, inputs, outputs)
def reshape(self, model):
# todo: complete otehr cases
shape = [layer.transform_param.crop_size,
layer.transform_param.crop_size,
3,
layer.batch_size]
model.vars[self.outputs[0]].shape = shape
def toMatlab(self):
return None
def toMatlabSimpleNN(self):
return None
# --------------------------------------------------------------------
# Convolution
# --------------------------------------------------------------------
class CaffeConv(CaffeLayer):
def __init__(self, name, inputs, outputs,
num_output,
bias_term,
pad,
kernel_size,
stride,
dilation,
group):
super(CaffeConv, self).__init__(name, inputs, outputs)
if len(kernel_size) == 1 : kernel_size = kernel_size * 2
if len(stride) == 1 : stride = stride * 2
if len(pad) == 1 : pad = pad * 4
elif len(pad) == 2 : pad = [pad[0], pad[0], pad[1], pad[1]]
self.num_output = num_output
self.bias_term = bias_term
self.pad = pad
self.kernel_size = kernel_size
self.stride = stride
self.dilation = dilation
self.group = group
self.params = [name + '_filter']
if bias_term: self.params.append(name + '_bias')
self.filter_depth = None
def display(self):
super(CaffeConv, self).display()
print " +- filter dimension:", self.filter_depth
print " c- num_output (num filters): %s" % self.num_output
print " c- bias_term: %s" % self.bias_term
print " c- pad: %s" % (self.pad,)
print " c- kernel_size: %s" % self.kernel_size
print " c- stride: %s" % (self.stride,)
print " c- dilation: %s" % (self.dilation,)
print " c- group: %s" % (self.group,)
def reshape(self, model):
varin = model.vars[self.inputs[0]]
varout = model.vars[self.outputs[0]]
if not varin.shape: return
varout.shape = getFilterOutputSize(varin.shape[0:2],
self.kernel_size,
self.stride,
self.pad) \
+ [self.num_output, varin.shape[3]]
self.filter_depth = varin.shape[2] / self.group
def getTransforms(self, model):
return [[getFilterTransform(self.kernel_size, self.stride, self.pad)]]
def setBlob(self, model, i, blob):
assert(i < 2)
if i == 0:
assert(blob.shape[0] == self.kernel_size[0])
assert(blob.shape[1] == self.kernel_size[1])
assert(blob.shape[3] == self.num_output)
self.filter_depth = blob.shape[2]
elif i == 1:
assert(blob.shape[0] == self.num_output)
model.params[self.params[i]].value = blob
model.params[self.params[i]].shape = blob.shape
def transpose(self, model):
self.kernel_size = reorder(self.kernel_size, [1,0])
self.stride = reorder(self.stride, [1,0])
self.pad = reorder(self.pad, [2,3,0,1])
if model.params[self.params[0]].hasValue():
print "Layer %s: transposing filters" % self.name
param = model.params[self.params[0]]
param.value = param.value.transpose([1,0,2,3])
if model.vars[self.inputs[0]].bgrInput:
print "Layer %s: BGR to RGB conversion" % self.name
param.value = param.value[:,:,: : -1,:]
def toMatlab(self):
size = self.kernel_size + [self.filter_depth, self.num_output]
mlayer = super(CaffeConv, self).toMatlab()
mlayer['type'][0] = u'dagnn.Conv'
mlayer['block'][0] = dictToMatlabStruct(
{'hasBias': self.bias_term,
'size': row(size),
'pad': row(self.pad),
'stride': row(self.stride)})
return mlayer
def toMatlabSimpleNN(self):
size = self.kernel_size + [self.filter_depth, self.num_output]
mlayer = super(CaffeConv, self).toMatlabSimpleNN()
mlayer['type'] = u'conv'
mlayer['weights'] = np.empty([1,len(self.params)], dtype=np.object)
mlayer['size'] = row(size)
mlayer['pad'] = row(self.pad)
mlayer['stride'] = row(self.stride)
for p, name in enumerate(self.params):
mlayer['weights'][0,p] = self.model.params[name].toMatlabSimpleNN()
return mlayer
# --------------------------------------------------------------------
# InnerProduct
# --------------------------------------------------------------------
# special case: inner product
class CaffeInnerProduct(CaffeConv):
def __init__(self, name, inputs, outputs, num_output, bias_term, axis):
super(CaffeInnerProduct, self).__init__(name, inputs, outputs,
num_output = num_output,
bias_term = bias_term,
pad = [0, 0, 0, 0],
kernel_size = [1, 1],
stride = [1, 1],
dilation = [],
group = 1)
self.axis = axis
assert(axis == 1)
def setBlob(self, model, i, blob):
assert(i < 1 + self.bias_term)
if i == 0:
self.filter_depth = blob.shape[0]
assert(blob.shape[1] == self.num_output)
blob = blob.reshape([1, 1, self.filter_depth, self.num_output])
elif i == 1:
assert(blob.shape[0] == self.num_output)
model.params[self.params[i]].value = blob
model.params[self.params[i]].shape = blob.shape
def reshape(self, model):
if not model.vars[self.inputs[0]].shape: return
s = model.vars[self.inputs[0]].shape
self.kernel_size = [s[0], s[1], s[2], self.num_output]
print "Layer %s: inner product converted to filter bank of shape %s" \
% (self.name, self.kernel_size)
param = model.params[self.params[0]]
if param.hasValue():
print "Layer %s: reshaping inner product paramters of shape %s into a filter bank" % (self.name, param.value.shape)
param.value = param.value.reshape(self.kernel_size, order='F')
super(CaffeInnerProduct, self).reshape(model)
# --------------------------------------------------------------------
# Deconvolution
# --------------------------------------------------------------------
class CaffeDeconvolution(CaffeConv):
def __init__(self, name, inputs, outputs,
num_output,
bias_term,
pad,
kernel_size,
stride,
dilation,
group):
super(CaffeDeconvolution, self).__init__(name, inputs, outputs,
num_output = num_output,
bias_term = bias_term,
pad = pad,
kernel_size = kernel_size,
stride = stride,
dilation = dilation,
group = group)
def setBlob(self, model, i, blob):
assert(i < 2)
if i == 0:
assert(blob.shape[0] == self.kernel_size[0])
assert(blob.shape[1] == self.kernel_size[1])
assert(blob.shape[2] == self.num_output)
self.filter_depth = blob.shape[3]
elif i == 1:
assert(blob.shape[0] == self.num_output)
model.params[self.params[i]].value = blob
model.params[self.params[i]].shape = blob.shape
def reshape(self, model):
inshape = model.vars[self.inputs[0]].shape
if not inshape: return
model.vars[self.outputs[0]].shape = \
getFilterOutputSize(inshape[0:2],
self.kernel_size, self.stride, self.pad) + \
[self.num_output, inshape[3]]
self.filter_depth = inshape[2]
def getTransforms(self, model):
t = getFilterTransform(self.kernel_size, self.stride, self.pad)
t = transposeTransform(t)
return [[t]]
def transpose(self, model):
self.kernel_size = reorder(self.kernel_size, [1,0])
self.stride = reorder(self.stride, [1,0])
self.pad = reorder(self.pad, [2,3,0,1])
if model.params[self.params[0]].hasValue():
print "Layer %s transposing filters" % self.name
param = model.params[self.params[0]]
param.value = param.value.transpose([1,0,2,3])
if model.vars[self.inputs[0]].bgrInput:
print "Layer %s BGR to RGB conversion" % self.name
param.value = param.value[:,:,:,: : -1]
def toMatlab(self):
size = self.kernel_size + [self.num_output, self.filter_depth / self.group]
mlayer = super(CaffeDeconvolution, self).toMatlab()
mlayer['type'][0] = u'dagnn.ConvTranspose'
mlayer['block'][0] = dictToMatlabStruct(
{'hasBias': self.bias_term,
'size': row(size),
'upsample': row(self.stride),
'crop': row(self.pad)})
return mlayer
def toMatlabSimpleNN(self):
size = self.kernel_size + [self.num_output, self.filter_depth / self.group]
mlayer = super(CaffeDeconvolution, self).toMatlabSimpleNN()
mlayer['type'] = u'convt'
mlayer['weights'] = np.empty([1,len(self.params)], dtype=np.object)
mlayer['size'] = row(size)
mlayer['upsample'] = row(self.stride)
mlayer['crop'] = row(self.pad)
for p, name in enumerate(self.params):
mlayer['weights'][0,p] = self.model.params[name].toMatlabSimpleNN()
return mlayer
# --------------------------------------------------------------------
# Pooling
# --------------------------------------------------------------------
class CaffePooling(CaffeLayer):
def __init__(self, name, inputs, outputs,
method,
pad,
kernel_size,
stride):
super(CaffePooling, self).__init__(name, inputs, outputs)
if len(kernel_size) == 1 : kernel_size = kernel_size * 2
if len(stride) == 1 : stride = stride * 2
if len(pad) == 1 : pad = pad * 4
elif len(pad) == 2 : pad = [pad[0], pad[0], pad[1], pad[1]]
self.method = method
self.pad = pad
self.kernel_size = kernel_size
self.stride = stride
self.pad_corrected = None
def display(self):
super(CaffePooling, self).display()
print " +- pad_corrected: %s" % (self.pad_corrected,)
print " c- method: ", self.method
print " c- pad: %s" % (self.pad,)
print " c- kernel_size: %s" % (self.kernel_size,)
print " c- stride: %s" % (self.stride,)
def reshape(self, model):
shape = model.vars[self.inputs[0]].shape
if not shape: return
# MatConvNet uses a slighly different definition of padding, which we think
# is the correct one (it corresponds to the filters)
self.pad_corrected = copy.deepcopy(self.pad)
for i in [0, 1]:
self.pad_corrected[1 + i*2] = min(
self.pad[1 + i*2] + self.stride[i] - 1,
self.kernel_size[i] - 1)
model.vars[self.outputs[0]].shape = \
getFilterOutputSize(shape[0:2],
self.kernel_size,
self.stride,
self.pad_corrected) + shape[2:5]
def getTransforms(self, model):
return [[getFilterTransform(self.kernel_size, self.stride, self.pad)]]
def transpose(self, model):
self.kernel_size = reorder(self.kernel_size, [1,0])
self.stride = reorder(self.stride, [1,0])
self.pad = reorder(self.pad, [2,3,0,1])
if self.pad_corrected:
self.pad_corrected = reorder(self.pad_corrected, [2,3,0,1])
def toMatlab(self):
mlayer = super(CaffePooling, self).toMatlab()
mlayer['type'][0] = u'dagnn.Pooling'
mlayer['block'][0] = dictToMatlabStruct(
{'method': self.method,
'poolSize': row(self.kernel_size),
'stride': row(self.stride),
'pad': row(self.pad_corrected)})
if not self.pad_corrected:
print "Warning: pad correction for layer %s could not be computed because the layer input shape could not be determined" % (self.name)
return mlayer
def toMatlabSimpleNN(self):
mlayer = super(CaffePooling, self).toMatlabSimpleNN()
mlayer['type'] = u'pool'
mlayer['method'] = self.method
mlayer['pool'] = row(self.kernel_size)
mlayer['stride'] = row(self.stride)
mlayer['pad'] = row(self.pad_corrected)
if not self.pad_corrected:
print "Warning: pad correction for layer %s could not be computed because the layer input shape could not be determined" % (self.name)
return mlayer
# --------------------------------------------------------------------
# ROIPooling
# --------------------------------------------------------------------
class CaffeROIPooling(CaffeLayer):
def __init__(self, name, inputs, outputs,
pooled_w,
pooled_h,
spatial_scale):
super(CaffeROIPooling, self).__init__(name, inputs, outputs)
self.pooled_w = pooled_w
self.pooled_h = pooled_h
self.spatial_scale = spatial_scale
self.flatten = True
def display(self):
super(CaffeROIPooling, self).display()
print " c- pooled_w: %s" % (self.pooled_w,)
print " c- pooled_h: %s" % (self.pooled_h,)
print " c- spatial_scale: %s" % (self.spatial_scale,)
print " c- flatten: %s" % (self.flatten,)
def reshape(self, model):
shape1 = model.vars[self.inputs[0]].shape
shape2 = model.vars[self.inputs[1]].shape
if not shape1 or not shape2: return
numChannels = shape1[2]
numROIs = reduce(mul, shape2, 1) / 5
if self.flatten:
oshape = [1,
1,
self.pooled_w * self.pooled_h * numChannels,
numROIs]
else:
oshape = [self.pooled_w,
self.pooled_h,
numChannels,
numROIs]
model.vars[self.outputs[0]].shape = oshape
def getTransforms(self, model):
# no transform
return [[CaffeTransform([1.,1.], [1.,1.], [1.,1.])]]
def transpose(self, model):
assert(not self.flatten)
tmp = self.pooled_w
self.pooled_w = self.pooled_h
self.pooled_h = tmp
def toMatlab(self):
mlayer = super(CaffeROIPooling, self).toMatlab()
mlayer['type'][0] = u'dagnn.ROIPooling'
mlayer['block'][0] = dictToMatlabStruct(
{'subdivisions':row([self.pooled_w, self.pooled_h]),
'transform':self.spatial_scale,
'flatten':self.flatten})
return mlayer
def toMatlabSimpleNN(self):
mlayer = super(CaffeROIPooling, self).toMatlabSimpleNN()
mlayer['type'] = u'roipool'
mlayer['subdivisions'] = row([self.pooled_w, self.pooled_h])
mlayer['transform'] = self.spatial_scale
mlayer['flatten'] = self.flatten
return mlayer
# --------------------------------------------------------------------
# Scale
# --------------------------------------------------------------------
class CaffeScale(CaffeLayer):
def __init__(self, name, inputs, outputs,
axis,
num_axes,
bias_term):
super(CaffeScale, self).__init__(name, inputs, outputs)
self.axis = axis
self.num_axes = num_axes
self.bias_term = bias_term
if len(self.inputs) == 1:
self.params.append(name + '_mult')
if len(self.inputs) < 2 and self.bias_term:
self.params.append(name + '_bias')
self.mult_size = [0, 0, 0, 0]
def display(self):
super(CaffeScale, self).display()
print " +- mult_size: %s" % (self.mult_size,)
print " c- axis: %s" % (self.axis,)
print " c- num_axes: %s" % (self.num_axes,)
print " c- bias_term: %s" % (self.bias_term,)
def reshape(self, model):
model.vars[self.outputs[0]].shape = model.vars[self.inputs[0]].shape
def setBlob(self, model, i, blob):
assert(i < self.bias_term + 1)
# Caffe *ends* with WIDTH, we start with it, blobs are already swapped here
k = 3 - self.axis
# This means that the MULT dimensions are aligned to the INPUT
# dimensions such that MULT[end] <-> INPUT[k]. For MatConvNet,
# we simply add singletion dimensions at the beginning of MULT
# to achieve this effect. BIAS is the same.
mshape = tuple([1] * (k - len(blob.shape) + 1) + list(blob.shape))
blob = blob.reshape(mshape)
model.params[self.params[i]].value = blob
model.params[self.params[i]].shape = blob.shape
if i == 0: self.mult_size = blob.shape
def getTransforms(self, model):
# The second input can be either a variable or a paramter; in
# both cases, there is no transform for it
return [[CaffeTransform([1.,1.], [1.,1.], [1.,1.])]]
def transpose(self, model):
if len(self.inputs) == 1:
# we only need to transpose if the scale is a parameter, not an input
for i in range(1 + self.bias_term):
param = model.params[self.params[i]]
n = len(param.shape)
if n >= 2:
order = range(n)
order[0] = 1
order[1] = 0
param.value = param.value.transpose(order)
def toMatlab(self):
mlayer = super(CaffeScale, self).toMatlab()
mlayer['type'][0] = u'dagnn.Scale'
mlayer['block'][0] = dictToMatlabStruct(
{'size': row(self.mult_size),
'hasBias': self.bias_term})
return mlayer
def toMatlabSimpleNN(self):
mlayer = super(CaffeScale, self).toMatlabSimpleNN()
# SimpleNN works only if the scaling blob is a parameter (and not a variable)
mlayer['type'] = u'scale'
mlayer['size'] = row(self.mult_size)
mlayer['hasBias'] = self.bias_term
return mlayer
# --------------------------------------------------------------------
# BatchNorm
# --------------------------------------------------------------------
class CaffeBatchNorm(CaffeLayer):
def __init__(self, name, inputs, outputs, use_global_stats, moving_average_fraction, eps):
super(CaffeBatchNorm, self).__init__(name, inputs, outputs)
self.use_global_stats = use_global_stats
self.moving_average_fraction = moving_average_fraction
self.eps = eps
self.params = [name + u'_mean',
name + u'_variance',
name + u'_scale_factor']
def display(self):
super(CaffeBatchNorm, self).display()
print " c- use_global_stats: %s" % (self.use_global_stats,)
print " c- moving_average_fraction: %s" % (self.moving_average_fraction,)
print " c- eps: %s" % (self.eps)
def setBlob(self, model, i, blob):
assert(i < 3)
model.params[self.params[i]].value = blob
model.params[self.params[i]].shape = blob.shape
def reshape(self, model):
shape = model.vars[self.inputs[0]].shape
mean = model.params[self.params[0]].value
variance = model.params[self.params[1]].value
scale_factor = model.params[self.params[2]].value
for i in range(3): del model.params[self.params[i]]
self.params = [self.name + u'_mult',
self.name + u'_bias',
self.name + u'_moments']
model.addParam(self.params[0])
model.addParam(self.params[1])
model.addParam(self.params[2])
if shape:
mult = np.ones((shape[2],),dtype='float32')
bias = np.zeros((shape[2],),dtype='float32')
model.params[self.params[0]].value = mult
model.params[self.params[0]].shape = mult.shape
model.params[self.params[1]].value = bias
model.params[self.params[1]].shape = bias.shape
if mean.size:
moments = np.concatenate(
(mean.reshape(-1,1) / scale_factor,
np.sqrt(variance.reshape(-1,1) / scale_factor + self.eps)),
axis=1)
model.params[self.params[2]].value = moments
model.params[self.params[2]].shape = moments.shape
model.vars[self.outputs[0]].shape = shape
def toMatlab(self):
mlayer = super(CaffeBatchNorm, self).toMatlab()
mlayer['type'][0] = u'dagnn.BatchNorm'
mlayer['block'][0] = dictToMatlabStruct(
{'epsilon': self.eps})
return mlayer
def toMatlabSimpleNN(self):
mlayer = super(CaffeBatchNorm, self).toMatlabSimpleNN()
mlayer['type'] = u'bnorm'
mlayer['epsilon'] = self.eps
return mlayer
# --------------------------------------------------------------------
# Concat
# --------------------------------------------------------------------
class CaffeConcat(CaffeLayer):
def __init__(self, name, inputs, outputs, concatDim):
super(CaffeConcat, self).__init__(name, inputs, outputs)
self.concatDim = concatDim
def transpose(self, model):
self.concatDim = [1, 0, 2, 3][self.concatDim]
def reshape(self, model):
sizes = [model.vars[x].shape for x in self.inputs]
osize = copy.deepcopy(sizes[0])
osize[self.concatDim] = 0
for thisSize in sizes:
for i in range(len(thisSize)):
if self.concatDim == i:
osize[i] = osize[i] + thisSize[i]
else:
if osize[i] != thisSize[i]:
print "Warning: concat layer: inconsistent input dimensions", sizes
model.vars[self.outputs[0]].shape = osize
def display(self):
super(CaffeConcat, self).display()
print " Concat Dim: ", self.concatDim
def toMatlab(self):
mlayer = super(CaffeConcat, self).toMatlab()
mlayer['type'][0] = u'dagnn.Concat'
mlayer['block'][0] = dictToMatlabStruct({'dim': float(self.concatDim) + 1})
return mlayer
def toMatlabSimpleNN(self):
raise ConversionError('Concat layers do not work in a SimpleNN network')
# --------------------------------------------------------------------
# EltWise (Sum, ...)
# --------------------------------------------------------------------
class CaffeEltWise(CaffeElementWise):
def __init__(self, name, inputs, outputs,
operation,
coeff,
stable_prod_grad):
super(CaffeEltWise, self).__init__(name, inputs, outputs)
self.operation = operation
self.coeff = coeff
self.stable_prod_grad = stable_prod_grad
def toMatlab(self):
mlayer = super(CaffeEltWise, self).toMatlab()
if self.operation == 'sum':
mlayer['type'][0] = u'dagnn.Sum'
else:
# not implemented
assert(False)
return mlayer
def display(self):
super(CaffeEltWise, self).display()
print " c- operation: ", self.operation
print " c- coeff: %s" % self.coeff
print " c- stable_prod_grad: %s" % self.stable_prod_grad
def reshape(self, model):
model.vars[self.outputs[0]].shape = \
model.vars[self.inputs[0]].shape
for i in range(1, len(self.inputs)):
assert(model.vars[self.inputs[0]].shape == model.vars[self.inputs[i]].shape)
def toMatlabSimpleNN(self):
raise ConversionError('EltWise (sum, ...) layers do not work in a SimpleNN network')
# --------------------------------------------------------------------
# Crop
# --------------------------------------------------------------------
class CaffeCrop(CaffeLayer):
def __init__(self, name, inputs, outputs):
super(CaffeCrop, self).__init__(name, inputs, outputs)
self.crop = []
def display(self):
super(CaffeCrop, self).display()
print " Crop: %s" % self.crop
def reshape(self, model):
# this is quite complex as we need to compute on the fly
# the geometry
tfs1 = model.getParentTransforms(self.inputs[0], self.name)
tfs2 = model.getParentTransforms(self.inputs[1], self.name)
print
print self.name, self.inputs[0]
for a,x in enumerate(tfs1): print "%10s %s" % (x,tfs1[x])
print self.name, self.inputs[1]
for a,x in enumerate(tfs2): print "%10s %s" % (x,tfs2[x])
# the goal is to crop inputs[0] to make it as big as inputs[1] and
# aligned to it; so now we find the map from inputs[0] to inputs[1]
tf = None
for name, tf2 in tfs2.items():
if tfs1.has_key(name):
tf1 = tfs1[name]
tf = composeTransforms(transposeTransform(tf2), tf1)
break
if tf is None:
print "Error: could not find common ancestor for inputs '%s' and '%s' of the CaffeCrop layer '%s'" % (self.inputs[0], self.inputs[1], self.name)
sys.exit(1)
print " Transformation %s -> %s = %s" % (self.inputs[0],
self.inputs[1], tf)
# for this to make sense it shoudl be tf.stride = 1
assert(tf.stride[0] == 1 and tf.stride[1] == 1)
# finally we can get the crops!
self.crop = [0.,0.]
for i in [0,1]:
# i' = alpha (i - 1) + beta + crop = 1 for i = 1
# crop = 1 - beta
self.crop[i] = round(1 - tf.offset[i])
print " Crop %s" % self.crop
# print
# print "resolved"
# tfs3 = model.getParentTransforms(self.outputs[0])
# for a,x in enumerate(tfs3): print "%10s %s" % (x,tfs3[x])
# now compute output variable size, which will be the size of the second input
model.vars[self.outputs[0]].shape = model.vars[self.inputs[1]].shape
def getTransforms(self, model):
t = CaffeTransform([1.,1.], [1.,1.], [1.+self.crop[0],1.+self.crop[1]])
return [[t],[None]]
def toMatlab(self):
mlayer = super(CaffeCrop, self).toMatlab()
mlayer['type'][0] = u'dagnn.Crop'
mlayer['block'][0] = dictToMatlabStruct({'crop': row(self.crop)})
return mlayer
def toMatlabSimpleNN(self):
# todo: simple 1 input crop layers should be supported though!
raise ConversionError('Crop layers do not work in a SimpleNN network')
# --------------------------------------------------------------------
# Caffe Model
# --------------------------------------------------------------------
class CaffeModel(object):
def __init__(self):
self.layers = OrderedDict()
self.vars = OrderedDict()
self.params = OrderedDict()
def addLayer(self, layer):
ename = layer.name
while self.layers.has_key(ename):
ename = ename + 'x'
if layer.name != ename:
print "Warning: a layer with name %s was already found, using %s instead" % \
(layer.name, ename)
layer.name = ename
for v in layer.inputs: self.addVar(v)
for v in layer.outputs: self.addVar(v)
for p in layer.params: self.addParam(p)
self.layers[layer.name] = layer
def addVar(self, name):
if not self.vars.has_key(name):
self.vars[name] = CaffeBlob(name)
def addParam(self, name):
if not self.params.has_key(name):
self.params[name] = CaffeBlob(name)
def renameLayer(self, old, new):
self.layers[old].name = new
# reinsert layer with new name -- this mess is to preserve the order
layers = OrderedDict([(new,v) if k==old else (k,v)
for k,v in self.layers.items()])
self.layers = layers
def renameVar(self, old, new, afterLayer=None):
self.vars[old].name = new
if afterLayer is not None:
start = self.layers.keys().index(afterLayer) + 1
else:
start = 0
# fix all references to the variable
for layer in self.layers.values()[start:-1]:
layer.inputs = [new if x==old else x for x in layer.inputs]
layer.outputs = [new if x==old else x for x in layer.outputs]
self.vars[new] = copy.deepcopy(self.vars[old])
# check if we can delete the old one (for afterLayet != None)
stillUsed = False
for layer in self.layers.values():
stillUsed = stillUsed or old in layer.inputs or old in layer.outputs
if not stillUsed:
del self.vars[old]
def renameParam(self, old, new):
self.params[old].name = new
# fix all references to the variable
for layer in self.layers.itervalues():
layer.params = [new if x==old else x for x in layer.params]
var = self.params[old]
del self.params[old]
self.params[new] = var
def removeParam(self, name):
del self.params[name]
def removeLayer(self, name):
# todo: fix this stuff for weight sharing
layer = self.layers[name]
for paramName in layer.params:
self.removeParam(paramName)
del self.layers[name]
def getLayersWithOutput(self, varName):
layerNames = []
for layer in self.layers.itervalues():
if varName in layer.outputs:
layerNames.append(layer.name)
return layerNames
def getLayersWithInput(self, varName):
layerNames = []
for layer in self.layers.itervalues():
if varName in layer.inputs:
layerNames.append(layer.name)
return layerNames
def reshape(self):
for layer in self.layers.itervalues():
layer.reshape(self)
def display(self):
for layer in self.layers.itervalues():
layer.display()
for var in self.vars.itervalues():
print 'Variable \'{}\''.format(var.name)
print ' + shape (computed): %s' % (var.shape,)
for par in self.params.itervalues():
print 'Parameter \'{}\''.format(par.name)
print ' + data found: %s' % (par.shape is not None)
print ' + data shape: %s' % (par.shape,)
def transpose(self):
for var in self.vars.itervalues():
if var.transposable: var.transpose()
for layer in self.layers.itervalues():
layer.transpose(self)
def getParentTransforms(self, variableName, topLayerName=None):
layerNames = self.layers.keys()
if topLayerName:
layerIndex = layerNames.index(topLayerName)
else:
layerIndex = len(self.layers) + 1
transforms = OrderedDict()
transforms[variableName] = CaffeTransform([1.,1.], [1.,1.], [1.,1.])
for layerName in reversed(layerNames[0:layerIndex]):
layer = self.layers[layerName]
layerTfs = layer.getTransforms(self)
for i, inputName in enumerate(layer.inputs):
tfs = []
if transforms.has_key(inputName):
tfs.append(transforms[inputName])
for j, outputName in enumerate(layer.outputs):
if layerTfs[i][j] is None: continue
if transforms.has_key(outputName):
composed = composeTransforms(layerTfs[i][j], transforms[outputName])
tfs.append(composed)
if len(tfs) > 0:
# should resolve conflicts, not simply pick the first tf
transforms[inputName] = tfs[0]
return transforms
| 43,791 | 36.493151 | 156 | py |
DRT | DRT-master/external_libs/matconvnet/utils/import-caffe.py | #! /usr/bin/python
# file: import-caffe.py
# brief: Caffe importer for DagNN and SimpleNN
# author: Karel Lenc and Andrea Vedaldi
# Requires Google Protobuf for Python and SciPy
import sys
import os
import argparse
import code
import re
import numpy as np
from math import floor, ceil
import numpy
from numpy import array
import scipy
import scipy.io
import scipy.misc
import google.protobuf.text_format
from ast import literal_eval as make_tuple
from layers import *
# --------------------------------------------------------------------
# Check NumPy version
# --------------------------------------------------------------------
def versiontuple(version):
return tuple(map(int, (version.split("."))))
min_numpy_version = "1.7.0"
if versiontuple(numpy.version.version) < versiontuple(min_numpy_version):
print 'Unsupported numpy version ({}), must be >= {}'.format(numpy.version.version,
min_numpy_version)
sys.exit(0)
# --------------------------------------------------------------------
# Helper functions
# --------------------------------------------------------------------
def find(seq, name):
for item in seq:
if item.name == name:
return item
return None
def blobproto_to_array(blob):
"""Convert a Caffe Blob to a numpy array.
It also reverses the order of all dimensions to [width, height,
channels, instance].
"""
dims = []
if hasattr(blob, 'shape'):
dims = tolist(blob.shape.dim)
if not dims:
dims = [blob.num, blob.channels, blob.height, blob.width]
return np.array(blob.data,dtype='float32').reshape(dims).transpose()
def dict_to_struct_array(d):
if not d:
return np.zeros((0,))
dt=[(x,object) for x in d.keys()]
y = np.empty((1,),dtype=dt)
for x in d.keys():
y[x][0] = d[x]
return y
def tolist(x):
"Convert x to a Python list. x can be a Protobuf container, a list or tuple, or scalar"
if isinstance(x,google.protobuf.internal.containers.RepeatedScalarFieldContainer):
return [z for z in x]
elif isinstance(x, (list,tuple)):
return [z for z in x]
else:
return [x]
def escape(name):
return name.replace('-','_')
# --------------------------------------------------------------------
# Parse options
# --------------------------------------------------------------------
parser = argparse.ArgumentParser(description='Convert a Caffe CNN into a MATLAB structure.')
parser.add_argument('caffe_proto',
type=argparse.FileType('rb'),
help='The Caffe CNN parameter file (ASCII .proto)')
parser.add_argument('--caffe-data',
type=argparse.FileType('rb'),
help='The Caffe CNN data file (binary .proto)')
parser.add_argument('output',
type=argparse.FileType('w'),
help='Output MATLAB file')
parser.add_argument('--full-image-size',
type=str,
nargs='?',
default=None,
help='Size of the full image')
parser.add_argument('--average-image',
type=argparse.FileType('rb'),
nargs='?',
help='Average image')
parser.add_argument('--average-value',
type=str,
nargs='?',
default=None,
help='Average image value')
parser.add_argument('--synsets',
type=argparse.FileType('r'),
nargs='?',
help='Synset file (ASCII)')
parser.add_argument('--class-names',
type=str,
nargs='?',
help='Class names')
parser.add_argument('--caffe-variant',
type=str,
nargs='?',
default='caffe',
help='Variant of Caffe software (use ? to get a list)')
parser.add_argument('--transpose',
dest='transpose',
action='store_true',
help='Transpose CNN in a sane MATLAB format')
parser.add_argument('--no-transpose',
dest='transpose',
action='store_false',
help='Do not transpose CNN')
parser.add_argument('--color-format',
dest='color_format',
default='bgr',
action='store',
help='Set the color format used by the network: ''rgb'' or ''bgr'' (default)')
parser.add_argument('--preproc',
type=str,
nargs='?',
default='caffe',
help='Variant of image preprocessing to use (use ? to get a list)')
parser.add_argument('--simplify',
dest='simplify',
action='store_true',
help='Apply simplifications')
parser.add_argument('--no-simplify',
dest='simplify',
action='store_false',
help='Do not apply simplifications')
parser.add_argument('--remove-dropout',
dest='remove_dropout',
action='store_true',
help='Remove dropout layers')
parser.add_argument('--no-remove-dropout',
dest='remove_dropout',
action='store_false',
help='Do not remove dropout layers')
parser.add_argument('--remove-loss',
dest='remove_loss',
action='store_true',
help='Remove loss layers')
parser.add_argument('--no-remove-loss',
dest='remove_loss',
action='store_false',
help='Do not remove loss layers')
parser.add_argument('--append-softmax',
dest='append_softmax',
action='append',
default=[],
help='Add a softmax layer after the specified layer')
parser.add_argument('--output-format',
dest='output_format',
default='dagnn',
help='Either ''dagnn'' or ''simplenn''')
parser.set_defaults(transpose=True)
parser.set_defaults(remove_dropout=False)
parser.set_defaults(remove_loss=False)
parser.set_defaults(simplify=True)
args = parser.parse_args()
print 'Caffe varaint set to', args.caffe_variant
if args.caffe_variant == 'vgg-caffe':
import proto.vgg_caffe_pb2 as caffe_pb2
elif args.caffe_variant == 'caffe-old':
import proto.caffe_old_pb2 as caffe_pb2
elif args.caffe_variant == 'caffe':
import proto.caffe_pb2 as caffe_pb2
elif args.caffe_variant == 'caffe_0115':
import proto.caffe_0115_pb2 as caffe_pb2
elif args.caffe_variant == 'caffe_6e3916':
import proto.caffe_6e3916_pb2 as caffe_pb2
elif args.caffe_variant == 'caffe_b590f1d':
import proto.caffe_b590f1d_pb2 as caffe_pb2
elif args.caffe_variant == 'caffe_fastrcnn':
import proto.caffe_fastrcnn_pb2 as caffe_pb2
elif args.caffe_variant == '?':
print 'Supported variants: caffe, vgg-caffe, caffe-old, caffe_0115, caffe_6e3916, caffe_b590f1d, caffe_fastrcnn'
sys.exit(0)
else:
print 'Unknown Caffe variant', args.caffe_variant
sys.exit(1)
if args.preproc == '?':
print 'Preprocessing variants: caffe, vgg, fcn'
sys.exit(0)
if args.preproc not in ['caffe', 'vgg-caffe', 'fcn']:
print 'Unknown preprocessing variant', args.preproc
sys.exit(1)
# --------------------------------------------------------------------
# Helper functions
# --------------------------------------------------------------------
def keyboard(banner=None):
''' Function that mimics the matlab keyboard command '''
# use exception trick to pick up the current frame
try:
raise None
except:
frame = sys.exc_info()[2].tb_frame.f_back
print "# Use quit() to exit :) Happy debugging!"
# evaluate commands in current namespace
namespace = frame.f_globals.copy()
namespace.update(frame.f_locals)
try:
code.interact(banner=banner, local=namespace)
except SystemExit:
return
def bilinear_interpolate(im, x, y):
x = np.asarray(x)
y = np.asarray(y)
x0 = np.floor(x).astype(int)
x1 = x0 + 1
y0 = np.floor(y).astype(int)
y1 = y0 + 1
x0 = np.clip(x0, 0, im.shape[1]-1);
x1 = np.clip(x1, 0, im.shape[1]-1);
y0 = np.clip(y0, 0, im.shape[0]-1);
y1 = np.clip(y1, 0, im.shape[0]-1);
Ia = im[ y0, x0 ]
Ib = im[ y1, x0 ]
Ic = im[ y0, x1 ]
Id = im[ y1, x1 ]
wa = (1-x+x0) * (1-y+y0)
wb = (1-x+x0) * (y-y0)
wc = (x-x0) * (1-y+y0)
wd = (x-x0) * (y-y0)
wa = wa.reshape(x.shape[0], x.shape[1], 1)
wb = wb.reshape(x.shape[0], x.shape[1], 1)
wc = wc.reshape(x.shape[0], x.shape[1], 1)
wd = wd.reshape(x.shape[0], x.shape[1], 1)
return wa*Ia + wb*Ib + wc*Ic + wd*Id
# Get the parameters for a layer from Caffe's proto entries
def getopts(layer, name):
if hasattr(layer, name):
return getattr(layer, name)
else:
# Older Caffe proto formats did not have sub-structures for layer
# specific parameters but mixed everything up! This falls back to
# that situation when fetching the parameters.
return layer
# --------------------------------------------------------------------
# Load average image
# --------------------------------------------------------------------
average_image = None
resize_average_image = False
if args.average_image:
print 'Loading average image from {}'.format(args.average_image.name)
resize_average_image = True # in case different from data size
avgim_nm, avgim_ext = os.path.splitext(args.average_image.name)
if avgim_ext == '.binaryproto':
blob=caffe_pb2.BlobProto()
blob.MergeFromString(args.average_image.read())
average_image = blobproto_to_array(blob).astype('float32')
average_image = np.squeeze(average_image,3)
if args.transpose and average_image is not None:
average_image = average_image.transpose([1,0,2])
average_image = average_image[:,:,: : -1] # to RGB
elif avgim_ext == '.mat':
avgim_data = scipy.io.loadmat(args.average_image)
average_image = avgim_data['mean_img']
else:
print 'Unsupported average image format {}'.format(avgim_ext)
if args.average_value:
rgb = make_tuple(args.average_value)
print 'Using average image value', rgb
# this will be resized later to a constant image
average_image = np.array(rgb,dtype=float).reshape(1,1,3,order='F')
resize_average_image = False
# --------------------------------------------------------------------
# Load ImageNet synseths (if any)
# --------------------------------------------------------------------
synsets_wnid=None
synsets_name=None
if args.synsets:
print 'Loading synsets from {}'.format(args.synsets.name)
r=re.compile('(?P<wnid>n[0-9]{8}?) (?P<name>.*)')
synsets_wnid=[]
synsets_name=[]
for line in args.synsets:
match = r.match(line)
synsets_wnid.append(match.group('wnid'))
synsets_name.append(match.group('name'))
if args.class_names:
synsets_wnid=list(make_tuple(args.class_names))
synsets_name=synsets_wnid
# --------------------------------------------------------------------
# Load layers
# --------------------------------------------------------------------
# Caffe stores the network structure and data into two different files
# We load them both and merge them into a single MATLAB structure
net=caffe_pb2.NetParameter()
data=caffe_pb2.NetParameter()
print 'Loading Caffe CNN structure from {}'.format(args.caffe_proto.name)
google.protobuf.text_format.Merge(args.caffe_proto.read(), net)
if args.caffe_data:
print 'Loading Caffe CNN parameters from {}'.format(args.caffe_data.name)
data.MergeFromString(args.caffe_data.read())
# --------------------------------------------------------------------
# Read layers in a CaffeModel object
# --------------------------------------------------------------------
if args.caffe_variant in ['caffe_b590f1d', 'caffe_fastrcnn']:
layers_list = net.layer
data_layers_list = data.layer
else:
layers_list = net.layers
data_layers_list = data.layers
print 'Converting {} layers'.format(len(layers_list))
cmodel = CaffeModel()
for layer in layers_list:
# Depending on how old the proto-buf, the top and bottom parameters
# are found at a different level than the others
top = layer.top
bottom = layer.bottom
if args.caffe_variant in ['vgg-caffe', 'caffe-old']:
layer = layer.layer
# get the type of layer
# depending on the Caffe variant, this is a string or a numeric
# ID, which we convert back to a string
ltype = layer.type
if not isinstance(ltype, basestring): ltype = layers_type[ltype]
print 'Added layer \'{}\' ({})'.format(ltype, layer.name)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
if ltype in ['conv', 'deconvolution', 'Convolution', 'Deconvolution']:
opts = getopts(layer, 'convolution_param')
if hasattr(opts, 'kernelsize'):
kernel_size = opts.kernelsize
else:
kernel_size = opts.kernel_size
if hasattr(opts, 'bias_term'):
bias_term = opts.bias_term
else:
bias_term = True
if hasattr(opts, 'dilation'):
dilation = opts.dilation
else:
dilation = 1
if ltype in ['conv', 'Convolution']:
clayer = CaffeConv(layer.name, bottom, top,
kernel_size = tolist(kernel_size),
bias_term = bias_term,
num_output = opts.num_output,
group = opts.group,
dilation = dilation,
stride = tolist(opts.stride),
pad = tolist(opts.pad))
else:
clayer = CaffeDeconvolution(layer.name, bottom, top,
kernel_size = tolist(kernel_size),
bias_term = bias_term,
num_output = opts.num_output,
group = opts.group,
dilation = dilation,
stride = tolist(opts.stride),
pad = tolist(opts.pad))
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['innerproduct', 'inner_product', 'InnerProduct']:
opts = getopts(layer, 'inner_product_param')
if hasattr(opts, 'bias_term'):
bias_term = opts.bias_term
else:
bias_term = True
if hasattr(opts, 'axis'):
axis = opts.axis
else:
axis = 1
clayer = CaffeInnerProduct(layer.name, bottom, top,
num_output = opts.num_output,
bias_term = bias_term,
axis = axis)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['relu', 'ReLU']:
clayer = CaffeReLU(layer.name, bottom, top)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['crop', 'Crop']:
clayer = CaffeCrop(layer.name, bottom, top)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['lrn', 'LRN']:
opts = getopts(layer, 'lrn_param')
local_size = float(opts.local_size)
alpha = float(opts.alpha)
beta = float(opts.beta)
kappa = opts.k if hasattr(opts,'k') else 1.
regions = ['across_channels', 'within_channel']
if hasattr(opts, 'norm_region'):
norm_region = opts.norm_region
else:
norm_region = 0
clayer = CaffeLRN(layer.name, bottom, top,
local_size = local_size,
alpha = alpha,
beta = beta,
norm_region = regions[norm_region],
kappa = kappa)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['pool', 'Pooling']:
opts = getopts(layer, 'pooling_param')
if hasattr(layer, 'kernelsize'):
kernel_size = opts.kernelsize
else:
kernel_size = opts.kernel_size
clayer = CaffePooling(layer.name, bottom, top,
method = ['max', 'avg'][opts.pool],
pad = tolist(opts.pad),
kernel_size = tolist(kernel_size),
stride = tolist(opts.stride))
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['dropout', 'Dropout']:
opts = getopts(layer, 'dropout_param')
clayer = CaffeDropout(layer.name, bottom, top,
opts.dropout_ratio)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['softmax', 'Softmax']:
clayer = CaffeSoftMax(layer.name, bottom, top)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['softmax_loss', 'SoftmaxLoss']:
clayer = CaffeSoftMaxLoss(layer.name, bottom, top)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['concat', 'Concat']:
opts = getopts(layer, 'concat_param')
clayer = CaffeConcat(layer.name, bottom, top,
3 - opts.concat_dim) # todo: depreceted in recent Caffes
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['Scale']:
opts = getopts(layer, 'scale_param')
clayer = CaffeScale(layer.name, bottom, top,
axis = opts.axis,
num_axes = opts.num_axes,
bias_term = opts.bias_term)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['BatchNorm']:
opts = getopts(layer, 'batch_norm_param')
clayer = CaffeBatchNorm(layer.name, bottom, top,
use_global_stats = opts.use_global_stats,
moving_average_fraction = opts.moving_average_fraction,
eps = opts.eps)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['eltwise', 'Eltwise']:
opts = getopts(layer, 'eltwise_param')
operations = ['prod', 'sum', 'max']
clayer = CaffeEltWise(layer.name, bottom, top,
operation = operations[opts.operation],
coeff = opts.coeff,
stable_prod_grad = opts.stable_prod_grad)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['data', 'Data']:
opts = getopts(layer, 'eltwise_param')
operations = ['prod', 'sum', 'max']
clayer = CaffeData(layer.name, bottom, top,
operation = operations[opts.operation],
coeff = opts.coeff,
stable_prod_grad = opts.stable_prod_grad)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['roipooling', 'ROIPooling']:
opts = getopts(layer, 'roi_pooling_param')
clayer = CaffeROIPooling(layer.name, bottom, top,
pooled_w = opts.pooled_w,
pooled_h = opts.pooled_h,
spatial_scale = opts.spatial_scale)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elif ltype in ['accuracy', 'Accuracy']:
continue
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
else:
print 'Warning: unknown layer type', ltype
continue
if clayer is not None:
clayer.model = cmodel
cmodel.addLayer(clayer)
# Fill parameters
for dlayer in data_layers_list:
if args.caffe_variant in ['vgg-caffe', 'caffe-old']:
dlayer = dlayer.layer
if dlayer.name == layer.name:
for i, blob in enumerate(dlayer.blobs):
blob = blobproto_to_array(blob).astype('float32')
print ' + parameter \'%s\' <-- blob%s' % (clayer.params[i], blob.shape)
clayer.setBlob(cmodel, i, blob)
# --------------------------------------------------------------------
# Get the size of the network variables
# --------------------------------------------------------------------
# Get the sizes of the network inputs
for i, inputVarName in enumerate(net.input):
if hasattr(net, 'input_shape') and net.input_shape:
shape = net.input_shape[i].dim._values
# ensure that shape is a list of dimensions
if isinstance(shape, caffe_pb2.BlobShape):
# shape.tolist() may not preserve the order of dimensions
shape = shape.dim._values
shape.reverse()
else:
shape = [net.input_dim[k + 4*i] for k in [3,2,1,0]]
cmodel.vars[inputVarName].shape = shape
print ' c- Input \'{}\' is {}'.format(inputVarName, shape)
# --------------------------------------------------------------------
# Sanitize
# --------------------------------------------------------------------
# Rename layers, parametrs, and variables if they contain symbols that
# are incompatible with MatConvNet.
layerNames = cmodel.layers.keys()
for name in layerNames:
ename = escape(name)
if ename == name: continue
# ensure unique
while cmodel.layers.has_key(ename): ename = ename + 'x'
print "Renaming layer {} to {}".format(name, ename)
cmodel.renameLayer(name, ename)
varNames = cmodel.vars.keys()
for name in varNames:
ename = escape(name)
if ename == name: continue
while cmodel.vars.has_key(ename): ename = ename + 'x'
print "Renaming variable {} to {}".format(name, ename)
cmodel.renameVar(name, ename)
parNames = cmodel.params.keys()
for name in parNames:
ename = escape(name)
if ename == name: continue
while cmodel.params.has_key(ename): ename = ename + 'x'
print "Renaming parameter {} to {}".format(name, ename)
cmodel.renameParam(name, ename)
# Split in-place layers. MatConvNet handles such optimizations
# differently.
for layer in cmodel.layers.itervalues():
if len(layer.inputs[0]) >= 1 and \
len(layer.outputs[0]) >= 1 and \
layer.inputs[0] == layer.outputs[0]:
name = layer.inputs[0]
ename = layer.inputs[0]
while cmodel.vars.has_key(ename): ename = ename + 'x'
print "Splitting in-place layer: renaming variable {} to {}".format(name, ename)
cmodel.addVar(ename)
cmodel.renameVar(name, ename, afterLayer=layer.name)
layer.inputs[0] = name
layer.outputs[0] = ename
# --------------------------------------------------------------------
# Get variable sizes
# --------------------------------------------------------------------
# Get the size of all other variables. This information is required
# for some special layer conversions:
#
# * For Pooling layers, fix incompatibility between padding in
# MatConvNet and Caffe.
#
# * For Crop layers (in FCNs), determine the amount of crop (in Caffe
# this is done at run time).
# Unflatten ROIPooling. ROIPooling will produce a H x W array instead
# of a stacked version of the same. The reshape operation below will
# convert the following InnerProduct layers in corresponding
# convolitions. This works well with transposition later.
layerNames = cmodel.layers.keys()
for name in layerNames:
layer = cmodel.layers[name]
if type(layer) is CaffeROIPooling:
childrenNames = cmodel.getLayersWithInput(layer.outputs[0])
for childName in childrenNames:
child = cmodel.layers[childName]
if type(child) is not CaffeInnerProduct:
print "Error: cannot unflatten ROIPooling if this is not followed only InnerProduct layers"
sys.exit(1)
layer.flatten = False
cmodel.reshape()
# --------------------------------------------------------------------
# Edit
# --------------------------------------------------------------------
# Remove dropout
if args.remove_dropout:
layerNames = cmodel.layers.keys()
for name in layerNames:
layer = cmodel.layers[name]
if type(layer) is CaffeDropout:
print "Removing dropout layer ", name
cmodel.renameVar(layer.outputs[0], layer.inputs[0])
cmodel.removeLayer(name)
# Remove loss
if args.remove_loss:
layerNames = cmodel.layers.keys()
for name in layerNames:
layer = cmodel.layers[name]
if type(layer) is CaffeSoftMaxLoss:
print "Removing loss layer ", name
cmodel.renameVar(layer.outputs[0], layer.inputs[0])
cmodel.removeLayer(name)
# Append softmax
for i, name in enumerate(args.append_softmax):
# search for the layer to append SoftMax to
if not cmodel.layers.has_key(name):
print 'Cannot append softmax to layer {} as no such layer could be found'.format(name)
sys.exit(1)
if len(args.append_softmax) > 1:
layerName = 'softmax' + (l + 1)
outputs= ['prob' + (l + 1)]
else:
layerName = 'softmax'
outputs = ['prob']
cmodel.addLayer(CaffeSoftMax(layerName,
cmodel.layers[name].outputs[0:1],
outputs))
# Simplifications
if args.simplify:
# Merge BatchNorm followed by Scale
layerNames = cmodel.layers.keys()
for name in layerNames:
layer = cmodel.layers[name]
if type(layer) is CaffeScale:
if len(layer.inputs) > 1:
continue # the scaling factor is an input, not a parameter
if len(cmodel.getLayersWithInput(layer.inputs[0])) > 1:
continue # other layers use the same input
parentNames = cmodel.getLayersWithOutput(layer.inputs[0])
if len(parentNames) != 1: continue
parent = cmodel.layers[parentNames[0]]
if type(parent) is not CaffeBatchNorm: continue
smult = cmodel.params[layer.params[0]]
sbias = cmodel.params[layer.params[1]]
mult = cmodel.params[parent.params[0]]
bias = cmodel.params[parent.params[1]]
# simplification can only occur if scale layer is 1x1xC
if smult.shape[0] != 1 or smult.shape[1] != 1: continue
C = smult.shape[2]
mult.value = np.reshape(smult.value, (C,)) * mult.value
bias.value = np.reshape(smult.value, (C,)) * bias.value + \
np.reshape(sbias.value, (C,))
print "Simplifying scale layer \'{}\'".format(name)
cmodel.renameVar(layer.outputs[0], layer.inputs[0])
cmodel.removeLayer(name)
# --------------------------------------------------------------------
# Transposition
# --------------------------------------------------------------------
#
# There are a few different conventions in MATLAB and Caffe:
#
# * In MATLAB, the frist spatial dimension is Y (vertical) followed by
# X (horizontal), whereas in Caffe the opposite is true.
#
# * In MATLAB, images are stored in RGB format, whereas Caffe uses
# BGR.
#
# * In MatConvNet, the first spatial coordinate is Y, whereas in Caffe
# it is X. This affects layers such as ROI pooling.
#
# These conventions means that, if the network is directly saved in
# MCN format, then images and spatial coordinates are transposed as
# just described. While this is not a deal breaker, it is
# inconvenient.
#
# Thus we transpose all X,Y spatial dimensions in the network. For now,
# this is partially heuristic. In the future, we should add adapter layer to
# convert from MCN inputs and outputs to Caffe input and outputs and then
# simplity those away using graph transformations.
# Mark variables:
# - requiring BGR -> RGB conversion
# - requiring XY transposition
for i, inputVarName in enumerate(net.input):
if inputVarName == 'data' or i == 0:
if cmodel.vars[inputVarName].shape[2] == 3:
cmodel.vars[inputVarName].bgrInput = (args.color_format == 'bgr')
if not inputVarName == 'rois':
cmodel.vars[inputVarName].transposable = True
else:
cmodel.vars[inputVarName].transposable = False
# Apply transformations
if args.transpose: cmodel.transpose()
cmodel.display()
# --------------------------------------------------------------------
# Normalization
# --------------------------------------------------------------------
minputs = np.empty(shape=[0,], dtype=minputdt)
# Determine the size of the inputs and input image (dataShape)
for i, inputVarName in enumerate(net.input):
shape = cmodel.vars[inputVarName].shape
# add metadata
minput = np.empty(shape=[1,], dtype=minputdt)
minput['name'][0] = inputVarName
minput['size'][0] = row(shape)
minputs = np.append(minputs, minput, axis=0)
# heuristic: the first input or 'data' is the input image
if i == 0 or inputVarName == 'data':
dataShape = shape
print "Input image data tensor shape:", dataShape
fullImageSize = [256, 256]
if args.full_image_size:
fullImageSize = list(make_tuple(args.full_image_size))
print "Full input image size:", fullImageSize
if average_image is not None:
if resize_average_image:
x = numpy.linspace(0, average_image.shape[1]-1, dataShape[0])
y = numpy.linspace(0, average_image.shape[0]-1, dataShape[1])
x, y = np.meshgrid(x, y, sparse=False, indexing='xy')
average_image = bilinear_interpolate(average_image, x, y)
else:
average_image = np.zeros((0,),dtype='float')
mnormalization = {
'imageSize': row(dataShape),
'averageImage': average_image,
'interpolation': 'bilinear',
'keepAspect': True,
'border': row([0,0]),
'cropSize': 1.0}
if len(fullImageSize) == 1:
fw = max(fullImageSize[0],dataShape[1])
fh = max(fullImageSize[0],dataShape[0])
mnormalization['border'] = max([float(fw - dataShape[1]),
float(fh - dataShape[0])])
mnormalization['cropSize'] = min([float(dataShape[1]) / fw,
float(dataShape[0]) / fh])
else:
fw = max(fullImageSize[0],dataShape[1])
fh = max(fullImageSize[1],dataShape[0])
mnormalization['border'] = row([float(fw - dataShape[1]),
float(fh - dataShape[0])])
mnormalization['cropSize'] = row([float(dataShape[1]) / fw,
float(dataShape[0]) / fh])
if args.caffe_variant == 'caffe_fastrcnn':
mnormalization['interpolation'] = 'bilinear'
if args.preproc == 'caffe':
mnormalization['interpolation'] = 'bicubic'
mnormalization['keepAspect'] = False
print 'Input image border: ', mnormalization['border']
print 'Full input image relative crop size: ', mnormalization['cropSize']
# --------------------------------------------------------------------
# Classes
# --------------------------------------------------------------------
mclassnames = np.empty((0,), dtype=np.object)
mclassdescriptions = np.array((0,), dtype=np.object)
if synsets_wnid:
mclassnames = np.array(synsets_wnid, dtype=np.object).reshape(1,-1)
if synsets_name:
mclassdescriptions = np.array(synsets_name, dtype=np.object).reshape(1,-1)
mclasses = dictToMatlabStruct({'name': mclassnames,
'description': mclassdescriptions})
# --------------------------------------------------------------------
# Convert to MATLAB
# --------------------------------------------------------------------
# net.meta
mmeta = dictToMatlabStruct({'inputs': minputs.reshape(1,-1),
'normalization': mnormalization,
'classes': mclasses})
if args.output_format == 'dagnn':
# This object should stay a dictionary and not a NumPy array due to
# how NumPy saves to MATLAB
mnet = {'layers': np.empty(shape=[0,], dtype=mlayerdt),
'params': np.empty(shape=[0,], dtype=mparamdt),
'meta': mmeta}
for layer in cmodel.layers.itervalues():
mnet['layers'] = np.append(mnet['layers'], layer.toMatlab(), axis=0)
for param in cmodel.params.itervalues():
mnet['params'] = np.append(mnet['params'], param.toMatlab(), axis=0)
# to row
mnet['layers'] = mnet['layers'].reshape(1,-1)
mnet['params'] = mnet['params'].reshape(1,-1)
elif args.output_format == 'simplenn':
# This object should stay a dictionary and not a NumPy array due to
# how NumPy saves to MATLAB
mnet = {'layers': np.empty(shape=[0,], dtype=np.object),
'meta': mmeta}
for layer in cmodel.layers.itervalues():
mnet['layers'] = np.append(mnet['layers'], np.object)
mnet['layers'][-1] = dictToMatlabStruct(layer.toMatlabSimpleNN())
# to row
mnet['layers'] = mnet['layers'].reshape(1,-1)
# --------------------------------------------------------------------
# Save output
# --------------------------------------------------------------------
print 'Saving network to {}'.format(args.output.name)
scipy.io.savemat(args.output, mnet, oned_as='column')
| 33,156 | 36.213244 | 114 | py |
DRT | DRT-master/external_libs/matconvnet/utils/proto/caffe_0115_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='caffe.proto',
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\x03(\x02\x12\x14\n\trand_skip\x18\x35 \x01(\r:\x01\x30\x12\x1d\n\x10\x64\x65t_fg_threshold\x18\x36 \x01(\x02:\x03\x30.5\x12\x1d\n\x10\x64\x65t_bg_threshold\x18\x37 \x01(\x02:\x03\x30.5\x12\x1d\n\x0f\x64\x65t_fg_fraction\x18\x38 \x01(\x02:\x04\x30.25\x12\x1a\n\x0f\x64\x65t_context_pad\x18: \x01(\r:\x01\x30\x12\x1b\n\rdet_crop_mode\x18; \x01(\t:\x04warp\x12\x12\n\x07new_num\x18< \x01(\x05:\x01\x30\x12\x17\n\x0cnew_channels\x18= \x01(\x05:\x01\x30\x12\x15\n\nnew_height\x18> \x01(\x05:\x01\x30\x12\x14\n\tnew_width\x18? \x01(\x05:\x01\x30\x12\x1d\n\x0eshuffle_images\x18@ \x01(\x08:\x05\x66\x61lse\x12\x15\n\nconcat_dim\x18\x41 \x01(\r:\x01\x31\x12\x36\n\x11hdf5_output_param\x18\xe9\x07 \x01(\x0b\x32\x1a.caffe.HDF5OutputParameter\".\n\nPoolMethod\x12\x07\n\x03MAX\x10\x00\x12\x07\n\x03\x41VE\x10\x01\x12\x0e\n\nSTOCHASTIC\x10\x02*\x1c\n\x05Phase\x12\t\n\x05TRAIN\x10\x00\x12\x08\n\x04TEST\x10\x01')
_PHASE = descriptor.EnumDescriptor(
name='Phase',
full_name='caffe.Phase',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='TRAIN', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='TEST', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=9074,
serialized_end=9102,
)
TRAIN = 0
TEST = 1
_SOLVERPARAMETER_SOLVERMODE = descriptor.EnumDescriptor(
name='SolverMode',
full_name='caffe.SolverParameter.SolverMode',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='CPU', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='GPU', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=1574,
serialized_end=1604,
)
_SOLVERPARAMETER_SOLVERTYPE = descriptor.EnumDescriptor(
name='SolverType',
full_name='caffe.SolverParameter.SolverType',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='SGD', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='NESTEROV', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ADAGRAD', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=1606,
serialized_end=1654,
)
_LAYERPARAMETER_LAYERTYPE = descriptor.EnumDescriptor(
name='LayerType',
full_name='caffe.LayerParameter.LayerType',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='NONE', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ABSVAL', index=1, number=35,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ACCURACY', index=2, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ARGMAX', index=3, number=30,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='BNLL', index=4, number=2,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONCAT', index=5, number=3,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONTRASTIVE_LOSS', index=6, number=37,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONVOLUTION', index=7, number=4,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DATA', index=8, number=5,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DROPOUT', index=9, number=6,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DUMMY_DATA', index=10, number=32,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='EUCLIDEAN_LOSS', index=11, number=7,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ELTWISE', index=12, number=25,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='FLATTEN', index=13, number=8,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HDF5_DATA', index=14, number=9,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HDF5_OUTPUT', index=15, number=10,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HINGE_LOSS', index=16, number=28,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='IM2COL', index=17, number=11,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='IMAGE_DATA', index=18, number=12,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='INFOGAIN_LOSS', index=19, number=13,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='INNER_PRODUCT', index=20, number=14,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='LRN', index=21, number=15,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MEMORY_DATA', index=22, number=29,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MULTINOMIAL_LOGISTIC_LOSS', index=23, number=16,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MVN', index=24, number=34,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='POOLING', index=25, number=17,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='POWER', index=26, number=26,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='RELU', index=27, number=18,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SIGMOID', index=28, number=19,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SIGMOID_CROSS_ENTROPY_LOSS', index=29, number=27,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SILENCE', index=30, number=36,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SOFTMAX', index=31, number=20,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SOFTMAX_LOSS', index=32, number=21,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SPLIT', index=33, number=22,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SLICE', index=34, number=33,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='TANH', index=35, number=23,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='WINDOW_DATA', index=36, number=24,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='THRESHOLD', index=37, number=31,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=3760,
serialized_end=4332,
)
_LAYERPARAMETER_DIMCHECKMODE = descriptor.EnumDescriptor(
name='DimCheckMode',
full_name='caffe.LayerParameter.DimCheckMode',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='STRICT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='PERMISSIVE', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=4334,
serialized_end=4376,
)
_CONVOLUTIONPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.ConvolutionParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5085,
serialized_end=5128,
)
_DATAPARAMETER_DB = descriptor.EnumDescriptor(
name='DB',
full_name='caffe.DataParameter.DB',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='LEVELDB', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='LMDB', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5339,
serialized_end=5366,
)
_ELTWISEPARAMETER_ELTWISEOP = descriptor.EnumDescriptor(
name='EltwiseOp',
full_name='caffe.EltwiseParameter.EltwiseOp',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='PROD', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SUM', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MAX', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5672,
serialized_end=5711,
)
_HINGELOSSPARAMETER_NORM = descriptor.EnumDescriptor(
name='Norm',
full_name='caffe.HingeLossParameter.Norm',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='L1', index=0, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='L2', index=1, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5928,
serialized_end=5950,
)
_LRNPARAMETER_NORMREGION = descriptor.EnumDescriptor(
name='NormRegion',
full_name='caffe.LRNParameter.NormRegion',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='ACROSS_CHANNELS', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='WITHIN_CHANNEL', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6536,
serialized_end=6589,
)
_POOLINGPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.PoolingParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=7062,
serialized_end=7108,
)
_POOLINGPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.PoolingParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5085,
serialized_end=5128,
)
_RELUPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.ReLUParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5085,
serialized_end=5128,
)
_SIGMOIDPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.SigmoidParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5085,
serialized_end=5128,
)
_SOFTMAXPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.SoftmaxParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5085,
serialized_end=5128,
)
_TANHPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.TanHParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5085,
serialized_end=5128,
)
_V0LAYERPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.V0LayerParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=7062,
serialized_end=7108,
)
_BLOBPROTO = descriptor.Descriptor(
name='BlobProto',
full_name='caffe.BlobProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num', full_name='caffe.BlobProto.num', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.BlobProto.channels', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.BlobProto.height', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.BlobProto.width', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.BlobProto.data', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='diff', full_name='caffe.BlobProto.diff', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=22,
serialized_end=143,
)
_BLOBPROTOVECTOR = descriptor.Descriptor(
name='BlobProtoVector',
full_name='caffe.BlobProtoVector',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.BlobProtoVector.blobs', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=145,
serialized_end=195,
)
_DATUM = descriptor.Descriptor(
name='Datum',
full_name='caffe.Datum',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='channels', full_name='caffe.Datum.channels', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.Datum.height', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.Datum.width', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.Datum.data', index=3,
number=4, type=12, cpp_type=9, label=1,
has_default_value=False, default_value="",
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='label', full_name='caffe.Datum.label', index=4,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='float_data', full_name='caffe.Datum.float_data', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=197,
serialized_end=302,
)
_FILLERPARAMETER = descriptor.Descriptor(
name='FillerParameter',
full_name='caffe.FillerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='type', full_name='caffe.FillerParameter.type', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("constant", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='value', full_name='caffe.FillerParameter.value', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='min', full_name='caffe.FillerParameter.min', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max', full_name='caffe.FillerParameter.max', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean', full_name='caffe.FillerParameter.mean', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='std', full_name='caffe.FillerParameter.std', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='sparse', full_name='caffe.FillerParameter.sparse', index=6,
number=7, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=305,
serialized_end=449,
)
_NETPARAMETER = descriptor.Descriptor(
name='NetParameter',
full_name='caffe.NetParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.NetParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layers', full_name='caffe.NetParameter.layers', index=1,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input', full_name='caffe.NetParameter.input', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input_dim', full_name='caffe.NetParameter.input_dim', index=3,
number=4, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_backward', full_name='caffe.NetParameter.force_backward', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='state', full_name='caffe.NetParameter.state', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=452,
serialized_end=616,
)
_SOLVERPARAMETER = descriptor.Descriptor(
name='SolverParameter',
full_name='caffe.SolverParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='net', full_name='caffe.SolverParameter.net', index=0,
number=24, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='net_param', full_name='caffe.SolverParameter.net_param', index=1,
number=25, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='train_net', full_name='caffe.SolverParameter.train_net', index=2,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_net', full_name='caffe.SolverParameter.test_net', index=3,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='train_net_param', full_name='caffe.SolverParameter.train_net_param', index=4,
number=21, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_net_param', full_name='caffe.SolverParameter.test_net_param', index=5,
number=22, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='train_state', full_name='caffe.SolverParameter.train_state', index=6,
number=26, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_state', full_name='caffe.SolverParameter.test_state', index=7,
number=27, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_iter', full_name='caffe.SolverParameter.test_iter', index=8,
number=3, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_interval', full_name='caffe.SolverParameter.test_interval', index=9,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_compute_loss', full_name='caffe.SolverParameter.test_compute_loss', index=10,
number=19, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_initialization', full_name='caffe.SolverParameter.test_initialization', index=11,
number=32, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='base_lr', full_name='caffe.SolverParameter.base_lr', index=12,
number=5, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='display', full_name='caffe.SolverParameter.display', index=13,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='average_loss', full_name='caffe.SolverParameter.average_loss', index=14,
number=33, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max_iter', full_name='caffe.SolverParameter.max_iter', index=15,
number=7, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lr_policy', full_name='caffe.SolverParameter.lr_policy', index=16,
number=8, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='gamma', full_name='caffe.SolverParameter.gamma', index=17,
number=9, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power', full_name='caffe.SolverParameter.power', index=18,
number=10, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='momentum', full_name='caffe.SolverParameter.momentum', index=19,
number=11, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.SolverParameter.weight_decay', index=20,
number=12, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='regularization_type', full_name='caffe.SolverParameter.regularization_type', index=21,
number=29, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("L2", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stepsize', full_name='caffe.SolverParameter.stepsize', index=22,
number=13, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stepvalue', full_name='caffe.SolverParameter.stepvalue', index=23,
number=34, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot', full_name='caffe.SolverParameter.snapshot', index=24,
number=14, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_prefix', full_name='caffe.SolverParameter.snapshot_prefix', index=25,
number=15, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_diff', full_name='caffe.SolverParameter.snapshot_diff', index=26,
number=16, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='solver_mode', full_name='caffe.SolverParameter.solver_mode', index=27,
number=17, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='device_id', full_name='caffe.SolverParameter.device_id', index=28,
number=18, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='random_seed', full_name='caffe.SolverParameter.random_seed', index=29,
number=20, type=3, cpp_type=2, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='solver_type', full_name='caffe.SolverParameter.solver_type', index=30,
number=30, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='delta', full_name='caffe.SolverParameter.delta', index=31,
number=31, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1e-08,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='debug_info', full_name='caffe.SolverParameter.debug_info', index=32,
number=23, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_after_train', full_name='caffe.SolverParameter.snapshot_after_train', index=33,
number=28, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SOLVERPARAMETER_SOLVERMODE,
_SOLVERPARAMETER_SOLVERTYPE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=619,
serialized_end=1654,
)
_SOLVERSTATE = descriptor.Descriptor(
name='SolverState',
full_name='caffe.SolverState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='iter', full_name='caffe.SolverState.iter', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='learned_net', full_name='caffe.SolverState.learned_net', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='history', full_name='caffe.SolverState.history', index=2,
number=3, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='current_step', full_name='caffe.SolverState.current_step', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1656,
serialized_end=1764,
)
_NETSTATE = descriptor.Descriptor(
name='NetState',
full_name='caffe.NetState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='phase', full_name='caffe.NetState.phase', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='level', full_name='caffe.NetState.level', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stage', full_name='caffe.NetState.stage', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1766,
serialized_end=1844,
)
_NETSTATERULE = descriptor.Descriptor(
name='NetStateRule',
full_name='caffe.NetStateRule',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='phase', full_name='caffe.NetStateRule.phase', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='min_level', full_name='caffe.NetStateRule.min_level', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max_level', full_name='caffe.NetStateRule.max_level', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stage', full_name='caffe.NetStateRule.stage', index=3,
number=4, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='not_stage', full_name='caffe.NetStateRule.not_stage', index=4,
number=5, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1846,
serialized_end=1961,
)
_LAYERPARAMETER = descriptor.Descriptor(
name='LayerParameter',
full_name='caffe.LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='bottom', full_name='caffe.LayerParameter.bottom', index=0,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top', full_name='caffe.LayerParameter.top', index=1,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='name', full_name='caffe.LayerParameter.name', index=2,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='include', full_name='caffe.LayerParameter.include', index=3,
number=32, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='exclude', full_name='caffe.LayerParameter.exclude', index=4,
number=33, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.LayerParameter.type', index=5,
number=5, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.LayerParameter.blobs', index=6,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='param', full_name='caffe.LayerParameter.param', index=7,
number=1001, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blob_share_mode', full_name='caffe.LayerParameter.blob_share_mode', index=8,
number=1002, type=14, cpp_type=8, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.LayerParameter.blobs_lr', index=9,
number=7, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.LayerParameter.weight_decay', index=10,
number=8, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='loss_weight', full_name='caffe.LayerParameter.loss_weight', index=11,
number=35, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='accuracy_param', full_name='caffe.LayerParameter.accuracy_param', index=12,
number=27, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='argmax_param', full_name='caffe.LayerParameter.argmax_param', index=13,
number=23, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_param', full_name='caffe.LayerParameter.concat_param', index=14,
number=9, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='contrastive_loss_param', full_name='caffe.LayerParameter.contrastive_loss_param', index=15,
number=40, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='convolution_param', full_name='caffe.LayerParameter.convolution_param', index=16,
number=10, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data_param', full_name='caffe.LayerParameter.data_param', index=17,
number=11, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_param', full_name='caffe.LayerParameter.dropout_param', index=18,
number=12, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dummy_data_param', full_name='caffe.LayerParameter.dummy_data_param', index=19,
number=26, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='eltwise_param', full_name='caffe.LayerParameter.eltwise_param', index=20,
number=24, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_data_param', full_name='caffe.LayerParameter.hdf5_data_param', index=21,
number=13, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.LayerParameter.hdf5_output_param', index=22,
number=14, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hinge_loss_param', full_name='caffe.LayerParameter.hinge_loss_param', index=23,
number=29, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='image_data_param', full_name='caffe.LayerParameter.image_data_param', index=24,
number=15, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='infogain_loss_param', full_name='caffe.LayerParameter.infogain_loss_param', index=25,
number=16, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='inner_product_param', full_name='caffe.LayerParameter.inner_product_param', index=26,
number=17, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lrn_param', full_name='caffe.LayerParameter.lrn_param', index=27,
number=18, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='memory_data_param', full_name='caffe.LayerParameter.memory_data_param', index=28,
number=22, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mvn_param', full_name='caffe.LayerParameter.mvn_param', index=29,
number=34, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pooling_param', full_name='caffe.LayerParameter.pooling_param', index=30,
number=19, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power_param', full_name='caffe.LayerParameter.power_param', index=31,
number=21, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='relu_param', full_name='caffe.LayerParameter.relu_param', index=32,
number=30, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='sigmoid_param', full_name='caffe.LayerParameter.sigmoid_param', index=33,
number=38, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='softmax_param', full_name='caffe.LayerParameter.softmax_param', index=34,
number=39, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='slice_param', full_name='caffe.LayerParameter.slice_param', index=35,
number=31, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='tanh_param', full_name='caffe.LayerParameter.tanh_param', index=36,
number=37, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='threshold_param', full_name='caffe.LayerParameter.threshold_param', index=37,
number=25, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='window_data_param', full_name='caffe.LayerParameter.window_data_param', index=38,
number=20, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='transform_param', full_name='caffe.LayerParameter.transform_param', index=39,
number=36, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layer', full_name='caffe.LayerParameter.layer', index=40,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_LAYERPARAMETER_LAYERTYPE,
_LAYERPARAMETER_DIMCHECKMODE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1964,
serialized_end=4376,
)
_TRANSFORMATIONPARAMETER = descriptor.Descriptor(
name='TransformationParameter',
full_name='caffe.TransformationParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='scale', full_name='caffe.TransformationParameter.scale', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.TransformationParameter.mirror', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.TransformationParameter.crop_size', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.TransformationParameter.mean_file', index=3,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4378,
serialized_end=4485,
)
_ACCURACYPARAMETER = descriptor.Descriptor(
name='AccuracyParameter',
full_name='caffe.AccuracyParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='top_k', full_name='caffe.AccuracyParameter.top_k', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4487,
serialized_end=4524,
)
_ARGMAXPARAMETER = descriptor.Descriptor(
name='ArgMaxParameter',
full_name='caffe.ArgMaxParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='out_max_val', full_name='caffe.ArgMaxParameter.out_max_val', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top_k', full_name='caffe.ArgMaxParameter.top_k', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4526,
serialized_end=4589,
)
_CONCATPARAMETER = descriptor.Descriptor(
name='ConcatParameter',
full_name='caffe.ConcatParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.ConcatParameter.concat_dim', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4591,
serialized_end=4631,
)
_CONTRASTIVELOSSPARAMETER = descriptor.Descriptor(
name='ContrastiveLossParameter',
full_name='caffe.ContrastiveLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='margin', full_name='caffe.ContrastiveLossParameter.margin', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4633,
serialized_end=4678,
)
_CONVOLUTIONPARAMETER = descriptor.Descriptor(
name='ConvolutionParameter',
full_name='caffe.ConvolutionParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.ConvolutionParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.ConvolutionParameter.bias_term', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.ConvolutionParameter.pad', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_h', full_name='caffe.ConvolutionParameter.pad_h', index=3,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_w', full_name='caffe.ConvolutionParameter.pad_w', index=4,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_size', full_name='caffe.ConvolutionParameter.kernel_size', index=5,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_h', full_name='caffe.ConvolutionParameter.kernel_h', index=6,
number=11, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_w', full_name='caffe.ConvolutionParameter.kernel_w', index=7,
number=12, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='group', full_name='caffe.ConvolutionParameter.group', index=8,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.ConvolutionParameter.stride', index=9,
number=6, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_h', full_name='caffe.ConvolutionParameter.stride_h', index=10,
number=13, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_w', full_name='caffe.ConvolutionParameter.stride_w', index=11,
number=14, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.ConvolutionParameter.weight_filler', index=12,
number=7, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.ConvolutionParameter.bias_filler', index=13,
number=8, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.ConvolutionParameter.engine', index=14,
number=15, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_CONVOLUTIONPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4681,
serialized_end=5128,
)
_DATAPARAMETER = descriptor.Descriptor(
name='DataParameter',
full_name='caffe.DataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.DataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.DataParameter.batch_size', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.DataParameter.rand_skip', index=2,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='backend', full_name='caffe.DataParameter.backend', index=3,
number=8, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.DataParameter.scale', index=4,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.DataParameter.mean_file', index=5,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.DataParameter.crop_size', index=6,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.DataParameter.mirror', index=7,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_DATAPARAMETER_DB,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5131,
serialized_end=5366,
)
_DROPOUTPARAMETER = descriptor.Descriptor(
name='DropoutParameter',
full_name='caffe.DropoutParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.DropoutParameter.dropout_ratio', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5368,
serialized_end=5414,
)
_DUMMYDATAPARAMETER = descriptor.Descriptor(
name='DummyDataParameter',
full_name='caffe.DummyDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='data_filler', full_name='caffe.DummyDataParameter.data_filler', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num', full_name='caffe.DummyDataParameter.num', index=1,
number=2, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.DummyDataParameter.channels', index=2,
number=3, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.DummyDataParameter.height', index=3,
number=4, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.DummyDataParameter.width', index=4,
number=5, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5416,
serialized_end=5543,
)
_ELTWISEPARAMETER = descriptor.Descriptor(
name='EltwiseParameter',
full_name='caffe.EltwiseParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='operation', full_name='caffe.EltwiseParameter.operation', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='coeff', full_name='caffe.EltwiseParameter.coeff', index=1,
number=2, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stable_prod_grad', full_name='caffe.EltwiseParameter.stable_prod_grad', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_ELTWISEPARAMETER_ELTWISEOP,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5546,
serialized_end=5711,
)
_THRESHOLDPARAMETER = descriptor.Descriptor(
name='ThresholdParameter',
full_name='caffe.ThresholdParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='threshold', full_name='caffe.ThresholdParameter.threshold', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5713,
serialized_end=5755,
)
_HDF5DATAPARAMETER = descriptor.Descriptor(
name='HDF5DataParameter',
full_name='caffe.HDF5DataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.HDF5DataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.HDF5DataParameter.batch_size', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5757,
serialized_end=5812,
)
_HDF5OUTPUTPARAMETER = descriptor.Descriptor(
name='HDF5OutputParameter',
full_name='caffe.HDF5OutputParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='file_name', full_name='caffe.HDF5OutputParameter.file_name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5814,
serialized_end=5854,
)
_HINGELOSSPARAMETER = descriptor.Descriptor(
name='HingeLossParameter',
full_name='caffe.HingeLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='norm', full_name='caffe.HingeLossParameter.norm', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_HINGELOSSPARAMETER_NORM,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5856,
serialized_end=5950,
)
_IMAGEDATAPARAMETER = descriptor.Descriptor(
name='ImageDataParameter',
full_name='caffe.ImageDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.ImageDataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.ImageDataParameter.batch_size', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.ImageDataParameter.rand_skip', index=2,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle', full_name='caffe.ImageDataParameter.shuffle', index=3,
number=8, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_height', full_name='caffe.ImageDataParameter.new_height', index=4,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_width', full_name='caffe.ImageDataParameter.new_width', index=5,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.ImageDataParameter.scale', index=6,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.ImageDataParameter.mean_file', index=7,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.ImageDataParameter.crop_size', index=8,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.ImageDataParameter.mirror', index=9,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5953,
serialized_end=6182,
)
_INFOGAINLOSSPARAMETER = descriptor.Descriptor(
name='InfogainLossParameter',
full_name='caffe.InfogainLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.InfogainLossParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6184,
serialized_end=6223,
)
_INNERPRODUCTPARAMETER = descriptor.Descriptor(
name='InnerProductParameter',
full_name='caffe.InnerProductParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.InnerProductParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.InnerProductParameter.bias_term', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.InnerProductParameter.weight_filler', index=2,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.InnerProductParameter.bias_filler', index=3,
number=4, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6226,
serialized_end=6386,
)
_LRNPARAMETER = descriptor.Descriptor(
name='LRNParameter',
full_name='caffe.LRNParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='local_size', full_name='caffe.LRNParameter.local_size', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.LRNParameter.alpha', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='beta', full_name='caffe.LRNParameter.beta', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='norm_region', full_name='caffe.LRNParameter.norm_region', index=3,
number=4, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_LRNPARAMETER_NORMREGION,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6389,
serialized_end=6589,
)
_MEMORYDATAPARAMETER = descriptor.Descriptor(
name='MemoryDataParameter',
full_name='caffe.MemoryDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.MemoryDataParameter.batch_size', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.MemoryDataParameter.channels', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.MemoryDataParameter.height', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.MemoryDataParameter.width', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6591,
serialized_end=6681,
)
_MVNPARAMETER = descriptor.Descriptor(
name='MVNParameter',
full_name='caffe.MVNParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='normalize_variance', full_name='caffe.MVNParameter.normalize_variance', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='across_channels', full_name='caffe.MVNParameter.across_channels', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6683,
serialized_end=6763,
)
_POOLINGPARAMETER = descriptor.Descriptor(
name='PoolingParameter',
full_name='caffe.PoolingParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='pool', full_name='caffe.PoolingParameter.pool', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.PoolingParameter.pad', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_h', full_name='caffe.PoolingParameter.pad_h', index=2,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_w', full_name='caffe.PoolingParameter.pad_w', index=3,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_size', full_name='caffe.PoolingParameter.kernel_size', index=4,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_h', full_name='caffe.PoolingParameter.kernel_h', index=5,
number=5, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_w', full_name='caffe.PoolingParameter.kernel_w', index=6,
number=6, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.PoolingParameter.stride', index=7,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_h', full_name='caffe.PoolingParameter.stride_h', index=8,
number=7, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_w', full_name='caffe.PoolingParameter.stride_w', index=9,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.PoolingParameter.engine', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_POOLINGPARAMETER_POOLMETHOD,
_POOLINGPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6766,
serialized_end=7153,
)
_POWERPARAMETER = descriptor.Descriptor(
name='PowerParameter',
full_name='caffe.PowerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='power', full_name='caffe.PowerParameter.power', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.PowerParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shift', full_name='caffe.PowerParameter.shift', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7155,
serialized_end=7225,
)
_RELUPARAMETER = descriptor.Descriptor(
name='ReLUParameter',
full_name='caffe.ReLUParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='negative_slope', full_name='caffe.ReLUParameter.negative_slope', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.ReLUParameter.engine', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_RELUPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7228,
serialized_end=7369,
)
_SIGMOIDPARAMETER = descriptor.Descriptor(
name='SigmoidParameter',
full_name='caffe.SigmoidParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='engine', full_name='caffe.SigmoidParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SIGMOIDPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7371,
serialized_end=7491,
)
_SLICEPARAMETER = descriptor.Descriptor(
name='SliceParameter',
full_name='caffe.SliceParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='slice_dim', full_name='caffe.SliceParameter.slice_dim', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='slice_point', full_name='caffe.SliceParameter.slice_point', index=1,
number=2, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7493,
serialized_end=7552,
)
_SOFTMAXPARAMETER = descriptor.Descriptor(
name='SoftmaxParameter',
full_name='caffe.SoftmaxParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='engine', full_name='caffe.SoftmaxParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SOFTMAXPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7554,
serialized_end=7674,
)
_TANHPARAMETER = descriptor.Descriptor(
name='TanHParameter',
full_name='caffe.TanHParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='engine', full_name='caffe.TanHParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_TANHPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7676,
serialized_end=7790,
)
_WINDOWDATAPARAMETER = descriptor.Descriptor(
name='WindowDataParameter',
full_name='caffe.WindowDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.WindowDataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.WindowDataParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.WindowDataParameter.mean_file', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.WindowDataParameter.batch_size', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.WindowDataParameter.crop_size', index=4,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.WindowDataParameter.mirror', index=5,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='fg_threshold', full_name='caffe.WindowDataParameter.fg_threshold', index=6,
number=7, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bg_threshold', full_name='caffe.WindowDataParameter.bg_threshold', index=7,
number=8, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='fg_fraction', full_name='caffe.WindowDataParameter.fg_fraction', index=8,
number=9, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='context_pad', full_name='caffe.WindowDataParameter.context_pad', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_mode', full_name='caffe.WindowDataParameter.crop_mode', index=10,
number=11, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7793,
serialized_end=8062,
)
_V0LAYERPARAMETER = descriptor.Descriptor(
name='V0LayerParameter',
full_name='caffe.V0LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.V0LayerParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.V0LayerParameter.type', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.V0LayerParameter.num_output', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='biasterm', full_name='caffe.V0LayerParameter.biasterm', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.V0LayerParameter.weight_filler', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.V0LayerParameter.bias_filler', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.V0LayerParameter.pad', index=6,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernelsize', full_name='caffe.V0LayerParameter.kernelsize', index=7,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='group', full_name='caffe.V0LayerParameter.group', index=8,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.V0LayerParameter.stride', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pool', full_name='caffe.V0LayerParameter.pool', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.V0LayerParameter.dropout_ratio', index=11,
number=12, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='local_size', full_name='caffe.V0LayerParameter.local_size', index=12,
number=13, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.V0LayerParameter.alpha', index=13,
number=14, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='beta', full_name='caffe.V0LayerParameter.beta', index=14,
number=15, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='source', full_name='caffe.V0LayerParameter.source', index=15,
number=16, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.V0LayerParameter.scale', index=16,
number=17, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='meanfile', full_name='caffe.V0LayerParameter.meanfile', index=17,
number=18, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batchsize', full_name='caffe.V0LayerParameter.batchsize', index=18,
number=19, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='cropsize', full_name='caffe.V0LayerParameter.cropsize', index=19,
number=20, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.V0LayerParameter.mirror', index=20,
number=21, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.V0LayerParameter.blobs', index=21,
number=50, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.V0LayerParameter.blobs_lr', index=22,
number=51, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.V0LayerParameter.weight_decay', index=23,
number=52, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.V0LayerParameter.rand_skip', index=24,
number=53, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_fg_threshold', full_name='caffe.V0LayerParameter.det_fg_threshold', index=25,
number=54, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_bg_threshold', full_name='caffe.V0LayerParameter.det_bg_threshold', index=26,
number=55, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_fg_fraction', full_name='caffe.V0LayerParameter.det_fg_fraction', index=27,
number=56, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_context_pad', full_name='caffe.V0LayerParameter.det_context_pad', index=28,
number=58, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_crop_mode', full_name='caffe.V0LayerParameter.det_crop_mode', index=29,
number=59, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_num', full_name='caffe.V0LayerParameter.new_num', index=30,
number=60, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_channels', full_name='caffe.V0LayerParameter.new_channels', index=31,
number=61, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_height', full_name='caffe.V0LayerParameter.new_height', index=32,
number=62, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_width', full_name='caffe.V0LayerParameter.new_width', index=33,
number=63, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle_images', full_name='caffe.V0LayerParameter.shuffle_images', index=34,
number=64, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.V0LayerParameter.concat_dim', index=35,
number=65, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.V0LayerParameter.hdf5_output_param', index=36,
number=1001, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_V0LAYERPARAMETER_POOLMETHOD,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8065,
serialized_end=9072,
)
_BLOBPROTOVECTOR.fields_by_name['blobs'].message_type = _BLOBPROTO
_NETPARAMETER.fields_by_name['layers'].message_type = _LAYERPARAMETER
_NETPARAMETER.fields_by_name['state'].message_type = _NETSTATE
_SOLVERPARAMETER.fields_by_name['net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['train_net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['test_net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['train_state'].message_type = _NETSTATE
_SOLVERPARAMETER.fields_by_name['test_state'].message_type = _NETSTATE
_SOLVERPARAMETER.fields_by_name['solver_mode'].enum_type = _SOLVERPARAMETER_SOLVERMODE
_SOLVERPARAMETER.fields_by_name['solver_type'].enum_type = _SOLVERPARAMETER_SOLVERTYPE
_SOLVERPARAMETER_SOLVERMODE.containing_type = _SOLVERPARAMETER;
_SOLVERPARAMETER_SOLVERTYPE.containing_type = _SOLVERPARAMETER;
_SOLVERSTATE.fields_by_name['history'].message_type = _BLOBPROTO
_NETSTATE.fields_by_name['phase'].enum_type = _PHASE
_NETSTATERULE.fields_by_name['phase'].enum_type = _PHASE
_LAYERPARAMETER.fields_by_name['include'].message_type = _NETSTATERULE
_LAYERPARAMETER.fields_by_name['exclude'].message_type = _NETSTATERULE
_LAYERPARAMETER.fields_by_name['type'].enum_type = _LAYERPARAMETER_LAYERTYPE
_LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_LAYERPARAMETER.fields_by_name['blob_share_mode'].enum_type = _LAYERPARAMETER_DIMCHECKMODE
_LAYERPARAMETER.fields_by_name['accuracy_param'].message_type = _ACCURACYPARAMETER
_LAYERPARAMETER.fields_by_name['argmax_param'].message_type = _ARGMAXPARAMETER
_LAYERPARAMETER.fields_by_name['concat_param'].message_type = _CONCATPARAMETER
_LAYERPARAMETER.fields_by_name['contrastive_loss_param'].message_type = _CONTRASTIVELOSSPARAMETER
_LAYERPARAMETER.fields_by_name['convolution_param'].message_type = _CONVOLUTIONPARAMETER
_LAYERPARAMETER.fields_by_name['data_param'].message_type = _DATAPARAMETER
_LAYERPARAMETER.fields_by_name['dropout_param'].message_type = _DROPOUTPARAMETER
_LAYERPARAMETER.fields_by_name['dummy_data_param'].message_type = _DUMMYDATAPARAMETER
_LAYERPARAMETER.fields_by_name['eltwise_param'].message_type = _ELTWISEPARAMETER
_LAYERPARAMETER.fields_by_name['hdf5_data_param'].message_type = _HDF5DATAPARAMETER
_LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_LAYERPARAMETER.fields_by_name['hinge_loss_param'].message_type = _HINGELOSSPARAMETER
_LAYERPARAMETER.fields_by_name['image_data_param'].message_type = _IMAGEDATAPARAMETER
_LAYERPARAMETER.fields_by_name['infogain_loss_param'].message_type = _INFOGAINLOSSPARAMETER
_LAYERPARAMETER.fields_by_name['inner_product_param'].message_type = _INNERPRODUCTPARAMETER
_LAYERPARAMETER.fields_by_name['lrn_param'].message_type = _LRNPARAMETER
_LAYERPARAMETER.fields_by_name['memory_data_param'].message_type = _MEMORYDATAPARAMETER
_LAYERPARAMETER.fields_by_name['mvn_param'].message_type = _MVNPARAMETER
_LAYERPARAMETER.fields_by_name['pooling_param'].message_type = _POOLINGPARAMETER
_LAYERPARAMETER.fields_by_name['power_param'].message_type = _POWERPARAMETER
_LAYERPARAMETER.fields_by_name['relu_param'].message_type = _RELUPARAMETER
_LAYERPARAMETER.fields_by_name['sigmoid_param'].message_type = _SIGMOIDPARAMETER
_LAYERPARAMETER.fields_by_name['softmax_param'].message_type = _SOFTMAXPARAMETER
_LAYERPARAMETER.fields_by_name['slice_param'].message_type = _SLICEPARAMETER
_LAYERPARAMETER.fields_by_name['tanh_param'].message_type = _TANHPARAMETER
_LAYERPARAMETER.fields_by_name['threshold_param'].message_type = _THRESHOLDPARAMETER
_LAYERPARAMETER.fields_by_name['window_data_param'].message_type = _WINDOWDATAPARAMETER
_LAYERPARAMETER.fields_by_name['transform_param'].message_type = _TRANSFORMATIONPARAMETER
_LAYERPARAMETER.fields_by_name['layer'].message_type = _V0LAYERPARAMETER
_LAYERPARAMETER_LAYERTYPE.containing_type = _LAYERPARAMETER;
_LAYERPARAMETER_DIMCHECKMODE.containing_type = _LAYERPARAMETER;
_CONVOLUTIONPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['engine'].enum_type = _CONVOLUTIONPARAMETER_ENGINE
_CONVOLUTIONPARAMETER_ENGINE.containing_type = _CONVOLUTIONPARAMETER;
_DATAPARAMETER.fields_by_name['backend'].enum_type = _DATAPARAMETER_DB
_DATAPARAMETER_DB.containing_type = _DATAPARAMETER;
_DUMMYDATAPARAMETER.fields_by_name['data_filler'].message_type = _FILLERPARAMETER
_ELTWISEPARAMETER.fields_by_name['operation'].enum_type = _ELTWISEPARAMETER_ELTWISEOP
_ELTWISEPARAMETER_ELTWISEOP.containing_type = _ELTWISEPARAMETER;
_HINGELOSSPARAMETER.fields_by_name['norm'].enum_type = _HINGELOSSPARAMETER_NORM
_HINGELOSSPARAMETER_NORM.containing_type = _HINGELOSSPARAMETER;
_INNERPRODUCTPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_INNERPRODUCTPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_LRNPARAMETER.fields_by_name['norm_region'].enum_type = _LRNPARAMETER_NORMREGION
_LRNPARAMETER_NORMREGION.containing_type = _LRNPARAMETER;
_POOLINGPARAMETER.fields_by_name['pool'].enum_type = _POOLINGPARAMETER_POOLMETHOD
_POOLINGPARAMETER.fields_by_name['engine'].enum_type = _POOLINGPARAMETER_ENGINE
_POOLINGPARAMETER_POOLMETHOD.containing_type = _POOLINGPARAMETER;
_POOLINGPARAMETER_ENGINE.containing_type = _POOLINGPARAMETER;
_RELUPARAMETER.fields_by_name['engine'].enum_type = _RELUPARAMETER_ENGINE
_RELUPARAMETER_ENGINE.containing_type = _RELUPARAMETER;
_SIGMOIDPARAMETER.fields_by_name['engine'].enum_type = _SIGMOIDPARAMETER_ENGINE
_SIGMOIDPARAMETER_ENGINE.containing_type = _SIGMOIDPARAMETER;
_SOFTMAXPARAMETER.fields_by_name['engine'].enum_type = _SOFTMAXPARAMETER_ENGINE
_SOFTMAXPARAMETER_ENGINE.containing_type = _SOFTMAXPARAMETER;
_TANHPARAMETER.fields_by_name['engine'].enum_type = _TANHPARAMETER_ENGINE
_TANHPARAMETER_ENGINE.containing_type = _TANHPARAMETER;
_V0LAYERPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_V0LAYERPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_V0LAYERPARAMETER.fields_by_name['pool'].enum_type = _V0LAYERPARAMETER_POOLMETHOD
_V0LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_V0LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_V0LAYERPARAMETER_POOLMETHOD.containing_type = _V0LAYERPARAMETER;
DESCRIPTOR.message_types_by_name['BlobProto'] = _BLOBPROTO
DESCRIPTOR.message_types_by_name['BlobProtoVector'] = _BLOBPROTOVECTOR
DESCRIPTOR.message_types_by_name['Datum'] = _DATUM
DESCRIPTOR.message_types_by_name['FillerParameter'] = _FILLERPARAMETER
DESCRIPTOR.message_types_by_name['NetParameter'] = _NETPARAMETER
DESCRIPTOR.message_types_by_name['SolverParameter'] = _SOLVERPARAMETER
DESCRIPTOR.message_types_by_name['SolverState'] = _SOLVERSTATE
DESCRIPTOR.message_types_by_name['NetState'] = _NETSTATE
DESCRIPTOR.message_types_by_name['NetStateRule'] = _NETSTATERULE
DESCRIPTOR.message_types_by_name['LayerParameter'] = _LAYERPARAMETER
DESCRIPTOR.message_types_by_name['TransformationParameter'] = _TRANSFORMATIONPARAMETER
DESCRIPTOR.message_types_by_name['AccuracyParameter'] = _ACCURACYPARAMETER
DESCRIPTOR.message_types_by_name['ArgMaxParameter'] = _ARGMAXPARAMETER
DESCRIPTOR.message_types_by_name['ConcatParameter'] = _CONCATPARAMETER
DESCRIPTOR.message_types_by_name['ContrastiveLossParameter'] = _CONTRASTIVELOSSPARAMETER
DESCRIPTOR.message_types_by_name['ConvolutionParameter'] = _CONVOLUTIONPARAMETER
DESCRIPTOR.message_types_by_name['DataParameter'] = _DATAPARAMETER
DESCRIPTOR.message_types_by_name['DropoutParameter'] = _DROPOUTPARAMETER
DESCRIPTOR.message_types_by_name['DummyDataParameter'] = _DUMMYDATAPARAMETER
DESCRIPTOR.message_types_by_name['EltwiseParameter'] = _ELTWISEPARAMETER
DESCRIPTOR.message_types_by_name['ThresholdParameter'] = _THRESHOLDPARAMETER
DESCRIPTOR.message_types_by_name['HDF5DataParameter'] = _HDF5DATAPARAMETER
DESCRIPTOR.message_types_by_name['HDF5OutputParameter'] = _HDF5OUTPUTPARAMETER
DESCRIPTOR.message_types_by_name['HingeLossParameter'] = _HINGELOSSPARAMETER
DESCRIPTOR.message_types_by_name['ImageDataParameter'] = _IMAGEDATAPARAMETER
DESCRIPTOR.message_types_by_name['InfogainLossParameter'] = _INFOGAINLOSSPARAMETER
DESCRIPTOR.message_types_by_name['InnerProductParameter'] = _INNERPRODUCTPARAMETER
DESCRIPTOR.message_types_by_name['LRNParameter'] = _LRNPARAMETER
DESCRIPTOR.message_types_by_name['MemoryDataParameter'] = _MEMORYDATAPARAMETER
DESCRIPTOR.message_types_by_name['MVNParameter'] = _MVNPARAMETER
DESCRIPTOR.message_types_by_name['PoolingParameter'] = _POOLINGPARAMETER
DESCRIPTOR.message_types_by_name['PowerParameter'] = _POWERPARAMETER
DESCRIPTOR.message_types_by_name['ReLUParameter'] = _RELUPARAMETER
DESCRIPTOR.message_types_by_name['SigmoidParameter'] = _SIGMOIDPARAMETER
DESCRIPTOR.message_types_by_name['SliceParameter'] = _SLICEPARAMETER
DESCRIPTOR.message_types_by_name['SoftmaxParameter'] = _SOFTMAXPARAMETER
DESCRIPTOR.message_types_by_name['TanHParameter'] = _TANHPARAMETER
DESCRIPTOR.message_types_by_name['WindowDataParameter'] = _WINDOWDATAPARAMETER
DESCRIPTOR.message_types_by_name['V0LayerParameter'] = _V0LAYERPARAMETER
class BlobProto(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTO
# @@protoc_insertion_point(class_scope:caffe.BlobProto)
class BlobProtoVector(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTOVECTOR
# @@protoc_insertion_point(class_scope:caffe.BlobProtoVector)
class Datum(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATUM
# @@protoc_insertion_point(class_scope:caffe.Datum)
class FillerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FILLERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.FillerParameter)
class NetParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETPARAMETER
# @@protoc_insertion_point(class_scope:caffe.NetParameter)
class SolverParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SolverParameter)
class SolverState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERSTATE
# @@protoc_insertion_point(class_scope:caffe.SolverState)
class NetState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATE
# @@protoc_insertion_point(class_scope:caffe.NetState)
class NetStateRule(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATERULE
# @@protoc_insertion_point(class_scope:caffe.NetStateRule)
class LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LayerParameter)
class TransformationParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TRANSFORMATIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.TransformationParameter)
class AccuracyParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ACCURACYPARAMETER
# @@protoc_insertion_point(class_scope:caffe.AccuracyParameter)
class ArgMaxParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ARGMAXPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ArgMaxParameter)
class ConcatParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONCATPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ConcatParameter)
class ContrastiveLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONTRASTIVELOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ContrastiveLossParameter)
class ConvolutionParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONVOLUTIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ConvolutionParameter)
class DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DataParameter)
class DropoutParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DROPOUTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DropoutParameter)
class DummyDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DUMMYDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DummyDataParameter)
class EltwiseParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ELTWISEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.EltwiseParameter)
class ThresholdParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _THRESHOLDPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ThresholdParameter)
class HDF5DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5DATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HDF5DataParameter)
class HDF5OutputParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5OUTPUTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HDF5OutputParameter)
class HingeLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HINGELOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HingeLossParameter)
class ImageDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _IMAGEDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ImageDataParameter)
class InfogainLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INFOGAINLOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.InfogainLossParameter)
class InnerProductParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INNERPRODUCTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.InnerProductParameter)
class LRNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LRNPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LRNParameter)
class MemoryDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MEMORYDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.MemoryDataParameter)
class MVNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MVNPARAMETER
# @@protoc_insertion_point(class_scope:caffe.MVNParameter)
class PoolingParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POOLINGPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PoolingParameter)
class PowerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POWERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PowerParameter)
class ReLUParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _RELUPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ReLUParameter)
class SigmoidParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SIGMOIDPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SigmoidParameter)
class SliceParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SLICEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SliceParameter)
class SoftmaxParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOFTMAXPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SoftmaxParameter)
class TanHParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TANHPARAMETER
# @@protoc_insertion_point(class_scope:caffe.TanHParameter)
class WindowDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _WINDOWDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.WindowDataParameter)
class V0LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _V0LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.V0LayerParameter)
# @@protoc_insertion_point(module_scope)
| 148,708 | 41.163028 | 17,413 | py |
DRT | DRT-master/external_libs/matconvnet/utils/proto/caffe_fastrcnn_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
# source: caffe_fastrcnn.proto
from google.protobuf.internal import enum_type_wrapper
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = _descriptor.FileDescriptor(
name='caffe_fastrcnn.proto',
package='caffe',
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_PHASE = _descriptor.EnumDescriptor(
name='Phase',
full_name='caffe.Phase',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='TRAIN', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='TEST', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=12254,
serialized_end=12282,
)
Phase = enum_type_wrapper.EnumTypeWrapper(_PHASE)
TRAIN = 0
TEST = 1
_SOLVERPARAMETER_SOLVERMODE = _descriptor.EnumDescriptor(
name='SolverMode',
full_name='caffe.SolverParameter.SolverMode',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='CPU', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='GPU', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=1806,
serialized_end=1836,
)
_SOLVERPARAMETER_SOLVERTYPE = _descriptor.EnumDescriptor(
name='SolverType',
full_name='caffe.SolverParameter.SolverType',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='SGD', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='NESTEROV', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='ADAGRAD', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=1838,
serialized_end=1886,
)
_PARAMSPEC_DIMCHECKMODE = _descriptor.EnumDescriptor(
name='DimCheckMode',
full_name='caffe.ParamSpec.DimCheckMode',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='STRICT', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='PERMISSIVE', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2317,
serialized_end=2359,
)
_CONVOLUTIONPARAMETER_ENGINE = _descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.ConvolutionParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5109,
serialized_end=5152,
)
_DATAPARAMETER_DB = _descriptor.EnumDescriptor(
name='DB',
full_name='caffe.DataParameter.DB',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='LEVELDB', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='LMDB', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5399,
serialized_end=5426,
)
_ELTWISEPARAMETER_ELTWISEOP = _descriptor.EnumDescriptor(
name='EltwiseOp',
full_name='caffe.EltwiseParameter.EltwiseOp',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='PROD', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='SUM', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='MAX', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5766,
serialized_end=5805,
)
_HINGELOSSPARAMETER_NORM = _descriptor.EnumDescriptor(
name='Norm',
full_name='caffe.HingeLossParameter.Norm',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='L1', index=0, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='L2', index=1, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6072,
serialized_end=6094,
)
_LRNPARAMETER_NORMREGION = _descriptor.EnumDescriptor(
name='NormRegion',
full_name='caffe.LRNParameter.NormRegion',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='ACROSS_CHANNELS', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='WITHIN_CHANNEL', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6758,
serialized_end=6811,
)
_POOLINGPARAMETER_POOLMETHOD = _descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.PoolingParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=7315,
serialized_end=7361,
)
_POOLINGPARAMETER_ENGINE = _descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.PoolingParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5109,
serialized_end=5152,
)
_RELUPARAMETER_ENGINE = _descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.ReLUParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5109,
serialized_end=5152,
)
_SIGMOIDPARAMETER_ENGINE = _descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.SigmoidParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5109,
serialized_end=5152,
)
_SOFTMAXPARAMETER_ENGINE = _descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.SoftmaxParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5109,
serialized_end=5152,
)
_TANHPARAMETER_ENGINE = _descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.TanHParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5109,
serialized_end=5152,
)
_V1LAYERPARAMETER_LAYERTYPE = _descriptor.EnumDescriptor(
name='LayerType',
full_name='caffe.V1LayerParameter.LayerType',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='NONE', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='ABSVAL', index=1, number=35,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='ACCURACY', index=2, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='ARGMAX', index=3, number=30,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='BNLL', index=4, number=2,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CONCAT', index=5, number=3,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CONTRASTIVE_LOSS', index=6, number=37,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='CONVOLUTION', index=7, number=4,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='DATA', index=8, number=5,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='DECONVOLUTION', index=9, number=39,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='DROPOUT', index=10, number=6,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='DUMMY_DATA', index=11, number=32,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='EUCLIDEAN_LOSS', index=12, number=7,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='ELTWISE', index=13, number=25,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='EXP', index=14, number=38,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='FLATTEN', index=15, number=8,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='HDF5_DATA', index=16, number=9,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='HDF5_OUTPUT', index=17, number=10,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='HINGE_LOSS', index=18, number=28,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='IM2COL', index=19, number=11,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='IMAGE_DATA', index=20, number=12,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='INFOGAIN_LOSS', index=21, number=13,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='INNER_PRODUCT', index=22, number=14,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='LRN', index=23, number=15,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='MEMORY_DATA', index=24, number=29,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='MULTINOMIAL_LOGISTIC_LOSS', index=25, number=16,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='MVN', index=26, number=34,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='POOLING', index=27, number=17,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='POWER', index=28, number=26,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='RELU', index=29, number=18,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='SIGMOID', index=30, number=19,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='SIGMOID_CROSS_ENTROPY_LOSS', index=31, number=27,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='SILENCE', index=32, number=36,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='SOFTMAX', index=33, number=20,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='SOFTMAX_LOSS', index=34, number=21,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='SPLIT', index=35, number=22,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='SLICE', index=36, number=33,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='TANH', index=37, number=23,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='WINDOW_DATA', index=38, number=24,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='THRESHOLD', index=39, number=31,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=10495,
serialized_end=11095,
)
_V1LAYERPARAMETER_DIMCHECKMODE = _descriptor.EnumDescriptor(
name='DimCheckMode',
full_name='caffe.V1LayerParameter.DimCheckMode',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='STRICT', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='PERMISSIVE', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2317,
serialized_end=2359,
)
_V0LAYERPARAMETER_POOLMETHOD = _descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.V0LayerParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=7315,
serialized_end=7361,
)
_BLOBSHAPE = _descriptor.Descriptor(
name='BlobShape',
full_name='caffe.BlobShape',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='dim', full_name='caffe.BlobShape.dim', index=0,
number=1, type=3, cpp_type=2, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=31,
serialized_end=59,
)
_BLOBPROTO = _descriptor.Descriptor(
name='BlobProto',
full_name='caffe.BlobProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='shape', full_name='caffe.BlobProto.shape', index=0,
number=7, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='data', full_name='caffe.BlobProto.data', index=1,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
_descriptor.FieldDescriptor(
name='diff', full_name='caffe.BlobProto.diff', index=2,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
_descriptor.FieldDescriptor(
name='num', full_name='caffe.BlobProto.num', index=3,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='channels', full_name='caffe.BlobProto.channels', index=4,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='height', full_name='caffe.BlobProto.height', index=5,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='width', full_name='caffe.BlobProto.width', index=6,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=62,
serialized_end=216,
)
_BLOBPROTOVECTOR = _descriptor.Descriptor(
name='BlobProtoVector',
full_name='caffe.BlobProtoVector',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='blobs', full_name='caffe.BlobProtoVector.blobs', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=218,
serialized_end=268,
)
_DATUM = _descriptor.Descriptor(
name='Datum',
full_name='caffe.Datum',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='channels', full_name='caffe.Datum.channels', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='height', full_name='caffe.Datum.height', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='width', full_name='caffe.Datum.width', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='data', full_name='caffe.Datum.data', index=3,
number=4, type=12, cpp_type=9, label=1,
has_default_value=False, default_value="",
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='label', full_name='caffe.Datum.label', index=4,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='float_data', full_name='caffe.Datum.float_data', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='encoded', full_name='caffe.Datum.encoded', index=6,
number=7, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=271,
serialized_end=400,
)
_FILLERPARAMETER = _descriptor.Descriptor(
name='FillerParameter',
full_name='caffe.FillerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='type', full_name='caffe.FillerParameter.type', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("constant", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='value', full_name='caffe.FillerParameter.value', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='min', full_name='caffe.FillerParameter.min', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='max', full_name='caffe.FillerParameter.max', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mean', full_name='caffe.FillerParameter.mean', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='std', full_name='caffe.FillerParameter.std', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='sparse', full_name='caffe.FillerParameter.sparse', index=6,
number=7, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=403,
serialized_end=547,
)
_NETPARAMETER = _descriptor.Descriptor(
name='NetParameter',
full_name='caffe.NetParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='name', full_name='caffe.NetParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='input', full_name='caffe.NetParameter.input', index=1,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='input_shape', full_name='caffe.NetParameter.input_shape', index=2,
number=8, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='input_dim', full_name='caffe.NetParameter.input_dim', index=3,
number=4, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='force_backward', full_name='caffe.NetParameter.force_backward', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='state', full_name='caffe.NetParameter.state', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='debug_info', full_name='caffe.NetParameter.debug_info', index=6,
number=7, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='layer', full_name='caffe.NetParameter.layer', index=7,
number=100, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='layers', full_name='caffe.NetParameter.layers', index=8,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=550,
serialized_end=820,
)
_SOLVERPARAMETER = _descriptor.Descriptor(
name='SolverParameter',
full_name='caffe.SolverParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='net', full_name='caffe.SolverParameter.net', index=0,
number=24, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='net_param', full_name='caffe.SolverParameter.net_param', index=1,
number=25, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='train_net', full_name='caffe.SolverParameter.train_net', index=2,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='test_net', full_name='caffe.SolverParameter.test_net', index=3,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='train_net_param', full_name='caffe.SolverParameter.train_net_param', index=4,
number=21, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='test_net_param', full_name='caffe.SolverParameter.test_net_param', index=5,
number=22, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='train_state', full_name='caffe.SolverParameter.train_state', index=6,
number=26, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='test_state', full_name='caffe.SolverParameter.test_state', index=7,
number=27, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='test_iter', full_name='caffe.SolverParameter.test_iter', index=8,
number=3, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='test_interval', full_name='caffe.SolverParameter.test_interval', index=9,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='test_compute_loss', full_name='caffe.SolverParameter.test_compute_loss', index=10,
number=19, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='test_initialization', full_name='caffe.SolverParameter.test_initialization', index=11,
number=32, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='base_lr', full_name='caffe.SolverParameter.base_lr', index=12,
number=5, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='display', full_name='caffe.SolverParameter.display', index=13,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='average_loss', full_name='caffe.SolverParameter.average_loss', index=14,
number=33, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='max_iter', full_name='caffe.SolverParameter.max_iter', index=15,
number=7, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='lr_policy', full_name='caffe.SolverParameter.lr_policy', index=16,
number=8, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='gamma', full_name='caffe.SolverParameter.gamma', index=17,
number=9, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='power', full_name='caffe.SolverParameter.power', index=18,
number=10, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='momentum', full_name='caffe.SolverParameter.momentum', index=19,
number=11, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.SolverParameter.weight_decay', index=20,
number=12, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='regularization_type', full_name='caffe.SolverParameter.regularization_type', index=21,
number=29, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("L2", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stepsize', full_name='caffe.SolverParameter.stepsize', index=22,
number=13, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stepvalue', full_name='caffe.SolverParameter.stepvalue', index=23,
number=34, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='clip_gradients', full_name='caffe.SolverParameter.clip_gradients', index=24,
number=35, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='snapshot', full_name='caffe.SolverParameter.snapshot', index=25,
number=14, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='snapshot_prefix', full_name='caffe.SolverParameter.snapshot_prefix', index=26,
number=15, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='snapshot_diff', full_name='caffe.SolverParameter.snapshot_diff', index=27,
number=16, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='solver_mode', full_name='caffe.SolverParameter.solver_mode', index=28,
number=17, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='device_id', full_name='caffe.SolverParameter.device_id', index=29,
number=18, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='random_seed', full_name='caffe.SolverParameter.random_seed', index=30,
number=20, type=3, cpp_type=2, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='solver_type', full_name='caffe.SolverParameter.solver_type', index=31,
number=30, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='delta', full_name='caffe.SolverParameter.delta', index=32,
number=31, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1e-08,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='debug_info', full_name='caffe.SolverParameter.debug_info', index=33,
number=23, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='snapshot_after_train', full_name='caffe.SolverParameter.snapshot_after_train', index=34,
number=28, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SOLVERPARAMETER_SOLVERMODE,
_SOLVERPARAMETER_SOLVERTYPE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=823,
serialized_end=1886,
)
_SOLVERSTATE = _descriptor.Descriptor(
name='SolverState',
full_name='caffe.SolverState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='iter', full_name='caffe.SolverState.iter', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='learned_net', full_name='caffe.SolverState.learned_net', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='history', full_name='caffe.SolverState.history', index=2,
number=3, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='current_step', full_name='caffe.SolverState.current_step', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1888,
serialized_end=1996,
)
_NETSTATE = _descriptor.Descriptor(
name='NetState',
full_name='caffe.NetState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='phase', full_name='caffe.NetState.phase', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='level', full_name='caffe.NetState.level', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stage', full_name='caffe.NetState.stage', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1998,
serialized_end=2076,
)
_NETSTATERULE = _descriptor.Descriptor(
name='NetStateRule',
full_name='caffe.NetStateRule',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='phase', full_name='caffe.NetStateRule.phase', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='min_level', full_name='caffe.NetStateRule.min_level', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='max_level', full_name='caffe.NetStateRule.max_level', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stage', full_name='caffe.NetStateRule.stage', index=3,
number=4, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='not_stage', full_name='caffe.NetStateRule.not_stage', index=4,
number=5, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2078,
serialized_end=2193,
)
_PARAMSPEC = _descriptor.Descriptor(
name='ParamSpec',
full_name='caffe.ParamSpec',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='name', full_name='caffe.ParamSpec.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='share_mode', full_name='caffe.ParamSpec.share_mode', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='lr_mult', full_name='caffe.ParamSpec.lr_mult', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='decay_mult', full_name='caffe.ParamSpec.decay_mult', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_PARAMSPEC_DIMCHECKMODE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2196,
serialized_end=2359,
)
_LAYERPARAMETER = _descriptor.Descriptor(
name='LayerParameter',
full_name='caffe.LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='name', full_name='caffe.LayerParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='type', full_name='caffe.LayerParameter.type', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='bottom', full_name='caffe.LayerParameter.bottom', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='top', full_name='caffe.LayerParameter.top', index=3,
number=4, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='phase', full_name='caffe.LayerParameter.phase', index=4,
number=10, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='loss_weight', full_name='caffe.LayerParameter.loss_weight', index=5,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='param', full_name='caffe.LayerParameter.param', index=6,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='blobs', full_name='caffe.LayerParameter.blobs', index=7,
number=7, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='include', full_name='caffe.LayerParameter.include', index=8,
number=8, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='exclude', full_name='caffe.LayerParameter.exclude', index=9,
number=9, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='transform_param', full_name='caffe.LayerParameter.transform_param', index=10,
number=100, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='loss_param', full_name='caffe.LayerParameter.loss_param', index=11,
number=101, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='accuracy_param', full_name='caffe.LayerParameter.accuracy_param', index=12,
number=102, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='argmax_param', full_name='caffe.LayerParameter.argmax_param', index=13,
number=103, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='concat_param', full_name='caffe.LayerParameter.concat_param', index=14,
number=104, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='contrastive_loss_param', full_name='caffe.LayerParameter.contrastive_loss_param', index=15,
number=105, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='convolution_param', full_name='caffe.LayerParameter.convolution_param', index=16,
number=106, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='data_param', full_name='caffe.LayerParameter.data_param', index=17,
number=107, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='dropout_param', full_name='caffe.LayerParameter.dropout_param', index=18,
number=108, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='dummy_data_param', full_name='caffe.LayerParameter.dummy_data_param', index=19,
number=109, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='eltwise_param', full_name='caffe.LayerParameter.eltwise_param', index=20,
number=110, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='exp_param', full_name='caffe.LayerParameter.exp_param', index=21,
number=111, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='hdf5_data_param', full_name='caffe.LayerParameter.hdf5_data_param', index=22,
number=112, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.LayerParameter.hdf5_output_param', index=23,
number=113, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='hinge_loss_param', full_name='caffe.LayerParameter.hinge_loss_param', index=24,
number=114, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='image_data_param', full_name='caffe.LayerParameter.image_data_param', index=25,
number=115, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='infogain_loss_param', full_name='caffe.LayerParameter.infogain_loss_param', index=26,
number=116, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='inner_product_param', full_name='caffe.LayerParameter.inner_product_param', index=27,
number=117, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='lrn_param', full_name='caffe.LayerParameter.lrn_param', index=28,
number=118, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='memory_data_param', full_name='caffe.LayerParameter.memory_data_param', index=29,
number=119, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mvn_param', full_name='caffe.LayerParameter.mvn_param', index=30,
number=120, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pooling_param', full_name='caffe.LayerParameter.pooling_param', index=31,
number=121, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='power_param', full_name='caffe.LayerParameter.power_param', index=32,
number=122, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='prelu_param', full_name='caffe.LayerParameter.prelu_param', index=33,
number=131, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='python_param', full_name='caffe.LayerParameter.python_param', index=34,
number=130, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='relu_param', full_name='caffe.LayerParameter.relu_param', index=35,
number=123, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='roi_pooling_param', full_name='caffe.LayerParameter.roi_pooling_param', index=36,
number=8266711, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='sigmoid_param', full_name='caffe.LayerParameter.sigmoid_param', index=37,
number=124, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='softmax_param', full_name='caffe.LayerParameter.softmax_param', index=38,
number=125, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='slice_param', full_name='caffe.LayerParameter.slice_param', index=39,
number=126, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='tanh_param', full_name='caffe.LayerParameter.tanh_param', index=40,
number=127, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='threshold_param', full_name='caffe.LayerParameter.threshold_param', index=41,
number=128, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='window_data_param', full_name='caffe.LayerParameter.window_data_param', index=42,
number=129, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2362,
serialized_end=4260,
)
_TRANSFORMATIONPARAMETER = _descriptor.Descriptor(
name='TransformationParameter',
full_name='caffe.TransformationParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='scale', full_name='caffe.TransformationParameter.scale', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mirror', full_name='caffe.TransformationParameter.mirror', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.TransformationParameter.crop_size', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.TransformationParameter.mean_file', index=3,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mean_value', full_name='caffe.TransformationParameter.mean_value', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4262,
serialized_end=4389,
)
_LOSSPARAMETER = _descriptor.Descriptor(
name='LossParameter',
full_name='caffe.LossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='ignore_label', full_name='caffe.LossParameter.ignore_label', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='normalize', full_name='caffe.LossParameter.normalize', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4391,
serialized_end=4453,
)
_ACCURACYPARAMETER = _descriptor.Descriptor(
name='AccuracyParameter',
full_name='caffe.AccuracyParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='top_k', full_name='caffe.AccuracyParameter.top_k', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='axis', full_name='caffe.AccuracyParameter.axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='ignore_label', full_name='caffe.AccuracyParameter.ignore_label', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4455,
serialized_end=4531,
)
_ARGMAXPARAMETER = _descriptor.Descriptor(
name='ArgMaxParameter',
full_name='caffe.ArgMaxParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='out_max_val', full_name='caffe.ArgMaxParameter.out_max_val', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='top_k', full_name='caffe.ArgMaxParameter.top_k', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4533,
serialized_end=4596,
)
_CONCATPARAMETER = _descriptor.Descriptor(
name='ConcatParameter',
full_name='caffe.ConcatParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='axis', full_name='caffe.ConcatParameter.axis', index=0,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.ConcatParameter.concat_dim', index=1,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4598,
serialized_end=4655,
)
_CONTRASTIVELOSSPARAMETER = _descriptor.Descriptor(
name='ContrastiveLossParameter',
full_name='caffe.ContrastiveLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='margin', full_name='caffe.ContrastiveLossParameter.margin', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4657,
serialized_end=4702,
)
_CONVOLUTIONPARAMETER = _descriptor.Descriptor(
name='ConvolutionParameter',
full_name='caffe.ConvolutionParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='num_output', full_name='caffe.ConvolutionParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.ConvolutionParameter.bias_term', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pad', full_name='caffe.ConvolutionParameter.pad', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pad_h', full_name='caffe.ConvolutionParameter.pad_h', index=3,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pad_w', full_name='caffe.ConvolutionParameter.pad_w', index=4,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='kernel_size', full_name='caffe.ConvolutionParameter.kernel_size', index=5,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='kernel_h', full_name='caffe.ConvolutionParameter.kernel_h', index=6,
number=11, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='kernel_w', full_name='caffe.ConvolutionParameter.kernel_w', index=7,
number=12, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='group', full_name='caffe.ConvolutionParameter.group', index=8,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stride', full_name='caffe.ConvolutionParameter.stride', index=9,
number=6, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stride_h', full_name='caffe.ConvolutionParameter.stride_h', index=10,
number=13, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stride_w', full_name='caffe.ConvolutionParameter.stride_w', index=11,
number=14, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.ConvolutionParameter.weight_filler', index=12,
number=7, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.ConvolutionParameter.bias_filler', index=13,
number=8, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='engine', full_name='caffe.ConvolutionParameter.engine', index=14,
number=15, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_CONVOLUTIONPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4705,
serialized_end=5152,
)
_DATAPARAMETER = _descriptor.Descriptor(
name='DataParameter',
full_name='caffe.DataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='source', full_name='caffe.DataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.DataParameter.batch_size', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.DataParameter.rand_skip', index=2,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='backend', full_name='caffe.DataParameter.backend', index=3,
number=8, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='scale', full_name='caffe.DataParameter.scale', index=4,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.DataParameter.mean_file', index=5,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.DataParameter.crop_size', index=6,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mirror', full_name='caffe.DataParameter.mirror', index=7,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='force_encoded_color', full_name='caffe.DataParameter.force_encoded_color', index=8,
number=9, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_DATAPARAMETER_DB,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5155,
serialized_end=5426,
)
_DROPOUTPARAMETER = _descriptor.Descriptor(
name='DropoutParameter',
full_name='caffe.DropoutParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.DropoutParameter.dropout_ratio', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5428,
serialized_end=5474,
)
_DUMMYDATAPARAMETER = _descriptor.Descriptor(
name='DummyDataParameter',
full_name='caffe.DummyDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='data_filler', full_name='caffe.DummyDataParameter.data_filler', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='shape', full_name='caffe.DummyDataParameter.shape', index=1,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='num', full_name='caffe.DummyDataParameter.num', index=2,
number=2, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='channels', full_name='caffe.DummyDataParameter.channels', index=3,
number=3, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='height', full_name='caffe.DummyDataParameter.height', index=4,
number=4, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='width', full_name='caffe.DummyDataParameter.width', index=5,
number=5, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5477,
serialized_end=5637,
)
_ELTWISEPARAMETER = _descriptor.Descriptor(
name='EltwiseParameter',
full_name='caffe.EltwiseParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='operation', full_name='caffe.EltwiseParameter.operation', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='coeff', full_name='caffe.EltwiseParameter.coeff', index=1,
number=2, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stable_prod_grad', full_name='caffe.EltwiseParameter.stable_prod_grad', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_ELTWISEPARAMETER_ELTWISEOP,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5640,
serialized_end=5805,
)
_EXPPARAMETER = _descriptor.Descriptor(
name='ExpParameter',
full_name='caffe.ExpParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='base', full_name='caffe.ExpParameter.base', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='scale', full_name='caffe.ExpParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='shift', full_name='caffe.ExpParameter.shift', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5807,
serialized_end=5875,
)
_HDF5DATAPARAMETER = _descriptor.Descriptor(
name='HDF5DataParameter',
full_name='caffe.HDF5DataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='source', full_name='caffe.HDF5DataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.HDF5DataParameter.batch_size', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='shuffle', full_name='caffe.HDF5DataParameter.shuffle', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5877,
serialized_end=5956,
)
_HDF5OUTPUTPARAMETER = _descriptor.Descriptor(
name='HDF5OutputParameter',
full_name='caffe.HDF5OutputParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='file_name', full_name='caffe.HDF5OutputParameter.file_name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5958,
serialized_end=5998,
)
_HINGELOSSPARAMETER = _descriptor.Descriptor(
name='HingeLossParameter',
full_name='caffe.HingeLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='norm', full_name='caffe.HingeLossParameter.norm', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_HINGELOSSPARAMETER_NORM,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6000,
serialized_end=6094,
)
_IMAGEDATAPARAMETER = _descriptor.Descriptor(
name='ImageDataParameter',
full_name='caffe.ImageDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='source', full_name='caffe.ImageDataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.ImageDataParameter.batch_size', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.ImageDataParameter.rand_skip', index=2,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='shuffle', full_name='caffe.ImageDataParameter.shuffle', index=3,
number=8, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='new_height', full_name='caffe.ImageDataParameter.new_height', index=4,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='new_width', full_name='caffe.ImageDataParameter.new_width', index=5,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='is_color', full_name='caffe.ImageDataParameter.is_color', index=6,
number=11, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='scale', full_name='caffe.ImageDataParameter.scale', index=7,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.ImageDataParameter.mean_file', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.ImageDataParameter.crop_size', index=9,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mirror', full_name='caffe.ImageDataParameter.mirror', index=10,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='root_folder', full_name='caffe.ImageDataParameter.root_folder', index=11,
number=12, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6097,
serialized_end=6373,
)
_INFOGAINLOSSPARAMETER = _descriptor.Descriptor(
name='InfogainLossParameter',
full_name='caffe.InfogainLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='source', full_name='caffe.InfogainLossParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6375,
serialized_end=6414,
)
_INNERPRODUCTPARAMETER = _descriptor.Descriptor(
name='InnerProductParameter',
full_name='caffe.InnerProductParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='num_output', full_name='caffe.InnerProductParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.InnerProductParameter.bias_term', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.InnerProductParameter.weight_filler', index=2,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.InnerProductParameter.bias_filler', index=3,
number=4, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='axis', full_name='caffe.InnerProductParameter.axis', index=4,
number=5, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6417,
serialized_end=6594,
)
_LRNPARAMETER = _descriptor.Descriptor(
name='LRNParameter',
full_name='caffe.LRNParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='local_size', full_name='caffe.LRNParameter.local_size', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='alpha', full_name='caffe.LRNParameter.alpha', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='beta', full_name='caffe.LRNParameter.beta', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='norm_region', full_name='caffe.LRNParameter.norm_region', index=3,
number=4, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='k', full_name='caffe.LRNParameter.k', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_LRNPARAMETER_NORMREGION,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6597,
serialized_end=6811,
)
_MEMORYDATAPARAMETER = _descriptor.Descriptor(
name='MemoryDataParameter',
full_name='caffe.MemoryDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.MemoryDataParameter.batch_size', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='channels', full_name='caffe.MemoryDataParameter.channels', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='height', full_name='caffe.MemoryDataParameter.height', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='width', full_name='caffe.MemoryDataParameter.width', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6813,
serialized_end=6903,
)
_MVNPARAMETER = _descriptor.Descriptor(
name='MVNParameter',
full_name='caffe.MVNParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='normalize_variance', full_name='caffe.MVNParameter.normalize_variance', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='across_channels', full_name='caffe.MVNParameter.across_channels', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6905,
serialized_end=6985,
)
_POOLINGPARAMETER = _descriptor.Descriptor(
name='PoolingParameter',
full_name='caffe.PoolingParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='pool', full_name='caffe.PoolingParameter.pool', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pad', full_name='caffe.PoolingParameter.pad', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pad_h', full_name='caffe.PoolingParameter.pad_h', index=2,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pad_w', full_name='caffe.PoolingParameter.pad_w', index=3,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='kernel_size', full_name='caffe.PoolingParameter.kernel_size', index=4,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='kernel_h', full_name='caffe.PoolingParameter.kernel_h', index=5,
number=5, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='kernel_w', full_name='caffe.PoolingParameter.kernel_w', index=6,
number=6, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stride', full_name='caffe.PoolingParameter.stride', index=7,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stride_h', full_name='caffe.PoolingParameter.stride_h', index=8,
number=7, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stride_w', full_name='caffe.PoolingParameter.stride_w', index=9,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='engine', full_name='caffe.PoolingParameter.engine', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='global_pooling', full_name='caffe.PoolingParameter.global_pooling', index=11,
number=12, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_POOLINGPARAMETER_POOLMETHOD,
_POOLINGPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6988,
serialized_end=7406,
)
_POWERPARAMETER = _descriptor.Descriptor(
name='PowerParameter',
full_name='caffe.PowerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='power', full_name='caffe.PowerParameter.power', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='scale', full_name='caffe.PowerParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='shift', full_name='caffe.PowerParameter.shift', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7408,
serialized_end=7478,
)
_PYTHONPARAMETER = _descriptor.Descriptor(
name='PythonParameter',
full_name='caffe.PythonParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='module', full_name='caffe.PythonParameter.module', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='layer', full_name='caffe.PythonParameter.layer', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='param_str', full_name='caffe.PythonParameter.param_str', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7480,
serialized_end=7549,
)
_RELUPARAMETER = _descriptor.Descriptor(
name='ReLUParameter',
full_name='caffe.ReLUParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='negative_slope', full_name='caffe.ReLUParameter.negative_slope', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='engine', full_name='caffe.ReLUParameter.engine', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_RELUPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7552,
serialized_end=7693,
)
_ROIPOOLINGPARAMETER = _descriptor.Descriptor(
name='ROIPoolingParameter',
full_name='caffe.ROIPoolingParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='pooled_h', full_name='caffe.ROIPoolingParameter.pooled_h', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pooled_w', full_name='caffe.ROIPoolingParameter.pooled_w', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='spatial_scale', full_name='caffe.ROIPoolingParameter.spatial_scale', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7695,
serialized_end=7784,
)
_SIGMOIDPARAMETER = _descriptor.Descriptor(
name='SigmoidParameter',
full_name='caffe.SigmoidParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='engine', full_name='caffe.SigmoidParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SIGMOIDPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7786,
serialized_end=7906,
)
_SLICEPARAMETER = _descriptor.Descriptor(
name='SliceParameter',
full_name='caffe.SliceParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='axis', full_name='caffe.SliceParameter.axis', index=0,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='slice_point', full_name='caffe.SliceParameter.slice_point', index=1,
number=2, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='slice_dim', full_name='caffe.SliceParameter.slice_dim', index=2,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7908,
serialized_end=7984,
)
_SOFTMAXPARAMETER = _descriptor.Descriptor(
name='SoftmaxParameter',
full_name='caffe.SoftmaxParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='engine', full_name='caffe.SoftmaxParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='axis', full_name='caffe.SoftmaxParameter.axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SOFTMAXPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7987,
serialized_end=8124,
)
_TANHPARAMETER = _descriptor.Descriptor(
name='TanHParameter',
full_name='caffe.TanHParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='engine', full_name='caffe.TanHParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_TANHPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8126,
serialized_end=8240,
)
_THRESHOLDPARAMETER = _descriptor.Descriptor(
name='ThresholdParameter',
full_name='caffe.ThresholdParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='threshold', full_name='caffe.ThresholdParameter.threshold', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8242,
serialized_end=8284,
)
_WINDOWDATAPARAMETER = _descriptor.Descriptor(
name='WindowDataParameter',
full_name='caffe.WindowDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='source', full_name='caffe.WindowDataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='scale', full_name='caffe.WindowDataParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.WindowDataParameter.mean_file', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.WindowDataParameter.batch_size', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.WindowDataParameter.crop_size', index=4,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mirror', full_name='caffe.WindowDataParameter.mirror', index=5,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='fg_threshold', full_name='caffe.WindowDataParameter.fg_threshold', index=6,
number=7, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='bg_threshold', full_name='caffe.WindowDataParameter.bg_threshold', index=7,
number=8, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='fg_fraction', full_name='caffe.WindowDataParameter.fg_fraction', index=8,
number=9, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='context_pad', full_name='caffe.WindowDataParameter.context_pad', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='crop_mode', full_name='caffe.WindowDataParameter.crop_mode', index=10,
number=11, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='cache_images', full_name='caffe.WindowDataParameter.cache_images', index=11,
number=12, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='root_folder', full_name='caffe.WindowDataParameter.root_folder', index=12,
number=13, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8287,
serialized_end=8608,
)
_V1LAYERPARAMETER = _descriptor.Descriptor(
name='V1LayerParameter',
full_name='caffe.V1LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='bottom', full_name='caffe.V1LayerParameter.bottom', index=0,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='top', full_name='caffe.V1LayerParameter.top', index=1,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='name', full_name='caffe.V1LayerParameter.name', index=2,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='include', full_name='caffe.V1LayerParameter.include', index=3,
number=32, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='exclude', full_name='caffe.V1LayerParameter.exclude', index=4,
number=33, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='type', full_name='caffe.V1LayerParameter.type', index=5,
number=5, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='blobs', full_name='caffe.V1LayerParameter.blobs', index=6,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='param', full_name='caffe.V1LayerParameter.param', index=7,
number=1001, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='blob_share_mode', full_name='caffe.V1LayerParameter.blob_share_mode', index=8,
number=1002, type=14, cpp_type=8, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.V1LayerParameter.blobs_lr', index=9,
number=7, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.V1LayerParameter.weight_decay', index=10,
number=8, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='loss_weight', full_name='caffe.V1LayerParameter.loss_weight', index=11,
number=35, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='accuracy_param', full_name='caffe.V1LayerParameter.accuracy_param', index=12,
number=27, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='argmax_param', full_name='caffe.V1LayerParameter.argmax_param', index=13,
number=23, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='concat_param', full_name='caffe.V1LayerParameter.concat_param', index=14,
number=9, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='contrastive_loss_param', full_name='caffe.V1LayerParameter.contrastive_loss_param', index=15,
number=40, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='convolution_param', full_name='caffe.V1LayerParameter.convolution_param', index=16,
number=10, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='data_param', full_name='caffe.V1LayerParameter.data_param', index=17,
number=11, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='dropout_param', full_name='caffe.V1LayerParameter.dropout_param', index=18,
number=12, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='dummy_data_param', full_name='caffe.V1LayerParameter.dummy_data_param', index=19,
number=26, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='eltwise_param', full_name='caffe.V1LayerParameter.eltwise_param', index=20,
number=24, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='exp_param', full_name='caffe.V1LayerParameter.exp_param', index=21,
number=41, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='hdf5_data_param', full_name='caffe.V1LayerParameter.hdf5_data_param', index=22,
number=13, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.V1LayerParameter.hdf5_output_param', index=23,
number=14, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='hinge_loss_param', full_name='caffe.V1LayerParameter.hinge_loss_param', index=24,
number=29, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='image_data_param', full_name='caffe.V1LayerParameter.image_data_param', index=25,
number=15, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='infogain_loss_param', full_name='caffe.V1LayerParameter.infogain_loss_param', index=26,
number=16, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='inner_product_param', full_name='caffe.V1LayerParameter.inner_product_param', index=27,
number=17, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='lrn_param', full_name='caffe.V1LayerParameter.lrn_param', index=28,
number=18, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='memory_data_param', full_name='caffe.V1LayerParameter.memory_data_param', index=29,
number=22, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mvn_param', full_name='caffe.V1LayerParameter.mvn_param', index=30,
number=34, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pooling_param', full_name='caffe.V1LayerParameter.pooling_param', index=31,
number=19, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='power_param', full_name='caffe.V1LayerParameter.power_param', index=32,
number=21, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='relu_param', full_name='caffe.V1LayerParameter.relu_param', index=33,
number=30, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='sigmoid_param', full_name='caffe.V1LayerParameter.sigmoid_param', index=34,
number=38, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='softmax_param', full_name='caffe.V1LayerParameter.softmax_param', index=35,
number=39, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='slice_param', full_name='caffe.V1LayerParameter.slice_param', index=36,
number=31, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='tanh_param', full_name='caffe.V1LayerParameter.tanh_param', index=37,
number=37, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='threshold_param', full_name='caffe.V1LayerParameter.threshold_param', index=38,
number=25, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='window_data_param', full_name='caffe.V1LayerParameter.window_data_param', index=39,
number=20, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='transform_param', full_name='caffe.V1LayerParameter.transform_param', index=40,
number=36, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='loss_param', full_name='caffe.V1LayerParameter.loss_param', index=41,
number=42, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='layer', full_name='caffe.V1LayerParameter.layer', index=42,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_V1LAYERPARAMETER_LAYERTYPE,
_V1LAYERPARAMETER_DIMCHECKMODE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8611,
serialized_end=11139,
)
_V0LAYERPARAMETER = _descriptor.Descriptor(
name='V0LayerParameter',
full_name='caffe.V0LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='name', full_name='caffe.V0LayerParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='type', full_name='caffe.V0LayerParameter.type', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='num_output', full_name='caffe.V0LayerParameter.num_output', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='biasterm', full_name='caffe.V0LayerParameter.biasterm', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.V0LayerParameter.weight_filler', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.V0LayerParameter.bias_filler', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pad', full_name='caffe.V0LayerParameter.pad', index=6,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='kernelsize', full_name='caffe.V0LayerParameter.kernelsize', index=7,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='group', full_name='caffe.V0LayerParameter.group', index=8,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='stride', full_name='caffe.V0LayerParameter.stride', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='pool', full_name='caffe.V0LayerParameter.pool', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.V0LayerParameter.dropout_ratio', index=11,
number=12, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='local_size', full_name='caffe.V0LayerParameter.local_size', index=12,
number=13, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='alpha', full_name='caffe.V0LayerParameter.alpha', index=13,
number=14, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='beta', full_name='caffe.V0LayerParameter.beta', index=14,
number=15, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='k', full_name='caffe.V0LayerParameter.k', index=15,
number=22, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='source', full_name='caffe.V0LayerParameter.source', index=16,
number=16, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='scale', full_name='caffe.V0LayerParameter.scale', index=17,
number=17, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='meanfile', full_name='caffe.V0LayerParameter.meanfile', index=18,
number=18, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='batchsize', full_name='caffe.V0LayerParameter.batchsize', index=19,
number=19, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='cropsize', full_name='caffe.V0LayerParameter.cropsize', index=20,
number=20, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='mirror', full_name='caffe.V0LayerParameter.mirror', index=21,
number=21, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='blobs', full_name='caffe.V0LayerParameter.blobs', index=22,
number=50, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.V0LayerParameter.blobs_lr', index=23,
number=51, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.V0LayerParameter.weight_decay', index=24,
number=52, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.V0LayerParameter.rand_skip', index=25,
number=53, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='det_fg_threshold', full_name='caffe.V0LayerParameter.det_fg_threshold', index=26,
number=54, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='det_bg_threshold', full_name='caffe.V0LayerParameter.det_bg_threshold', index=27,
number=55, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='det_fg_fraction', full_name='caffe.V0LayerParameter.det_fg_fraction', index=28,
number=56, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='det_context_pad', full_name='caffe.V0LayerParameter.det_context_pad', index=29,
number=58, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='det_crop_mode', full_name='caffe.V0LayerParameter.det_crop_mode', index=30,
number=59, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='new_num', full_name='caffe.V0LayerParameter.new_num', index=31,
number=60, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='new_channels', full_name='caffe.V0LayerParameter.new_channels', index=32,
number=61, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='new_height', full_name='caffe.V0LayerParameter.new_height', index=33,
number=62, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='new_width', full_name='caffe.V0LayerParameter.new_width', index=34,
number=63, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='shuffle_images', full_name='caffe.V0LayerParameter.shuffle_images', index=35,
number=64, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.V0LayerParameter.concat_dim', index=36,
number=65, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.V0LayerParameter.hdf5_output_param', index=37,
number=1001, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_V0LAYERPARAMETER_POOLMETHOD,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=11142,
serialized_end=12163,
)
_PRELUPARAMETER = _descriptor.Descriptor(
name='PReLUParameter',
full_name='caffe.PReLUParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='filler', full_name='caffe.PReLUParameter.filler', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='channel_shared', full_name='caffe.PReLUParameter.channel_shared', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=12165,
serialized_end=12252,
)
_BLOBPROTO.fields_by_name['shape'].message_type = _BLOBSHAPE
_BLOBPROTOVECTOR.fields_by_name['blobs'].message_type = _BLOBPROTO
_NETPARAMETER.fields_by_name['input_shape'].message_type = _BLOBSHAPE
_NETPARAMETER.fields_by_name['state'].message_type = _NETSTATE
_NETPARAMETER.fields_by_name['layer'].message_type = _LAYERPARAMETER
_NETPARAMETER.fields_by_name['layers'].message_type = _V1LAYERPARAMETER
_SOLVERPARAMETER.fields_by_name['net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['train_net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['test_net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['train_state'].message_type = _NETSTATE
_SOLVERPARAMETER.fields_by_name['test_state'].message_type = _NETSTATE
_SOLVERPARAMETER.fields_by_name['solver_mode'].enum_type = _SOLVERPARAMETER_SOLVERMODE
_SOLVERPARAMETER.fields_by_name['solver_type'].enum_type = _SOLVERPARAMETER_SOLVERTYPE
_SOLVERPARAMETER_SOLVERMODE.containing_type = _SOLVERPARAMETER;
_SOLVERPARAMETER_SOLVERTYPE.containing_type = _SOLVERPARAMETER;
_SOLVERSTATE.fields_by_name['history'].message_type = _BLOBPROTO
_NETSTATE.fields_by_name['phase'].enum_type = _PHASE
_NETSTATERULE.fields_by_name['phase'].enum_type = _PHASE
_PARAMSPEC.fields_by_name['share_mode'].enum_type = _PARAMSPEC_DIMCHECKMODE
_PARAMSPEC_DIMCHECKMODE.containing_type = _PARAMSPEC;
_LAYERPARAMETER.fields_by_name['phase'].enum_type = _PHASE
_LAYERPARAMETER.fields_by_name['param'].message_type = _PARAMSPEC
_LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_LAYERPARAMETER.fields_by_name['include'].message_type = _NETSTATERULE
_LAYERPARAMETER.fields_by_name['exclude'].message_type = _NETSTATERULE
_LAYERPARAMETER.fields_by_name['transform_param'].message_type = _TRANSFORMATIONPARAMETER
_LAYERPARAMETER.fields_by_name['loss_param'].message_type = _LOSSPARAMETER
_LAYERPARAMETER.fields_by_name['accuracy_param'].message_type = _ACCURACYPARAMETER
_LAYERPARAMETER.fields_by_name['argmax_param'].message_type = _ARGMAXPARAMETER
_LAYERPARAMETER.fields_by_name['concat_param'].message_type = _CONCATPARAMETER
_LAYERPARAMETER.fields_by_name['contrastive_loss_param'].message_type = _CONTRASTIVELOSSPARAMETER
_LAYERPARAMETER.fields_by_name['convolution_param'].message_type = _CONVOLUTIONPARAMETER
_LAYERPARAMETER.fields_by_name['data_param'].message_type = _DATAPARAMETER
_LAYERPARAMETER.fields_by_name['dropout_param'].message_type = _DROPOUTPARAMETER
_LAYERPARAMETER.fields_by_name['dummy_data_param'].message_type = _DUMMYDATAPARAMETER
_LAYERPARAMETER.fields_by_name['eltwise_param'].message_type = _ELTWISEPARAMETER
_LAYERPARAMETER.fields_by_name['exp_param'].message_type = _EXPPARAMETER
_LAYERPARAMETER.fields_by_name['hdf5_data_param'].message_type = _HDF5DATAPARAMETER
_LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_LAYERPARAMETER.fields_by_name['hinge_loss_param'].message_type = _HINGELOSSPARAMETER
_LAYERPARAMETER.fields_by_name['image_data_param'].message_type = _IMAGEDATAPARAMETER
_LAYERPARAMETER.fields_by_name['infogain_loss_param'].message_type = _INFOGAINLOSSPARAMETER
_LAYERPARAMETER.fields_by_name['inner_product_param'].message_type = _INNERPRODUCTPARAMETER
_LAYERPARAMETER.fields_by_name['lrn_param'].message_type = _LRNPARAMETER
_LAYERPARAMETER.fields_by_name['memory_data_param'].message_type = _MEMORYDATAPARAMETER
_LAYERPARAMETER.fields_by_name['mvn_param'].message_type = _MVNPARAMETER
_LAYERPARAMETER.fields_by_name['pooling_param'].message_type = _POOLINGPARAMETER
_LAYERPARAMETER.fields_by_name['power_param'].message_type = _POWERPARAMETER
_LAYERPARAMETER.fields_by_name['prelu_param'].message_type = _PRELUPARAMETER
_LAYERPARAMETER.fields_by_name['python_param'].message_type = _PYTHONPARAMETER
_LAYERPARAMETER.fields_by_name['relu_param'].message_type = _RELUPARAMETER
_LAYERPARAMETER.fields_by_name['roi_pooling_param'].message_type = _ROIPOOLINGPARAMETER
_LAYERPARAMETER.fields_by_name['sigmoid_param'].message_type = _SIGMOIDPARAMETER
_LAYERPARAMETER.fields_by_name['softmax_param'].message_type = _SOFTMAXPARAMETER
_LAYERPARAMETER.fields_by_name['slice_param'].message_type = _SLICEPARAMETER
_LAYERPARAMETER.fields_by_name['tanh_param'].message_type = _TANHPARAMETER
_LAYERPARAMETER.fields_by_name['threshold_param'].message_type = _THRESHOLDPARAMETER
_LAYERPARAMETER.fields_by_name['window_data_param'].message_type = _WINDOWDATAPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['engine'].enum_type = _CONVOLUTIONPARAMETER_ENGINE
_CONVOLUTIONPARAMETER_ENGINE.containing_type = _CONVOLUTIONPARAMETER;
_DATAPARAMETER.fields_by_name['backend'].enum_type = _DATAPARAMETER_DB
_DATAPARAMETER_DB.containing_type = _DATAPARAMETER;
_DUMMYDATAPARAMETER.fields_by_name['data_filler'].message_type = _FILLERPARAMETER
_DUMMYDATAPARAMETER.fields_by_name['shape'].message_type = _BLOBSHAPE
_ELTWISEPARAMETER.fields_by_name['operation'].enum_type = _ELTWISEPARAMETER_ELTWISEOP
_ELTWISEPARAMETER_ELTWISEOP.containing_type = _ELTWISEPARAMETER;
_HINGELOSSPARAMETER.fields_by_name['norm'].enum_type = _HINGELOSSPARAMETER_NORM
_HINGELOSSPARAMETER_NORM.containing_type = _HINGELOSSPARAMETER;
_INNERPRODUCTPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_INNERPRODUCTPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_LRNPARAMETER.fields_by_name['norm_region'].enum_type = _LRNPARAMETER_NORMREGION
_LRNPARAMETER_NORMREGION.containing_type = _LRNPARAMETER;
_POOLINGPARAMETER.fields_by_name['pool'].enum_type = _POOLINGPARAMETER_POOLMETHOD
_POOLINGPARAMETER.fields_by_name['engine'].enum_type = _POOLINGPARAMETER_ENGINE
_POOLINGPARAMETER_POOLMETHOD.containing_type = _POOLINGPARAMETER;
_POOLINGPARAMETER_ENGINE.containing_type = _POOLINGPARAMETER;
_RELUPARAMETER.fields_by_name['engine'].enum_type = _RELUPARAMETER_ENGINE
_RELUPARAMETER_ENGINE.containing_type = _RELUPARAMETER;
_SIGMOIDPARAMETER.fields_by_name['engine'].enum_type = _SIGMOIDPARAMETER_ENGINE
_SIGMOIDPARAMETER_ENGINE.containing_type = _SIGMOIDPARAMETER;
_SOFTMAXPARAMETER.fields_by_name['engine'].enum_type = _SOFTMAXPARAMETER_ENGINE
_SOFTMAXPARAMETER_ENGINE.containing_type = _SOFTMAXPARAMETER;
_TANHPARAMETER.fields_by_name['engine'].enum_type = _TANHPARAMETER_ENGINE
_TANHPARAMETER_ENGINE.containing_type = _TANHPARAMETER;
_V1LAYERPARAMETER.fields_by_name['include'].message_type = _NETSTATERULE
_V1LAYERPARAMETER.fields_by_name['exclude'].message_type = _NETSTATERULE
_V1LAYERPARAMETER.fields_by_name['type'].enum_type = _V1LAYERPARAMETER_LAYERTYPE
_V1LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_V1LAYERPARAMETER.fields_by_name['blob_share_mode'].enum_type = _V1LAYERPARAMETER_DIMCHECKMODE
_V1LAYERPARAMETER.fields_by_name['accuracy_param'].message_type = _ACCURACYPARAMETER
_V1LAYERPARAMETER.fields_by_name['argmax_param'].message_type = _ARGMAXPARAMETER
_V1LAYERPARAMETER.fields_by_name['concat_param'].message_type = _CONCATPARAMETER
_V1LAYERPARAMETER.fields_by_name['contrastive_loss_param'].message_type = _CONTRASTIVELOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['convolution_param'].message_type = _CONVOLUTIONPARAMETER
_V1LAYERPARAMETER.fields_by_name['data_param'].message_type = _DATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['dropout_param'].message_type = _DROPOUTPARAMETER
_V1LAYERPARAMETER.fields_by_name['dummy_data_param'].message_type = _DUMMYDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['eltwise_param'].message_type = _ELTWISEPARAMETER
_V1LAYERPARAMETER.fields_by_name['exp_param'].message_type = _EXPPARAMETER
_V1LAYERPARAMETER.fields_by_name['hdf5_data_param'].message_type = _HDF5DATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_V1LAYERPARAMETER.fields_by_name['hinge_loss_param'].message_type = _HINGELOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['image_data_param'].message_type = _IMAGEDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['infogain_loss_param'].message_type = _INFOGAINLOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['inner_product_param'].message_type = _INNERPRODUCTPARAMETER
_V1LAYERPARAMETER.fields_by_name['lrn_param'].message_type = _LRNPARAMETER
_V1LAYERPARAMETER.fields_by_name['memory_data_param'].message_type = _MEMORYDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['mvn_param'].message_type = _MVNPARAMETER
_V1LAYERPARAMETER.fields_by_name['pooling_param'].message_type = _POOLINGPARAMETER
_V1LAYERPARAMETER.fields_by_name['power_param'].message_type = _POWERPARAMETER
_V1LAYERPARAMETER.fields_by_name['relu_param'].message_type = _RELUPARAMETER
_V1LAYERPARAMETER.fields_by_name['sigmoid_param'].message_type = _SIGMOIDPARAMETER
_V1LAYERPARAMETER.fields_by_name['softmax_param'].message_type = _SOFTMAXPARAMETER
_V1LAYERPARAMETER.fields_by_name['slice_param'].message_type = _SLICEPARAMETER
_V1LAYERPARAMETER.fields_by_name['tanh_param'].message_type = _TANHPARAMETER
_V1LAYERPARAMETER.fields_by_name['threshold_param'].message_type = _THRESHOLDPARAMETER
_V1LAYERPARAMETER.fields_by_name['window_data_param'].message_type = _WINDOWDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['transform_param'].message_type = _TRANSFORMATIONPARAMETER
_V1LAYERPARAMETER.fields_by_name['loss_param'].message_type = _LOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['layer'].message_type = _V0LAYERPARAMETER
_V1LAYERPARAMETER_LAYERTYPE.containing_type = _V1LAYERPARAMETER;
_V1LAYERPARAMETER_DIMCHECKMODE.containing_type = _V1LAYERPARAMETER;
_V0LAYERPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_V0LAYERPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_V0LAYERPARAMETER.fields_by_name['pool'].enum_type = _V0LAYERPARAMETER_POOLMETHOD
_V0LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_V0LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_V0LAYERPARAMETER_POOLMETHOD.containing_type = _V0LAYERPARAMETER;
_PRELUPARAMETER.fields_by_name['filler'].message_type = _FILLERPARAMETER
DESCRIPTOR.message_types_by_name['BlobShape'] = _BLOBSHAPE
DESCRIPTOR.message_types_by_name['BlobProto'] = _BLOBPROTO
DESCRIPTOR.message_types_by_name['BlobProtoVector'] = _BLOBPROTOVECTOR
DESCRIPTOR.message_types_by_name['Datum'] = _DATUM
DESCRIPTOR.message_types_by_name['FillerParameter'] = _FILLERPARAMETER
DESCRIPTOR.message_types_by_name['NetParameter'] = _NETPARAMETER
DESCRIPTOR.message_types_by_name['SolverParameter'] = _SOLVERPARAMETER
DESCRIPTOR.message_types_by_name['SolverState'] = _SOLVERSTATE
DESCRIPTOR.message_types_by_name['NetState'] = _NETSTATE
DESCRIPTOR.message_types_by_name['NetStateRule'] = _NETSTATERULE
DESCRIPTOR.message_types_by_name['ParamSpec'] = _PARAMSPEC
DESCRIPTOR.message_types_by_name['LayerParameter'] = _LAYERPARAMETER
DESCRIPTOR.message_types_by_name['TransformationParameter'] = _TRANSFORMATIONPARAMETER
DESCRIPTOR.message_types_by_name['LossParameter'] = _LOSSPARAMETER
DESCRIPTOR.message_types_by_name['AccuracyParameter'] = _ACCURACYPARAMETER
DESCRIPTOR.message_types_by_name['ArgMaxParameter'] = _ARGMAXPARAMETER
DESCRIPTOR.message_types_by_name['ConcatParameter'] = _CONCATPARAMETER
DESCRIPTOR.message_types_by_name['ContrastiveLossParameter'] = _CONTRASTIVELOSSPARAMETER
DESCRIPTOR.message_types_by_name['ConvolutionParameter'] = _CONVOLUTIONPARAMETER
DESCRIPTOR.message_types_by_name['DataParameter'] = _DATAPARAMETER
DESCRIPTOR.message_types_by_name['DropoutParameter'] = _DROPOUTPARAMETER
DESCRIPTOR.message_types_by_name['DummyDataParameter'] = _DUMMYDATAPARAMETER
DESCRIPTOR.message_types_by_name['EltwiseParameter'] = _ELTWISEPARAMETER
DESCRIPTOR.message_types_by_name['ExpParameter'] = _EXPPARAMETER
DESCRIPTOR.message_types_by_name['HDF5DataParameter'] = _HDF5DATAPARAMETER
DESCRIPTOR.message_types_by_name['HDF5OutputParameter'] = _HDF5OUTPUTPARAMETER
DESCRIPTOR.message_types_by_name['HingeLossParameter'] = _HINGELOSSPARAMETER
DESCRIPTOR.message_types_by_name['ImageDataParameter'] = _IMAGEDATAPARAMETER
DESCRIPTOR.message_types_by_name['InfogainLossParameter'] = _INFOGAINLOSSPARAMETER
DESCRIPTOR.message_types_by_name['InnerProductParameter'] = _INNERPRODUCTPARAMETER
DESCRIPTOR.message_types_by_name['LRNParameter'] = _LRNPARAMETER
DESCRIPTOR.message_types_by_name['MemoryDataParameter'] = _MEMORYDATAPARAMETER
DESCRIPTOR.message_types_by_name['MVNParameter'] = _MVNPARAMETER
DESCRIPTOR.message_types_by_name['PoolingParameter'] = _POOLINGPARAMETER
DESCRIPTOR.message_types_by_name['PowerParameter'] = _POWERPARAMETER
DESCRIPTOR.message_types_by_name['PythonParameter'] = _PYTHONPARAMETER
DESCRIPTOR.message_types_by_name['ReLUParameter'] = _RELUPARAMETER
DESCRIPTOR.message_types_by_name['ROIPoolingParameter'] = _ROIPOOLINGPARAMETER
DESCRIPTOR.message_types_by_name['SigmoidParameter'] = _SIGMOIDPARAMETER
DESCRIPTOR.message_types_by_name['SliceParameter'] = _SLICEPARAMETER
DESCRIPTOR.message_types_by_name['SoftmaxParameter'] = _SOFTMAXPARAMETER
DESCRIPTOR.message_types_by_name['TanHParameter'] = _TANHPARAMETER
DESCRIPTOR.message_types_by_name['ThresholdParameter'] = _THRESHOLDPARAMETER
DESCRIPTOR.message_types_by_name['WindowDataParameter'] = _WINDOWDATAPARAMETER
DESCRIPTOR.message_types_by_name['V1LayerParameter'] = _V1LAYERPARAMETER
DESCRIPTOR.message_types_by_name['V0LayerParameter'] = _V0LAYERPARAMETER
DESCRIPTOR.message_types_by_name['PReLUParameter'] = _PRELUPARAMETER
class BlobShape(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBSHAPE
# @@protoc_insertion_point(class_scope:caffe.BlobShape)
class BlobProto(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTO
# @@protoc_insertion_point(class_scope:caffe.BlobProto)
class BlobProtoVector(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTOVECTOR
# @@protoc_insertion_point(class_scope:caffe.BlobProtoVector)
class Datum(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATUM
# @@protoc_insertion_point(class_scope:caffe.Datum)
class FillerParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FILLERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.FillerParameter)
class NetParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETPARAMETER
# @@protoc_insertion_point(class_scope:caffe.NetParameter)
class SolverParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SolverParameter)
class SolverState(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERSTATE
# @@protoc_insertion_point(class_scope:caffe.SolverState)
class NetState(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATE
# @@protoc_insertion_point(class_scope:caffe.NetState)
class NetStateRule(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATERULE
# @@protoc_insertion_point(class_scope:caffe.NetStateRule)
class ParamSpec(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PARAMSPEC
# @@protoc_insertion_point(class_scope:caffe.ParamSpec)
class LayerParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LayerParameter)
class TransformationParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TRANSFORMATIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.TransformationParameter)
class LossParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LossParameter)
class AccuracyParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ACCURACYPARAMETER
# @@protoc_insertion_point(class_scope:caffe.AccuracyParameter)
class ArgMaxParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ARGMAXPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ArgMaxParameter)
class ConcatParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONCATPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ConcatParameter)
class ContrastiveLossParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONTRASTIVELOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ContrastiveLossParameter)
class ConvolutionParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONVOLUTIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ConvolutionParameter)
class DataParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DataParameter)
class DropoutParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DROPOUTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DropoutParameter)
class DummyDataParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DUMMYDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DummyDataParameter)
class EltwiseParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ELTWISEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.EltwiseParameter)
class ExpParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _EXPPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ExpParameter)
class HDF5DataParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5DATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HDF5DataParameter)
class HDF5OutputParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5OUTPUTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HDF5OutputParameter)
class HingeLossParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HINGELOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HingeLossParameter)
class ImageDataParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _IMAGEDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ImageDataParameter)
class InfogainLossParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INFOGAINLOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.InfogainLossParameter)
class InnerProductParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INNERPRODUCTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.InnerProductParameter)
class LRNParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LRNPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LRNParameter)
class MemoryDataParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MEMORYDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.MemoryDataParameter)
class MVNParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MVNPARAMETER
# @@protoc_insertion_point(class_scope:caffe.MVNParameter)
class PoolingParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POOLINGPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PoolingParameter)
class PowerParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POWERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PowerParameter)
class PythonParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PYTHONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PythonParameter)
class ReLUParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _RELUPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ReLUParameter)
class ROIPoolingParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ROIPOOLINGPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ROIPoolingParameter)
class SigmoidParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SIGMOIDPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SigmoidParameter)
class SliceParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SLICEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SliceParameter)
class SoftmaxParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOFTMAXPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SoftmaxParameter)
class TanHParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TANHPARAMETER
# @@protoc_insertion_point(class_scope:caffe.TanHParameter)
class ThresholdParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _THRESHOLDPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ThresholdParameter)
class WindowDataParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _WINDOWDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.WindowDataParameter)
class V1LayerParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _V1LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.V1LayerParameter)
class V0LayerParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _V0LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.V0LayerParameter)
class PReLUParameter(_message.Message):
__metaclass__ = _reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PRELUPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PReLUParameter)
_BLOBSHAPE.fields_by_name['dim'].has_options = True
_BLOBSHAPE.fields_by_name['dim']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')
_BLOBPROTO.fields_by_name['data'].has_options = True
_BLOBPROTO.fields_by_name['data']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')
_BLOBPROTO.fields_by_name['diff'].has_options = True
_BLOBPROTO.fields_by_name['diff']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')
# @@protoc_insertion_point(module_scope)
| 194,370 | 42.777252 | 22,943 | py |
DRT | DRT-master/external_libs/matconvnet/utils/proto/caffe_6e3916_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='caffe_6e3916.proto',
package='caffe',
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_PHASE = descriptor.EnumDescriptor(
name='Phase',
full_name='caffe.Phase',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='TRAIN', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='TEST', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=13880,
serialized_end=13908,
)
TRAIN = 0
TEST = 1
_FILLERPARAMETER_VARIANCENORM = descriptor.EnumDescriptor(
name='VarianceNorm',
full_name='caffe.FillerParameter.VarianceNorm',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='FAN_IN', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='FAN_OUT', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVERAGE', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=665,
serialized_end=717,
)
_SOLVERPARAMETER_SNAPSHOTFORMAT = descriptor.EnumDescriptor(
name='SnapshotFormat',
full_name='caffe.SolverParameter.SnapshotFormat',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='HDF5', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='BINARYPROTO', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2120,
serialized_end=2163,
)
_SOLVERPARAMETER_SOLVERMODE = descriptor.EnumDescriptor(
name='SolverMode',
full_name='caffe.SolverParameter.SolverMode',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='CPU', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='GPU', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2165,
serialized_end=2195,
)
_SOLVERPARAMETER_SOLVERTYPE = descriptor.EnumDescriptor(
name='SolverType',
full_name='caffe.SolverParameter.SolverType',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='SGD', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='NESTEROV', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ADAGRAD', index=2, number=2,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='RMSPROP', index=3, number=3,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ADADELTA', index=4, number=4,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ADAM', index=5, number=5,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2197,
serialized_end=2282,
)
_PARAMSPEC_DIMCHECKMODE = descriptor.EnumDescriptor(
name='DimCheckMode',
full_name='caffe.ParamSpec.DimCheckMode',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='STRICT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='PERMISSIVE', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2713,
serialized_end=2755,
)
_CONVOLUTIONPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.ConvolutionParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5879,
serialized_end=5922,
)
_DATAPARAMETER_DB = descriptor.EnumDescriptor(
name='DB',
full_name='caffe.DataParameter.DB',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='LEVELDB', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='LMDB', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6190,
serialized_end=6217,
)
_ELTWISEPARAMETER_ELTWISEOP = descriptor.EnumDescriptor(
name='EltwiseOp',
full_name='caffe.EltwiseParameter.EltwiseOp',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='PROD', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SUM', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MAX', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6557,
serialized_end=6596,
)
_HINGELOSSPARAMETER_NORM = descriptor.EnumDescriptor(
name='Norm',
full_name='caffe.HingeLossParameter.Norm',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='L1', index=0, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='L2', index=1, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=7097,
serialized_end=7119,
)
_LRNPARAMETER_NORMREGION = descriptor.EnumDescriptor(
name='NormRegion',
full_name='caffe.LRNParameter.NormRegion',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='ACROSS_CHANNELS', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='WITHIN_CHANNEL', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=7856,
serialized_end=7909,
)
_POOLINGPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.PoolingParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=8433,
serialized_end=8479,
)
_POOLINGPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.PoolingParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5879,
serialized_end=5922,
)
_REDUCTIONPARAMETER_REDUCTIONOP = descriptor.EnumDescriptor(
name='ReductionOp',
full_name='caffe.ReductionParameter.ReductionOp',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='SUM', index=0, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ASUM', index=1, number=2,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SUMSQ', index=2, number=3,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MEAN', index=3, number=4,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=8824,
serialized_end=8877,
)
_RELUPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.ReLUParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5879,
serialized_end=5922,
)
_SIGMOIDPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.SigmoidParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5879,
serialized_end=5922,
)
_SOFTMAXPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.SoftmaxParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5879,
serialized_end=5922,
)
_TANHPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.TanHParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5879,
serialized_end=5922,
)
_SPPPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.SPPParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=8433,
serialized_end=8479,
)
_SPPPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.SPPParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5879,
serialized_end=5922,
)
_V1LAYERPARAMETER_LAYERTYPE = descriptor.EnumDescriptor(
name='LayerType',
full_name='caffe.V1LayerParameter.LayerType',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='NONE', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ABSVAL', index=1, number=35,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ACCURACY', index=2, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ARGMAX', index=3, number=30,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='BNLL', index=4, number=2,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONCAT', index=5, number=3,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONTRASTIVE_LOSS', index=6, number=37,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONVOLUTION', index=7, number=4,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CROP', index=8, number=40,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DATA', index=9, number=5,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DECONVOLUTION', index=10, number=39,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DROPOUT', index=11, number=6,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DUMMY_DATA', index=12, number=32,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='EUCLIDEAN_LOSS', index=13, number=7,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ELTWISE', index=14, number=25,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='EXP', index=15, number=38,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='FLATTEN', index=16, number=8,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HDF5_DATA', index=17, number=9,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HDF5_OUTPUT', index=18, number=10,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HINGE_LOSS', index=19, number=28,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='IM2COL', index=20, number=11,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='IMAGE_DATA', index=21, number=12,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='INFOGAIN_LOSS', index=22, number=13,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='INNER_PRODUCT', index=23, number=14,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='LRN', index=24, number=15,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MEMORY_DATA', index=25, number=29,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MULTINOMIAL_LOGISTIC_LOSS', index=26, number=16,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MVN', index=27, number=34,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='POOLING', index=28, number=17,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='POWER', index=29, number=26,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='RELU', index=30, number=18,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SIGMOID', index=31, number=19,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SIGMOID_CROSS_ENTROPY_LOSS', index=32, number=27,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SILENCE', index=33, number=36,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SOFTMAX', index=34, number=20,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SOFTMAX_LOSS', index=35, number=21,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SPLIT', index=36, number=22,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SLICE', index=37, number=33,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='TANH', index=38, number=23,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='WINDOW_DATA', index=39, number=24,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='THRESHOLD', index=40, number=31,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=12111,
serialized_end=12721,
)
_V1LAYERPARAMETER_DIMCHECKMODE = descriptor.EnumDescriptor(
name='DimCheckMode',
full_name='caffe.V1LayerParameter.DimCheckMode',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='STRICT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='PERMISSIVE', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2713,
serialized_end=2755,
)
_V0LAYERPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.V0LayerParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=8433,
serialized_end=8479,
)
_BLOBSHAPE = descriptor.Descriptor(
name='BlobShape',
full_name='caffe.BlobShape',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='dim', full_name='caffe.BlobShape.dim', index=0,
number=1, type=3, cpp_type=2, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=29,
serialized_end=57,
)
_BLOBPROTO = descriptor.Descriptor(
name='BlobProto',
full_name='caffe.BlobProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='shape', full_name='caffe.BlobProto.shape', index=0,
number=7, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.BlobProto.data', index=1,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='diff', full_name='caffe.BlobProto.diff', index=2,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='double_data', full_name='caffe.BlobProto.double_data', index=3,
number=8, type=1, cpp_type=5, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='double_diff', full_name='caffe.BlobProto.double_diff', index=4,
number=9, type=1, cpp_type=5, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='num', full_name='caffe.BlobProto.num', index=5,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.BlobProto.channels', index=6,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.BlobProto.height', index=7,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.BlobProto.width', index=8,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=60,
serialized_end=264,
)
_BLOBPROTOVECTOR = descriptor.Descriptor(
name='BlobProtoVector',
full_name='caffe.BlobProtoVector',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.BlobProtoVector.blobs', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=266,
serialized_end=316,
)
_DATUM = descriptor.Descriptor(
name='Datum',
full_name='caffe.Datum',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='channels', full_name='caffe.Datum.channels', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.Datum.height', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.Datum.width', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.Datum.data', index=3,
number=4, type=12, cpp_type=9, label=1,
has_default_value=False, default_value="",
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='label', full_name='caffe.Datum.label', index=4,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='float_data', full_name='caffe.Datum.float_data', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='encoded', full_name='caffe.Datum.encoded', index=6,
number=7, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=319,
serialized_end=448,
)
_FILLERPARAMETER = descriptor.Descriptor(
name='FillerParameter',
full_name='caffe.FillerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='type', full_name='caffe.FillerParameter.type', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("constant", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='value', full_name='caffe.FillerParameter.value', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='min', full_name='caffe.FillerParameter.min', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max', full_name='caffe.FillerParameter.max', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean', full_name='caffe.FillerParameter.mean', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='std', full_name='caffe.FillerParameter.std', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='sparse', full_name='caffe.FillerParameter.sparse', index=6,
number=7, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='variance_norm', full_name='caffe.FillerParameter.variance_norm', index=7,
number=8, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_FILLERPARAMETER_VARIANCENORM,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=451,
serialized_end=717,
)
_NETPARAMETER = descriptor.Descriptor(
name='NetParameter',
full_name='caffe.NetParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.NetParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input', full_name='caffe.NetParameter.input', index=1,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input_shape', full_name='caffe.NetParameter.input_shape', index=2,
number=8, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input_dim', full_name='caffe.NetParameter.input_dim', index=3,
number=4, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_backward', full_name='caffe.NetParameter.force_backward', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='state', full_name='caffe.NetParameter.state', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='debug_info', full_name='caffe.NetParameter.debug_info', index=6,
number=7, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layer', full_name='caffe.NetParameter.layer', index=7,
number=100, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layers', full_name='caffe.NetParameter.layers', index=8,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=720,
serialized_end=990,
)
_SOLVERPARAMETER = descriptor.Descriptor(
name='SolverParameter',
full_name='caffe.SolverParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='net', full_name='caffe.SolverParameter.net', index=0,
number=24, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='net_param', full_name='caffe.SolverParameter.net_param', index=1,
number=25, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='train_net', full_name='caffe.SolverParameter.train_net', index=2,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_net', full_name='caffe.SolverParameter.test_net', index=3,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='train_net_param', full_name='caffe.SolverParameter.train_net_param', index=4,
number=21, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_net_param', full_name='caffe.SolverParameter.test_net_param', index=5,
number=22, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='train_state', full_name='caffe.SolverParameter.train_state', index=6,
number=26, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_state', full_name='caffe.SolverParameter.test_state', index=7,
number=27, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_iter', full_name='caffe.SolverParameter.test_iter', index=8,
number=3, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_interval', full_name='caffe.SolverParameter.test_interval', index=9,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_compute_loss', full_name='caffe.SolverParameter.test_compute_loss', index=10,
number=19, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_initialization', full_name='caffe.SolverParameter.test_initialization', index=11,
number=32, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='base_lr', full_name='caffe.SolverParameter.base_lr', index=12,
number=5, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='display', full_name='caffe.SolverParameter.display', index=13,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='average_loss', full_name='caffe.SolverParameter.average_loss', index=14,
number=33, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max_iter', full_name='caffe.SolverParameter.max_iter', index=15,
number=7, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='iter_size', full_name='caffe.SolverParameter.iter_size', index=16,
number=36, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lr_policy', full_name='caffe.SolverParameter.lr_policy', index=17,
number=8, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='gamma', full_name='caffe.SolverParameter.gamma', index=18,
number=9, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power', full_name='caffe.SolverParameter.power', index=19,
number=10, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='momentum', full_name='caffe.SolverParameter.momentum', index=20,
number=11, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.SolverParameter.weight_decay', index=21,
number=12, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='regularization_type', full_name='caffe.SolverParameter.regularization_type', index=22,
number=29, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("L2", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stepsize', full_name='caffe.SolverParameter.stepsize', index=23,
number=13, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stepvalue', full_name='caffe.SolverParameter.stepvalue', index=24,
number=34, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='clip_gradients', full_name='caffe.SolverParameter.clip_gradients', index=25,
number=35, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot', full_name='caffe.SolverParameter.snapshot', index=26,
number=14, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_prefix', full_name='caffe.SolverParameter.snapshot_prefix', index=27,
number=15, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_diff', full_name='caffe.SolverParameter.snapshot_diff', index=28,
number=16, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_format', full_name='caffe.SolverParameter.snapshot_format', index=29,
number=37, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='solver_mode', full_name='caffe.SolverParameter.solver_mode', index=30,
number=17, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='device_id', full_name='caffe.SolverParameter.device_id', index=31,
number=18, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='random_seed', full_name='caffe.SolverParameter.random_seed', index=32,
number=20, type=3, cpp_type=2, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='solver_type', full_name='caffe.SolverParameter.solver_type', index=33,
number=30, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='delta', full_name='caffe.SolverParameter.delta', index=34,
number=31, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1e-08,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='momentum2', full_name='caffe.SolverParameter.momentum2', index=35,
number=39, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.999,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rms_decay', full_name='caffe.SolverParameter.rms_decay', index=36,
number=38, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='debug_info', full_name='caffe.SolverParameter.debug_info', index=37,
number=23, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_after_train', full_name='caffe.SolverParameter.snapshot_after_train', index=38,
number=28, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SOLVERPARAMETER_SNAPSHOTFORMAT,
_SOLVERPARAMETER_SOLVERMODE,
_SOLVERPARAMETER_SOLVERTYPE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=993,
serialized_end=2282,
)
_SOLVERSTATE = descriptor.Descriptor(
name='SolverState',
full_name='caffe.SolverState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='iter', full_name='caffe.SolverState.iter', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='learned_net', full_name='caffe.SolverState.learned_net', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='history', full_name='caffe.SolverState.history', index=2,
number=3, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='current_step', full_name='caffe.SolverState.current_step', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2284,
serialized_end=2392,
)
_NETSTATE = descriptor.Descriptor(
name='NetState',
full_name='caffe.NetState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='phase', full_name='caffe.NetState.phase', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='level', full_name='caffe.NetState.level', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stage', full_name='caffe.NetState.stage', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2394,
serialized_end=2472,
)
_NETSTATERULE = descriptor.Descriptor(
name='NetStateRule',
full_name='caffe.NetStateRule',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='phase', full_name='caffe.NetStateRule.phase', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='min_level', full_name='caffe.NetStateRule.min_level', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max_level', full_name='caffe.NetStateRule.max_level', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stage', full_name='caffe.NetStateRule.stage', index=3,
number=4, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='not_stage', full_name='caffe.NetStateRule.not_stage', index=4,
number=5, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2474,
serialized_end=2589,
)
_PARAMSPEC = descriptor.Descriptor(
name='ParamSpec',
full_name='caffe.ParamSpec',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.ParamSpec.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='share_mode', full_name='caffe.ParamSpec.share_mode', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lr_mult', full_name='caffe.ParamSpec.lr_mult', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='decay_mult', full_name='caffe.ParamSpec.decay_mult', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_PARAMSPEC_DIMCHECKMODE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2592,
serialized_end=2755,
)
_LAYERPARAMETER = descriptor.Descriptor(
name='LayerParameter',
full_name='caffe.LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.LayerParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.LayerParameter.type', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bottom', full_name='caffe.LayerParameter.bottom', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top', full_name='caffe.LayerParameter.top', index=3,
number=4, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='phase', full_name='caffe.LayerParameter.phase', index=4,
number=10, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='loss_weight', full_name='caffe.LayerParameter.loss_weight', index=5,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='param', full_name='caffe.LayerParameter.param', index=6,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.LayerParameter.blobs', index=7,
number=7, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='propagate_down', full_name='caffe.LayerParameter.propagate_down', index=8,
number=11, type=8, cpp_type=7, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='include', full_name='caffe.LayerParameter.include', index=9,
number=8, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='exclude', full_name='caffe.LayerParameter.exclude', index=10,
number=9, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='transform_param', full_name='caffe.LayerParameter.transform_param', index=11,
number=100, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='loss_param', full_name='caffe.LayerParameter.loss_param', index=12,
number=101, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='accuracy_param', full_name='caffe.LayerParameter.accuracy_param', index=13,
number=102, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='argmax_param', full_name='caffe.LayerParameter.argmax_param', index=14,
number=103, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_param', full_name='caffe.LayerParameter.concat_param', index=15,
number=104, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='contrastive_loss_param', full_name='caffe.LayerParameter.contrastive_loss_param', index=16,
number=105, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='convolution_param', full_name='caffe.LayerParameter.convolution_param', index=17,
number=106, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data_param', full_name='caffe.LayerParameter.data_param', index=18,
number=107, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_param', full_name='caffe.LayerParameter.dropout_param', index=19,
number=108, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dummy_data_param', full_name='caffe.LayerParameter.dummy_data_param', index=20,
number=109, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='eltwise_param', full_name='caffe.LayerParameter.eltwise_param', index=21,
number=110, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='embed_param', full_name='caffe.LayerParameter.embed_param', index=22,
number=137, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='exp_param', full_name='caffe.LayerParameter.exp_param', index=23,
number=111, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='flatten_param', full_name='caffe.LayerParameter.flatten_param', index=24,
number=135, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_data_param', full_name='caffe.LayerParameter.hdf5_data_param', index=25,
number=112, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.LayerParameter.hdf5_output_param', index=26,
number=113, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hinge_loss_param', full_name='caffe.LayerParameter.hinge_loss_param', index=27,
number=114, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='image_data_param', full_name='caffe.LayerParameter.image_data_param', index=28,
number=115, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='infogain_loss_param', full_name='caffe.LayerParameter.infogain_loss_param', index=29,
number=116, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='inner_product_param', full_name='caffe.LayerParameter.inner_product_param', index=30,
number=117, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='log_param', full_name='caffe.LayerParameter.log_param', index=31,
number=134, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lrn_param', full_name='caffe.LayerParameter.lrn_param', index=32,
number=118, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='memory_data_param', full_name='caffe.LayerParameter.memory_data_param', index=33,
number=119, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mvn_param', full_name='caffe.LayerParameter.mvn_param', index=34,
number=120, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pooling_param', full_name='caffe.LayerParameter.pooling_param', index=35,
number=121, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power_param', full_name='caffe.LayerParameter.power_param', index=36,
number=122, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='prelu_param', full_name='caffe.LayerParameter.prelu_param', index=37,
number=131, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='python_param', full_name='caffe.LayerParameter.python_param', index=38,
number=130, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='reduction_param', full_name='caffe.LayerParameter.reduction_param', index=39,
number=136, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='relu_param', full_name='caffe.LayerParameter.relu_param', index=40,
number=123, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='reshape_param', full_name='caffe.LayerParameter.reshape_param', index=41,
number=133, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='sigmoid_param', full_name='caffe.LayerParameter.sigmoid_param', index=42,
number=124, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='softmax_param', full_name='caffe.LayerParameter.softmax_param', index=43,
number=125, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='spp_param', full_name='caffe.LayerParameter.spp_param', index=44,
number=132, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='slice_param', full_name='caffe.LayerParameter.slice_param', index=45,
number=126, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='tanh_param', full_name='caffe.LayerParameter.tanh_param', index=46,
number=127, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='threshold_param', full_name='caffe.LayerParameter.threshold_param', index=47,
number=128, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='tile_param', full_name='caffe.LayerParameter.tile_param', index=48,
number=138, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='window_data_param', full_name='caffe.LayerParameter.window_data_param', index=49,
number=129, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2758,
serialized_end=4943,
)
_TRANSFORMATIONPARAMETER = descriptor.Descriptor(
name='TransformationParameter',
full_name='caffe.TransformationParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='scale', full_name='caffe.TransformationParameter.scale', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.TransformationParameter.mirror', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.TransformationParameter.crop_size', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.TransformationParameter.mean_file', index=3,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_value', full_name='caffe.TransformationParameter.mean_value', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_color', full_name='caffe.TransformationParameter.force_color', index=5,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_gray', full_name='caffe.TransformationParameter.force_gray', index=6,
number=7, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4946,
serialized_end=5128,
)
_LOSSPARAMETER = descriptor.Descriptor(
name='LossParameter',
full_name='caffe.LossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='ignore_label', full_name='caffe.LossParameter.ignore_label', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='normalize', full_name='caffe.LossParameter.normalize', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5130,
serialized_end=5192,
)
_ACCURACYPARAMETER = descriptor.Descriptor(
name='AccuracyParameter',
full_name='caffe.AccuracyParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='top_k', full_name='caffe.AccuracyParameter.top_k', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.AccuracyParameter.axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='ignore_label', full_name='caffe.AccuracyParameter.ignore_label', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5194,
serialized_end=5270,
)
_ARGMAXPARAMETER = descriptor.Descriptor(
name='ArgMaxParameter',
full_name='caffe.ArgMaxParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='out_max_val', full_name='caffe.ArgMaxParameter.out_max_val', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top_k', full_name='caffe.ArgMaxParameter.top_k', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5272,
serialized_end=5335,
)
_CONCATPARAMETER = descriptor.Descriptor(
name='ConcatParameter',
full_name='caffe.ConcatParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='axis', full_name='caffe.ConcatParameter.axis', index=0,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.ConcatParameter.concat_dim', index=1,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5337,
serialized_end=5394,
)
_CONTRASTIVELOSSPARAMETER = descriptor.Descriptor(
name='ContrastiveLossParameter',
full_name='caffe.ContrastiveLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='margin', full_name='caffe.ContrastiveLossParameter.margin', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='legacy_version', full_name='caffe.ContrastiveLossParameter.legacy_version', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5396,
serialized_end=5472,
)
_CONVOLUTIONPARAMETER = descriptor.Descriptor(
name='ConvolutionParameter',
full_name='caffe.ConvolutionParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.ConvolutionParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.ConvolutionParameter.bias_term', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.ConvolutionParameter.pad', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_h', full_name='caffe.ConvolutionParameter.pad_h', index=3,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_w', full_name='caffe.ConvolutionParameter.pad_w', index=4,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_size', full_name='caffe.ConvolutionParameter.kernel_size', index=5,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_h', full_name='caffe.ConvolutionParameter.kernel_h', index=6,
number=11, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_w', full_name='caffe.ConvolutionParameter.kernel_w', index=7,
number=12, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='group', full_name='caffe.ConvolutionParameter.group', index=8,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.ConvolutionParameter.stride', index=9,
number=6, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_h', full_name='caffe.ConvolutionParameter.stride_h', index=10,
number=13, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_w', full_name='caffe.ConvolutionParameter.stride_w', index=11,
number=14, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.ConvolutionParameter.weight_filler', index=12,
number=7, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.ConvolutionParameter.bias_filler', index=13,
number=8, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.ConvolutionParameter.engine', index=14,
number=15, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_CONVOLUTIONPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5475,
serialized_end=5922,
)
_DATAPARAMETER = descriptor.Descriptor(
name='DataParameter',
full_name='caffe.DataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.DataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.DataParameter.batch_size', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.DataParameter.rand_skip', index=2,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='backend', full_name='caffe.DataParameter.backend', index=3,
number=8, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.DataParameter.scale', index=4,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.DataParameter.mean_file', index=5,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.DataParameter.crop_size', index=6,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.DataParameter.mirror', index=7,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_encoded_color', full_name='caffe.DataParameter.force_encoded_color', index=8,
number=9, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='prefetch', full_name='caffe.DataParameter.prefetch', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=4,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_DATAPARAMETER_DB,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5925,
serialized_end=6217,
)
_DROPOUTPARAMETER = descriptor.Descriptor(
name='DropoutParameter',
full_name='caffe.DropoutParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.DropoutParameter.dropout_ratio', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6219,
serialized_end=6265,
)
_DUMMYDATAPARAMETER = descriptor.Descriptor(
name='DummyDataParameter',
full_name='caffe.DummyDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='data_filler', full_name='caffe.DummyDataParameter.data_filler', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shape', full_name='caffe.DummyDataParameter.shape', index=1,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num', full_name='caffe.DummyDataParameter.num', index=2,
number=2, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.DummyDataParameter.channels', index=3,
number=3, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.DummyDataParameter.height', index=4,
number=4, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.DummyDataParameter.width', index=5,
number=5, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6268,
serialized_end=6428,
)
_ELTWISEPARAMETER = descriptor.Descriptor(
name='EltwiseParameter',
full_name='caffe.EltwiseParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='operation', full_name='caffe.EltwiseParameter.operation', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='coeff', full_name='caffe.EltwiseParameter.coeff', index=1,
number=2, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stable_prod_grad', full_name='caffe.EltwiseParameter.stable_prod_grad', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_ELTWISEPARAMETER_ELTWISEOP,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6431,
serialized_end=6596,
)
_EMBEDPARAMETER = descriptor.Descriptor(
name='EmbedParameter',
full_name='caffe.EmbedParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.EmbedParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input_dim', full_name='caffe.EmbedParameter.input_dim', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.EmbedParameter.bias_term', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.EmbedParameter.weight_filler', index=3,
number=4, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.EmbedParameter.bias_filler', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6599,
serialized_end=6771,
)
_EXPPARAMETER = descriptor.Descriptor(
name='ExpParameter',
full_name='caffe.ExpParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='base', full_name='caffe.ExpParameter.base', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.ExpParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shift', full_name='caffe.ExpParameter.shift', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6773,
serialized_end=6841,
)
_FLATTENPARAMETER = descriptor.Descriptor(
name='FlattenParameter',
full_name='caffe.FlattenParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='axis', full_name='caffe.FlattenParameter.axis', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='end_axis', full_name='caffe.FlattenParameter.end_axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6843,
serialized_end=6900,
)
_HDF5DATAPARAMETER = descriptor.Descriptor(
name='HDF5DataParameter',
full_name='caffe.HDF5DataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.HDF5DataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.HDF5DataParameter.batch_size', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle', full_name='caffe.HDF5DataParameter.shuffle', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6902,
serialized_end=6981,
)
_HDF5OUTPUTPARAMETER = descriptor.Descriptor(
name='HDF5OutputParameter',
full_name='caffe.HDF5OutputParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='file_name', full_name='caffe.HDF5OutputParameter.file_name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6983,
serialized_end=7023,
)
_HINGELOSSPARAMETER = descriptor.Descriptor(
name='HingeLossParameter',
full_name='caffe.HingeLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='norm', full_name='caffe.HingeLossParameter.norm', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_HINGELOSSPARAMETER_NORM,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7025,
serialized_end=7119,
)
_IMAGEDATAPARAMETER = descriptor.Descriptor(
name='ImageDataParameter',
full_name='caffe.ImageDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.ImageDataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.ImageDataParameter.batch_size', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.ImageDataParameter.rand_skip', index=2,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle', full_name='caffe.ImageDataParameter.shuffle', index=3,
number=8, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_height', full_name='caffe.ImageDataParameter.new_height', index=4,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_width', full_name='caffe.ImageDataParameter.new_width', index=5,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='is_color', full_name='caffe.ImageDataParameter.is_color', index=6,
number=11, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.ImageDataParameter.scale', index=7,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.ImageDataParameter.mean_file', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.ImageDataParameter.crop_size', index=9,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.ImageDataParameter.mirror', index=10,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='root_folder', full_name='caffe.ImageDataParameter.root_folder', index=11,
number=12, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7122,
serialized_end=7401,
)
_INFOGAINLOSSPARAMETER = descriptor.Descriptor(
name='InfogainLossParameter',
full_name='caffe.InfogainLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.InfogainLossParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7403,
serialized_end=7442,
)
_INNERPRODUCTPARAMETER = descriptor.Descriptor(
name='InnerProductParameter',
full_name='caffe.InnerProductParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.InnerProductParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.InnerProductParameter.bias_term', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.InnerProductParameter.weight_filler', index=2,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.InnerProductParameter.bias_filler', index=3,
number=4, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.InnerProductParameter.axis', index=4,
number=5, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7445,
serialized_end=7622,
)
_LOGPARAMETER = descriptor.Descriptor(
name='LogParameter',
full_name='caffe.LogParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='base', full_name='caffe.LogParameter.base', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.LogParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shift', full_name='caffe.LogParameter.shift', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7624,
serialized_end=7692,
)
_LRNPARAMETER = descriptor.Descriptor(
name='LRNParameter',
full_name='caffe.LRNParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='local_size', full_name='caffe.LRNParameter.local_size', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.LRNParameter.alpha', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='beta', full_name='caffe.LRNParameter.beta', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='norm_region', full_name='caffe.LRNParameter.norm_region', index=3,
number=4, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='k', full_name='caffe.LRNParameter.k', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_LRNPARAMETER_NORMREGION,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7695,
serialized_end=7909,
)
_MEMORYDATAPARAMETER = descriptor.Descriptor(
name='MemoryDataParameter',
full_name='caffe.MemoryDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.MemoryDataParameter.batch_size', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.MemoryDataParameter.channels', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.MemoryDataParameter.height', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.MemoryDataParameter.width', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7911,
serialized_end=8001,
)
_MVNPARAMETER = descriptor.Descriptor(
name='MVNParameter',
full_name='caffe.MVNParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='normalize_variance', full_name='caffe.MVNParameter.normalize_variance', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='across_channels', full_name='caffe.MVNParameter.across_channels', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='eps', full_name='caffe.MVNParameter.eps', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1e-09,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8003,
serialized_end=8103,
)
_POOLINGPARAMETER = descriptor.Descriptor(
name='PoolingParameter',
full_name='caffe.PoolingParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='pool', full_name='caffe.PoolingParameter.pool', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.PoolingParameter.pad', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_h', full_name='caffe.PoolingParameter.pad_h', index=2,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_w', full_name='caffe.PoolingParameter.pad_w', index=3,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_size', full_name='caffe.PoolingParameter.kernel_size', index=4,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_h', full_name='caffe.PoolingParameter.kernel_h', index=5,
number=5, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_w', full_name='caffe.PoolingParameter.kernel_w', index=6,
number=6, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.PoolingParameter.stride', index=7,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_h', full_name='caffe.PoolingParameter.stride_h', index=8,
number=7, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_w', full_name='caffe.PoolingParameter.stride_w', index=9,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.PoolingParameter.engine', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='global_pooling', full_name='caffe.PoolingParameter.global_pooling', index=11,
number=12, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_POOLINGPARAMETER_POOLMETHOD,
_POOLINGPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8106,
serialized_end=8524,
)
_POWERPARAMETER = descriptor.Descriptor(
name='PowerParameter',
full_name='caffe.PowerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='power', full_name='caffe.PowerParameter.power', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.PowerParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shift', full_name='caffe.PowerParameter.shift', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8526,
serialized_end=8596,
)
_PYTHONPARAMETER = descriptor.Descriptor(
name='PythonParameter',
full_name='caffe.PythonParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='module', full_name='caffe.PythonParameter.module', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layer', full_name='caffe.PythonParameter.layer', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='param_str', full_name='caffe.PythonParameter.param_str', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='share_in_parallel', full_name='caffe.PythonParameter.share_in_parallel', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8598,
serialized_end=8701,
)
_REDUCTIONPARAMETER = descriptor.Descriptor(
name='ReductionParameter',
full_name='caffe.ReductionParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='operation', full_name='caffe.ReductionParameter.operation', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.ReductionParameter.axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='coeff', full_name='caffe.ReductionParameter.coeff', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_REDUCTIONPARAMETER_REDUCTIONOP,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8704,
serialized_end=8877,
)
_RELUPARAMETER = descriptor.Descriptor(
name='ReLUParameter',
full_name='caffe.ReLUParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='negative_slope', full_name='caffe.ReLUParameter.negative_slope', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.ReLUParameter.engine', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_RELUPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8880,
serialized_end=9021,
)
_RESHAPEPARAMETER = descriptor.Descriptor(
name='ReshapeParameter',
full_name='caffe.ReshapeParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='shape', full_name='caffe.ReshapeParameter.shape', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.ReshapeParameter.axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num_axes', full_name='caffe.ReshapeParameter.num_axes', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9023,
serialized_end=9113,
)
_SIGMOIDPARAMETER = descriptor.Descriptor(
name='SigmoidParameter',
full_name='caffe.SigmoidParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='engine', full_name='caffe.SigmoidParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SIGMOIDPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9115,
serialized_end=9235,
)
_SLICEPARAMETER = descriptor.Descriptor(
name='SliceParameter',
full_name='caffe.SliceParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='axis', full_name='caffe.SliceParameter.axis', index=0,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='slice_point', full_name='caffe.SliceParameter.slice_point', index=1,
number=2, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='slice_dim', full_name='caffe.SliceParameter.slice_dim', index=2,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9237,
serialized_end=9313,
)
_SOFTMAXPARAMETER = descriptor.Descriptor(
name='SoftmaxParameter',
full_name='caffe.SoftmaxParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='engine', full_name='caffe.SoftmaxParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.SoftmaxParameter.axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SOFTMAXPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9316,
serialized_end=9453,
)
_TANHPARAMETER = descriptor.Descriptor(
name='TanHParameter',
full_name='caffe.TanHParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='engine', full_name='caffe.TanHParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_TANHPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9455,
serialized_end=9569,
)
_TILEPARAMETER = descriptor.Descriptor(
name='TileParameter',
full_name='caffe.TileParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='axis', full_name='caffe.TileParameter.axis', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='tiles', full_name='caffe.TileParameter.tiles', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9571,
serialized_end=9618,
)
_THRESHOLDPARAMETER = descriptor.Descriptor(
name='ThresholdParameter',
full_name='caffe.ThresholdParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='threshold', full_name='caffe.ThresholdParameter.threshold', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9620,
serialized_end=9662,
)
_WINDOWDATAPARAMETER = descriptor.Descriptor(
name='WindowDataParameter',
full_name='caffe.WindowDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.WindowDataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.WindowDataParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.WindowDataParameter.mean_file', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.WindowDataParameter.batch_size', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.WindowDataParameter.crop_size', index=4,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.WindowDataParameter.mirror', index=5,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='fg_threshold', full_name='caffe.WindowDataParameter.fg_threshold', index=6,
number=7, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bg_threshold', full_name='caffe.WindowDataParameter.bg_threshold', index=7,
number=8, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='fg_fraction', full_name='caffe.WindowDataParameter.fg_fraction', index=8,
number=9, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='context_pad', full_name='caffe.WindowDataParameter.context_pad', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_mode', full_name='caffe.WindowDataParameter.crop_mode', index=10,
number=11, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='cache_images', full_name='caffe.WindowDataParameter.cache_images', index=11,
number=12, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='root_folder', full_name='caffe.WindowDataParameter.root_folder', index=12,
number=13, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9665,
serialized_end=9986,
)
_SPPPARAMETER = descriptor.Descriptor(
name='SPPParameter',
full_name='caffe.SPPParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='pyramid_height', full_name='caffe.SPPParameter.pyramid_height', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pool', full_name='caffe.SPPParameter.pool', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.SPPParameter.engine', index=2,
number=6, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SPPPARAMETER_POOLMETHOD,
_SPPPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9989,
serialized_end=10224,
)
_V1LAYERPARAMETER = descriptor.Descriptor(
name='V1LayerParameter',
full_name='caffe.V1LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='bottom', full_name='caffe.V1LayerParameter.bottom', index=0,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top', full_name='caffe.V1LayerParameter.top', index=1,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='name', full_name='caffe.V1LayerParameter.name', index=2,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='include', full_name='caffe.V1LayerParameter.include', index=3,
number=32, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='exclude', full_name='caffe.V1LayerParameter.exclude', index=4,
number=33, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.V1LayerParameter.type', index=5,
number=5, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.V1LayerParameter.blobs', index=6,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='param', full_name='caffe.V1LayerParameter.param', index=7,
number=1001, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blob_share_mode', full_name='caffe.V1LayerParameter.blob_share_mode', index=8,
number=1002, type=14, cpp_type=8, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.V1LayerParameter.blobs_lr', index=9,
number=7, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.V1LayerParameter.weight_decay', index=10,
number=8, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='loss_weight', full_name='caffe.V1LayerParameter.loss_weight', index=11,
number=35, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='accuracy_param', full_name='caffe.V1LayerParameter.accuracy_param', index=12,
number=27, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='argmax_param', full_name='caffe.V1LayerParameter.argmax_param', index=13,
number=23, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_param', full_name='caffe.V1LayerParameter.concat_param', index=14,
number=9, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='contrastive_loss_param', full_name='caffe.V1LayerParameter.contrastive_loss_param', index=15,
number=40, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='convolution_param', full_name='caffe.V1LayerParameter.convolution_param', index=16,
number=10, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data_param', full_name='caffe.V1LayerParameter.data_param', index=17,
number=11, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_param', full_name='caffe.V1LayerParameter.dropout_param', index=18,
number=12, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dummy_data_param', full_name='caffe.V1LayerParameter.dummy_data_param', index=19,
number=26, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='eltwise_param', full_name='caffe.V1LayerParameter.eltwise_param', index=20,
number=24, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='exp_param', full_name='caffe.V1LayerParameter.exp_param', index=21,
number=41, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_data_param', full_name='caffe.V1LayerParameter.hdf5_data_param', index=22,
number=13, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.V1LayerParameter.hdf5_output_param', index=23,
number=14, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hinge_loss_param', full_name='caffe.V1LayerParameter.hinge_loss_param', index=24,
number=29, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='image_data_param', full_name='caffe.V1LayerParameter.image_data_param', index=25,
number=15, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='infogain_loss_param', full_name='caffe.V1LayerParameter.infogain_loss_param', index=26,
number=16, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='inner_product_param', full_name='caffe.V1LayerParameter.inner_product_param', index=27,
number=17, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lrn_param', full_name='caffe.V1LayerParameter.lrn_param', index=28,
number=18, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='memory_data_param', full_name='caffe.V1LayerParameter.memory_data_param', index=29,
number=22, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mvn_param', full_name='caffe.V1LayerParameter.mvn_param', index=30,
number=34, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pooling_param', full_name='caffe.V1LayerParameter.pooling_param', index=31,
number=19, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power_param', full_name='caffe.V1LayerParameter.power_param', index=32,
number=21, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='relu_param', full_name='caffe.V1LayerParameter.relu_param', index=33,
number=30, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='sigmoid_param', full_name='caffe.V1LayerParameter.sigmoid_param', index=34,
number=38, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='softmax_param', full_name='caffe.V1LayerParameter.softmax_param', index=35,
number=39, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='slice_param', full_name='caffe.V1LayerParameter.slice_param', index=36,
number=31, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='tanh_param', full_name='caffe.V1LayerParameter.tanh_param', index=37,
number=37, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='threshold_param', full_name='caffe.V1LayerParameter.threshold_param', index=38,
number=25, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='window_data_param', full_name='caffe.V1LayerParameter.window_data_param', index=39,
number=20, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='transform_param', full_name='caffe.V1LayerParameter.transform_param', index=40,
number=36, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='loss_param', full_name='caffe.V1LayerParameter.loss_param', index=41,
number=42, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layer', full_name='caffe.V1LayerParameter.layer', index=42,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_V1LAYERPARAMETER_LAYERTYPE,
_V1LAYERPARAMETER_DIMCHECKMODE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=10227,
serialized_end=12765,
)
_V0LAYERPARAMETER = descriptor.Descriptor(
name='V0LayerParameter',
full_name='caffe.V0LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.V0LayerParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.V0LayerParameter.type', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.V0LayerParameter.num_output', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='biasterm', full_name='caffe.V0LayerParameter.biasterm', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.V0LayerParameter.weight_filler', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.V0LayerParameter.bias_filler', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.V0LayerParameter.pad', index=6,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernelsize', full_name='caffe.V0LayerParameter.kernelsize', index=7,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='group', full_name='caffe.V0LayerParameter.group', index=8,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.V0LayerParameter.stride', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pool', full_name='caffe.V0LayerParameter.pool', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.V0LayerParameter.dropout_ratio', index=11,
number=12, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='local_size', full_name='caffe.V0LayerParameter.local_size', index=12,
number=13, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.V0LayerParameter.alpha', index=13,
number=14, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='beta', full_name='caffe.V0LayerParameter.beta', index=14,
number=15, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='k', full_name='caffe.V0LayerParameter.k', index=15,
number=22, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='source', full_name='caffe.V0LayerParameter.source', index=16,
number=16, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.V0LayerParameter.scale', index=17,
number=17, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='meanfile', full_name='caffe.V0LayerParameter.meanfile', index=18,
number=18, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batchsize', full_name='caffe.V0LayerParameter.batchsize', index=19,
number=19, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='cropsize', full_name='caffe.V0LayerParameter.cropsize', index=20,
number=20, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.V0LayerParameter.mirror', index=21,
number=21, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.V0LayerParameter.blobs', index=22,
number=50, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.V0LayerParameter.blobs_lr', index=23,
number=51, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.V0LayerParameter.weight_decay', index=24,
number=52, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.V0LayerParameter.rand_skip', index=25,
number=53, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_fg_threshold', full_name='caffe.V0LayerParameter.det_fg_threshold', index=26,
number=54, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_bg_threshold', full_name='caffe.V0LayerParameter.det_bg_threshold', index=27,
number=55, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_fg_fraction', full_name='caffe.V0LayerParameter.det_fg_fraction', index=28,
number=56, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_context_pad', full_name='caffe.V0LayerParameter.det_context_pad', index=29,
number=58, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_crop_mode', full_name='caffe.V0LayerParameter.det_crop_mode', index=30,
number=59, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_num', full_name='caffe.V0LayerParameter.new_num', index=31,
number=60, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_channels', full_name='caffe.V0LayerParameter.new_channels', index=32,
number=61, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_height', full_name='caffe.V0LayerParameter.new_height', index=33,
number=62, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_width', full_name='caffe.V0LayerParameter.new_width', index=34,
number=63, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle_images', full_name='caffe.V0LayerParameter.shuffle_images', index=35,
number=64, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.V0LayerParameter.concat_dim', index=36,
number=65, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.V0LayerParameter.hdf5_output_param', index=37,
number=1001, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_V0LAYERPARAMETER_POOLMETHOD,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=12768,
serialized_end=13789,
)
_PRELUPARAMETER = descriptor.Descriptor(
name='PReLUParameter',
full_name='caffe.PReLUParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='filler', full_name='caffe.PReLUParameter.filler', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channel_shared', full_name='caffe.PReLUParameter.channel_shared', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=13791,
serialized_end=13878,
)
_BLOBPROTO.fields_by_name['shape'].message_type = _BLOBSHAPE
_BLOBPROTOVECTOR.fields_by_name['blobs'].message_type = _BLOBPROTO
_FILLERPARAMETER.fields_by_name['variance_norm'].enum_type = _FILLERPARAMETER_VARIANCENORM
_FILLERPARAMETER_VARIANCENORM.containing_type = _FILLERPARAMETER;
_NETPARAMETER.fields_by_name['input_shape'].message_type = _BLOBSHAPE
_NETPARAMETER.fields_by_name['state'].message_type = _NETSTATE
_NETPARAMETER.fields_by_name['layer'].message_type = _LAYERPARAMETER
_NETPARAMETER.fields_by_name['layers'].message_type = _V1LAYERPARAMETER
_SOLVERPARAMETER.fields_by_name['net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['train_net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['test_net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['train_state'].message_type = _NETSTATE
_SOLVERPARAMETER.fields_by_name['test_state'].message_type = _NETSTATE
_SOLVERPARAMETER.fields_by_name['snapshot_format'].enum_type = _SOLVERPARAMETER_SNAPSHOTFORMAT
_SOLVERPARAMETER.fields_by_name['solver_mode'].enum_type = _SOLVERPARAMETER_SOLVERMODE
_SOLVERPARAMETER.fields_by_name['solver_type'].enum_type = _SOLVERPARAMETER_SOLVERTYPE
_SOLVERPARAMETER_SNAPSHOTFORMAT.containing_type = _SOLVERPARAMETER;
_SOLVERPARAMETER_SOLVERMODE.containing_type = _SOLVERPARAMETER;
_SOLVERPARAMETER_SOLVERTYPE.containing_type = _SOLVERPARAMETER;
_SOLVERSTATE.fields_by_name['history'].message_type = _BLOBPROTO
_NETSTATE.fields_by_name['phase'].enum_type = _PHASE
_NETSTATERULE.fields_by_name['phase'].enum_type = _PHASE
_PARAMSPEC.fields_by_name['share_mode'].enum_type = _PARAMSPEC_DIMCHECKMODE
_PARAMSPEC_DIMCHECKMODE.containing_type = _PARAMSPEC;
_LAYERPARAMETER.fields_by_name['phase'].enum_type = _PHASE
_LAYERPARAMETER.fields_by_name['param'].message_type = _PARAMSPEC
_LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_LAYERPARAMETER.fields_by_name['include'].message_type = _NETSTATERULE
_LAYERPARAMETER.fields_by_name['exclude'].message_type = _NETSTATERULE
_LAYERPARAMETER.fields_by_name['transform_param'].message_type = _TRANSFORMATIONPARAMETER
_LAYERPARAMETER.fields_by_name['loss_param'].message_type = _LOSSPARAMETER
_LAYERPARAMETER.fields_by_name['accuracy_param'].message_type = _ACCURACYPARAMETER
_LAYERPARAMETER.fields_by_name['argmax_param'].message_type = _ARGMAXPARAMETER
_LAYERPARAMETER.fields_by_name['concat_param'].message_type = _CONCATPARAMETER
_LAYERPARAMETER.fields_by_name['contrastive_loss_param'].message_type = _CONTRASTIVELOSSPARAMETER
_LAYERPARAMETER.fields_by_name['convolution_param'].message_type = _CONVOLUTIONPARAMETER
_LAYERPARAMETER.fields_by_name['data_param'].message_type = _DATAPARAMETER
_LAYERPARAMETER.fields_by_name['dropout_param'].message_type = _DROPOUTPARAMETER
_LAYERPARAMETER.fields_by_name['dummy_data_param'].message_type = _DUMMYDATAPARAMETER
_LAYERPARAMETER.fields_by_name['eltwise_param'].message_type = _ELTWISEPARAMETER
_LAYERPARAMETER.fields_by_name['embed_param'].message_type = _EMBEDPARAMETER
_LAYERPARAMETER.fields_by_name['exp_param'].message_type = _EXPPARAMETER
_LAYERPARAMETER.fields_by_name['flatten_param'].message_type = _FLATTENPARAMETER
_LAYERPARAMETER.fields_by_name['hdf5_data_param'].message_type = _HDF5DATAPARAMETER
_LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_LAYERPARAMETER.fields_by_name['hinge_loss_param'].message_type = _HINGELOSSPARAMETER
_LAYERPARAMETER.fields_by_name['image_data_param'].message_type = _IMAGEDATAPARAMETER
_LAYERPARAMETER.fields_by_name['infogain_loss_param'].message_type = _INFOGAINLOSSPARAMETER
_LAYERPARAMETER.fields_by_name['inner_product_param'].message_type = _INNERPRODUCTPARAMETER
_LAYERPARAMETER.fields_by_name['log_param'].message_type = _LOGPARAMETER
_LAYERPARAMETER.fields_by_name['lrn_param'].message_type = _LRNPARAMETER
_LAYERPARAMETER.fields_by_name['memory_data_param'].message_type = _MEMORYDATAPARAMETER
_LAYERPARAMETER.fields_by_name['mvn_param'].message_type = _MVNPARAMETER
_LAYERPARAMETER.fields_by_name['pooling_param'].message_type = _POOLINGPARAMETER
_LAYERPARAMETER.fields_by_name['power_param'].message_type = _POWERPARAMETER
_LAYERPARAMETER.fields_by_name['prelu_param'].message_type = _PRELUPARAMETER
_LAYERPARAMETER.fields_by_name['python_param'].message_type = _PYTHONPARAMETER
_LAYERPARAMETER.fields_by_name['reduction_param'].message_type = _REDUCTIONPARAMETER
_LAYERPARAMETER.fields_by_name['relu_param'].message_type = _RELUPARAMETER
_LAYERPARAMETER.fields_by_name['reshape_param'].message_type = _RESHAPEPARAMETER
_LAYERPARAMETER.fields_by_name['sigmoid_param'].message_type = _SIGMOIDPARAMETER
_LAYERPARAMETER.fields_by_name['softmax_param'].message_type = _SOFTMAXPARAMETER
_LAYERPARAMETER.fields_by_name['spp_param'].message_type = _SPPPARAMETER
_LAYERPARAMETER.fields_by_name['slice_param'].message_type = _SLICEPARAMETER
_LAYERPARAMETER.fields_by_name['tanh_param'].message_type = _TANHPARAMETER
_LAYERPARAMETER.fields_by_name['threshold_param'].message_type = _THRESHOLDPARAMETER
_LAYERPARAMETER.fields_by_name['tile_param'].message_type = _TILEPARAMETER
_LAYERPARAMETER.fields_by_name['window_data_param'].message_type = _WINDOWDATAPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['engine'].enum_type = _CONVOLUTIONPARAMETER_ENGINE
_CONVOLUTIONPARAMETER_ENGINE.containing_type = _CONVOLUTIONPARAMETER;
_DATAPARAMETER.fields_by_name['backend'].enum_type = _DATAPARAMETER_DB
_DATAPARAMETER_DB.containing_type = _DATAPARAMETER;
_DUMMYDATAPARAMETER.fields_by_name['data_filler'].message_type = _FILLERPARAMETER
_DUMMYDATAPARAMETER.fields_by_name['shape'].message_type = _BLOBSHAPE
_ELTWISEPARAMETER.fields_by_name['operation'].enum_type = _ELTWISEPARAMETER_ELTWISEOP
_ELTWISEPARAMETER_ELTWISEOP.containing_type = _ELTWISEPARAMETER;
_EMBEDPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_EMBEDPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_HINGELOSSPARAMETER.fields_by_name['norm'].enum_type = _HINGELOSSPARAMETER_NORM
_HINGELOSSPARAMETER_NORM.containing_type = _HINGELOSSPARAMETER;
_INNERPRODUCTPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_INNERPRODUCTPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_LRNPARAMETER.fields_by_name['norm_region'].enum_type = _LRNPARAMETER_NORMREGION
_LRNPARAMETER_NORMREGION.containing_type = _LRNPARAMETER;
_POOLINGPARAMETER.fields_by_name['pool'].enum_type = _POOLINGPARAMETER_POOLMETHOD
_POOLINGPARAMETER.fields_by_name['engine'].enum_type = _POOLINGPARAMETER_ENGINE
_POOLINGPARAMETER_POOLMETHOD.containing_type = _POOLINGPARAMETER;
_POOLINGPARAMETER_ENGINE.containing_type = _POOLINGPARAMETER;
_REDUCTIONPARAMETER.fields_by_name['operation'].enum_type = _REDUCTIONPARAMETER_REDUCTIONOP
_REDUCTIONPARAMETER_REDUCTIONOP.containing_type = _REDUCTIONPARAMETER;
_RELUPARAMETER.fields_by_name['engine'].enum_type = _RELUPARAMETER_ENGINE
_RELUPARAMETER_ENGINE.containing_type = _RELUPARAMETER;
_RESHAPEPARAMETER.fields_by_name['shape'].message_type = _BLOBSHAPE
_SIGMOIDPARAMETER.fields_by_name['engine'].enum_type = _SIGMOIDPARAMETER_ENGINE
_SIGMOIDPARAMETER_ENGINE.containing_type = _SIGMOIDPARAMETER;
_SOFTMAXPARAMETER.fields_by_name['engine'].enum_type = _SOFTMAXPARAMETER_ENGINE
_SOFTMAXPARAMETER_ENGINE.containing_type = _SOFTMAXPARAMETER;
_TANHPARAMETER.fields_by_name['engine'].enum_type = _TANHPARAMETER_ENGINE
_TANHPARAMETER_ENGINE.containing_type = _TANHPARAMETER;
_SPPPARAMETER.fields_by_name['pool'].enum_type = _SPPPARAMETER_POOLMETHOD
_SPPPARAMETER.fields_by_name['engine'].enum_type = _SPPPARAMETER_ENGINE
_SPPPARAMETER_POOLMETHOD.containing_type = _SPPPARAMETER;
_SPPPARAMETER_ENGINE.containing_type = _SPPPARAMETER;
_V1LAYERPARAMETER.fields_by_name['include'].message_type = _NETSTATERULE
_V1LAYERPARAMETER.fields_by_name['exclude'].message_type = _NETSTATERULE
_V1LAYERPARAMETER.fields_by_name['type'].enum_type = _V1LAYERPARAMETER_LAYERTYPE
_V1LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_V1LAYERPARAMETER.fields_by_name['blob_share_mode'].enum_type = _V1LAYERPARAMETER_DIMCHECKMODE
_V1LAYERPARAMETER.fields_by_name['accuracy_param'].message_type = _ACCURACYPARAMETER
_V1LAYERPARAMETER.fields_by_name['argmax_param'].message_type = _ARGMAXPARAMETER
_V1LAYERPARAMETER.fields_by_name['concat_param'].message_type = _CONCATPARAMETER
_V1LAYERPARAMETER.fields_by_name['contrastive_loss_param'].message_type = _CONTRASTIVELOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['convolution_param'].message_type = _CONVOLUTIONPARAMETER
_V1LAYERPARAMETER.fields_by_name['data_param'].message_type = _DATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['dropout_param'].message_type = _DROPOUTPARAMETER
_V1LAYERPARAMETER.fields_by_name['dummy_data_param'].message_type = _DUMMYDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['eltwise_param'].message_type = _ELTWISEPARAMETER
_V1LAYERPARAMETER.fields_by_name['exp_param'].message_type = _EXPPARAMETER
_V1LAYERPARAMETER.fields_by_name['hdf5_data_param'].message_type = _HDF5DATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_V1LAYERPARAMETER.fields_by_name['hinge_loss_param'].message_type = _HINGELOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['image_data_param'].message_type = _IMAGEDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['infogain_loss_param'].message_type = _INFOGAINLOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['inner_product_param'].message_type = _INNERPRODUCTPARAMETER
_V1LAYERPARAMETER.fields_by_name['lrn_param'].message_type = _LRNPARAMETER
_V1LAYERPARAMETER.fields_by_name['memory_data_param'].message_type = _MEMORYDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['mvn_param'].message_type = _MVNPARAMETER
_V1LAYERPARAMETER.fields_by_name['pooling_param'].message_type = _POOLINGPARAMETER
_V1LAYERPARAMETER.fields_by_name['power_param'].message_type = _POWERPARAMETER
_V1LAYERPARAMETER.fields_by_name['relu_param'].message_type = _RELUPARAMETER
_V1LAYERPARAMETER.fields_by_name['sigmoid_param'].message_type = _SIGMOIDPARAMETER
_V1LAYERPARAMETER.fields_by_name['softmax_param'].message_type = _SOFTMAXPARAMETER
_V1LAYERPARAMETER.fields_by_name['slice_param'].message_type = _SLICEPARAMETER
_V1LAYERPARAMETER.fields_by_name['tanh_param'].message_type = _TANHPARAMETER
_V1LAYERPARAMETER.fields_by_name['threshold_param'].message_type = _THRESHOLDPARAMETER
_V1LAYERPARAMETER.fields_by_name['window_data_param'].message_type = _WINDOWDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['transform_param'].message_type = _TRANSFORMATIONPARAMETER
_V1LAYERPARAMETER.fields_by_name['loss_param'].message_type = _LOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['layer'].message_type = _V0LAYERPARAMETER
_V1LAYERPARAMETER_LAYERTYPE.containing_type = _V1LAYERPARAMETER;
_V1LAYERPARAMETER_DIMCHECKMODE.containing_type = _V1LAYERPARAMETER;
_V0LAYERPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_V0LAYERPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_V0LAYERPARAMETER.fields_by_name['pool'].enum_type = _V0LAYERPARAMETER_POOLMETHOD
_V0LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_V0LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_V0LAYERPARAMETER_POOLMETHOD.containing_type = _V0LAYERPARAMETER;
_PRELUPARAMETER.fields_by_name['filler'].message_type = _FILLERPARAMETER
DESCRIPTOR.message_types_by_name['BlobShape'] = _BLOBSHAPE
DESCRIPTOR.message_types_by_name['BlobProto'] = _BLOBPROTO
DESCRIPTOR.message_types_by_name['BlobProtoVector'] = _BLOBPROTOVECTOR
DESCRIPTOR.message_types_by_name['Datum'] = _DATUM
DESCRIPTOR.message_types_by_name['FillerParameter'] = _FILLERPARAMETER
DESCRIPTOR.message_types_by_name['NetParameter'] = _NETPARAMETER
DESCRIPTOR.message_types_by_name['SolverParameter'] = _SOLVERPARAMETER
DESCRIPTOR.message_types_by_name['SolverState'] = _SOLVERSTATE
DESCRIPTOR.message_types_by_name['NetState'] = _NETSTATE
DESCRIPTOR.message_types_by_name['NetStateRule'] = _NETSTATERULE
DESCRIPTOR.message_types_by_name['ParamSpec'] = _PARAMSPEC
DESCRIPTOR.message_types_by_name['LayerParameter'] = _LAYERPARAMETER
DESCRIPTOR.message_types_by_name['TransformationParameter'] = _TRANSFORMATIONPARAMETER
DESCRIPTOR.message_types_by_name['LossParameter'] = _LOSSPARAMETER
DESCRIPTOR.message_types_by_name['AccuracyParameter'] = _ACCURACYPARAMETER
DESCRIPTOR.message_types_by_name['ArgMaxParameter'] = _ARGMAXPARAMETER
DESCRIPTOR.message_types_by_name['ConcatParameter'] = _CONCATPARAMETER
DESCRIPTOR.message_types_by_name['ContrastiveLossParameter'] = _CONTRASTIVELOSSPARAMETER
DESCRIPTOR.message_types_by_name['ConvolutionParameter'] = _CONVOLUTIONPARAMETER
DESCRIPTOR.message_types_by_name['DataParameter'] = _DATAPARAMETER
DESCRIPTOR.message_types_by_name['DropoutParameter'] = _DROPOUTPARAMETER
DESCRIPTOR.message_types_by_name['DummyDataParameter'] = _DUMMYDATAPARAMETER
DESCRIPTOR.message_types_by_name['EltwiseParameter'] = _ELTWISEPARAMETER
DESCRIPTOR.message_types_by_name['EmbedParameter'] = _EMBEDPARAMETER
DESCRIPTOR.message_types_by_name['ExpParameter'] = _EXPPARAMETER
DESCRIPTOR.message_types_by_name['FlattenParameter'] = _FLATTENPARAMETER
DESCRIPTOR.message_types_by_name['HDF5DataParameter'] = _HDF5DATAPARAMETER
DESCRIPTOR.message_types_by_name['HDF5OutputParameter'] = _HDF5OUTPUTPARAMETER
DESCRIPTOR.message_types_by_name['HingeLossParameter'] = _HINGELOSSPARAMETER
DESCRIPTOR.message_types_by_name['ImageDataParameter'] = _IMAGEDATAPARAMETER
DESCRIPTOR.message_types_by_name['InfogainLossParameter'] = _INFOGAINLOSSPARAMETER
DESCRIPTOR.message_types_by_name['InnerProductParameter'] = _INNERPRODUCTPARAMETER
DESCRIPTOR.message_types_by_name['LogParameter'] = _LOGPARAMETER
DESCRIPTOR.message_types_by_name['LRNParameter'] = _LRNPARAMETER
DESCRIPTOR.message_types_by_name['MemoryDataParameter'] = _MEMORYDATAPARAMETER
DESCRIPTOR.message_types_by_name['MVNParameter'] = _MVNPARAMETER
DESCRIPTOR.message_types_by_name['PoolingParameter'] = _POOLINGPARAMETER
DESCRIPTOR.message_types_by_name['PowerParameter'] = _POWERPARAMETER
DESCRIPTOR.message_types_by_name['PythonParameter'] = _PYTHONPARAMETER
DESCRIPTOR.message_types_by_name['ReductionParameter'] = _REDUCTIONPARAMETER
DESCRIPTOR.message_types_by_name['ReLUParameter'] = _RELUPARAMETER
DESCRIPTOR.message_types_by_name['ReshapeParameter'] = _RESHAPEPARAMETER
DESCRIPTOR.message_types_by_name['SigmoidParameter'] = _SIGMOIDPARAMETER
DESCRIPTOR.message_types_by_name['SliceParameter'] = _SLICEPARAMETER
DESCRIPTOR.message_types_by_name['SoftmaxParameter'] = _SOFTMAXPARAMETER
DESCRIPTOR.message_types_by_name['TanHParameter'] = _TANHPARAMETER
DESCRIPTOR.message_types_by_name['TileParameter'] = _TILEPARAMETER
DESCRIPTOR.message_types_by_name['ThresholdParameter'] = _THRESHOLDPARAMETER
DESCRIPTOR.message_types_by_name['WindowDataParameter'] = _WINDOWDATAPARAMETER
DESCRIPTOR.message_types_by_name['SPPParameter'] = _SPPPARAMETER
DESCRIPTOR.message_types_by_name['V1LayerParameter'] = _V1LAYERPARAMETER
DESCRIPTOR.message_types_by_name['V0LayerParameter'] = _V0LAYERPARAMETER
DESCRIPTOR.message_types_by_name['PReLUParameter'] = _PRELUPARAMETER
class BlobShape(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBSHAPE
# @@protoc_insertion_point(class_scope:caffe.BlobShape)
class BlobProto(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTO
# @@protoc_insertion_point(class_scope:caffe.BlobProto)
class BlobProtoVector(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTOVECTOR
# @@protoc_insertion_point(class_scope:caffe.BlobProtoVector)
class Datum(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATUM
# @@protoc_insertion_point(class_scope:caffe.Datum)
class FillerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FILLERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.FillerParameter)
class NetParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETPARAMETER
# @@protoc_insertion_point(class_scope:caffe.NetParameter)
class SolverParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SolverParameter)
class SolverState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERSTATE
# @@protoc_insertion_point(class_scope:caffe.SolverState)
class NetState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATE
# @@protoc_insertion_point(class_scope:caffe.NetState)
class NetStateRule(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATERULE
# @@protoc_insertion_point(class_scope:caffe.NetStateRule)
class ParamSpec(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PARAMSPEC
# @@protoc_insertion_point(class_scope:caffe.ParamSpec)
class LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LayerParameter)
class TransformationParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TRANSFORMATIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.TransformationParameter)
class LossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LossParameter)
class AccuracyParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ACCURACYPARAMETER
# @@protoc_insertion_point(class_scope:caffe.AccuracyParameter)
class ArgMaxParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ARGMAXPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ArgMaxParameter)
class ConcatParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONCATPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ConcatParameter)
class ContrastiveLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONTRASTIVELOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ContrastiveLossParameter)
class ConvolutionParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONVOLUTIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ConvolutionParameter)
class DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DataParameter)
class DropoutParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DROPOUTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DropoutParameter)
class DummyDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DUMMYDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DummyDataParameter)
class EltwiseParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ELTWISEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.EltwiseParameter)
class EmbedParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _EMBEDPARAMETER
# @@protoc_insertion_point(class_scope:caffe.EmbedParameter)
class ExpParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _EXPPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ExpParameter)
class FlattenParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FLATTENPARAMETER
# @@protoc_insertion_point(class_scope:caffe.FlattenParameter)
class HDF5DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5DATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HDF5DataParameter)
class HDF5OutputParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5OUTPUTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HDF5OutputParameter)
class HingeLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HINGELOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HingeLossParameter)
class ImageDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _IMAGEDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ImageDataParameter)
class InfogainLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INFOGAINLOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.InfogainLossParameter)
class InnerProductParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INNERPRODUCTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.InnerProductParameter)
class LogParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LOGPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LogParameter)
class LRNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LRNPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LRNParameter)
class MemoryDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MEMORYDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.MemoryDataParameter)
class MVNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MVNPARAMETER
# @@protoc_insertion_point(class_scope:caffe.MVNParameter)
class PoolingParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POOLINGPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PoolingParameter)
class PowerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POWERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PowerParameter)
class PythonParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PYTHONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PythonParameter)
class ReductionParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _REDUCTIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ReductionParameter)
class ReLUParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _RELUPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ReLUParameter)
class ReshapeParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _RESHAPEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ReshapeParameter)
class SigmoidParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SIGMOIDPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SigmoidParameter)
class SliceParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SLICEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SliceParameter)
class SoftmaxParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOFTMAXPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SoftmaxParameter)
class TanHParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TANHPARAMETER
# @@protoc_insertion_point(class_scope:caffe.TanHParameter)
class TileParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TILEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.TileParameter)
class ThresholdParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _THRESHOLDPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ThresholdParameter)
class WindowDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _WINDOWDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.WindowDataParameter)
class SPPParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SPPPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SPPParameter)
class V1LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _V1LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.V1LayerParameter)
class V0LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _V0LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.V0LayerParameter)
class PReLUParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PRELUPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PReLUParameter)
# @@protoc_insertion_point(module_scope)
| 218,004 | 42.349572 | 26,073 | py |
DRT | DRT-master/external_libs/matconvnet/utils/proto/caffe_old_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='caffe-old.proto',
package='caffe',
serialized_pb='\n\x0f\x63\x61\x66\x66\x65-old.proto\x12\x05\x63\x61\x66\x66\x65\"y\n\tBlobProto\x12\x0e\n\x03num\x18\x01 \x01(\x05:\x01\x30\x12\x13\n\x08\x63hannels\x18\x02 \x01(\x05:\x01\x30\x12\x11\n\x06height\x18\x03 \x01(\x05:\x01\x30\x12\x10\n\x05width\x18\x04 \x01(\x05:\x01\x30\x12\x10\n\x04\x64\x61ta\x18\x05 \x03(\x02\x42\x02\x10\x01\x12\x10\n\x04\x64iff\x18\x06 \x03(\x02\x42\x02\x10\x01\"2\n\x0f\x42lobProtoVector\x12\x1f\n\x05\x62lobs\x18\x01 \x03(\x0b\x32\x10.caffe.BlobProto\"i\n\x05\x44\x61tum\x12\x10\n\x08\x63hannels\x18\x01 \x01(\x05\x12\x0e\n\x06height\x18\x02 \x01(\x05\x12\r\n\x05width\x18\x03 \x01(\x05\x12\x0c\n\x04\x64\x61ta\x18\x04 \x01(\x0c\x12\r\n\x05label\x18\x05 \x01(\x05\x12\x12\n\nfloat_data\x18\x06 \x03(\x02\"|\n\x0f\x46illerParameter\x12\x16\n\x04type\x18\x01 \x01(\t:\x08\x63onstant\x12\x10\n\x05value\x18\x02 \x01(\x02:\x01\x30\x12\x0e\n\x03min\x18\x03 \x01(\x02:\x01\x30\x12\x0e\n\x03max\x18\x04 \x01(\x02:\x01\x31\x12\x0f\n\x04mean\x18\x05 \x01(\x02:\x01\x30\x12\x0e\n\x03std\x18\x06 \x01(\x02:\x01\x31\"\xb3\x07\n\x0eLayerParameter\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0c\n\x04type\x18\x02 \x01(\t\x12\x12\n\nnum_output\x18\x03 \x01(\r\x12\x16\n\x08\x62iasterm\x18\x04 \x01(\x08:\x04true\x12-\n\rweight_filler\x18\x05 \x01(\x0b\x32\x16.caffe.FillerParameter\x12+\n\x0b\x62ias_filler\x18\x06 \x01(\x0b\x32\x16.caffe.FillerParameter\x12\x0e\n\x03pad\x18\x07 \x01(\r:\x01\x30\x12\x12\n\nkernelsize\x18\x08 \x01(\r\x12\x10\n\x05group\x18\t \x01(\r:\x01\x31\x12\x11\n\x06stride\x18\n \x01(\r:\x01\x31\x12\x33\n\x04pool\x18\x0b \x01(\x0e\x32 .caffe.LayerParameter.PoolMethod:\x03MAX\x12\x1a\n\rdropout_ratio\x18\x0c \x01(\x02:\x03\x30.5\x12\x15\n\nlocal_size\x18\r \x01(\r:\x01\x35\x12\x10\n\x05\x61lpha\x18\x0e \x01(\x02:\x01\x31\x12\x12\n\x04\x62\x65ta\x18\x0f \x01(\x02:\x04\x30.75\x12\x0e\n\x06source\x18\x10 \x01(\t\x12\x10\n\x05scale\x18\x11 \x01(\x02:\x01\x31\x12\x10\n\x08meanfile\x18\x12 \x01(\t\x12\x11\n\tbatchsize\x18\x13 \x01(\r\x12\x13\n\x08\x63ropsize\x18\x14 \x01(\r:\x01\x30\x12\x15\n\x06mirror\x18\x15 \x01(\x08:\x05\x66\x61lse\x12\x1f\n\x05\x62lobs\x18\x32 \x03(\x0b\x32\x10.caffe.BlobProto\x12\x10\n\x08\x62lobs_lr\x18\x33 \x03(\x02\x12\x14\n\x0cweight_decay\x18\x34 \x03(\x02\x12\x14\n\trand_skip\x18\x35 \x01(\r:\x01\x30\x12\x1d\n\x10\x64\x65t_fg_threshold\x18\x36 \x01(\x02:\x03\x30.5\x12\x1d\n\x10\x64\x65t_bg_threshold\x18\x37 \x01(\x02:\x03\x30.5\x12\x1d\n\x0f\x64\x65t_fg_fraction\x18\x38 \x01(\x02:\x04\x30.25\x12\x1a\n\x0f\x64\x65t_context_pad\x18: \x01(\r:\x01\x30\x12\x1b\n\rdet_crop_mode\x18; \x01(\t:\x04warp\x12\x12\n\x07new_num\x18< \x01(\x05:\x01\x30\x12\x17\n\x0cnew_channels\x18= \x01(\x05:\x01\x30\x12\x15\n\nnew_height\x18> \x01(\x05:\x01\x30\x12\x14\n\tnew_width\x18? \x01(\x05:\x01\x30\x12\x1d\n\x0eshuffle_images\x18@ \x01(\x08:\x05\x66\x61lse\x12\x15\n\nconcat_dim\x18\x41 \x01(\r:\x01\x31\".\n\nPoolMethod\x12\x07\n\x03MAX\x10\x00\x12\x07\n\x03\x41VE\x10\x01\x12\x0e\n\nSTOCHASTIC\x10\x02\"T\n\x0fLayerConnection\x12$\n\x05layer\x18\x01 \x01(\x0b\x32\x15.caffe.LayerParameter\x12\x0e\n\x06\x62ottom\x18\x02 \x03(\t\x12\x0b\n\x03top\x18\x03 \x03(\t\"\x85\x01\n\x0cNetParameter\x12\x0c\n\x04name\x18\x01 \x01(\t\x12&\n\x06layers\x18\x02 \x03(\x0b\x32\x16.caffe.LayerConnection\x12\r\n\x05input\x18\x03 \x03(\t\x12\x11\n\tinput_dim\x18\x04 \x03(\x05\x12\x1d\n\x0e\x66orce_backward\x18\x05 \x01(\x08:\x05\x66\x61lse\"\xff\x02\n\x0fSolverParameter\x12\x11\n\ttrain_net\x18\x01 \x01(\t\x12\x10\n\x08test_net\x18\x02 \x01(\t\x12\x14\n\ttest_iter\x18\x03 \x01(\x05:\x01\x30\x12\x18\n\rtest_interval\x18\x04 \x01(\x05:\x01\x30\x12\x0f\n\x07\x62\x61se_lr\x18\x05 \x01(\x02\x12\x0f\n\x07\x64isplay\x18\x06 \x01(\x05\x12\x10\n\x08max_iter\x18\x07 \x01(\x05\x12\x11\n\tlr_policy\x18\x08 \x01(\t\x12\r\n\x05gamma\x18\t \x01(\x02\x12\r\n\x05power\x18\n \x01(\x02\x12\x10\n\x08momentum\x18\x0b \x01(\x02\x12\x14\n\x0cweight_decay\x18\x0c \x01(\x02\x12\x10\n\x08stepsize\x18\r \x01(\x05\x12\x13\n\x08snapshot\x18\x0e \x01(\x05:\x01\x30\x12\x17\n\x0fsnapshot_prefix\x18\x0f \x01(\t\x12\x1c\n\rsnapshot_diff\x18\x10 \x01(\x08:\x05\x66\x61lse\x12\x16\n\x0bsolver_mode\x18\x11 \x01(\x05:\x01\x31\x12\x14\n\tdevice_id\x18\x12 \x01(\x05:\x01\x30\"S\n\x0bSolverState\x12\x0c\n\x04iter\x18\x01 \x01(\x05\x12\x13\n\x0blearned_net\x18\x02 \x01(\t\x12!\n\x07history\x18\x03 \x03(\x0b\x32\x10.caffe.BlobProto')
_LAYERPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.LayerParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=1336,
serialized_end=1382,
)
_BLOBPROTO = descriptor.Descriptor(
name='BlobProto',
full_name='caffe.BlobProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num', full_name='caffe.BlobProto.num', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.BlobProto.channels', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.BlobProto.height', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.BlobProto.width', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.BlobProto.data', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='diff', full_name='caffe.BlobProto.diff', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=26,
serialized_end=147,
)
_BLOBPROTOVECTOR = descriptor.Descriptor(
name='BlobProtoVector',
full_name='caffe.BlobProtoVector',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.BlobProtoVector.blobs', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=149,
serialized_end=199,
)
_DATUM = descriptor.Descriptor(
name='Datum',
full_name='caffe.Datum',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='channels', full_name='caffe.Datum.channels', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.Datum.height', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.Datum.width', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.Datum.data', index=3,
number=4, type=12, cpp_type=9, label=1,
has_default_value=False, default_value="",
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='label', full_name='caffe.Datum.label', index=4,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='float_data', full_name='caffe.Datum.float_data', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=201,
serialized_end=306,
)
_FILLERPARAMETER = descriptor.Descriptor(
name='FillerParameter',
full_name='caffe.FillerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='type', full_name='caffe.FillerParameter.type', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("constant", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='value', full_name='caffe.FillerParameter.value', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='min', full_name='caffe.FillerParameter.min', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max', full_name='caffe.FillerParameter.max', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean', full_name='caffe.FillerParameter.mean', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='std', full_name='caffe.FillerParameter.std', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=308,
serialized_end=432,
)
_LAYERPARAMETER = descriptor.Descriptor(
name='LayerParameter',
full_name='caffe.LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.LayerParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.LayerParameter.type', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.LayerParameter.num_output', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='biasterm', full_name='caffe.LayerParameter.biasterm', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.LayerParameter.weight_filler', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.LayerParameter.bias_filler', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.LayerParameter.pad', index=6,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernelsize', full_name='caffe.LayerParameter.kernelsize', index=7,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='group', full_name='caffe.LayerParameter.group', index=8,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.LayerParameter.stride', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pool', full_name='caffe.LayerParameter.pool', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.LayerParameter.dropout_ratio', index=11,
number=12, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='local_size', full_name='caffe.LayerParameter.local_size', index=12,
number=13, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.LayerParameter.alpha', index=13,
number=14, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='beta', full_name='caffe.LayerParameter.beta', index=14,
number=15, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='source', full_name='caffe.LayerParameter.source', index=15,
number=16, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.LayerParameter.scale', index=16,
number=17, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='meanfile', full_name='caffe.LayerParameter.meanfile', index=17,
number=18, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batchsize', full_name='caffe.LayerParameter.batchsize', index=18,
number=19, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='cropsize', full_name='caffe.LayerParameter.cropsize', index=19,
number=20, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.LayerParameter.mirror', index=20,
number=21, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.LayerParameter.blobs', index=21,
number=50, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.LayerParameter.blobs_lr', index=22,
number=51, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.LayerParameter.weight_decay', index=23,
number=52, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.LayerParameter.rand_skip', index=24,
number=53, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_fg_threshold', full_name='caffe.LayerParameter.det_fg_threshold', index=25,
number=54, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_bg_threshold', full_name='caffe.LayerParameter.det_bg_threshold', index=26,
number=55, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_fg_fraction', full_name='caffe.LayerParameter.det_fg_fraction', index=27,
number=56, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_context_pad', full_name='caffe.LayerParameter.det_context_pad', index=28,
number=58, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_crop_mode', full_name='caffe.LayerParameter.det_crop_mode', index=29,
number=59, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_num', full_name='caffe.LayerParameter.new_num', index=30,
number=60, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_channels', full_name='caffe.LayerParameter.new_channels', index=31,
number=61, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_height', full_name='caffe.LayerParameter.new_height', index=32,
number=62, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_width', full_name='caffe.LayerParameter.new_width', index=33,
number=63, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle_images', full_name='caffe.LayerParameter.shuffle_images', index=34,
number=64, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.LayerParameter.concat_dim', index=35,
number=65, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_LAYERPARAMETER_POOLMETHOD,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=435,
serialized_end=1382,
)
_LAYERCONNECTION = descriptor.Descriptor(
name='LayerConnection',
full_name='caffe.LayerConnection',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='layer', full_name='caffe.LayerConnection.layer', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bottom', full_name='caffe.LayerConnection.bottom', index=1,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top', full_name='caffe.LayerConnection.top', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1384,
serialized_end=1468,
)
_NETPARAMETER = descriptor.Descriptor(
name='NetParameter',
full_name='caffe.NetParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.NetParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layers', full_name='caffe.NetParameter.layers', index=1,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input', full_name='caffe.NetParameter.input', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input_dim', full_name='caffe.NetParameter.input_dim', index=3,
number=4, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_backward', full_name='caffe.NetParameter.force_backward', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1471,
serialized_end=1604,
)
_SOLVERPARAMETER = descriptor.Descriptor(
name='SolverParameter',
full_name='caffe.SolverParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='train_net', full_name='caffe.SolverParameter.train_net', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_net', full_name='caffe.SolverParameter.test_net', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_iter', full_name='caffe.SolverParameter.test_iter', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_interval', full_name='caffe.SolverParameter.test_interval', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='base_lr', full_name='caffe.SolverParameter.base_lr', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='display', full_name='caffe.SolverParameter.display', index=5,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max_iter', full_name='caffe.SolverParameter.max_iter', index=6,
number=7, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lr_policy', full_name='caffe.SolverParameter.lr_policy', index=7,
number=8, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='gamma', full_name='caffe.SolverParameter.gamma', index=8,
number=9, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power', full_name='caffe.SolverParameter.power', index=9,
number=10, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='momentum', full_name='caffe.SolverParameter.momentum', index=10,
number=11, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.SolverParameter.weight_decay', index=11,
number=12, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stepsize', full_name='caffe.SolverParameter.stepsize', index=12,
number=13, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot', full_name='caffe.SolverParameter.snapshot', index=13,
number=14, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_prefix', full_name='caffe.SolverParameter.snapshot_prefix', index=14,
number=15, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_diff', full_name='caffe.SolverParameter.snapshot_diff', index=15,
number=16, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='solver_mode', full_name='caffe.SolverParameter.solver_mode', index=16,
number=17, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='device_id', full_name='caffe.SolverParameter.device_id', index=17,
number=18, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1607,
serialized_end=1990,
)
_SOLVERSTATE = descriptor.Descriptor(
name='SolverState',
full_name='caffe.SolverState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='iter', full_name='caffe.SolverState.iter', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='learned_net', full_name='caffe.SolverState.learned_net', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='history', full_name='caffe.SolverState.history', index=2,
number=3, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1992,
serialized_end=2075,
)
_BLOBPROTOVECTOR.fields_by_name['blobs'].message_type = _BLOBPROTO
_LAYERPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_LAYERPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_LAYERPARAMETER.fields_by_name['pool'].enum_type = _LAYERPARAMETER_POOLMETHOD
_LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_LAYERPARAMETER_POOLMETHOD.containing_type = _LAYERPARAMETER;
_LAYERCONNECTION.fields_by_name['layer'].message_type = _LAYERPARAMETER
_NETPARAMETER.fields_by_name['layers'].message_type = _LAYERCONNECTION
_SOLVERSTATE.fields_by_name['history'].message_type = _BLOBPROTO
DESCRIPTOR.message_types_by_name['BlobProto'] = _BLOBPROTO
DESCRIPTOR.message_types_by_name['BlobProtoVector'] = _BLOBPROTOVECTOR
DESCRIPTOR.message_types_by_name['Datum'] = _DATUM
DESCRIPTOR.message_types_by_name['FillerParameter'] = _FILLERPARAMETER
DESCRIPTOR.message_types_by_name['LayerParameter'] = _LAYERPARAMETER
DESCRIPTOR.message_types_by_name['LayerConnection'] = _LAYERCONNECTION
DESCRIPTOR.message_types_by_name['NetParameter'] = _NETPARAMETER
DESCRIPTOR.message_types_by_name['SolverParameter'] = _SOLVERPARAMETER
DESCRIPTOR.message_types_by_name['SolverState'] = _SOLVERSTATE
class BlobProto(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTO
# @@protoc_insertion_point(class_scope:caffe.BlobProto)
class BlobProtoVector(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTOVECTOR
# @@protoc_insertion_point(class_scope:caffe.BlobProtoVector)
class Datum(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATUM
# @@protoc_insertion_point(class_scope:caffe.Datum)
class FillerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FILLERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.FillerParameter)
class LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LayerParameter)
class LayerConnection(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERCONNECTION
# @@protoc_insertion_point(class_scope:caffe.LayerConnection)
class NetParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETPARAMETER
# @@protoc_insertion_point(class_scope:caffe.NetParameter)
class SolverParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SolverParameter)
class SolverState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERSTATE
# @@protoc_insertion_point(class_scope:caffe.SolverState)
# @@protoc_insertion_point(module_scope)
| 39,691 | 43.348603 | 4,364 | py |
DRT | DRT-master/external_libs/matconvnet/utils/proto/caffe_b590f1d_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='caffe_b590f1d.proto',
package='caffe',
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_PHASE = descriptor.EnumDescriptor(
name='Phase',
full_name='caffe.Phase',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='TRAIN', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='TEST', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=14784,
serialized_end=14812,
)
TRAIN = 0
TEST = 1
_FILLERPARAMETER_VARIANCENORM = descriptor.EnumDescriptor(
name='VarianceNorm',
full_name='caffe.FillerParameter.VarianceNorm',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='FAN_IN', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='FAN_OUT', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVERAGE', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=666,
serialized_end=718,
)
_SOLVERPARAMETER_SNAPSHOTFORMAT = descriptor.EnumDescriptor(
name='SnapshotFormat',
full_name='caffe.SolverParameter.SnapshotFormat',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='HDF5', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='BINARYPROTO', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2140,
serialized_end=2183,
)
_SOLVERPARAMETER_SOLVERMODE = descriptor.EnumDescriptor(
name='SolverMode',
full_name='caffe.SolverParameter.SolverMode',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='CPU', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='GPU', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2185,
serialized_end=2215,
)
_SOLVERPARAMETER_SOLVERTYPE = descriptor.EnumDescriptor(
name='SolverType',
full_name='caffe.SolverParameter.SolverType',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='SGD', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='NESTEROV', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ADAGRAD', index=2, number=2,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='RMSPROP', index=3, number=3,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ADADELTA', index=4, number=4,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ADAM', index=5, number=5,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2217,
serialized_end=2302,
)
_PARAMSPEC_DIMCHECKMODE = descriptor.EnumDescriptor(
name='DimCheckMode',
full_name='caffe.ParamSpec.DimCheckMode',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='STRICT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='PERMISSIVE', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2733,
serialized_end=2775,
)
_LOSSPARAMETER_NORMALIZATIONMODE = descriptor.EnumDescriptor(
name='NormalizationMode',
full_name='caffe.LossParameter.NormalizationMode',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='FULL', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='VALID', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='BATCH_SIZE', index=2, number=2,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='NONE', index=3, number=3,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=5462,
serialized_end=5528,
)
_CONVOLUTIONPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.ConvolutionParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6493,
serialized_end=6536,
)
_DATAPARAMETER_DB = descriptor.EnumDescriptor(
name='DB',
full_name='caffe.DataParameter.DB',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='LEVELDB', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='LMDB', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6804,
serialized_end=6831,
)
_ELTWISEPARAMETER_ELTWISEOP = descriptor.EnumDescriptor(
name='EltwiseOp',
full_name='caffe.EltwiseParameter.EltwiseOp',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='PROD', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SUM', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MAX', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=7171,
serialized_end=7210,
)
_HINGELOSSPARAMETER_NORM = descriptor.EnumDescriptor(
name='Norm',
full_name='caffe.HingeLossParameter.Norm',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='L1', index=0, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='L2', index=1, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=7745,
serialized_end=7767,
)
_LRNPARAMETER_NORMREGION = descriptor.EnumDescriptor(
name='NormRegion',
full_name='caffe.LRNParameter.NormRegion',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='ACROSS_CHANNELS', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='WITHIN_CHANNEL', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=8557,
serialized_end=8610,
)
_LRNPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.LRNParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6493,
serialized_end=6536,
)
_POOLINGPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.PoolingParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=9179,
serialized_end=9225,
)
_POOLINGPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.PoolingParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6493,
serialized_end=6536,
)
_REDUCTIONPARAMETER_REDUCTIONOP = descriptor.EnumDescriptor(
name='ReductionOp',
full_name='caffe.ReductionParameter.ReductionOp',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='SUM', index=0, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ASUM', index=1, number=2,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SUMSQ', index=2, number=3,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MEAN', index=3, number=4,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=9570,
serialized_end=9623,
)
_RELUPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.ReLUParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6493,
serialized_end=6536,
)
_SIGMOIDPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.SigmoidParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6493,
serialized_end=6536,
)
_SOFTMAXPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.SoftmaxParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6493,
serialized_end=6536,
)
_TANHPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.TanHParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6493,
serialized_end=6536,
)
_SPPPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.SPPParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=9179,
serialized_end=9225,
)
_SPPPARAMETER_ENGINE = descriptor.EnumDescriptor(
name='Engine',
full_name='caffe.SPPParameter.Engine',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='DEFAULT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CAFFE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CUDNN', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=6493,
serialized_end=6536,
)
_V1LAYERPARAMETER_LAYERTYPE = descriptor.EnumDescriptor(
name='LayerType',
full_name='caffe.V1LayerParameter.LayerType',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='NONE', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ABSVAL', index=1, number=35,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ACCURACY', index=2, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ARGMAX', index=3, number=30,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='BNLL', index=4, number=2,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONCAT', index=5, number=3,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONTRASTIVE_LOSS', index=6, number=37,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONVOLUTION', index=7, number=4,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DATA', index=8, number=5,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DECONVOLUTION', index=9, number=39,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DROPOUT', index=10, number=6,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DUMMY_DATA', index=11, number=32,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='EUCLIDEAN_LOSS', index=12, number=7,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ELTWISE', index=13, number=25,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='EXP', index=14, number=38,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='FLATTEN', index=15, number=8,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HDF5_DATA', index=16, number=9,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HDF5_OUTPUT', index=17, number=10,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HINGE_LOSS', index=18, number=28,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='IM2COL', index=19, number=11,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='IMAGE_DATA', index=20, number=12,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='INFOGAIN_LOSS', index=21, number=13,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='INNER_PRODUCT', index=22, number=14,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='LRN', index=23, number=15,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MEMORY_DATA', index=24, number=29,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MULTINOMIAL_LOGISTIC_LOSS', index=25, number=16,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MVN', index=26, number=34,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='POOLING', index=27, number=17,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='POWER', index=28, number=26,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='RELU', index=29, number=18,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SIGMOID', index=30, number=19,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SIGMOID_CROSS_ENTROPY_LOSS', index=31, number=27,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SILENCE', index=32, number=36,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SOFTMAX', index=33, number=20,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SOFTMAX_LOSS', index=34, number=21,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SPLIT', index=35, number=22,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SLICE', index=36, number=33,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='TANH', index=37, number=23,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='WINDOW_DATA', index=38, number=24,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='THRESHOLD', index=39, number=31,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=13025,
serialized_end=13625,
)
_V1LAYERPARAMETER_DIMCHECKMODE = descriptor.EnumDescriptor(
name='DimCheckMode',
full_name='caffe.V1LayerParameter.DimCheckMode',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='STRICT', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='PERMISSIVE', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2733,
serialized_end=2775,
)
_V0LAYERPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.V0LayerParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=9179,
serialized_end=9225,
)
_BLOBSHAPE = descriptor.Descriptor(
name='BlobShape',
full_name='caffe.BlobShape',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='dim', full_name='caffe.BlobShape.dim', index=0,
number=1, type=3, cpp_type=2, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=30,
serialized_end=58,
)
_BLOBPROTO = descriptor.Descriptor(
name='BlobProto',
full_name='caffe.BlobProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='shape', full_name='caffe.BlobProto.shape', index=0,
number=7, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.BlobProto.data', index=1,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='diff', full_name='caffe.BlobProto.diff', index=2,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='double_data', full_name='caffe.BlobProto.double_data', index=3,
number=8, type=1, cpp_type=5, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='double_diff', full_name='caffe.BlobProto.double_diff', index=4,
number=9, type=1, cpp_type=5, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='num', full_name='caffe.BlobProto.num', index=5,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.BlobProto.channels', index=6,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.BlobProto.height', index=7,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.BlobProto.width', index=8,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=61,
serialized_end=265,
)
_BLOBPROTOVECTOR = descriptor.Descriptor(
name='BlobProtoVector',
full_name='caffe.BlobProtoVector',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.BlobProtoVector.blobs', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=267,
serialized_end=317,
)
_DATUM = descriptor.Descriptor(
name='Datum',
full_name='caffe.Datum',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='channels', full_name='caffe.Datum.channels', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.Datum.height', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.Datum.width', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.Datum.data', index=3,
number=4, type=12, cpp_type=9, label=1,
has_default_value=False, default_value="",
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='label', full_name='caffe.Datum.label', index=4,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='float_data', full_name='caffe.Datum.float_data', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='encoded', full_name='caffe.Datum.encoded', index=6,
number=7, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=320,
serialized_end=449,
)
_FILLERPARAMETER = descriptor.Descriptor(
name='FillerParameter',
full_name='caffe.FillerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='type', full_name='caffe.FillerParameter.type', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("constant", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='value', full_name='caffe.FillerParameter.value', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='min', full_name='caffe.FillerParameter.min', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max', full_name='caffe.FillerParameter.max', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean', full_name='caffe.FillerParameter.mean', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='std', full_name='caffe.FillerParameter.std', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='sparse', full_name='caffe.FillerParameter.sparse', index=6,
number=7, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='variance_norm', full_name='caffe.FillerParameter.variance_norm', index=7,
number=8, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_FILLERPARAMETER_VARIANCENORM,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=452,
serialized_end=718,
)
_NETPARAMETER = descriptor.Descriptor(
name='NetParameter',
full_name='caffe.NetParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.NetParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input', full_name='caffe.NetParameter.input', index=1,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input_shape', full_name='caffe.NetParameter.input_shape', index=2,
number=8, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input_dim', full_name='caffe.NetParameter.input_dim', index=3,
number=4, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_backward', full_name='caffe.NetParameter.force_backward', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='state', full_name='caffe.NetParameter.state', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='debug_info', full_name='caffe.NetParameter.debug_info', index=6,
number=7, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layer', full_name='caffe.NetParameter.layer', index=7,
number=100, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layers', full_name='caffe.NetParameter.layers', index=8,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=721,
serialized_end=991,
)
_SOLVERPARAMETER = descriptor.Descriptor(
name='SolverParameter',
full_name='caffe.SolverParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='net', full_name='caffe.SolverParameter.net', index=0,
number=24, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='net_param', full_name='caffe.SolverParameter.net_param', index=1,
number=25, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='train_net', full_name='caffe.SolverParameter.train_net', index=2,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_net', full_name='caffe.SolverParameter.test_net', index=3,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='train_net_param', full_name='caffe.SolverParameter.train_net_param', index=4,
number=21, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_net_param', full_name='caffe.SolverParameter.test_net_param', index=5,
number=22, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='train_state', full_name='caffe.SolverParameter.train_state', index=6,
number=26, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_state', full_name='caffe.SolverParameter.test_state', index=7,
number=27, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_iter', full_name='caffe.SolverParameter.test_iter', index=8,
number=3, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_interval', full_name='caffe.SolverParameter.test_interval', index=9,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_compute_loss', full_name='caffe.SolverParameter.test_compute_loss', index=10,
number=19, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_initialization', full_name='caffe.SolverParameter.test_initialization', index=11,
number=32, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='base_lr', full_name='caffe.SolverParameter.base_lr', index=12,
number=5, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='display', full_name='caffe.SolverParameter.display', index=13,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='average_loss', full_name='caffe.SolverParameter.average_loss', index=14,
number=33, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max_iter', full_name='caffe.SolverParameter.max_iter', index=15,
number=7, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='iter_size', full_name='caffe.SolverParameter.iter_size', index=16,
number=36, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lr_policy', full_name='caffe.SolverParameter.lr_policy', index=17,
number=8, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='gamma', full_name='caffe.SolverParameter.gamma', index=18,
number=9, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power', full_name='caffe.SolverParameter.power', index=19,
number=10, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='momentum', full_name='caffe.SolverParameter.momentum', index=20,
number=11, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.SolverParameter.weight_decay', index=21,
number=12, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='regularization_type', full_name='caffe.SolverParameter.regularization_type', index=22,
number=29, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("L2", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stepsize', full_name='caffe.SolverParameter.stepsize', index=23,
number=13, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stepvalue', full_name='caffe.SolverParameter.stepvalue', index=24,
number=34, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='clip_gradients', full_name='caffe.SolverParameter.clip_gradients', index=25,
number=35, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot', full_name='caffe.SolverParameter.snapshot', index=26,
number=14, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_prefix', full_name='caffe.SolverParameter.snapshot_prefix', index=27,
number=15, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_diff', full_name='caffe.SolverParameter.snapshot_diff', index=28,
number=16, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_format', full_name='caffe.SolverParameter.snapshot_format', index=29,
number=37, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='solver_mode', full_name='caffe.SolverParameter.solver_mode', index=30,
number=17, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='device_id', full_name='caffe.SolverParameter.device_id', index=31,
number=18, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='random_seed', full_name='caffe.SolverParameter.random_seed', index=32,
number=20, type=3, cpp_type=2, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.SolverParameter.type', index=33,
number=40, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("SGD", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='delta', full_name='caffe.SolverParameter.delta', index=34,
number=31, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1e-08,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='momentum2', full_name='caffe.SolverParameter.momentum2', index=35,
number=39, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.999,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rms_decay', full_name='caffe.SolverParameter.rms_decay', index=36,
number=38, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='debug_info', full_name='caffe.SolverParameter.debug_info', index=37,
number=23, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_after_train', full_name='caffe.SolverParameter.snapshot_after_train', index=38,
number=28, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='solver_type', full_name='caffe.SolverParameter.solver_type', index=39,
number=30, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SOLVERPARAMETER_SNAPSHOTFORMAT,
_SOLVERPARAMETER_SOLVERMODE,
_SOLVERPARAMETER_SOLVERTYPE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=994,
serialized_end=2302,
)
_SOLVERSTATE = descriptor.Descriptor(
name='SolverState',
full_name='caffe.SolverState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='iter', full_name='caffe.SolverState.iter', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='learned_net', full_name='caffe.SolverState.learned_net', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='history', full_name='caffe.SolverState.history', index=2,
number=3, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='current_step', full_name='caffe.SolverState.current_step', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2304,
serialized_end=2412,
)
_NETSTATE = descriptor.Descriptor(
name='NetState',
full_name='caffe.NetState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='phase', full_name='caffe.NetState.phase', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='level', full_name='caffe.NetState.level', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stage', full_name='caffe.NetState.stage', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2414,
serialized_end=2492,
)
_NETSTATERULE = descriptor.Descriptor(
name='NetStateRule',
full_name='caffe.NetStateRule',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='phase', full_name='caffe.NetStateRule.phase', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='min_level', full_name='caffe.NetStateRule.min_level', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max_level', full_name='caffe.NetStateRule.max_level', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stage', full_name='caffe.NetStateRule.stage', index=3,
number=4, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='not_stage', full_name='caffe.NetStateRule.not_stage', index=4,
number=5, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2494,
serialized_end=2609,
)
_PARAMSPEC = descriptor.Descriptor(
name='ParamSpec',
full_name='caffe.ParamSpec',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.ParamSpec.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='share_mode', full_name='caffe.ParamSpec.share_mode', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lr_mult', full_name='caffe.ParamSpec.lr_mult', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='decay_mult', full_name='caffe.ParamSpec.decay_mult', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_PARAMSPEC_DIMCHECKMODE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2612,
serialized_end=2775,
)
_LAYERPARAMETER = descriptor.Descriptor(
name='LayerParameter',
full_name='caffe.LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.LayerParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.LayerParameter.type', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bottom', full_name='caffe.LayerParameter.bottom', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top', full_name='caffe.LayerParameter.top', index=3,
number=4, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='phase', full_name='caffe.LayerParameter.phase', index=4,
number=10, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='loss_weight', full_name='caffe.LayerParameter.loss_weight', index=5,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='param', full_name='caffe.LayerParameter.param', index=6,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.LayerParameter.blobs', index=7,
number=7, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='propagate_down', full_name='caffe.LayerParameter.propagate_down', index=8,
number=11, type=8, cpp_type=7, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='include', full_name='caffe.LayerParameter.include', index=9,
number=8, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='exclude', full_name='caffe.LayerParameter.exclude', index=10,
number=9, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='transform_param', full_name='caffe.LayerParameter.transform_param', index=11,
number=100, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='loss_param', full_name='caffe.LayerParameter.loss_param', index=12,
number=101, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='accuracy_param', full_name='caffe.LayerParameter.accuracy_param', index=13,
number=102, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='argmax_param', full_name='caffe.LayerParameter.argmax_param', index=14,
number=103, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_norm_param', full_name='caffe.LayerParameter.batch_norm_param', index=15,
number=139, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_param', full_name='caffe.LayerParameter.bias_param', index=16,
number=141, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_param', full_name='caffe.LayerParameter.concat_param', index=17,
number=104, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='contrastive_loss_param', full_name='caffe.LayerParameter.contrastive_loss_param', index=18,
number=105, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='convolution_param', full_name='caffe.LayerParameter.convolution_param', index=19,
number=106, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data_param', full_name='caffe.LayerParameter.data_param', index=20,
number=107, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_param', full_name='caffe.LayerParameter.dropout_param', index=21,
number=108, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dummy_data_param', full_name='caffe.LayerParameter.dummy_data_param', index=22,
number=109, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='eltwise_param', full_name='caffe.LayerParameter.eltwise_param', index=23,
number=110, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='elu_param', full_name='caffe.LayerParameter.elu_param', index=24,
number=140, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='embed_param', full_name='caffe.LayerParameter.embed_param', index=25,
number=137, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='exp_param', full_name='caffe.LayerParameter.exp_param', index=26,
number=111, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='flatten_param', full_name='caffe.LayerParameter.flatten_param', index=27,
number=135, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_data_param', full_name='caffe.LayerParameter.hdf5_data_param', index=28,
number=112, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.LayerParameter.hdf5_output_param', index=29,
number=113, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hinge_loss_param', full_name='caffe.LayerParameter.hinge_loss_param', index=30,
number=114, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='image_data_param', full_name='caffe.LayerParameter.image_data_param', index=31,
number=115, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='infogain_loss_param', full_name='caffe.LayerParameter.infogain_loss_param', index=32,
number=116, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='inner_product_param', full_name='caffe.LayerParameter.inner_product_param', index=33,
number=117, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='log_param', full_name='caffe.LayerParameter.log_param', index=34,
number=134, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lrn_param', full_name='caffe.LayerParameter.lrn_param', index=35,
number=118, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='memory_data_param', full_name='caffe.LayerParameter.memory_data_param', index=36,
number=119, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mvn_param', full_name='caffe.LayerParameter.mvn_param', index=37,
number=120, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pooling_param', full_name='caffe.LayerParameter.pooling_param', index=38,
number=121, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power_param', full_name='caffe.LayerParameter.power_param', index=39,
number=122, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='prelu_param', full_name='caffe.LayerParameter.prelu_param', index=40,
number=131, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='python_param', full_name='caffe.LayerParameter.python_param', index=41,
number=130, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='reduction_param', full_name='caffe.LayerParameter.reduction_param', index=42,
number=136, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='relu_param', full_name='caffe.LayerParameter.relu_param', index=43,
number=123, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='reshape_param', full_name='caffe.LayerParameter.reshape_param', index=44,
number=133, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale_param', full_name='caffe.LayerParameter.scale_param', index=45,
number=142, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='sigmoid_param', full_name='caffe.LayerParameter.sigmoid_param', index=46,
number=124, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='softmax_param', full_name='caffe.LayerParameter.softmax_param', index=47,
number=125, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='spp_param', full_name='caffe.LayerParameter.spp_param', index=48,
number=132, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='slice_param', full_name='caffe.LayerParameter.slice_param', index=49,
number=126, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='tanh_param', full_name='caffe.LayerParameter.tanh_param', index=50,
number=127, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='threshold_param', full_name='caffe.LayerParameter.threshold_param', index=51,
number=128, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='tile_param', full_name='caffe.LayerParameter.tile_param', index=52,
number=138, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='window_data_param', full_name='caffe.LayerParameter.window_data_param', index=53,
number=129, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2778,
serialized_end=5146,
)
_TRANSFORMATIONPARAMETER = descriptor.Descriptor(
name='TransformationParameter',
full_name='caffe.TransformationParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='scale', full_name='caffe.TransformationParameter.scale', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.TransformationParameter.mirror', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.TransformationParameter.crop_size', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.TransformationParameter.mean_file', index=3,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_value', full_name='caffe.TransformationParameter.mean_value', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_color', full_name='caffe.TransformationParameter.force_color', index=5,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_gray', full_name='caffe.TransformationParameter.force_gray', index=6,
number=7, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5149,
serialized_end=5331,
)
_LOSSPARAMETER = descriptor.Descriptor(
name='LossParameter',
full_name='caffe.LossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='ignore_label', full_name='caffe.LossParameter.ignore_label', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='normalization', full_name='caffe.LossParameter.normalization', index=1,
number=3, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='normalize', full_name='caffe.LossParameter.normalize', index=2,
number=2, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_LOSSPARAMETER_NORMALIZATIONMODE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5334,
serialized_end=5528,
)
_ACCURACYPARAMETER = descriptor.Descriptor(
name='AccuracyParameter',
full_name='caffe.AccuracyParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='top_k', full_name='caffe.AccuracyParameter.top_k', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.AccuracyParameter.axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='ignore_label', full_name='caffe.AccuracyParameter.ignore_label', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5530,
serialized_end=5606,
)
_ARGMAXPARAMETER = descriptor.Descriptor(
name='ArgMaxParameter',
full_name='caffe.ArgMaxParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='out_max_val', full_name='caffe.ArgMaxParameter.out_max_val', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top_k', full_name='caffe.ArgMaxParameter.top_k', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.ArgMaxParameter.axis', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5608,
serialized_end=5685,
)
_CONCATPARAMETER = descriptor.Descriptor(
name='ConcatParameter',
full_name='caffe.ConcatParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='axis', full_name='caffe.ConcatParameter.axis', index=0,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.ConcatParameter.concat_dim', index=1,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5687,
serialized_end=5744,
)
_BATCHNORMPARAMETER = descriptor.Descriptor(
name='BatchNormParameter',
full_name='caffe.BatchNormParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='use_global_stats', full_name='caffe.BatchNormParameter.use_global_stats', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='moving_average_fraction', full_name='caffe.BatchNormParameter.moving_average_fraction', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.999,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='eps', full_name='caffe.BatchNormParameter.eps', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1e-05,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5746,
serialized_end=5852,
)
_BIASPARAMETER = descriptor.Descriptor(
name='BiasParameter',
full_name='caffe.BiasParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='axis', full_name='caffe.BiasParameter.axis', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num_axes', full_name='caffe.BiasParameter.num_axes', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='filler', full_name='caffe.BiasParameter.filler', index=2,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5854,
serialized_end=5947,
)
_CONTRASTIVELOSSPARAMETER = descriptor.Descriptor(
name='ContrastiveLossParameter',
full_name='caffe.ContrastiveLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='margin', full_name='caffe.ContrastiveLossParameter.margin', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='legacy_version', full_name='caffe.ContrastiveLossParameter.legacy_version', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=5949,
serialized_end=6025,
)
_CONVOLUTIONPARAMETER = descriptor.Descriptor(
name='ConvolutionParameter',
full_name='caffe.ConvolutionParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.ConvolutionParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.ConvolutionParameter.bias_term', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.ConvolutionParameter.pad', index=2,
number=3, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_size', full_name='caffe.ConvolutionParameter.kernel_size', index=3,
number=4, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.ConvolutionParameter.stride', index=4,
number=6, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dilation', full_name='caffe.ConvolutionParameter.dilation', index=5,
number=18, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_h', full_name='caffe.ConvolutionParameter.pad_h', index=6,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_w', full_name='caffe.ConvolutionParameter.pad_w', index=7,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_h', full_name='caffe.ConvolutionParameter.kernel_h', index=8,
number=11, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_w', full_name='caffe.ConvolutionParameter.kernel_w', index=9,
number=12, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_h', full_name='caffe.ConvolutionParameter.stride_h', index=10,
number=13, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_w', full_name='caffe.ConvolutionParameter.stride_w', index=11,
number=14, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='group', full_name='caffe.ConvolutionParameter.group', index=12,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.ConvolutionParameter.weight_filler', index=13,
number=7, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.ConvolutionParameter.bias_filler', index=14,
number=8, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.ConvolutionParameter.engine', index=15,
number=15, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.ConvolutionParameter.axis', index=16,
number=16, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_nd_im2col', full_name='caffe.ConvolutionParameter.force_nd_im2col', index=17,
number=17, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_CONVOLUTIONPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6028,
serialized_end=6536,
)
_DATAPARAMETER = descriptor.Descriptor(
name='DataParameter',
full_name='caffe.DataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.DataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.DataParameter.batch_size', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.DataParameter.rand_skip', index=2,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='backend', full_name='caffe.DataParameter.backend', index=3,
number=8, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.DataParameter.scale', index=4,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.DataParameter.mean_file', index=5,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.DataParameter.crop_size', index=6,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.DataParameter.mirror', index=7,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_encoded_color', full_name='caffe.DataParameter.force_encoded_color', index=8,
number=9, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='prefetch', full_name='caffe.DataParameter.prefetch', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=4,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_DATAPARAMETER_DB,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6539,
serialized_end=6831,
)
_DROPOUTPARAMETER = descriptor.Descriptor(
name='DropoutParameter',
full_name='caffe.DropoutParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.DropoutParameter.dropout_ratio', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6833,
serialized_end=6879,
)
_DUMMYDATAPARAMETER = descriptor.Descriptor(
name='DummyDataParameter',
full_name='caffe.DummyDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='data_filler', full_name='caffe.DummyDataParameter.data_filler', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shape', full_name='caffe.DummyDataParameter.shape', index=1,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num', full_name='caffe.DummyDataParameter.num', index=2,
number=2, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.DummyDataParameter.channels', index=3,
number=3, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.DummyDataParameter.height', index=4,
number=4, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.DummyDataParameter.width', index=5,
number=5, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=6882,
serialized_end=7042,
)
_ELTWISEPARAMETER = descriptor.Descriptor(
name='EltwiseParameter',
full_name='caffe.EltwiseParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='operation', full_name='caffe.EltwiseParameter.operation', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='coeff', full_name='caffe.EltwiseParameter.coeff', index=1,
number=2, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stable_prod_grad', full_name='caffe.EltwiseParameter.stable_prod_grad', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_ELTWISEPARAMETER_ELTWISEOP,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7045,
serialized_end=7210,
)
_ELUPARAMETER = descriptor.Descriptor(
name='ELUParameter',
full_name='caffe.ELUParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.ELUParameter.alpha', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7212,
serialized_end=7244,
)
_EMBEDPARAMETER = descriptor.Descriptor(
name='EmbedParameter',
full_name='caffe.EmbedParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.EmbedParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input_dim', full_name='caffe.EmbedParameter.input_dim', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.EmbedParameter.bias_term', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.EmbedParameter.weight_filler', index=3,
number=4, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.EmbedParameter.bias_filler', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7247,
serialized_end=7419,
)
_EXPPARAMETER = descriptor.Descriptor(
name='ExpParameter',
full_name='caffe.ExpParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='base', full_name='caffe.ExpParameter.base', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.ExpParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shift', full_name='caffe.ExpParameter.shift', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7421,
serialized_end=7489,
)
_FLATTENPARAMETER = descriptor.Descriptor(
name='FlattenParameter',
full_name='caffe.FlattenParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='axis', full_name='caffe.FlattenParameter.axis', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='end_axis', full_name='caffe.FlattenParameter.end_axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7491,
serialized_end=7548,
)
_HDF5DATAPARAMETER = descriptor.Descriptor(
name='HDF5DataParameter',
full_name='caffe.HDF5DataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.HDF5DataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.HDF5DataParameter.batch_size', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle', full_name='caffe.HDF5DataParameter.shuffle', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7550,
serialized_end=7629,
)
_HDF5OUTPUTPARAMETER = descriptor.Descriptor(
name='HDF5OutputParameter',
full_name='caffe.HDF5OutputParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='file_name', full_name='caffe.HDF5OutputParameter.file_name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7631,
serialized_end=7671,
)
_HINGELOSSPARAMETER = descriptor.Descriptor(
name='HingeLossParameter',
full_name='caffe.HingeLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='norm', full_name='caffe.HingeLossParameter.norm', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_HINGELOSSPARAMETER_NORM,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7673,
serialized_end=7767,
)
_IMAGEDATAPARAMETER = descriptor.Descriptor(
name='ImageDataParameter',
full_name='caffe.ImageDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.ImageDataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.ImageDataParameter.batch_size', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.ImageDataParameter.rand_skip', index=2,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle', full_name='caffe.ImageDataParameter.shuffle', index=3,
number=8, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_height', full_name='caffe.ImageDataParameter.new_height', index=4,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_width', full_name='caffe.ImageDataParameter.new_width', index=5,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='is_color', full_name='caffe.ImageDataParameter.is_color', index=6,
number=11, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.ImageDataParameter.scale', index=7,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.ImageDataParameter.mean_file', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.ImageDataParameter.crop_size', index=9,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.ImageDataParameter.mirror', index=10,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='root_folder', full_name='caffe.ImageDataParameter.root_folder', index=11,
number=12, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=7770,
serialized_end=8049,
)
_INFOGAINLOSSPARAMETER = descriptor.Descriptor(
name='InfogainLossParameter',
full_name='caffe.InfogainLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.InfogainLossParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8051,
serialized_end=8090,
)
_INNERPRODUCTPARAMETER = descriptor.Descriptor(
name='InnerProductParameter',
full_name='caffe.InnerProductParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.InnerProductParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.InnerProductParameter.bias_term', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.InnerProductParameter.weight_filler', index=2,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.InnerProductParameter.bias_filler', index=3,
number=4, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.InnerProductParameter.axis', index=4,
number=5, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8093,
serialized_end=8270,
)
_LOGPARAMETER = descriptor.Descriptor(
name='LogParameter',
full_name='caffe.LogParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='base', full_name='caffe.LogParameter.base', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.LogParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shift', full_name='caffe.LogParameter.shift', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8272,
serialized_end=8340,
)
_LRNPARAMETER = descriptor.Descriptor(
name='LRNParameter',
full_name='caffe.LRNParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='local_size', full_name='caffe.LRNParameter.local_size', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.LRNParameter.alpha', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='beta', full_name='caffe.LRNParameter.beta', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='norm_region', full_name='caffe.LRNParameter.norm_region', index=3,
number=4, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='k', full_name='caffe.LRNParameter.k', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.LRNParameter.engine', index=5,
number=6, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_LRNPARAMETER_NORMREGION,
_LRNPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8343,
serialized_end=8655,
)
_MEMORYDATAPARAMETER = descriptor.Descriptor(
name='MemoryDataParameter',
full_name='caffe.MemoryDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.MemoryDataParameter.batch_size', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.MemoryDataParameter.channels', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.MemoryDataParameter.height', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.MemoryDataParameter.width', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8657,
serialized_end=8747,
)
_MVNPARAMETER = descriptor.Descriptor(
name='MVNParameter',
full_name='caffe.MVNParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='normalize_variance', full_name='caffe.MVNParameter.normalize_variance', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='across_channels', full_name='caffe.MVNParameter.across_channels', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='eps', full_name='caffe.MVNParameter.eps', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1e-09,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8749,
serialized_end=8849,
)
_POOLINGPARAMETER = descriptor.Descriptor(
name='PoolingParameter',
full_name='caffe.PoolingParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='pool', full_name='caffe.PoolingParameter.pool', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.PoolingParameter.pad', index=1,
number=4, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_h', full_name='caffe.PoolingParameter.pad_h', index=2,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad_w', full_name='caffe.PoolingParameter.pad_w', index=3,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_size', full_name='caffe.PoolingParameter.kernel_size', index=4,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_h', full_name='caffe.PoolingParameter.kernel_h', index=5,
number=5, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_w', full_name='caffe.PoolingParameter.kernel_w', index=6,
number=6, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.PoolingParameter.stride', index=7,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_h', full_name='caffe.PoolingParameter.stride_h', index=8,
number=7, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride_w', full_name='caffe.PoolingParameter.stride_w', index=9,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.PoolingParameter.engine', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='global_pooling', full_name='caffe.PoolingParameter.global_pooling', index=11,
number=12, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_POOLINGPARAMETER_POOLMETHOD,
_POOLINGPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=8852,
serialized_end=9270,
)
_POWERPARAMETER = descriptor.Descriptor(
name='PowerParameter',
full_name='caffe.PowerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='power', full_name='caffe.PowerParameter.power', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.PowerParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shift', full_name='caffe.PowerParameter.shift', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9272,
serialized_end=9342,
)
_PYTHONPARAMETER = descriptor.Descriptor(
name='PythonParameter',
full_name='caffe.PythonParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='module', full_name='caffe.PythonParameter.module', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layer', full_name='caffe.PythonParameter.layer', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='param_str', full_name='caffe.PythonParameter.param_str', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='share_in_parallel', full_name='caffe.PythonParameter.share_in_parallel', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9344,
serialized_end=9447,
)
_REDUCTIONPARAMETER = descriptor.Descriptor(
name='ReductionParameter',
full_name='caffe.ReductionParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='operation', full_name='caffe.ReductionParameter.operation', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.ReductionParameter.axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='coeff', full_name='caffe.ReductionParameter.coeff', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_REDUCTIONPARAMETER_REDUCTIONOP,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9450,
serialized_end=9623,
)
_RELUPARAMETER = descriptor.Descriptor(
name='ReLUParameter',
full_name='caffe.ReLUParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='negative_slope', full_name='caffe.ReLUParameter.negative_slope', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.ReLUParameter.engine', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_RELUPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9626,
serialized_end=9767,
)
_RESHAPEPARAMETER = descriptor.Descriptor(
name='ReshapeParameter',
full_name='caffe.ReshapeParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='shape', full_name='caffe.ReshapeParameter.shape', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.ReshapeParameter.axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num_axes', full_name='caffe.ReshapeParameter.num_axes', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9769,
serialized_end=9859,
)
_SCALEPARAMETER = descriptor.Descriptor(
name='ScaleParameter',
full_name='caffe.ScaleParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='axis', full_name='caffe.ScaleParameter.axis', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num_axes', full_name='caffe.ScaleParameter.num_axes', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='filler', full_name='caffe.ScaleParameter.filler', index=2,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.ScaleParameter.bias_term', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.ScaleParameter.bias_filler', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=9862,
serialized_end=10027,
)
_SIGMOIDPARAMETER = descriptor.Descriptor(
name='SigmoidParameter',
full_name='caffe.SigmoidParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='engine', full_name='caffe.SigmoidParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SIGMOIDPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=10029,
serialized_end=10149,
)
_SLICEPARAMETER = descriptor.Descriptor(
name='SliceParameter',
full_name='caffe.SliceParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='axis', full_name='caffe.SliceParameter.axis', index=0,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='slice_point', full_name='caffe.SliceParameter.slice_point', index=1,
number=2, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='slice_dim', full_name='caffe.SliceParameter.slice_dim', index=2,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=10151,
serialized_end=10227,
)
_SOFTMAXPARAMETER = descriptor.Descriptor(
name='SoftmaxParameter',
full_name='caffe.SoftmaxParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='engine', full_name='caffe.SoftmaxParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='axis', full_name='caffe.SoftmaxParameter.axis', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SOFTMAXPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=10230,
serialized_end=10367,
)
_TANHPARAMETER = descriptor.Descriptor(
name='TanHParameter',
full_name='caffe.TanHParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='engine', full_name='caffe.TanHParameter.engine', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_TANHPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=10369,
serialized_end=10483,
)
_TILEPARAMETER = descriptor.Descriptor(
name='TileParameter',
full_name='caffe.TileParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='axis', full_name='caffe.TileParameter.axis', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='tiles', full_name='caffe.TileParameter.tiles', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=10485,
serialized_end=10532,
)
_THRESHOLDPARAMETER = descriptor.Descriptor(
name='ThresholdParameter',
full_name='caffe.ThresholdParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='threshold', full_name='caffe.ThresholdParameter.threshold', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=10534,
serialized_end=10576,
)
_WINDOWDATAPARAMETER = descriptor.Descriptor(
name='WindowDataParameter',
full_name='caffe.WindowDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.WindowDataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.WindowDataParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.WindowDataParameter.mean_file', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.WindowDataParameter.batch_size', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.WindowDataParameter.crop_size', index=4,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.WindowDataParameter.mirror', index=5,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='fg_threshold', full_name='caffe.WindowDataParameter.fg_threshold', index=6,
number=7, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bg_threshold', full_name='caffe.WindowDataParameter.bg_threshold', index=7,
number=8, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='fg_fraction', full_name='caffe.WindowDataParameter.fg_fraction', index=8,
number=9, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='context_pad', full_name='caffe.WindowDataParameter.context_pad', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_mode', full_name='caffe.WindowDataParameter.crop_mode', index=10,
number=11, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='cache_images', full_name='caffe.WindowDataParameter.cache_images', index=11,
number=12, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='root_folder', full_name='caffe.WindowDataParameter.root_folder', index=12,
number=13, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=10579,
serialized_end=10900,
)
_SPPPARAMETER = descriptor.Descriptor(
name='SPPParameter',
full_name='caffe.SPPParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='pyramid_height', full_name='caffe.SPPParameter.pyramid_height', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pool', full_name='caffe.SPPParameter.pool', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='engine', full_name='caffe.SPPParameter.engine', index=2,
number=6, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SPPPARAMETER_POOLMETHOD,
_SPPPARAMETER_ENGINE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=10903,
serialized_end=11138,
)
_V1LAYERPARAMETER = descriptor.Descriptor(
name='V1LayerParameter',
full_name='caffe.V1LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='bottom', full_name='caffe.V1LayerParameter.bottom', index=0,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top', full_name='caffe.V1LayerParameter.top', index=1,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='name', full_name='caffe.V1LayerParameter.name', index=2,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='include', full_name='caffe.V1LayerParameter.include', index=3,
number=32, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='exclude', full_name='caffe.V1LayerParameter.exclude', index=4,
number=33, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.V1LayerParameter.type', index=5,
number=5, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.V1LayerParameter.blobs', index=6,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='param', full_name='caffe.V1LayerParameter.param', index=7,
number=1001, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blob_share_mode', full_name='caffe.V1LayerParameter.blob_share_mode', index=8,
number=1002, type=14, cpp_type=8, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.V1LayerParameter.blobs_lr', index=9,
number=7, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.V1LayerParameter.weight_decay', index=10,
number=8, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='loss_weight', full_name='caffe.V1LayerParameter.loss_weight', index=11,
number=35, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='accuracy_param', full_name='caffe.V1LayerParameter.accuracy_param', index=12,
number=27, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='argmax_param', full_name='caffe.V1LayerParameter.argmax_param', index=13,
number=23, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_param', full_name='caffe.V1LayerParameter.concat_param', index=14,
number=9, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='contrastive_loss_param', full_name='caffe.V1LayerParameter.contrastive_loss_param', index=15,
number=40, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='convolution_param', full_name='caffe.V1LayerParameter.convolution_param', index=16,
number=10, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data_param', full_name='caffe.V1LayerParameter.data_param', index=17,
number=11, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_param', full_name='caffe.V1LayerParameter.dropout_param', index=18,
number=12, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dummy_data_param', full_name='caffe.V1LayerParameter.dummy_data_param', index=19,
number=26, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='eltwise_param', full_name='caffe.V1LayerParameter.eltwise_param', index=20,
number=24, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='exp_param', full_name='caffe.V1LayerParameter.exp_param', index=21,
number=41, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_data_param', full_name='caffe.V1LayerParameter.hdf5_data_param', index=22,
number=13, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.V1LayerParameter.hdf5_output_param', index=23,
number=14, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hinge_loss_param', full_name='caffe.V1LayerParameter.hinge_loss_param', index=24,
number=29, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='image_data_param', full_name='caffe.V1LayerParameter.image_data_param', index=25,
number=15, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='infogain_loss_param', full_name='caffe.V1LayerParameter.infogain_loss_param', index=26,
number=16, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='inner_product_param', full_name='caffe.V1LayerParameter.inner_product_param', index=27,
number=17, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lrn_param', full_name='caffe.V1LayerParameter.lrn_param', index=28,
number=18, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='memory_data_param', full_name='caffe.V1LayerParameter.memory_data_param', index=29,
number=22, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mvn_param', full_name='caffe.V1LayerParameter.mvn_param', index=30,
number=34, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pooling_param', full_name='caffe.V1LayerParameter.pooling_param', index=31,
number=19, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power_param', full_name='caffe.V1LayerParameter.power_param', index=32,
number=21, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='relu_param', full_name='caffe.V1LayerParameter.relu_param', index=33,
number=30, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='sigmoid_param', full_name='caffe.V1LayerParameter.sigmoid_param', index=34,
number=38, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='softmax_param', full_name='caffe.V1LayerParameter.softmax_param', index=35,
number=39, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='slice_param', full_name='caffe.V1LayerParameter.slice_param', index=36,
number=31, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='tanh_param', full_name='caffe.V1LayerParameter.tanh_param', index=37,
number=37, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='threshold_param', full_name='caffe.V1LayerParameter.threshold_param', index=38,
number=25, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='window_data_param', full_name='caffe.V1LayerParameter.window_data_param', index=39,
number=20, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='transform_param', full_name='caffe.V1LayerParameter.transform_param', index=40,
number=36, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='loss_param', full_name='caffe.V1LayerParameter.loss_param', index=41,
number=42, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layer', full_name='caffe.V1LayerParameter.layer', index=42,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_V1LAYERPARAMETER_LAYERTYPE,
_V1LAYERPARAMETER_DIMCHECKMODE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=11141,
serialized_end=13669,
)
_V0LAYERPARAMETER = descriptor.Descriptor(
name='V0LayerParameter',
full_name='caffe.V0LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.V0LayerParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.V0LayerParameter.type', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.V0LayerParameter.num_output', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='biasterm', full_name='caffe.V0LayerParameter.biasterm', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.V0LayerParameter.weight_filler', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.V0LayerParameter.bias_filler', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.V0LayerParameter.pad', index=6,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernelsize', full_name='caffe.V0LayerParameter.kernelsize', index=7,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='group', full_name='caffe.V0LayerParameter.group', index=8,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.V0LayerParameter.stride', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pool', full_name='caffe.V0LayerParameter.pool', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.V0LayerParameter.dropout_ratio', index=11,
number=12, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='local_size', full_name='caffe.V0LayerParameter.local_size', index=12,
number=13, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.V0LayerParameter.alpha', index=13,
number=14, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='beta', full_name='caffe.V0LayerParameter.beta', index=14,
number=15, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='k', full_name='caffe.V0LayerParameter.k', index=15,
number=22, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='source', full_name='caffe.V0LayerParameter.source', index=16,
number=16, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.V0LayerParameter.scale', index=17,
number=17, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='meanfile', full_name='caffe.V0LayerParameter.meanfile', index=18,
number=18, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batchsize', full_name='caffe.V0LayerParameter.batchsize', index=19,
number=19, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='cropsize', full_name='caffe.V0LayerParameter.cropsize', index=20,
number=20, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.V0LayerParameter.mirror', index=21,
number=21, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.V0LayerParameter.blobs', index=22,
number=50, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.V0LayerParameter.blobs_lr', index=23,
number=51, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.V0LayerParameter.weight_decay', index=24,
number=52, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.V0LayerParameter.rand_skip', index=25,
number=53, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_fg_threshold', full_name='caffe.V0LayerParameter.det_fg_threshold', index=26,
number=54, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_bg_threshold', full_name='caffe.V0LayerParameter.det_bg_threshold', index=27,
number=55, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_fg_fraction', full_name='caffe.V0LayerParameter.det_fg_fraction', index=28,
number=56, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_context_pad', full_name='caffe.V0LayerParameter.det_context_pad', index=29,
number=58, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_crop_mode', full_name='caffe.V0LayerParameter.det_crop_mode', index=30,
number=59, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_num', full_name='caffe.V0LayerParameter.new_num', index=31,
number=60, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_channels', full_name='caffe.V0LayerParameter.new_channels', index=32,
number=61, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_height', full_name='caffe.V0LayerParameter.new_height', index=33,
number=62, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_width', full_name='caffe.V0LayerParameter.new_width', index=34,
number=63, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle_images', full_name='caffe.V0LayerParameter.shuffle_images', index=35,
number=64, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.V0LayerParameter.concat_dim', index=36,
number=65, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.V0LayerParameter.hdf5_output_param', index=37,
number=1001, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_V0LAYERPARAMETER_POOLMETHOD,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=13672,
serialized_end=14693,
)
_PRELUPARAMETER = descriptor.Descriptor(
name='PReLUParameter',
full_name='caffe.PReLUParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='filler', full_name='caffe.PReLUParameter.filler', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channel_shared', full_name='caffe.PReLUParameter.channel_shared', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=14695,
serialized_end=14782,
)
_BLOBPROTO.fields_by_name['shape'].message_type = _BLOBSHAPE
_BLOBPROTOVECTOR.fields_by_name['blobs'].message_type = _BLOBPROTO
_FILLERPARAMETER.fields_by_name['variance_norm'].enum_type = _FILLERPARAMETER_VARIANCENORM
_FILLERPARAMETER_VARIANCENORM.containing_type = _FILLERPARAMETER;
_NETPARAMETER.fields_by_name['input_shape'].message_type = _BLOBSHAPE
_NETPARAMETER.fields_by_name['state'].message_type = _NETSTATE
_NETPARAMETER.fields_by_name['layer'].message_type = _LAYERPARAMETER
_NETPARAMETER.fields_by_name['layers'].message_type = _V1LAYERPARAMETER
_SOLVERPARAMETER.fields_by_name['net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['train_net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['test_net_param'].message_type = _NETPARAMETER
_SOLVERPARAMETER.fields_by_name['train_state'].message_type = _NETSTATE
_SOLVERPARAMETER.fields_by_name['test_state'].message_type = _NETSTATE
_SOLVERPARAMETER.fields_by_name['snapshot_format'].enum_type = _SOLVERPARAMETER_SNAPSHOTFORMAT
_SOLVERPARAMETER.fields_by_name['solver_mode'].enum_type = _SOLVERPARAMETER_SOLVERMODE
_SOLVERPARAMETER.fields_by_name['solver_type'].enum_type = _SOLVERPARAMETER_SOLVERTYPE
_SOLVERPARAMETER_SNAPSHOTFORMAT.containing_type = _SOLVERPARAMETER;
_SOLVERPARAMETER_SOLVERMODE.containing_type = _SOLVERPARAMETER;
_SOLVERPARAMETER_SOLVERTYPE.containing_type = _SOLVERPARAMETER;
_SOLVERSTATE.fields_by_name['history'].message_type = _BLOBPROTO
_NETSTATE.fields_by_name['phase'].enum_type = _PHASE
_NETSTATERULE.fields_by_name['phase'].enum_type = _PHASE
_PARAMSPEC.fields_by_name['share_mode'].enum_type = _PARAMSPEC_DIMCHECKMODE
_PARAMSPEC_DIMCHECKMODE.containing_type = _PARAMSPEC;
_LAYERPARAMETER.fields_by_name['phase'].enum_type = _PHASE
_LAYERPARAMETER.fields_by_name['param'].message_type = _PARAMSPEC
_LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_LAYERPARAMETER.fields_by_name['include'].message_type = _NETSTATERULE
_LAYERPARAMETER.fields_by_name['exclude'].message_type = _NETSTATERULE
_LAYERPARAMETER.fields_by_name['transform_param'].message_type = _TRANSFORMATIONPARAMETER
_LAYERPARAMETER.fields_by_name['loss_param'].message_type = _LOSSPARAMETER
_LAYERPARAMETER.fields_by_name['accuracy_param'].message_type = _ACCURACYPARAMETER
_LAYERPARAMETER.fields_by_name['argmax_param'].message_type = _ARGMAXPARAMETER
_LAYERPARAMETER.fields_by_name['batch_norm_param'].message_type = _BATCHNORMPARAMETER
_LAYERPARAMETER.fields_by_name['bias_param'].message_type = _BIASPARAMETER
_LAYERPARAMETER.fields_by_name['concat_param'].message_type = _CONCATPARAMETER
_LAYERPARAMETER.fields_by_name['contrastive_loss_param'].message_type = _CONTRASTIVELOSSPARAMETER
_LAYERPARAMETER.fields_by_name['convolution_param'].message_type = _CONVOLUTIONPARAMETER
_LAYERPARAMETER.fields_by_name['data_param'].message_type = _DATAPARAMETER
_LAYERPARAMETER.fields_by_name['dropout_param'].message_type = _DROPOUTPARAMETER
_LAYERPARAMETER.fields_by_name['dummy_data_param'].message_type = _DUMMYDATAPARAMETER
_LAYERPARAMETER.fields_by_name['eltwise_param'].message_type = _ELTWISEPARAMETER
_LAYERPARAMETER.fields_by_name['elu_param'].message_type = _ELUPARAMETER
_LAYERPARAMETER.fields_by_name['embed_param'].message_type = _EMBEDPARAMETER
_LAYERPARAMETER.fields_by_name['exp_param'].message_type = _EXPPARAMETER
_LAYERPARAMETER.fields_by_name['flatten_param'].message_type = _FLATTENPARAMETER
_LAYERPARAMETER.fields_by_name['hdf5_data_param'].message_type = _HDF5DATAPARAMETER
_LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_LAYERPARAMETER.fields_by_name['hinge_loss_param'].message_type = _HINGELOSSPARAMETER
_LAYERPARAMETER.fields_by_name['image_data_param'].message_type = _IMAGEDATAPARAMETER
_LAYERPARAMETER.fields_by_name['infogain_loss_param'].message_type = _INFOGAINLOSSPARAMETER
_LAYERPARAMETER.fields_by_name['inner_product_param'].message_type = _INNERPRODUCTPARAMETER
_LAYERPARAMETER.fields_by_name['log_param'].message_type = _LOGPARAMETER
_LAYERPARAMETER.fields_by_name['lrn_param'].message_type = _LRNPARAMETER
_LAYERPARAMETER.fields_by_name['memory_data_param'].message_type = _MEMORYDATAPARAMETER
_LAYERPARAMETER.fields_by_name['mvn_param'].message_type = _MVNPARAMETER
_LAYERPARAMETER.fields_by_name['pooling_param'].message_type = _POOLINGPARAMETER
_LAYERPARAMETER.fields_by_name['power_param'].message_type = _POWERPARAMETER
_LAYERPARAMETER.fields_by_name['prelu_param'].message_type = _PRELUPARAMETER
_LAYERPARAMETER.fields_by_name['python_param'].message_type = _PYTHONPARAMETER
_LAYERPARAMETER.fields_by_name['reduction_param'].message_type = _REDUCTIONPARAMETER
_LAYERPARAMETER.fields_by_name['relu_param'].message_type = _RELUPARAMETER
_LAYERPARAMETER.fields_by_name['reshape_param'].message_type = _RESHAPEPARAMETER
_LAYERPARAMETER.fields_by_name['scale_param'].message_type = _SCALEPARAMETER
_LAYERPARAMETER.fields_by_name['sigmoid_param'].message_type = _SIGMOIDPARAMETER
_LAYERPARAMETER.fields_by_name['softmax_param'].message_type = _SOFTMAXPARAMETER
_LAYERPARAMETER.fields_by_name['spp_param'].message_type = _SPPPARAMETER
_LAYERPARAMETER.fields_by_name['slice_param'].message_type = _SLICEPARAMETER
_LAYERPARAMETER.fields_by_name['tanh_param'].message_type = _TANHPARAMETER
_LAYERPARAMETER.fields_by_name['threshold_param'].message_type = _THRESHOLDPARAMETER
_LAYERPARAMETER.fields_by_name['tile_param'].message_type = _TILEPARAMETER
_LAYERPARAMETER.fields_by_name['window_data_param'].message_type = _WINDOWDATAPARAMETER
_LOSSPARAMETER.fields_by_name['normalization'].enum_type = _LOSSPARAMETER_NORMALIZATIONMODE
_LOSSPARAMETER_NORMALIZATIONMODE.containing_type = _LOSSPARAMETER;
_BIASPARAMETER.fields_by_name['filler'].message_type = _FILLERPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['engine'].enum_type = _CONVOLUTIONPARAMETER_ENGINE
_CONVOLUTIONPARAMETER_ENGINE.containing_type = _CONVOLUTIONPARAMETER;
_DATAPARAMETER.fields_by_name['backend'].enum_type = _DATAPARAMETER_DB
_DATAPARAMETER_DB.containing_type = _DATAPARAMETER;
_DUMMYDATAPARAMETER.fields_by_name['data_filler'].message_type = _FILLERPARAMETER
_DUMMYDATAPARAMETER.fields_by_name['shape'].message_type = _BLOBSHAPE
_ELTWISEPARAMETER.fields_by_name['operation'].enum_type = _ELTWISEPARAMETER_ELTWISEOP
_ELTWISEPARAMETER_ELTWISEOP.containing_type = _ELTWISEPARAMETER;
_EMBEDPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_EMBEDPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_HINGELOSSPARAMETER.fields_by_name['norm'].enum_type = _HINGELOSSPARAMETER_NORM
_HINGELOSSPARAMETER_NORM.containing_type = _HINGELOSSPARAMETER;
_INNERPRODUCTPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_INNERPRODUCTPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_LRNPARAMETER.fields_by_name['norm_region'].enum_type = _LRNPARAMETER_NORMREGION
_LRNPARAMETER.fields_by_name['engine'].enum_type = _LRNPARAMETER_ENGINE
_LRNPARAMETER_NORMREGION.containing_type = _LRNPARAMETER;
_LRNPARAMETER_ENGINE.containing_type = _LRNPARAMETER;
_POOLINGPARAMETER.fields_by_name['pool'].enum_type = _POOLINGPARAMETER_POOLMETHOD
_POOLINGPARAMETER.fields_by_name['engine'].enum_type = _POOLINGPARAMETER_ENGINE
_POOLINGPARAMETER_POOLMETHOD.containing_type = _POOLINGPARAMETER;
_POOLINGPARAMETER_ENGINE.containing_type = _POOLINGPARAMETER;
_REDUCTIONPARAMETER.fields_by_name['operation'].enum_type = _REDUCTIONPARAMETER_REDUCTIONOP
_REDUCTIONPARAMETER_REDUCTIONOP.containing_type = _REDUCTIONPARAMETER;
_RELUPARAMETER.fields_by_name['engine'].enum_type = _RELUPARAMETER_ENGINE
_RELUPARAMETER_ENGINE.containing_type = _RELUPARAMETER;
_RESHAPEPARAMETER.fields_by_name['shape'].message_type = _BLOBSHAPE
_SCALEPARAMETER.fields_by_name['filler'].message_type = _FILLERPARAMETER
_SCALEPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_SIGMOIDPARAMETER.fields_by_name['engine'].enum_type = _SIGMOIDPARAMETER_ENGINE
_SIGMOIDPARAMETER_ENGINE.containing_type = _SIGMOIDPARAMETER;
_SOFTMAXPARAMETER.fields_by_name['engine'].enum_type = _SOFTMAXPARAMETER_ENGINE
_SOFTMAXPARAMETER_ENGINE.containing_type = _SOFTMAXPARAMETER;
_TANHPARAMETER.fields_by_name['engine'].enum_type = _TANHPARAMETER_ENGINE
_TANHPARAMETER_ENGINE.containing_type = _TANHPARAMETER;
_SPPPARAMETER.fields_by_name['pool'].enum_type = _SPPPARAMETER_POOLMETHOD
_SPPPARAMETER.fields_by_name['engine'].enum_type = _SPPPARAMETER_ENGINE
_SPPPARAMETER_POOLMETHOD.containing_type = _SPPPARAMETER;
_SPPPARAMETER_ENGINE.containing_type = _SPPPARAMETER;
_V1LAYERPARAMETER.fields_by_name['include'].message_type = _NETSTATERULE
_V1LAYERPARAMETER.fields_by_name['exclude'].message_type = _NETSTATERULE
_V1LAYERPARAMETER.fields_by_name['type'].enum_type = _V1LAYERPARAMETER_LAYERTYPE
_V1LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_V1LAYERPARAMETER.fields_by_name['blob_share_mode'].enum_type = _V1LAYERPARAMETER_DIMCHECKMODE
_V1LAYERPARAMETER.fields_by_name['accuracy_param'].message_type = _ACCURACYPARAMETER
_V1LAYERPARAMETER.fields_by_name['argmax_param'].message_type = _ARGMAXPARAMETER
_V1LAYERPARAMETER.fields_by_name['concat_param'].message_type = _CONCATPARAMETER
_V1LAYERPARAMETER.fields_by_name['contrastive_loss_param'].message_type = _CONTRASTIVELOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['convolution_param'].message_type = _CONVOLUTIONPARAMETER
_V1LAYERPARAMETER.fields_by_name['data_param'].message_type = _DATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['dropout_param'].message_type = _DROPOUTPARAMETER
_V1LAYERPARAMETER.fields_by_name['dummy_data_param'].message_type = _DUMMYDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['eltwise_param'].message_type = _ELTWISEPARAMETER
_V1LAYERPARAMETER.fields_by_name['exp_param'].message_type = _EXPPARAMETER
_V1LAYERPARAMETER.fields_by_name['hdf5_data_param'].message_type = _HDF5DATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_V1LAYERPARAMETER.fields_by_name['hinge_loss_param'].message_type = _HINGELOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['image_data_param'].message_type = _IMAGEDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['infogain_loss_param'].message_type = _INFOGAINLOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['inner_product_param'].message_type = _INNERPRODUCTPARAMETER
_V1LAYERPARAMETER.fields_by_name['lrn_param'].message_type = _LRNPARAMETER
_V1LAYERPARAMETER.fields_by_name['memory_data_param'].message_type = _MEMORYDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['mvn_param'].message_type = _MVNPARAMETER
_V1LAYERPARAMETER.fields_by_name['pooling_param'].message_type = _POOLINGPARAMETER
_V1LAYERPARAMETER.fields_by_name['power_param'].message_type = _POWERPARAMETER
_V1LAYERPARAMETER.fields_by_name['relu_param'].message_type = _RELUPARAMETER
_V1LAYERPARAMETER.fields_by_name['sigmoid_param'].message_type = _SIGMOIDPARAMETER
_V1LAYERPARAMETER.fields_by_name['softmax_param'].message_type = _SOFTMAXPARAMETER
_V1LAYERPARAMETER.fields_by_name['slice_param'].message_type = _SLICEPARAMETER
_V1LAYERPARAMETER.fields_by_name['tanh_param'].message_type = _TANHPARAMETER
_V1LAYERPARAMETER.fields_by_name['threshold_param'].message_type = _THRESHOLDPARAMETER
_V1LAYERPARAMETER.fields_by_name['window_data_param'].message_type = _WINDOWDATAPARAMETER
_V1LAYERPARAMETER.fields_by_name['transform_param'].message_type = _TRANSFORMATIONPARAMETER
_V1LAYERPARAMETER.fields_by_name['loss_param'].message_type = _LOSSPARAMETER
_V1LAYERPARAMETER.fields_by_name['layer'].message_type = _V0LAYERPARAMETER
_V1LAYERPARAMETER_LAYERTYPE.containing_type = _V1LAYERPARAMETER;
_V1LAYERPARAMETER_DIMCHECKMODE.containing_type = _V1LAYERPARAMETER;
_V0LAYERPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_V0LAYERPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_V0LAYERPARAMETER.fields_by_name['pool'].enum_type = _V0LAYERPARAMETER_POOLMETHOD
_V0LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_V0LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_V0LAYERPARAMETER_POOLMETHOD.containing_type = _V0LAYERPARAMETER;
_PRELUPARAMETER.fields_by_name['filler'].message_type = _FILLERPARAMETER
DESCRIPTOR.message_types_by_name['BlobShape'] = _BLOBSHAPE
DESCRIPTOR.message_types_by_name['BlobProto'] = _BLOBPROTO
DESCRIPTOR.message_types_by_name['BlobProtoVector'] = _BLOBPROTOVECTOR
DESCRIPTOR.message_types_by_name['Datum'] = _DATUM
DESCRIPTOR.message_types_by_name['FillerParameter'] = _FILLERPARAMETER
DESCRIPTOR.message_types_by_name['NetParameter'] = _NETPARAMETER
DESCRIPTOR.message_types_by_name['SolverParameter'] = _SOLVERPARAMETER
DESCRIPTOR.message_types_by_name['SolverState'] = _SOLVERSTATE
DESCRIPTOR.message_types_by_name['NetState'] = _NETSTATE
DESCRIPTOR.message_types_by_name['NetStateRule'] = _NETSTATERULE
DESCRIPTOR.message_types_by_name['ParamSpec'] = _PARAMSPEC
DESCRIPTOR.message_types_by_name['LayerParameter'] = _LAYERPARAMETER
DESCRIPTOR.message_types_by_name['TransformationParameter'] = _TRANSFORMATIONPARAMETER
DESCRIPTOR.message_types_by_name['LossParameter'] = _LOSSPARAMETER
DESCRIPTOR.message_types_by_name['AccuracyParameter'] = _ACCURACYPARAMETER
DESCRIPTOR.message_types_by_name['ArgMaxParameter'] = _ARGMAXPARAMETER
DESCRIPTOR.message_types_by_name['ConcatParameter'] = _CONCATPARAMETER
DESCRIPTOR.message_types_by_name['BatchNormParameter'] = _BATCHNORMPARAMETER
DESCRIPTOR.message_types_by_name['BiasParameter'] = _BIASPARAMETER
DESCRIPTOR.message_types_by_name['ContrastiveLossParameter'] = _CONTRASTIVELOSSPARAMETER
DESCRIPTOR.message_types_by_name['ConvolutionParameter'] = _CONVOLUTIONPARAMETER
DESCRIPTOR.message_types_by_name['DataParameter'] = _DATAPARAMETER
DESCRIPTOR.message_types_by_name['DropoutParameter'] = _DROPOUTPARAMETER
DESCRIPTOR.message_types_by_name['DummyDataParameter'] = _DUMMYDATAPARAMETER
DESCRIPTOR.message_types_by_name['EltwiseParameter'] = _ELTWISEPARAMETER
DESCRIPTOR.message_types_by_name['ELUParameter'] = _ELUPARAMETER
DESCRIPTOR.message_types_by_name['EmbedParameter'] = _EMBEDPARAMETER
DESCRIPTOR.message_types_by_name['ExpParameter'] = _EXPPARAMETER
DESCRIPTOR.message_types_by_name['FlattenParameter'] = _FLATTENPARAMETER
DESCRIPTOR.message_types_by_name['HDF5DataParameter'] = _HDF5DATAPARAMETER
DESCRIPTOR.message_types_by_name['HDF5OutputParameter'] = _HDF5OUTPUTPARAMETER
DESCRIPTOR.message_types_by_name['HingeLossParameter'] = _HINGELOSSPARAMETER
DESCRIPTOR.message_types_by_name['ImageDataParameter'] = _IMAGEDATAPARAMETER
DESCRIPTOR.message_types_by_name['InfogainLossParameter'] = _INFOGAINLOSSPARAMETER
DESCRIPTOR.message_types_by_name['InnerProductParameter'] = _INNERPRODUCTPARAMETER
DESCRIPTOR.message_types_by_name['LogParameter'] = _LOGPARAMETER
DESCRIPTOR.message_types_by_name['LRNParameter'] = _LRNPARAMETER
DESCRIPTOR.message_types_by_name['MemoryDataParameter'] = _MEMORYDATAPARAMETER
DESCRIPTOR.message_types_by_name['MVNParameter'] = _MVNPARAMETER
DESCRIPTOR.message_types_by_name['PoolingParameter'] = _POOLINGPARAMETER
DESCRIPTOR.message_types_by_name['PowerParameter'] = _POWERPARAMETER
DESCRIPTOR.message_types_by_name['PythonParameter'] = _PYTHONPARAMETER
DESCRIPTOR.message_types_by_name['ReductionParameter'] = _REDUCTIONPARAMETER
DESCRIPTOR.message_types_by_name['ReLUParameter'] = _RELUPARAMETER
DESCRIPTOR.message_types_by_name['ReshapeParameter'] = _RESHAPEPARAMETER
DESCRIPTOR.message_types_by_name['ScaleParameter'] = _SCALEPARAMETER
DESCRIPTOR.message_types_by_name['SigmoidParameter'] = _SIGMOIDPARAMETER
DESCRIPTOR.message_types_by_name['SliceParameter'] = _SLICEPARAMETER
DESCRIPTOR.message_types_by_name['SoftmaxParameter'] = _SOFTMAXPARAMETER
DESCRIPTOR.message_types_by_name['TanHParameter'] = _TANHPARAMETER
DESCRIPTOR.message_types_by_name['TileParameter'] = _TILEPARAMETER
DESCRIPTOR.message_types_by_name['ThresholdParameter'] = _THRESHOLDPARAMETER
DESCRIPTOR.message_types_by_name['WindowDataParameter'] = _WINDOWDATAPARAMETER
DESCRIPTOR.message_types_by_name['SPPParameter'] = _SPPPARAMETER
DESCRIPTOR.message_types_by_name['V1LayerParameter'] = _V1LAYERPARAMETER
DESCRIPTOR.message_types_by_name['V0LayerParameter'] = _V0LAYERPARAMETER
DESCRIPTOR.message_types_by_name['PReLUParameter'] = _PRELUPARAMETER
class BlobShape(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBSHAPE
# @@protoc_insertion_point(class_scope:caffe.BlobShape)
class BlobProto(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTO
# @@protoc_insertion_point(class_scope:caffe.BlobProto)
class BlobProtoVector(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTOVECTOR
# @@protoc_insertion_point(class_scope:caffe.BlobProtoVector)
class Datum(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATUM
# @@protoc_insertion_point(class_scope:caffe.Datum)
class FillerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FILLERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.FillerParameter)
class NetParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETPARAMETER
# @@protoc_insertion_point(class_scope:caffe.NetParameter)
class SolverParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SolverParameter)
class SolverState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERSTATE
# @@protoc_insertion_point(class_scope:caffe.SolverState)
class NetState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATE
# @@protoc_insertion_point(class_scope:caffe.NetState)
class NetStateRule(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATERULE
# @@protoc_insertion_point(class_scope:caffe.NetStateRule)
class ParamSpec(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PARAMSPEC
# @@protoc_insertion_point(class_scope:caffe.ParamSpec)
class LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LayerParameter)
class TransformationParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TRANSFORMATIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.TransformationParameter)
class LossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LossParameter)
class AccuracyParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ACCURACYPARAMETER
# @@protoc_insertion_point(class_scope:caffe.AccuracyParameter)
class ArgMaxParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ARGMAXPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ArgMaxParameter)
class ConcatParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONCATPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ConcatParameter)
class BatchNormParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BATCHNORMPARAMETER
# @@protoc_insertion_point(class_scope:caffe.BatchNormParameter)
class BiasParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BIASPARAMETER
# @@protoc_insertion_point(class_scope:caffe.BiasParameter)
class ContrastiveLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONTRASTIVELOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ContrastiveLossParameter)
class ConvolutionParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONVOLUTIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ConvolutionParameter)
class DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DataParameter)
class DropoutParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DROPOUTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DropoutParameter)
class DummyDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DUMMYDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DummyDataParameter)
class EltwiseParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ELTWISEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.EltwiseParameter)
class ELUParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ELUPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ELUParameter)
class EmbedParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _EMBEDPARAMETER
# @@protoc_insertion_point(class_scope:caffe.EmbedParameter)
class ExpParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _EXPPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ExpParameter)
class FlattenParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FLATTENPARAMETER
# @@protoc_insertion_point(class_scope:caffe.FlattenParameter)
class HDF5DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5DATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HDF5DataParameter)
class HDF5OutputParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5OUTPUTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HDF5OutputParameter)
class HingeLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HINGELOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HingeLossParameter)
class ImageDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _IMAGEDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ImageDataParameter)
class InfogainLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INFOGAINLOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.InfogainLossParameter)
class InnerProductParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INNERPRODUCTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.InnerProductParameter)
class LogParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LOGPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LogParameter)
class LRNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LRNPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LRNParameter)
class MemoryDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MEMORYDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.MemoryDataParameter)
class MVNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MVNPARAMETER
# @@protoc_insertion_point(class_scope:caffe.MVNParameter)
class PoolingParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POOLINGPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PoolingParameter)
class PowerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POWERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PowerParameter)
class PythonParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PYTHONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PythonParameter)
class ReductionParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _REDUCTIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ReductionParameter)
class ReLUParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _RELUPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ReLUParameter)
class ReshapeParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _RESHAPEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ReshapeParameter)
class ScaleParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SCALEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ScaleParameter)
class SigmoidParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SIGMOIDPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SigmoidParameter)
class SliceParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SLICEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SliceParameter)
class SoftmaxParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOFTMAXPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SoftmaxParameter)
class TanHParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TANHPARAMETER
# @@protoc_insertion_point(class_scope:caffe.TanHParameter)
class TileParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TILEPARAMETER
# @@protoc_insertion_point(class_scope:caffe.TileParameter)
class ThresholdParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _THRESHOLDPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ThresholdParameter)
class WindowDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _WINDOWDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.WindowDataParameter)
class SPPParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SPPPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SPPParameter)
class V1LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _V1LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.V1LayerParameter)
class V0LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _V0LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.V0LayerParameter)
class PReLUParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PRELUPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PReLUParameter)
# @@protoc_insertion_point(module_scope)
| 232,112 | 42.264306 | 27,801 | py |
DRT | DRT-master/external_libs/matconvnet/utils/proto/caffe_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='caffe.proto',
package='caffe',
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_SOLVERPARAMETER_SOLVERMODE = descriptor.EnumDescriptor(
name='SolverMode',
full_name='caffe.SolverParameter.SolverMode',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='CPU', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='GPU', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=1068,
serialized_end=1098,
)
_LAYERPARAMETER_LAYERTYPE = descriptor.EnumDescriptor(
name='LayerType',
full_name='caffe.LayerParameter.LayerType',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='NONE', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ACCURACY', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='BNLL', index=2, number=2,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONCAT', index=3, number=3,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='CONVOLUTION', index=4, number=4,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DATA', index=5, number=5,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='DROPOUT', index=6, number=6,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='EUCLIDEAN_LOSS', index=7, number=7,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='ELTWISE_PRODUCT', index=8, number=25,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='FLATTEN', index=9, number=8,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HDF5_DATA', index=10, number=9,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HDF5_OUTPUT', index=11, number=10,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='HINGE_LOSS', index=12, number=28,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='IM2COL', index=13, number=11,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='IMAGE_DATA', index=14, number=12,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='INFOGAIN_LOSS', index=15, number=13,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='INNER_PRODUCT', index=16, number=14,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='LRN', index=17, number=15,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MEMORY_DATA', index=18, number=29,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='MULTINOMIAL_LOGISTIC_LOSS', index=19, number=16,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='POOLING', index=20, number=17,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='POWER', index=21, number=26,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='RELU', index=22, number=18,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SIGMOID', index=23, number=19,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SIGMOID_CROSS_ENTROPY_LOSS', index=24, number=27,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SOFTMAX', index=25, number=20,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SOFTMAX_LOSS', index=26, number=21,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='SPLIT', index=27, number=22,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='TANH', index=28, number=23,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='WINDOW_DATA', index=29, number=24,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2119,
serialized_end=2589,
)
_LRNPARAMETER_NORMREGION = descriptor.EnumDescriptor(
name='NormRegion',
full_name='caffe.LRNParameter.NormRegion',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='ACROSS_CHANNELS', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='WITHIN_CHANNEL', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=3758,
serialized_end=3811,
)
_POOLINGPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.PoolingParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=4037,
serialized_end=4083,
)
_V0LAYERPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.V0LayerParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=4037,
serialized_end=4083,
)
_BLOBPROTO = descriptor.Descriptor(
name='BlobProto',
full_name='caffe.BlobProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num', full_name='caffe.BlobProto.num', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.BlobProto.channels', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.BlobProto.height', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.BlobProto.width', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.BlobProto.data', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='diff', full_name='caffe.BlobProto.diff', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=22,
serialized_end=143,
)
_BLOBPROTOVECTOR = descriptor.Descriptor(
name='BlobProtoVector',
full_name='caffe.BlobProtoVector',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.BlobProtoVector.blobs', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=145,
serialized_end=195,
)
_DATUM = descriptor.Descriptor(
name='Datum',
full_name='caffe.Datum',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='channels', full_name='caffe.Datum.channels', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.Datum.height', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.Datum.width', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.Datum.data', index=3,
number=4, type=12, cpp_type=9, label=1,
has_default_value=False, default_value="",
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='label', full_name='caffe.Datum.label', index=4,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='float_data', full_name='caffe.Datum.float_data', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=197,
serialized_end=302,
)
_FILLERPARAMETER = descriptor.Descriptor(
name='FillerParameter',
full_name='caffe.FillerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='type', full_name='caffe.FillerParameter.type', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("constant", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='value', full_name='caffe.FillerParameter.value', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='min', full_name='caffe.FillerParameter.min', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max', full_name='caffe.FillerParameter.max', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean', full_name='caffe.FillerParameter.mean', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='std', full_name='caffe.FillerParameter.std', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='sparse', full_name='caffe.FillerParameter.sparse', index=6,
number=7, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=305,
serialized_end=449,
)
_NETPARAMETER = descriptor.Descriptor(
name='NetParameter',
full_name='caffe.NetParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.NetParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layers', full_name='caffe.NetParameter.layers', index=1,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input', full_name='caffe.NetParameter.input', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input_dim', full_name='caffe.NetParameter.input_dim', index=3,
number=4, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_backward', full_name='caffe.NetParameter.force_backward', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=452,
serialized_end=584,
)
_SOLVERPARAMETER = descriptor.Descriptor(
name='SolverParameter',
full_name='caffe.SolverParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='train_net', full_name='caffe.SolverParameter.train_net', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_net', full_name='caffe.SolverParameter.test_net', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_iter', full_name='caffe.SolverParameter.test_iter', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_interval', full_name='caffe.SolverParameter.test_interval', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_compute_loss', full_name='caffe.SolverParameter.test_compute_loss', index=4,
number=19, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='base_lr', full_name='caffe.SolverParameter.base_lr', index=5,
number=5, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='display', full_name='caffe.SolverParameter.display', index=6,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max_iter', full_name='caffe.SolverParameter.max_iter', index=7,
number=7, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lr_policy', full_name='caffe.SolverParameter.lr_policy', index=8,
number=8, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='gamma', full_name='caffe.SolverParameter.gamma', index=9,
number=9, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power', full_name='caffe.SolverParameter.power', index=10,
number=10, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='momentum', full_name='caffe.SolverParameter.momentum', index=11,
number=11, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.SolverParameter.weight_decay', index=12,
number=12, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stepsize', full_name='caffe.SolverParameter.stepsize', index=13,
number=13, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot', full_name='caffe.SolverParameter.snapshot', index=14,
number=14, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_prefix', full_name='caffe.SolverParameter.snapshot_prefix', index=15,
number=15, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_diff', full_name='caffe.SolverParameter.snapshot_diff', index=16,
number=16, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='solver_mode', full_name='caffe.SolverParameter.solver_mode', index=17,
number=17, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='device_id', full_name='caffe.SolverParameter.device_id', index=18,
number=18, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='random_seed', full_name='caffe.SolverParameter.random_seed', index=19,
number=20, type=3, cpp_type=2, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_SOLVERPARAMETER_SOLVERMODE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=587,
serialized_end=1098,
)
_SOLVERSTATE = descriptor.Descriptor(
name='SolverState',
full_name='caffe.SolverState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='iter', full_name='caffe.SolverState.iter', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='learned_net', full_name='caffe.SolverState.learned_net', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='history', full_name='caffe.SolverState.history', index=2,
number=3, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1100,
serialized_end=1183,
)
_LAYERPARAMETER = descriptor.Descriptor(
name='LayerParameter',
full_name='caffe.LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='bottom', full_name='caffe.LayerParameter.bottom', index=0,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top', full_name='caffe.LayerParameter.top', index=1,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='name', full_name='caffe.LayerParameter.name', index=2,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.LayerParameter.type', index=3,
number=5, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.LayerParameter.blobs', index=4,
number=6, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.LayerParameter.blobs_lr', index=5,
number=7, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.LayerParameter.weight_decay', index=6,
number=8, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_param', full_name='caffe.LayerParameter.concat_param', index=7,
number=9, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='convolution_param', full_name='caffe.LayerParameter.convolution_param', index=8,
number=10, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data_param', full_name='caffe.LayerParameter.data_param', index=9,
number=11, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_param', full_name='caffe.LayerParameter.dropout_param', index=10,
number=12, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_data_param', full_name='caffe.LayerParameter.hdf5_data_param', index=11,
number=13, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.LayerParameter.hdf5_output_param', index=12,
number=14, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='image_data_param', full_name='caffe.LayerParameter.image_data_param', index=13,
number=15, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='infogain_loss_param', full_name='caffe.LayerParameter.infogain_loss_param', index=14,
number=16, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='inner_product_param', full_name='caffe.LayerParameter.inner_product_param', index=15,
number=17, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lrn_param', full_name='caffe.LayerParameter.lrn_param', index=16,
number=18, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='memory_data_param', full_name='caffe.LayerParameter.memory_data_param', index=17,
number=22, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pooling_param', full_name='caffe.LayerParameter.pooling_param', index=18,
number=19, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power_param', full_name='caffe.LayerParameter.power_param', index=19,
number=21, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='window_data_param', full_name='caffe.LayerParameter.window_data_param', index=20,
number=20, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layer', full_name='caffe.LayerParameter.layer', index=21,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_LAYERPARAMETER_LAYERTYPE,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1186,
serialized_end=2589,
)
_CONCATPARAMETER = descriptor.Descriptor(
name='ConcatParameter',
full_name='caffe.ConcatParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.ConcatParameter.concat_dim', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2591,
serialized_end=2631,
)
_CONVOLUTIONPARAMETER = descriptor.Descriptor(
name='ConvolutionParameter',
full_name='caffe.ConvolutionParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.ConvolutionParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.ConvolutionParameter.bias_term', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.ConvolutionParameter.pad', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_size', full_name='caffe.ConvolutionParameter.kernel_size', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='group', full_name='caffe.ConvolutionParameter.group', index=4,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.ConvolutionParameter.stride', index=5,
number=6, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.ConvolutionParameter.weight_filler', index=6,
number=7, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.ConvolutionParameter.bias_filler', index=7,
number=8, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2634,
serialized_end=2867,
)
_DATAPARAMETER = descriptor.Descriptor(
name='DataParameter',
full_name='caffe.DataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.DataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.DataParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.DataParameter.mean_file', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.DataParameter.batch_size', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.DataParameter.crop_size', index=4,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.DataParameter.mirror', index=5,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.DataParameter.rand_skip', index=6,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2870,
serialized_end=3025,
)
_DROPOUTPARAMETER = descriptor.Descriptor(
name='DropoutParameter',
full_name='caffe.DropoutParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.DropoutParameter.dropout_ratio', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=3027,
serialized_end=3073,
)
_HDF5DATAPARAMETER = descriptor.Descriptor(
name='HDF5DataParameter',
full_name='caffe.HDF5DataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.HDF5DataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.HDF5DataParameter.batch_size', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=3075,
serialized_end=3130,
)
_HDF5OUTPUTPARAMETER = descriptor.Descriptor(
name='HDF5OutputParameter',
full_name='caffe.HDF5OutputParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='file_name', full_name='caffe.HDF5OutputParameter.file_name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=3132,
serialized_end=3172,
)
_IMAGEDATAPARAMETER = descriptor.Descriptor(
name='ImageDataParameter',
full_name='caffe.ImageDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.ImageDataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.ImageDataParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.ImageDataParameter.mean_file', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.ImageDataParameter.batch_size', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.ImageDataParameter.crop_size', index=4,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.ImageDataParameter.mirror', index=5,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.ImageDataParameter.rand_skip', index=6,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle', full_name='caffe.ImageDataParameter.shuffle', index=7,
number=8, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_height', full_name='caffe.ImageDataParameter.new_height', index=8,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_width', full_name='caffe.ImageDataParameter.new_width', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=3175,
serialized_end=3404,
)
_INFOGAINLOSSPARAMETER = descriptor.Descriptor(
name='InfogainLossParameter',
full_name='caffe.InfogainLossParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.InfogainLossParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=3406,
serialized_end=3445,
)
_INNERPRODUCTPARAMETER = descriptor.Descriptor(
name='InnerProductParameter',
full_name='caffe.InnerProductParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.InnerProductParameter.num_output', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_term', full_name='caffe.InnerProductParameter.bias_term', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.InnerProductParameter.weight_filler', index=2,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.InnerProductParameter.bias_filler', index=3,
number=4, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=3448,
serialized_end=3608,
)
_LRNPARAMETER = descriptor.Descriptor(
name='LRNParameter',
full_name='caffe.LRNParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='local_size', full_name='caffe.LRNParameter.local_size', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.LRNParameter.alpha', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='beta', full_name='caffe.LRNParameter.beta', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='norm_region', full_name='caffe.LRNParameter.norm_region', index=3,
number=4, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_LRNPARAMETER_NORMREGION,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=3611,
serialized_end=3811,
)
_MEMORYDATAPARAMETER = descriptor.Descriptor(
name='MemoryDataParameter',
full_name='caffe.MemoryDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.MemoryDataParameter.batch_size', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.MemoryDataParameter.channels', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.MemoryDataParameter.height', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.MemoryDataParameter.width', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=3813,
serialized_end=3903,
)
_POOLINGPARAMETER = descriptor.Descriptor(
name='PoolingParameter',
full_name='caffe.PoolingParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='pool', full_name='caffe.PoolingParameter.pool', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernel_size', full_name='caffe.PoolingParameter.kernel_size', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.PoolingParameter.stride', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.PoolingParameter.pad', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_POOLINGPARAMETER_POOLMETHOD,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=3906,
serialized_end=4083,
)
_POWERPARAMETER = descriptor.Descriptor(
name='PowerParameter',
full_name='caffe.PowerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='power', full_name='caffe.PowerParameter.power', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.PowerParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shift', full_name='caffe.PowerParameter.shift', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4085,
serialized_end=4155,
)
_WINDOWDATAPARAMETER = descriptor.Descriptor(
name='WindowDataParameter',
full_name='caffe.WindowDataParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='source', full_name='caffe.WindowDataParameter.source', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.WindowDataParameter.scale', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean_file', full_name='caffe.WindowDataParameter.mean_file', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batch_size', full_name='caffe.WindowDataParameter.batch_size', index=3,
number=4, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_size', full_name='caffe.WindowDataParameter.crop_size', index=4,
number=5, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.WindowDataParameter.mirror', index=5,
number=6, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='fg_threshold', full_name='caffe.WindowDataParameter.fg_threshold', index=6,
number=7, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bg_threshold', full_name='caffe.WindowDataParameter.bg_threshold', index=7,
number=8, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='fg_fraction', full_name='caffe.WindowDataParameter.fg_fraction', index=8,
number=9, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='context_pad', full_name='caffe.WindowDataParameter.context_pad', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_mode', full_name='caffe.WindowDataParameter.crop_mode', index=10,
number=11, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4158,
serialized_end=4427,
)
_V0LAYERPARAMETER = descriptor.Descriptor(
name='V0LayerParameter',
full_name='caffe.V0LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.V0LayerParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.V0LayerParameter.type', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.V0LayerParameter.num_output', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='biasterm', full_name='caffe.V0LayerParameter.biasterm', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.V0LayerParameter.weight_filler', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.V0LayerParameter.bias_filler', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.V0LayerParameter.pad', index=6,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernelsize', full_name='caffe.V0LayerParameter.kernelsize', index=7,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='group', full_name='caffe.V0LayerParameter.group', index=8,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.V0LayerParameter.stride', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pool', full_name='caffe.V0LayerParameter.pool', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.V0LayerParameter.dropout_ratio', index=11,
number=12, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='local_size', full_name='caffe.V0LayerParameter.local_size', index=12,
number=13, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.V0LayerParameter.alpha', index=13,
number=14, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='beta', full_name='caffe.V0LayerParameter.beta', index=14,
number=15, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='source', full_name='caffe.V0LayerParameter.source', index=15,
number=16, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.V0LayerParameter.scale', index=16,
number=17, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='meanfile', full_name='caffe.V0LayerParameter.meanfile', index=17,
number=18, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batchsize', full_name='caffe.V0LayerParameter.batchsize', index=18,
number=19, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='cropsize', full_name='caffe.V0LayerParameter.cropsize', index=19,
number=20, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.V0LayerParameter.mirror', index=20,
number=21, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.V0LayerParameter.blobs', index=21,
number=50, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.V0LayerParameter.blobs_lr', index=22,
number=51, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.V0LayerParameter.weight_decay', index=23,
number=52, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.V0LayerParameter.rand_skip', index=24,
number=53, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_fg_threshold', full_name='caffe.V0LayerParameter.det_fg_threshold', index=25,
number=54, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_bg_threshold', full_name='caffe.V0LayerParameter.det_bg_threshold', index=26,
number=55, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_fg_fraction', full_name='caffe.V0LayerParameter.det_fg_fraction', index=27,
number=56, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.25,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_context_pad', full_name='caffe.V0LayerParameter.det_context_pad', index=28,
number=58, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='det_crop_mode', full_name='caffe.V0LayerParameter.det_crop_mode', index=29,
number=59, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("warp", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_num', full_name='caffe.V0LayerParameter.new_num', index=30,
number=60, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_channels', full_name='caffe.V0LayerParameter.new_channels', index=31,
number=61, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_height', full_name='caffe.V0LayerParameter.new_height', index=32,
number=62, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='new_width', full_name='caffe.V0LayerParameter.new_width', index=33,
number=63, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='shuffle_images', full_name='caffe.V0LayerParameter.shuffle_images', index=34,
number=64, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='concat_dim', full_name='caffe.V0LayerParameter.concat_dim', index=35,
number=65, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='hdf5_output_param', full_name='caffe.V0LayerParameter.hdf5_output_param', index=36,
number=1001, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_V0LAYERPARAMETER_POOLMETHOD,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=4430,
serialized_end=5437,
)
_BLOBPROTOVECTOR.fields_by_name['blobs'].message_type = _BLOBPROTO
_NETPARAMETER.fields_by_name['layers'].message_type = _LAYERPARAMETER
_SOLVERPARAMETER.fields_by_name['solver_mode'].enum_type = _SOLVERPARAMETER_SOLVERMODE
_SOLVERPARAMETER_SOLVERMODE.containing_type = _SOLVERPARAMETER;
_SOLVERSTATE.fields_by_name['history'].message_type = _BLOBPROTO
_LAYERPARAMETER.fields_by_name['type'].enum_type = _LAYERPARAMETER_LAYERTYPE
_LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_LAYERPARAMETER.fields_by_name['concat_param'].message_type = _CONCATPARAMETER
_LAYERPARAMETER.fields_by_name['convolution_param'].message_type = _CONVOLUTIONPARAMETER
_LAYERPARAMETER.fields_by_name['data_param'].message_type = _DATAPARAMETER
_LAYERPARAMETER.fields_by_name['dropout_param'].message_type = _DROPOUTPARAMETER
_LAYERPARAMETER.fields_by_name['hdf5_data_param'].message_type = _HDF5DATAPARAMETER
_LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_LAYERPARAMETER.fields_by_name['image_data_param'].message_type = _IMAGEDATAPARAMETER
_LAYERPARAMETER.fields_by_name['infogain_loss_param'].message_type = _INFOGAINLOSSPARAMETER
_LAYERPARAMETER.fields_by_name['inner_product_param'].message_type = _INNERPRODUCTPARAMETER
_LAYERPARAMETER.fields_by_name['lrn_param'].message_type = _LRNPARAMETER
_LAYERPARAMETER.fields_by_name['memory_data_param'].message_type = _MEMORYDATAPARAMETER
_LAYERPARAMETER.fields_by_name['pooling_param'].message_type = _POOLINGPARAMETER
_LAYERPARAMETER.fields_by_name['power_param'].message_type = _POWERPARAMETER
_LAYERPARAMETER.fields_by_name['window_data_param'].message_type = _WINDOWDATAPARAMETER
_LAYERPARAMETER.fields_by_name['layer'].message_type = _V0LAYERPARAMETER
_LAYERPARAMETER_LAYERTYPE.containing_type = _LAYERPARAMETER;
_CONVOLUTIONPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_CONVOLUTIONPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_INNERPRODUCTPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_INNERPRODUCTPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_LRNPARAMETER.fields_by_name['norm_region'].enum_type = _LRNPARAMETER_NORMREGION
_LRNPARAMETER_NORMREGION.containing_type = _LRNPARAMETER;
_POOLINGPARAMETER.fields_by_name['pool'].enum_type = _POOLINGPARAMETER_POOLMETHOD
_POOLINGPARAMETER_POOLMETHOD.containing_type = _POOLINGPARAMETER;
_V0LAYERPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_V0LAYERPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_V0LAYERPARAMETER.fields_by_name['pool'].enum_type = _V0LAYERPARAMETER_POOLMETHOD
_V0LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_V0LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER
_V0LAYERPARAMETER_POOLMETHOD.containing_type = _V0LAYERPARAMETER;
DESCRIPTOR.message_types_by_name['BlobProto'] = _BLOBPROTO
DESCRIPTOR.message_types_by_name['BlobProtoVector'] = _BLOBPROTOVECTOR
DESCRIPTOR.message_types_by_name['Datum'] = _DATUM
DESCRIPTOR.message_types_by_name['FillerParameter'] = _FILLERPARAMETER
DESCRIPTOR.message_types_by_name['NetParameter'] = _NETPARAMETER
DESCRIPTOR.message_types_by_name['SolverParameter'] = _SOLVERPARAMETER
DESCRIPTOR.message_types_by_name['SolverState'] = _SOLVERSTATE
DESCRIPTOR.message_types_by_name['LayerParameter'] = _LAYERPARAMETER
DESCRIPTOR.message_types_by_name['ConcatParameter'] = _CONCATPARAMETER
DESCRIPTOR.message_types_by_name['ConvolutionParameter'] = _CONVOLUTIONPARAMETER
DESCRIPTOR.message_types_by_name['DataParameter'] = _DATAPARAMETER
DESCRIPTOR.message_types_by_name['DropoutParameter'] = _DROPOUTPARAMETER
DESCRIPTOR.message_types_by_name['HDF5DataParameter'] = _HDF5DATAPARAMETER
DESCRIPTOR.message_types_by_name['HDF5OutputParameter'] = _HDF5OUTPUTPARAMETER
DESCRIPTOR.message_types_by_name['ImageDataParameter'] = _IMAGEDATAPARAMETER
DESCRIPTOR.message_types_by_name['InfogainLossParameter'] = _INFOGAINLOSSPARAMETER
DESCRIPTOR.message_types_by_name['InnerProductParameter'] = _INNERPRODUCTPARAMETER
DESCRIPTOR.message_types_by_name['LRNParameter'] = _LRNPARAMETER
DESCRIPTOR.message_types_by_name['MemoryDataParameter'] = _MEMORYDATAPARAMETER
DESCRIPTOR.message_types_by_name['PoolingParameter'] = _POOLINGPARAMETER
DESCRIPTOR.message_types_by_name['PowerParameter'] = _POWERPARAMETER
DESCRIPTOR.message_types_by_name['WindowDataParameter'] = _WINDOWDATAPARAMETER
DESCRIPTOR.message_types_by_name['V0LayerParameter'] = _V0LAYERPARAMETER
class BlobProto(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTO
# @@protoc_insertion_point(class_scope:caffe.BlobProto)
class BlobProtoVector(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTOVECTOR
# @@protoc_insertion_point(class_scope:caffe.BlobProtoVector)
class Datum(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATUM
# @@protoc_insertion_point(class_scope:caffe.Datum)
class FillerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FILLERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.FillerParameter)
class NetParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETPARAMETER
# @@protoc_insertion_point(class_scope:caffe.NetParameter)
class SolverParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SolverParameter)
class SolverState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERSTATE
# @@protoc_insertion_point(class_scope:caffe.SolverState)
class LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LayerParameter)
class ConcatParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONCATPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ConcatParameter)
class ConvolutionParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONVOLUTIONPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ConvolutionParameter)
class DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DataParameter)
class DropoutParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DROPOUTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.DropoutParameter)
class HDF5DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5DATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HDF5DataParameter)
class HDF5OutputParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5OUTPUTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.HDF5OutputParameter)
class ImageDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _IMAGEDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.ImageDataParameter)
class InfogainLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INFOGAINLOSSPARAMETER
# @@protoc_insertion_point(class_scope:caffe.InfogainLossParameter)
class InnerProductParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INNERPRODUCTPARAMETER
# @@protoc_insertion_point(class_scope:caffe.InnerProductParameter)
class LRNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LRNPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LRNParameter)
class MemoryDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MEMORYDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.MemoryDataParameter)
class PoolingParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POOLINGPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PoolingParameter)
class PowerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POWERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.PowerParameter)
class WindowDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _WINDOWDATAPARAMETER
# @@protoc_insertion_point(class_scope:caffe.WindowDataParameter)
class V0LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _V0LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.V0LayerParameter)
# @@protoc_insertion_point(module_scope)
| 91,458 | 42.407214 | 10,562 | py |
DRT | DRT-master/external_libs/matconvnet/utils/proto/vgg_caffe_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='vgg_caffe.proto',
package='caffe',
serialized_pb='\n\x0fvgg_caffe.proto\x12\x05\x63\x61\x66\x66\x65\"y\n\tBlobProto\x12\x0e\n\x03num\x18\x01 \x01(\x05:\x01\x30\x12\x13\n\x08\x63hannels\x18\x02 \x01(\x05:\x01\x30\x12\x11\n\x06height\x18\x03 \x01(\x05:\x01\x30\x12\x10\n\x05width\x18\x04 \x01(\x05:\x01\x30\x12\x10\n\x04\x64\x61ta\x18\x05 \x03(\x02\x42\x02\x10\x01\x12\x10\n\x04\x64iff\x18\x06 \x03(\x02\x42\x02\x10\x01\"2\n\x0f\x42lobProtoVector\x12\x1f\n\x05\x62lobs\x18\x01 \x03(\x0b\x32\x10.caffe.BlobProto\"i\n\x05\x44\x61tum\x12\x10\n\x08\x63hannels\x18\x01 \x01(\x05\x12\x0e\n\x06height\x18\x02 \x01(\x05\x12\r\n\x05width\x18\x03 \x01(\x05\x12\x0c\n\x04\x64\x61ta\x18\x04 \x01(\x0c\x12\r\n\x05label\x18\x05 \x01(\x05\x12\x12\n\nfloat_data\x18\x06 \x03(\x02\"\xaa\x01\n\x0f\x46illerParameter\x12\x16\n\x04type\x18\x01 \x01(\t:\x08\x63onstant\x12\x10\n\x05value\x18\x02 \x01(\x02:\x01\x30\x12\x0e\n\x03min\x18\x03 \x01(\x02:\x01\x30\x12\x0e\n\x03max\x18\x04 \x01(\x02:\x01\x31\x12\x0f\n\x04mean\x18\x05 \x01(\x02:\x01\x30\x12\x0e\n\x03std\x18\x06 \x01(\x02:\x01\x31\x12\x12\n\nmodel_path\x18\x07 \x01(\t\x12\x18\n\x10model_layer_name\x18\x08 \x01(\t\"\xe9\x06\n\x0eLayerParameter\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0c\n\x04type\x18\x02 \x01(\t\x12\x12\n\nnum_output\x18\x03 \x01(\r\x12\x16\n\x08\x62iasterm\x18\x04 \x01(\x08:\x04true\x12-\n\rweight_filler\x18\x05 \x01(\x0b\x32\x16.caffe.FillerParameter\x12+\n\x0b\x62ias_filler\x18\x06 \x01(\x0b\x32\x16.caffe.FillerParameter\x12\x0e\n\x03pad\x18\x07 \x01(\r:\x01\x30\x12\x12\n\nkernelsize\x18\x08 \x01(\r\x12\x10\n\x05group\x18\t \x01(\r:\x01\x31\x12\x11\n\x06stride\x18\n \x01(\r:\x01\x31\x12\x33\n\x04pool\x18\x0b \x01(\x0e\x32 .caffe.LayerParameter.PoolMethod:\x03MAX\x12\x1a\n\rdropout_ratio\x18\x0c \x01(\x02:\x03\x30.5\x12\x15\n\nlocal_size\x18\r \x01(\r:\x01\x35\x12\x10\n\x05\x61lpha\x18\x0e \x01(\x02:\x01\x31\x12\x12\n\x04\x62\x65ta\x18\x0f \x01(\x02:\x04\x30.75\x12\x0c\n\x01k\x18t \x01(\x02:\x01\x31\x12\x0e\n\x06source\x18\x10 \x01(\t\x12\x14\n\x0croot_img_dir\x18u \x01(\t\x12\x10\n\x05scale\x18\x11 \x01(\x02:\x01\x31\x12\x10\n\x08meanfile\x18\x12 \x01(\t\x12\x15\n\rcrop_meanfile\x18w \x01(\t\x12\x11\n\tbatchsize\x18\x13 \x01(\r\x12\x13\n\x08\x63ropsize\x18\x14 \x01(\r:\x01\x30\x12\x15\n\x06mirror\x18\x15 \x01(\x08:\x05\x66\x61lse\x12\x14\n\x0cimg_aug_type\x18\x16 \x01(\r\x12\x19\n\x11img_sampling_type\x18\x17 \x01(\r\x12\r\n\x05top_k\x18\x1f \x03(\r\x12\x11\n\tvis_label\x18\x18 \x01(\x05\x12\x10\n\x08\x63hannels\x18\x19 \x01(\x05\x12\x10\n\x08save_dir\x18\x1a \x01(\t\x12\x15\n\nlabel_rank\x18\x1e \x01(\r:\x01\x30\x12\x11\n\x06margin\x18 \x01(\x02:\x01\x31\x12\x1f\n\x05\x62lobs\x18\x32 \x03(\x0b\x32\x10.caffe.BlobProto\x12\x10\n\x08\x62lobs_lr\x18\x33 \x03(\x02\x12\x14\n\x0cweight_decay\x18\x34 \x03(\x02\x12\x14\n\trand_skip\x18\x35 \x01(\r:\x01\x30\".\n\nPoolMethod\x12\x07\n\x03MAX\x10\x00\x12\x07\n\x03\x41VE\x10\x01\x12\x0e\n\nSTOCHASTIC\x10\x02\"T\n\x0fLayerConnection\x12$\n\x05layer\x18\x01 \x01(\x0b\x32\x15.caffe.LayerParameter\x12\x0e\n\x06\x62ottom\x18\x02 \x03(\t\x12\x0b\n\x03top\x18\x03 \x03(\t\"\x85\x01\n\x0cNetParameter\x12\x0c\n\x04name\x18\x01 \x01(\t\x12&\n\x06layers\x18\x02 \x03(\x0b\x32\x16.caffe.LayerConnection\x12\r\n\x05input\x18\x03 \x03(\t\x12\x11\n\tinput_dim\x18\x04 \x03(\x05\x12\x1d\n\x0e\x66orce_backward\x18\x05 \x01(\x08:\x05\x66\x61lse\"\xef\x03\n\x0fSolverParameter\x12\x11\n\ttrain_net\x18\x01 \x01(\t\x12\x10\n\x08test_net\x18\x02 \x01(\t\x12\x14\n\ttest_iter\x18\x03 \x01(\x05:\x01\x30\x12\x18\n\rtest_interval\x18\x04 \x01(\x05:\x01\x30\x12\x0f\n\x07\x62\x61se_lr\x18\x05 \x01(\x02\x12\x0f\n\x07\x64isplay\x18\x06 \x01(\x05\x12\x10\n\x08max_iter\x18\x07 \x01(\x05\x12\x11\n\tlr_policy\x18\x08 \x01(\t\x12\r\n\x05gamma\x18\t \x01(\x02\x12\r\n\x05power\x18\n \x01(\x02\x12\x10\n\x08momentum\x18\x0b \x01(\x02\x12\x14\n\x0cweight_decay\x18\x0c \x01(\x02\x12\x10\n\x08stepsize\x18\r \x01(\x05\x12\x12\n\nbreakpoint\x18\x16 \x03(\x05\x12\x13\n\x08snapshot\x18\x0e \x01(\x05:\x01\x30\x12\x17\n\x0fsnapshot_prefix\x18\x0f \x01(\t\x12\"\n\x17snapshot_history_length\x18\x12 \x01(\x05:\x01\x30\x12\x1c\n\rsnapshot_diff\x18\x10 \x01(\x08:\x05\x66\x61lse\x12\x16\n\x0bsolver_mode\x18\x11 \x01(\x05:\x01\x31\x12\x11\n\tbatchsize\x18\x13 \x01(\x05\x12\x18\n\rdisplay_debug\x18\x14 \x01(\x05:\x01\x30\x12\x1f\n\x11load_solver_state\x18\x15 \x01(\x08:\x04true\"-\n\x0f\x45valHistoryIter\x12\x0c\n\x04iter\x18\x01 \x01(\x05\x12\x0c\n\x04perf\x18\x02 \x03(\x02\";\n\x0b\x45valHistory\x12,\n\x0cmeasurements\x18\x01 \x03(\x0b\x32\x16.caffe.EvalHistoryIter\"|\n\x0bSolverState\x12\x0c\n\x04iter\x18\x01 \x01(\x05\x12\x13\n\x0blearned_net\x18\x02 \x01(\t\x12!\n\x07history\x18\x03 \x03(\x0b\x32\x10.caffe.BlobProto\x12\'\n\x0bval_history\x18\x04 \x01(\x0b\x32\x12.caffe.EvalHistory')
_LAYERPARAMETER_POOLMETHOD = descriptor.EnumDescriptor(
name='PoolMethod',
full_name='caffe.LayerParameter.PoolMethod',
filename=None,
file=DESCRIPTOR,
values=[
descriptor.EnumValueDescriptor(
name='MAX', index=0, number=0,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='AVE', index=1, number=1,
options=None,
type=None),
descriptor.EnumValueDescriptor(
name='STOCHASTIC', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=1309,
serialized_end=1355,
)
_BLOBPROTO = descriptor.Descriptor(
name='BlobProto',
full_name='caffe.BlobProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='num', full_name='caffe.BlobProto.num', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.BlobProto.channels', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.BlobProto.height', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.BlobProto.width', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.BlobProto.data', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
descriptor.FieldDescriptor(
name='diff', full_name='caffe.BlobProto.diff', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=26,
serialized_end=147,
)
_BLOBPROTOVECTOR = descriptor.Descriptor(
name='BlobProtoVector',
full_name='caffe.BlobProtoVector',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.BlobProtoVector.blobs', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=149,
serialized_end=199,
)
_DATUM = descriptor.Descriptor(
name='Datum',
full_name='caffe.Datum',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='channels', full_name='caffe.Datum.channels', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='height', full_name='caffe.Datum.height', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='width', full_name='caffe.Datum.width', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='data', full_name='caffe.Datum.data', index=3,
number=4, type=12, cpp_type=9, label=1,
has_default_value=False, default_value="",
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='label', full_name='caffe.Datum.label', index=4,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='float_data', full_name='caffe.Datum.float_data', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=201,
serialized_end=306,
)
_FILLERPARAMETER = descriptor.Descriptor(
name='FillerParameter',
full_name='caffe.FillerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='type', full_name='caffe.FillerParameter.type', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=True, default_value=unicode("constant", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='value', full_name='caffe.FillerParameter.value', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='min', full_name='caffe.FillerParameter.min', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max', full_name='caffe.FillerParameter.max', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mean', full_name='caffe.FillerParameter.mean', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='std', full_name='caffe.FillerParameter.std', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='model_path', full_name='caffe.FillerParameter.model_path', index=6,
number=7, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='model_layer_name', full_name='caffe.FillerParameter.model_layer_name', index=7,
number=8, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=309,
serialized_end=479,
)
_LAYERPARAMETER = descriptor.Descriptor(
name='LayerParameter',
full_name='caffe.LayerParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.LayerParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='type', full_name='caffe.LayerParameter.type', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='num_output', full_name='caffe.LayerParameter.num_output', index=2,
number=3, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='biasterm', full_name='caffe.LayerParameter.biasterm', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_filler', full_name='caffe.LayerParameter.weight_filler', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bias_filler', full_name='caffe.LayerParameter.bias_filler', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pad', full_name='caffe.LayerParameter.pad', index=6,
number=7, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='kernelsize', full_name='caffe.LayerParameter.kernelsize', index=7,
number=8, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='group', full_name='caffe.LayerParameter.group', index=8,
number=9, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stride', full_name='caffe.LayerParameter.stride', index=9,
number=10, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='pool', full_name='caffe.LayerParameter.pool', index=10,
number=11, type=14, cpp_type=8, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='dropout_ratio', full_name='caffe.LayerParameter.dropout_ratio', index=11,
number=12, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='local_size', full_name='caffe.LayerParameter.local_size', index=12,
number=13, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=5,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='alpha', full_name='caffe.LayerParameter.alpha', index=13,
number=14, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='beta', full_name='caffe.LayerParameter.beta', index=14,
number=15, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=0.75,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='k', full_name='caffe.LayerParameter.k', index=15,
number=116, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='source', full_name='caffe.LayerParameter.source', index=16,
number=16, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='root_img_dir', full_name='caffe.LayerParameter.root_img_dir', index=17,
number=117, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='scale', full_name='caffe.LayerParameter.scale', index=18,
number=17, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='meanfile', full_name='caffe.LayerParameter.meanfile', index=19,
number=18, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='crop_meanfile', full_name='caffe.LayerParameter.crop_meanfile', index=20,
number=119, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batchsize', full_name='caffe.LayerParameter.batchsize', index=21,
number=19, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='cropsize', full_name='caffe.LayerParameter.cropsize', index=22,
number=20, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='mirror', full_name='caffe.LayerParameter.mirror', index=23,
number=21, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='img_aug_type', full_name='caffe.LayerParameter.img_aug_type', index=24,
number=22, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='img_sampling_type', full_name='caffe.LayerParameter.img_sampling_type', index=25,
number=23, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top_k', full_name='caffe.LayerParameter.top_k', index=26,
number=31, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='vis_label', full_name='caffe.LayerParameter.vis_label', index=27,
number=24, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='channels', full_name='caffe.LayerParameter.channels', index=28,
number=25, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='save_dir', full_name='caffe.LayerParameter.save_dir', index=29,
number=26, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='label_rank', full_name='caffe.LayerParameter.label_rank', index=30,
number=30, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='margin', full_name='caffe.LayerParameter.margin', index=31,
number=32, type=2, cpp_type=6, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs', full_name='caffe.LayerParameter.blobs', index=32,
number=50, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='blobs_lr', full_name='caffe.LayerParameter.blobs_lr', index=33,
number=51, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.LayerParameter.weight_decay', index=34,
number=52, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='rand_skip', full_name='caffe.LayerParameter.rand_skip', index=35,
number=53, type=13, cpp_type=3, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_LAYERPARAMETER_POOLMETHOD,
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=482,
serialized_end=1355,
)
_LAYERCONNECTION = descriptor.Descriptor(
name='LayerConnection',
full_name='caffe.LayerConnection',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='layer', full_name='caffe.LayerConnection.layer', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='bottom', full_name='caffe.LayerConnection.bottom', index=1,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='top', full_name='caffe.LayerConnection.top', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1357,
serialized_end=1441,
)
_NETPARAMETER = descriptor.Descriptor(
name='NetParameter',
full_name='caffe.NetParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='name', full_name='caffe.NetParameter.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='layers', full_name='caffe.NetParameter.layers', index=1,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input', full_name='caffe.NetParameter.input', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='input_dim', full_name='caffe.NetParameter.input_dim', index=3,
number=4, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='force_backward', full_name='caffe.NetParameter.force_backward', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1444,
serialized_end=1577,
)
_SOLVERPARAMETER = descriptor.Descriptor(
name='SolverParameter',
full_name='caffe.SolverParameter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='train_net', full_name='caffe.SolverParameter.train_net', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_net', full_name='caffe.SolverParameter.test_net', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_iter', full_name='caffe.SolverParameter.test_iter', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='test_interval', full_name='caffe.SolverParameter.test_interval', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='base_lr', full_name='caffe.SolverParameter.base_lr', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='display', full_name='caffe.SolverParameter.display', index=5,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='max_iter', full_name='caffe.SolverParameter.max_iter', index=6,
number=7, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='lr_policy', full_name='caffe.SolverParameter.lr_policy', index=7,
number=8, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='gamma', full_name='caffe.SolverParameter.gamma', index=8,
number=9, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='power', full_name='caffe.SolverParameter.power', index=9,
number=10, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='momentum', full_name='caffe.SolverParameter.momentum', index=10,
number=11, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='weight_decay', full_name='caffe.SolverParameter.weight_decay', index=11,
number=12, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='stepsize', full_name='caffe.SolverParameter.stepsize', index=12,
number=13, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='breakpoint', full_name='caffe.SolverParameter.breakpoint', index=13,
number=22, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot', full_name='caffe.SolverParameter.snapshot', index=14,
number=14, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_prefix', full_name='caffe.SolverParameter.snapshot_prefix', index=15,
number=15, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_history_length', full_name='caffe.SolverParameter.snapshot_history_length', index=16,
number=18, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='snapshot_diff', full_name='caffe.SolverParameter.snapshot_diff', index=17,
number=16, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='solver_mode', full_name='caffe.SolverParameter.solver_mode', index=18,
number=17, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='batchsize', full_name='caffe.SolverParameter.batchsize', index=19,
number=19, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='display_debug', full_name='caffe.SolverParameter.display_debug', index=20,
number=20, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='load_solver_state', full_name='caffe.SolverParameter.load_solver_state', index=21,
number=21, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=True,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=1580,
serialized_end=2075,
)
_EVALHISTORYITER = descriptor.Descriptor(
name='EvalHistoryIter',
full_name='caffe.EvalHistoryIter',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='iter', full_name='caffe.EvalHistoryIter.iter', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='perf', full_name='caffe.EvalHistoryIter.perf', index=1,
number=2, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2077,
serialized_end=2122,
)
_EVALHISTORY = descriptor.Descriptor(
name='EvalHistory',
full_name='caffe.EvalHistory',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='measurements', full_name='caffe.EvalHistory.measurements', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2124,
serialized_end=2183,
)
_SOLVERSTATE = descriptor.Descriptor(
name='SolverState',
full_name='caffe.SolverState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
descriptor.FieldDescriptor(
name='iter', full_name='caffe.SolverState.iter', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='learned_net', full_name='caffe.SolverState.learned_net', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=unicode("", "utf-8"),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='history', full_name='caffe.SolverState.history', index=2,
number=3, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
descriptor.FieldDescriptor(
name='val_history', full_name='caffe.SolverState.val_history', index=3,
number=4, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
extension_ranges=[],
serialized_start=2185,
serialized_end=2309,
)
_BLOBPROTOVECTOR.fields_by_name['blobs'].message_type = _BLOBPROTO
_LAYERPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER
_LAYERPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER
_LAYERPARAMETER.fields_by_name['pool'].enum_type = _LAYERPARAMETER_POOLMETHOD
_LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO
_LAYERPARAMETER_POOLMETHOD.containing_type = _LAYERPARAMETER;
_LAYERCONNECTION.fields_by_name['layer'].message_type = _LAYERPARAMETER
_NETPARAMETER.fields_by_name['layers'].message_type = _LAYERCONNECTION
_EVALHISTORY.fields_by_name['measurements'].message_type = _EVALHISTORYITER
_SOLVERSTATE.fields_by_name['history'].message_type = _BLOBPROTO
_SOLVERSTATE.fields_by_name['val_history'].message_type = _EVALHISTORY
DESCRIPTOR.message_types_by_name['BlobProto'] = _BLOBPROTO
DESCRIPTOR.message_types_by_name['BlobProtoVector'] = _BLOBPROTOVECTOR
DESCRIPTOR.message_types_by_name['Datum'] = _DATUM
DESCRIPTOR.message_types_by_name['FillerParameter'] = _FILLERPARAMETER
DESCRIPTOR.message_types_by_name['LayerParameter'] = _LAYERPARAMETER
DESCRIPTOR.message_types_by_name['LayerConnection'] = _LAYERCONNECTION
DESCRIPTOR.message_types_by_name['NetParameter'] = _NETPARAMETER
DESCRIPTOR.message_types_by_name['SolverParameter'] = _SOLVERPARAMETER
DESCRIPTOR.message_types_by_name['EvalHistoryIter'] = _EVALHISTORYITER
DESCRIPTOR.message_types_by_name['EvalHistory'] = _EVALHISTORY
DESCRIPTOR.message_types_by_name['SolverState'] = _SOLVERSTATE
class BlobProto(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTO
# @@protoc_insertion_point(class_scope:caffe.BlobProto)
class BlobProtoVector(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTOVECTOR
# @@protoc_insertion_point(class_scope:caffe.BlobProtoVector)
class Datum(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATUM
# @@protoc_insertion_point(class_scope:caffe.Datum)
class FillerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FILLERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.FillerParameter)
class LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.LayerParameter)
class LayerConnection(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERCONNECTION
# @@protoc_insertion_point(class_scope:caffe.LayerConnection)
class NetParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETPARAMETER
# @@protoc_insertion_point(class_scope:caffe.NetParameter)
class SolverParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERPARAMETER
# @@protoc_insertion_point(class_scope:caffe.SolverParameter)
class EvalHistoryIter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _EVALHISTORYITER
# @@protoc_insertion_point(class_scope:caffe.EvalHistoryIter)
class EvalHistory(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _EVALHISTORY
# @@protoc_insertion_point(class_scope:caffe.EvalHistory)
class SolverState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERSTATE
# @@protoc_insertion_point(class_scope:caffe.SolverState)
# @@protoc_insertion_point(module_scope)
| 44,873 | 42.865103 | 4,761 | py |
DRT | DRT-master/caffe/tools/extra/parse_log.py | #!/usr/bin/env python
"""
Parse training log
Evolved from parse_log.sh
"""
import os
import re
import extract_seconds
import argparse
import csv
from collections import OrderedDict
def parse_log(path_to_log):
"""Parse log file
Returns (train_dict_list, train_dict_names, test_dict_list, test_dict_names)
train_dict_list and test_dict_list are lists of dicts that define the table
rows
train_dict_names and test_dict_names are ordered tuples of the column names
for the two dict_lists
"""
regex_iteration = re.compile('Iteration (\d+)')
regex_train_output = re.compile('Train net output #(\d+): (\S+) = ([\.\deE+-]+)')
regex_test_output = re.compile('Test net output #(\d+): (\S+) = ([\.\deE+-]+)')
regex_learning_rate = re.compile('lr = ([-+]?[0-9]*\.?[0-9]+([eE]?[-+]?[0-9]+)?)')
# Pick out lines of interest
iteration = -1
learning_rate = float('NaN')
train_dict_list = []
test_dict_list = []
train_row = None
test_row = None
logfile_year = extract_seconds.get_log_created_year(path_to_log)
with open(path_to_log) as f:
start_time = extract_seconds.get_start_time(f, logfile_year)
for line in f:
iteration_match = regex_iteration.search(line)
if iteration_match:
iteration = float(iteration_match.group(1))
if iteration == -1:
# Only start parsing for other stuff if we've found the first
# iteration
continue
time = extract_seconds.extract_datetime_from_line(line,
logfile_year)
seconds = (time - start_time).total_seconds()
learning_rate_match = regex_learning_rate.search(line)
if learning_rate_match:
learning_rate = float(learning_rate_match.group(1))
train_dict_list, train_row = parse_line_for_net_output(
regex_train_output, train_row, train_dict_list,
line, iteration, seconds, learning_rate
)
test_dict_list, test_row = parse_line_for_net_output(
regex_test_output, test_row, test_dict_list,
line, iteration, seconds, learning_rate
)
fix_initial_nan_learning_rate(train_dict_list)
fix_initial_nan_learning_rate(test_dict_list)
return train_dict_list, test_dict_list
def parse_line_for_net_output(regex_obj, row, row_dict_list,
line, iteration, seconds, learning_rate):
"""Parse a single line for training or test output
Returns a a tuple with (row_dict_list, row)
row: may be either a new row or an augmented version of the current row
row_dict_list: may be either the current row_dict_list or an augmented
version of the current row_dict_list
"""
output_match = regex_obj.search(line)
if output_match:
if not row or row['NumIters'] != iteration:
# Push the last row and start a new one
if row:
# If we're on a new iteration, push the last row
# This will probably only happen for the first row; otherwise
# the full row checking logic below will push and clear full
# rows
row_dict_list.append(row)
row = OrderedDict([
('NumIters', iteration),
('Seconds', seconds),
('LearningRate', learning_rate)
])
# output_num is not used; may be used in the future
# output_num = output_match.group(1)
output_name = output_match.group(2)
output_val = output_match.group(3)
row[output_name] = float(output_val)
if row and len(row_dict_list) >= 1 and len(row) == len(row_dict_list[0]):
# The row is full, based on the fact that it has the same number of
# columns as the first row; append it to the list
row_dict_list.append(row)
row = None
return row_dict_list, row
def fix_initial_nan_learning_rate(dict_list):
"""Correct initial value of learning rate
Learning rate is normally not printed until after the initial test and
training step, which means the initial testing and training rows have
LearningRate = NaN. Fix this by copying over the LearningRate from the
second row, if it exists.
"""
if len(dict_list) > 1:
dict_list[0]['LearningRate'] = dict_list[1]['LearningRate']
def save_csv_files(logfile_path, output_dir, train_dict_list, test_dict_list,
delimiter=',', verbose=False):
"""Save CSV files to output_dir
If the input log file is, e.g., caffe.INFO, the names will be
caffe.INFO.train and caffe.INFO.test
"""
log_basename = os.path.basename(logfile_path)
train_filename = os.path.join(output_dir, log_basename + '.train')
write_csv(train_filename, train_dict_list, delimiter, verbose)
test_filename = os.path.join(output_dir, log_basename + '.test')
write_csv(test_filename, test_dict_list, delimiter, verbose)
def write_csv(output_filename, dict_list, delimiter, verbose=False):
"""Write a CSV file
"""
dialect = csv.excel
dialect.delimiter = delimiter
with open(output_filename, 'w') as f:
dict_writer = csv.DictWriter(f, fieldnames=dict_list[0].keys(),
dialect=dialect)
dict_writer.writeheader()
dict_writer.writerows(dict_list)
if verbose:
print 'Wrote %s' % output_filename
def parse_args():
description = ('Parse a Caffe training log into two CSV files '
'containing training and testing information')
parser = argparse.ArgumentParser(description=description)
parser.add_argument('logfile_path',
help='Path to log file')
parser.add_argument('output_dir',
help='Directory in which to place output CSV files')
parser.add_argument('--verbose',
action='store_true',
help='Print some extra info (e.g., output filenames)')
parser.add_argument('--delimiter',
default=',',
help=('Column delimiter in output files '
'(default: \'%(default)s\')'))
args = parser.parse_args()
return args
def main():
args = parse_args()
train_dict_list, test_dict_list = parse_log(args.logfile_path)
save_csv_files(args.logfile_path, args.output_dir, train_dict_list,
test_dict_list, delimiter=args.delimiter)
if __name__ == '__main__':
main()
| 6,700 | 33.015228 | 86 | py |
DRT | DRT-master/caffe/examples/web_demo/app.py | import os
import time
import cPickle
import datetime
import logging
import flask
import werkzeug
import optparse
import tornado.wsgi
import tornado.httpserver
import numpy as np
import pandas as pd
from PIL import Image
import cStringIO as StringIO
import urllib
import exifutil
import caffe
REPO_DIRNAME = os.path.abspath(os.path.dirname(os.path.abspath(__file__)) + '/../..')
UPLOAD_FOLDER = '/tmp/caffe_demos_uploads'
ALLOWED_IMAGE_EXTENSIONS = set(['png', 'bmp', 'jpg', 'jpe', 'jpeg', 'gif'])
# Obtain the flask app object
app = flask.Flask(__name__)
@app.route('/')
def index():
return flask.render_template('index.html', has_result=False)
@app.route('/classify_url', methods=['GET'])
def classify_url():
imageurl = flask.request.args.get('imageurl', '')
try:
string_buffer = StringIO.StringIO(
urllib.urlopen(imageurl).read())
image = caffe.io.load_image(string_buffer)
except Exception as err:
# For any exception we encounter in reading the image, we will just
# not continue.
logging.info('URL Image open error: %s', err)
return flask.render_template(
'index.html', has_result=True,
result=(False, 'Cannot open image from URL.')
)
logging.info('Image: %s', imageurl)
result = app.clf.classify_image(image)
return flask.render_template(
'index.html', has_result=True, result=result, imagesrc=imageurl)
@app.route('/classify_upload', methods=['POST'])
def classify_upload():
try:
# We will save the file to disk for possible data collection.
imagefile = flask.request.files['imagefile']
filename_ = str(datetime.datetime.now()).replace(' ', '_') + \
werkzeug.secure_filename(imagefile.filename)
filename = os.path.join(UPLOAD_FOLDER, filename_)
imagefile.save(filename)
logging.info('Saving to %s.', filename)
image = exifutil.open_oriented_im(filename)
except Exception as err:
logging.info('Uploaded image open error: %s', err)
return flask.render_template(
'index.html', has_result=True,
result=(False, 'Cannot open uploaded image.')
)
result = app.clf.classify_image(image)
return flask.render_template(
'index.html', has_result=True, result=result,
imagesrc=embed_image_html(image)
)
def embed_image_html(image):
"""Creates an image embedded in HTML base64 format."""
image_pil = Image.fromarray((255 * image).astype('uint8'))
image_pil = image_pil.resize((256, 256))
string_buf = StringIO.StringIO()
image_pil.save(string_buf, format='png')
data = string_buf.getvalue().encode('base64').replace('\n', '')
return 'data:image/png;base64,' + data
def allowed_file(filename):
return (
'.' in filename and
filename.rsplit('.', 1)[1] in ALLOWED_IMAGE_EXTENSIONS
)
class ImagenetClassifier(object):
default_args = {
'model_def_file': (
'{}/models/bvlc_reference_caffenet/deploy.prototxt'.format(REPO_DIRNAME)),
'pretrained_model_file': (
'{}/models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'.format(REPO_DIRNAME)),
'mean_file': (
'{}/python/caffe/imagenet/ilsvrc_2012_mean.npy'.format(REPO_DIRNAME)),
'class_labels_file': (
'{}/data/ilsvrc12/synset_words.txt'.format(REPO_DIRNAME)),
'bet_file': (
'{}/data/ilsvrc12/imagenet.bet.pickle'.format(REPO_DIRNAME)),
}
for key, val in default_args.iteritems():
if not os.path.exists(val):
raise Exception(
"File for {} is missing. Should be at: {}".format(key, val))
default_args['image_dim'] = 256
default_args['raw_scale'] = 255.
def __init__(self, model_def_file, pretrained_model_file, mean_file,
raw_scale, class_labels_file, bet_file, image_dim, gpu_mode):
logging.info('Loading net and associated files...')
if gpu_mode:
caffe.set_mode_gpu()
else:
caffe.set_mode_cpu()
self.net = caffe.Classifier(
model_def_file, pretrained_model_file,
image_dims=(image_dim, image_dim), raw_scale=raw_scale,
mean=np.load(mean_file).mean(1).mean(1), channel_swap=(2, 1, 0)
)
with open(class_labels_file) as f:
labels_df = pd.DataFrame([
{
'synset_id': l.strip().split(' ')[0],
'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]
}
for l in f.readlines()
])
self.labels = labels_df.sort('synset_id')['name'].values
self.bet = cPickle.load(open(bet_file))
# A bias to prefer children nodes in single-chain paths
# I am setting the value to 0.1 as a quick, simple model.
# We could use better psychological models here...
self.bet['infogain'] -= np.array(self.bet['preferences']) * 0.1
def classify_image(self, image):
try:
starttime = time.time()
scores = self.net.predict([image], oversample=True).flatten()
endtime = time.time()
indices = (-scores).argsort()[:5]
predictions = self.labels[indices]
# In addition to the prediction text, we will also produce
# the length for the progress bar visualization.
meta = [
(p, '%.5f' % scores[i])
for i, p in zip(indices, predictions)
]
logging.info('result: %s', str(meta))
# Compute expected information gain
expected_infogain = np.dot(
self.bet['probmat'], scores[self.bet['idmapping']])
expected_infogain *= self.bet['infogain']
# sort the scores
infogain_sort = expected_infogain.argsort()[::-1]
bet_result = [(self.bet['words'][v], '%.5f' % expected_infogain[v])
for v in infogain_sort[:5]]
logging.info('bet result: %s', str(bet_result))
return (True, meta, bet_result, '%.3f' % (endtime - starttime))
except Exception as err:
logging.info('Classification error: %s', err)
return (False, 'Something went wrong when classifying the '
'image. Maybe try another one?')
def start_tornado(app, port=5000):
http_server = tornado.httpserver.HTTPServer(
tornado.wsgi.WSGIContainer(app))
http_server.listen(port)
print("Tornado server starting on port {}".format(port))
tornado.ioloop.IOLoop.instance().start()
def start_from_terminal(app):
"""
Parse command line options and start the server.
"""
parser = optparse.OptionParser()
parser.add_option(
'-d', '--debug',
help="enable debug mode",
action="store_true", default=False)
parser.add_option(
'-p', '--port',
help="which port to serve content on",
type='int', default=5000)
parser.add_option(
'-g', '--gpu',
help="use gpu mode",
action='store_true', default=False)
opts, args = parser.parse_args()
ImagenetClassifier.default_args.update({'gpu_mode': opts.gpu})
# Initialize classifier + warm start by forward for allocation
app.clf = ImagenetClassifier(**ImagenetClassifier.default_args)
app.clf.net.forward()
if opts.debug:
app.run(debug=True, host='0.0.0.0', port=opts.port)
else:
start_tornado(app, opts.port)
if __name__ == '__main__':
logging.getLogger().setLevel(logging.INFO)
if not os.path.exists(UPLOAD_FOLDER):
os.makedirs(UPLOAD_FOLDER)
start_from_terminal(app)
| 7,793 | 33.184211 | 105 | py |
DRT | DRT-master/caffe/examples/pycaffe/caffenet.py | from __future__ import print_function
from caffe import layers as L, params as P, to_proto
from caffe.proto import caffe_pb2
# helper function for common structures
def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):
conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
num_output=nout, pad=pad, group=group)
return conv, L.ReLU(conv, in_place=True)
def fc_relu(bottom, nout):
fc = L.InnerProduct(bottom, num_output=nout)
return fc, L.ReLU(fc, in_place=True)
def max_pool(bottom, ks, stride=1):
return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
def caffenet(lmdb, batch_size=256, include_acc=False):
data, label = L.Data(source=lmdb, backend=P.Data.LMDB, batch_size=batch_size, ntop=2,
transform_param=dict(crop_size=227, mean_value=[104, 117, 123], mirror=True))
# the net itself
conv1, relu1 = conv_relu(data, 11, 96, stride=4)
pool1 = max_pool(relu1, 3, stride=2)
norm1 = L.LRN(pool1, local_size=5, alpha=1e-4, beta=0.75)
conv2, relu2 = conv_relu(norm1, 5, 256, pad=2, group=2)
pool2 = max_pool(relu2, 3, stride=2)
norm2 = L.LRN(pool2, local_size=5, alpha=1e-4, beta=0.75)
conv3, relu3 = conv_relu(norm2, 3, 384, pad=1)
conv4, relu4 = conv_relu(relu3, 3, 384, pad=1, group=2)
conv5, relu5 = conv_relu(relu4, 3, 256, pad=1, group=2)
pool5 = max_pool(relu5, 3, stride=2)
fc6, relu6 = fc_relu(pool5, 4096)
drop6 = L.Dropout(relu6, in_place=True)
fc7, relu7 = fc_relu(drop6, 4096)
drop7 = L.Dropout(relu7, in_place=True)
fc8 = L.InnerProduct(drop7, num_output=1000)
loss = L.SoftmaxWithLoss(fc8, label)
if include_acc:
acc = L.Accuracy(fc8, label)
return to_proto(loss, acc)
else:
return to_proto(loss)
def make_net():
with open('train.prototxt', 'w') as f:
print(caffenet('/path/to/caffe-train-lmdb'), file=f)
with open('test.prototxt', 'w') as f:
print(caffenet('/path/to/caffe-val-lmdb', batch_size=50, include_acc=True), file=f)
if __name__ == '__main__':
make_net()
| 2,112 | 36.732143 | 91 | py |
DRT | DRT-master/caffe/examples/pycaffe/layers/pyloss.py | import caffe
import numpy as np
class EuclideanLossLayer(caffe.Layer):
"""
Compute the Euclidean Loss in the same manner as the C++ EuclideanLossLayer
to demonstrate the class interface for developing layers in Python.
"""
def setup(self, bottom, top):
# check input pair
if len(bottom) != 2:
raise Exception("Need two inputs to compute distance.")
def reshape(self, bottom, top):
# check input dimensions match
if bottom[0].count != bottom[1].count:
raise Exception("Inputs must have the same dimension.")
# difference is shape of inputs
self.diff = np.zeros_like(bottom[0].data, dtype=np.float32)
# loss output is scalar
top[0].reshape(1)
def forward(self, bottom, top):
self.diff[...] = bottom[0].data - bottom[1].data
top[0].data[...] = np.sum(self.diff**2) / bottom[0].num / 2.
def backward(self, top, propagate_down, bottom):
for i in range(2):
if not propagate_down[i]:
continue
if i == 0:
sign = 1
else:
sign = -1
bottom[i].diff[...] = sign * self.diff / bottom[i].num
| 1,223 | 31.210526 | 79 | py |
DRT | DRT-master/caffe/examples/finetune_flickr_style/assemble_data.py | #!/usr/bin/env python
"""
Form a subset of the Flickr Style data, download images to dirname, and write
Caffe ImagesDataLayer training file.
"""
import os
import urllib
import hashlib
import argparse
import numpy as np
import pandas as pd
from skimage import io
import multiprocessing
# Flickr returns a special image if the request is unavailable.
MISSING_IMAGE_SHA1 = '6a92790b1c2a301c6e7ddef645dca1f53ea97ac2'
example_dirname = os.path.abspath(os.path.dirname(__file__))
caffe_dirname = os.path.abspath(os.path.join(example_dirname, '../..'))
training_dirname = os.path.join(caffe_dirname, 'data/flickr_style')
def download_image(args_tuple):
"For use with multiprocessing map. Returns filename on fail."
try:
url, filename = args_tuple
if not os.path.exists(filename):
urllib.urlretrieve(url, filename)
with open(filename) as f:
assert hashlib.sha1(f.read()).hexdigest() != MISSING_IMAGE_SHA1
test_read_image = io.imread(filename)
return True
except KeyboardInterrupt:
raise Exception() # multiprocessing doesn't catch keyboard exceptions
except:
return False
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Download a subset of Flickr Style to a directory')
parser.add_argument(
'-s', '--seed', type=int, default=0,
help="random seed")
parser.add_argument(
'-i', '--images', type=int, default=-1,
help="number of images to use (-1 for all [default])",
)
parser.add_argument(
'-w', '--workers', type=int, default=-1,
help="num workers used to download images. -x uses (all - x) cores [-1 default]."
)
parser.add_argument(
'-l', '--labels', type=int, default=0,
help="if set to a positive value, only sample images from the first number of labels."
)
args = parser.parse_args()
np.random.seed(args.seed)
# Read data, shuffle order, and subsample.
csv_filename = os.path.join(example_dirname, 'flickr_style.csv.gz')
df = pd.read_csv(csv_filename, index_col=0, compression='gzip')
df = df.iloc[np.random.permutation(df.shape[0])]
if args.labels > 0:
df = df.loc[df['label'] < args.labels]
if args.images > 0 and args.images < df.shape[0]:
df = df.iloc[:args.images]
# Make directory for images and get local filenames.
if training_dirname is None:
training_dirname = os.path.join(caffe_dirname, 'data/flickr_style')
images_dirname = os.path.join(training_dirname, 'images')
if not os.path.exists(images_dirname):
os.makedirs(images_dirname)
df['image_filename'] = [
os.path.join(images_dirname, _.split('/')[-1]) for _ in df['image_url']
]
# Download images.
num_workers = args.workers
if num_workers <= 0:
num_workers = multiprocessing.cpu_count() + num_workers
print('Downloading {} images with {} workers...'.format(
df.shape[0], num_workers))
pool = multiprocessing.Pool(processes=num_workers)
map_args = zip(df['image_url'], df['image_filename'])
results = pool.map(download_image, map_args)
# Only keep rows with valid images, and write out training file lists.
df = df[results]
for split in ['train', 'test']:
split_df = df[df['_split'] == split]
filename = os.path.join(training_dirname, '{}.txt'.format(split))
split_df[['image_filename', 'label']].to_csv(
filename, sep=' ', header=None, index=None)
print('Writing train/val for {} successfully downloaded images.'.format(
df.shape[0]))
| 3,636 | 35.737374 | 94 | py |
DRT | DRT-master/caffe/examples/coco_caption/captioner.py | #!/usr/bin/env python
from collections import OrderedDict
import h5py
import math
import matplotlib.pyplot as plt
import numpy as np
import os
import random
import sys
sys.path.append('./python/')
import caffe
class Captioner():
def __init__(self, weights_path, image_net_proto, lstm_net_proto,
vocab_path, device_id=-1):
if device_id >= 0:
caffe.set_mode_gpu()
caffe.set_device(device_id)
else:
caffe.set_mode_cpu()
# Setup image processing net.
phase = caffe.TEST
self.image_net = caffe.Net(image_net_proto, weights_path, phase)
image_data_shape = self.image_net.blobs['data'].data.shape
self.transformer = caffe.io.Transformer({'data': image_data_shape})
channel_mean = np.zeros(image_data_shape[1:])
channel_mean_values = [104, 117, 123]
assert channel_mean.shape[0] == len(channel_mean_values)
for channel_index, mean_val in enumerate(channel_mean_values):
channel_mean[channel_index, ...] = mean_val
self.transformer.set_mean('data', channel_mean)
self.transformer.set_channel_swap('data', (2, 1, 0))
self.transformer.set_transpose('data', (2, 0, 1))
# Setup sentence prediction net.
self.lstm_net = caffe.Net(lstm_net_proto, weights_path, phase)
self.vocab = ['<EOS>']
with open(vocab_path, 'r') as vocab_file:
self.vocab += [word.strip() for word in vocab_file.readlines()]
net_vocab_size = self.lstm_net.blobs['predict'].data.shape[2]
if len(self.vocab) != net_vocab_size:
raise Exception('Invalid vocab file: contains %d words; '
'net expects vocab with %d words' % (len(self.vocab), net_vocab_size))
def set_image_batch_size(self, batch_size):
self.image_net.blobs['data'].reshape(batch_size,
*self.image_net.blobs['data'].data.shape[1:])
def caption_batch_size(self):
return self.lstm_net.blobs['cont_sentence'].data.shape[1]
def set_caption_batch_size(self, batch_size):
self.lstm_net.blobs['cont_sentence'].reshape(1, batch_size)
self.lstm_net.blobs['input_sentence'].reshape(1, batch_size)
self.lstm_net.blobs['image_features'].reshape(batch_size,
*self.lstm_net.blobs['image_features'].data.shape[1:])
self.lstm_net.reshape()
def preprocess_image(self, image, verbose=False):
if type(image) in (str, unicode):
image = plt.imread(image)
crop_edge_ratio = (256. - 227.) / 256. / 2
ch = int(image.shape[0] * crop_edge_ratio + 0.5)
cw = int(image.shape[1] * crop_edge_ratio + 0.5)
cropped_image = image[ch:-ch, cw:-cw]
if len(cropped_image.shape) == 2:
cropped_image = np.tile(cropped_image[:, :, np.newaxis], (1, 1, 3))
preprocessed_image = self.transformer.preprocess('data', cropped_image)
if verbose:
print 'Preprocessed image has shape %s, range (%f, %f)' % \
(preprocessed_image.shape,
preprocessed_image.min(),
preprocessed_image.max())
return preprocessed_image
def preprocessed_image_to_descriptor(self, image, output_name='fc8'):
net = self.image_net
if net.blobs['data'].data.shape[0] > 1:
batch = np.zeros_like(net.blobs['data'].data)
batch[0] = image[0]
else:
batch = image
net.forward(data=batch)
descriptor = net.blobs[output_name].data[0].copy()
return descriptor
def image_to_descriptor(self, image, output_name='fc8'):
return self.preprocessed_image_to_descriptor(self.preprocess_image(image))
def predict_single_word(self, descriptor, previous_word, output='probs'):
net = self.lstm_net
cont = 0 if previous_word == 0 else 1
cont_input = np.array([cont])
word_input = np.array([previous_word])
image_features = np.zeros_like(net.blobs['image_features'].data)
image_features[:] = descriptor
net.forward(image_features=image_features, cont_sentence=cont_input,
input_sentence=word_input)
output_preds = net.blobs[output].data[0, 0, :]
return output_preds
def predict_single_word_from_all_previous(self, descriptor, previous_words):
for word in [0] + previous_words:
probs = self.predict_single_word(descriptor, word)
return probs
# Strategy must be either 'beam' or 'sample'.
# If 'beam', do a max likelihood beam search with beam size num_samples.
# Otherwise, sample with temperature temp.
def predict_caption(self, descriptor, strategy={'type': 'beam'}):
assert 'type' in strategy
assert strategy['type'] in ('beam', 'sample')
if strategy['type'] == 'beam':
return self.predict_caption_beam_search(descriptor, strategy)
num_samples = strategy['num'] if 'num' in strategy else 1
samples = []
sample_probs = []
for _ in range(num_samples):
sample, sample_prob = self.sample_caption(descriptor, strategy)
samples.append(sample)
sample_probs.append(sample_prob)
return samples, sample_probs
def sample_caption(self, descriptor, strategy,
net_output='predict', max_length=50):
sentence = []
probs = []
eps_prob = 1e-8
temp = strategy['temp'] if 'temp' in strategy else 1.0
if max_length < 0: max_length = float('inf')
while len(sentence) < max_length and (not sentence or sentence[-1] != 0):
previous_word = sentence[-1] if sentence else 0
softmax_inputs = self.predict_single_word(descriptor, previous_word,
output=net_output)
word = random_choice_from_probs(softmax_inputs, temp)
sentence.append(word)
probs.append(softmax(softmax_inputs, 1.0)[word])
return sentence, probs
def predict_caption_beam_search(self, descriptor, strategy, max_length=50):
orig_batch_size = self.caption_batch_size()
if orig_batch_size != 1: self.set_caption_batch_size(1)
beam_size = strategy['beam_size'] if 'beam_size' in strategy else 1
assert beam_size >= 1
beams = [[]]
beams_complete = 0
beam_probs = [[]]
beam_log_probs = [0.]
while beams_complete < len(beams):
expansions = []
for beam_index, beam_log_prob, beam in \
zip(range(len(beams)), beam_log_probs, beams):
if beam:
previous_word = beam[-1]
if len(beam) >= max_length or previous_word == 0:
exp = {'prefix_beam_index': beam_index, 'extension': [],
'prob_extension': [], 'log_prob': beam_log_prob}
expansions.append(exp)
# Don't expand this beam; it was already ended with an EOS,
# or is the max length.
continue
else:
previous_word = 0 # EOS is first word
if beam_size == 1:
probs = self.predict_single_word(descriptor, previous_word)
else:
probs = self.predict_single_word_from_all_previous(descriptor, beam)
assert len(probs.shape) == 1
assert probs.shape[0] == len(self.vocab)
expansion_inds = probs.argsort()[-beam_size:]
for ind in expansion_inds:
prob = probs[ind]
extended_beam_log_prob = beam_log_prob + math.log(prob)
exp = {'prefix_beam_index': beam_index, 'extension': [ind],
'prob_extension': [prob], 'log_prob': extended_beam_log_prob}
expansions.append(exp)
# Sort expansions in decreasing order of probability.
expansions.sort(key=lambda expansion: -1 * expansion['log_prob'])
expansions = expansions[:beam_size]
new_beams = \
[beams[e['prefix_beam_index']] + e['extension'] for e in expansions]
new_beam_probs = \
[beam_probs[e['prefix_beam_index']] + e['prob_extension'] for e in expansions]
beam_log_probs = [e['log_prob'] for e in expansions]
beams_complete = 0
for beam in new_beams:
if beam[-1] == 0 or len(beam) >= max_length: beams_complete += 1
beams, beam_probs = new_beams, new_beam_probs
if orig_batch_size != 1: self.set_caption_batch_size(orig_batch_size)
return beams, beam_probs
def score_caption(self, descriptor, caption, is_gt=True, caption_source='gt'):
output = {}
output['caption'] = caption
output['gt'] = is_gt
output['source'] = caption_source
output['prob'] = []
probs = self.predict_single_word(descriptor, 0)
for word in caption:
output['prob'].append(probs[word])
probs = self.predict_single_word(descriptor, word)
return output
def compute_descriptors(self, image_list, output_name='fc8'):
batch = np.zeros_like(self.image_net.blobs['data'].data)
batch_shape = batch.shape
batch_size = batch_shape[0]
descriptors_shape = (len(image_list), ) + \
self.image_net.blobs[output_name].data.shape[1:]
descriptors = np.zeros(descriptors_shape)
for batch_start_index in range(0, len(image_list), batch_size):
batch_list = image_list[batch_start_index:(batch_start_index + batch_size)]
for batch_index, image_path in enumerate(batch_list):
batch[batch_index:(batch_index + 1)] = self.preprocess_image(image_path)
current_batch_size = min(batch_size, len(image_list) - batch_start_index)
print 'Computing descriptors for images %d-%d of %d' % \
(batch_start_index, batch_start_index + current_batch_size - 1,
len(image_list))
self.image_net.forward(data=batch)
descriptors[batch_start_index:(batch_start_index + current_batch_size)] = \
self.image_net.blobs[output_name].data[:current_batch_size]
return descriptors
def score_captions(self, descriptor, captions,
output_name='probs', caption_source='gt', verbose=True):
net = self.lstm_net
cont_input = np.zeros_like(net.blobs['cont_sentence'].data)
word_input = np.zeros_like(net.blobs['input_sentence'].data)
image_features = np.zeros_like(net.blobs['image_features'].data)
batch_size = image_features.shape[0]
assert descriptor.shape == image_features.shape[1:]
for index in range(batch_size):
image_features[index] = descriptor
outputs = []
input_data_initialized = False
for batch_start_index in range(0, len(captions), batch_size):
caption_batch = captions[batch_start_index:(batch_start_index + batch_size)]
current_batch_size = len(caption_batch)
caption_index = 0
probs_batch = [[] for b in range(current_batch_size)]
num_done = 0
while num_done < current_batch_size:
if caption_index == 0:
cont_input[:] = 0
elif caption_index == 1:
cont_input[:] = 1
for index, caption in enumerate(caption_batch):
word_input[0, index] = \
caption['caption'][caption_index - 1] if \
0 < caption_index < len(caption['caption']) else 0
if input_data_initialized:
net.forward(start="embedding", input_sentence=word_input,
cont_sentence=cont_input, image_features=image_features)
else:
net.forward(input_sentence=word_input, cont_sentence=cont_input,
image_features=image_features)
input_data_initialized = True
output_probs = net.blobs[output_name].data
for index, probs, caption in \
zip(range(current_batch_size), probs_batch, caption_batch):
if caption_index == len(caption['caption']) - 1:
num_done += 1
if caption_index < len(caption['caption']):
word = caption['caption'][caption_index]
probs.append(output_probs[0, index, word].reshape(-1)[0])
if verbose:
print 'Computed probs for word %d of captions %d-%d (%d done)' % \
(caption_index, batch_start_index,
batch_start_index + current_batch_size - 1, num_done)
caption_index += 1
for prob, caption in zip(probs_batch, caption_batch):
output = {}
output['caption'] = caption['caption']
output['prob'] = prob
output['gt'] = True
output['source'] = caption_source
outputs.append(output)
return outputs
def sample_captions(self, descriptor, prob_output_name='probs',
pred_output_name='predict', temp=1, max_length=50):
descriptor = np.array(descriptor)
batch_size = descriptor.shape[0]
self.set_caption_batch_size(batch_size)
net = self.lstm_net
cont_input = np.zeros_like(net.blobs['cont_sentence'].data)
word_input = np.zeros_like(net.blobs['input_sentence'].data)
image_features = np.zeros_like(net.blobs['image_features'].data)
image_features[:] = descriptor
outputs = []
output_captions = [[] for b in range(batch_size)]
output_probs = [[] for b in range(batch_size)]
caption_index = 0
num_done = 0
while num_done < batch_size and caption_index < max_length:
if caption_index == 0:
cont_input[:] = 0
elif caption_index == 1:
cont_input[:] = 1
if caption_index == 0:
word_input[:] = 0
else:
for index in range(batch_size):
word_input[0, index] = \
output_captions[index][caption_index - 1] if \
caption_index <= len(output_captions[index]) else 0
net.forward(image_features=image_features, cont_sentence=cont_input,
input_sentence=word_input)
if temp == 1.0 or temp == float('inf'):
net_output_probs = net.blobs[prob_output_name].data[0]
samples = [
random_choice_from_probs(dist, temp=temp, already_softmaxed=True)
for dist in net_output_probs
]
else:
net_output_preds = net.blobs[pred_output_name].data[0]
samples = [
random_choice_from_probs(preds, temp=temp, already_softmaxed=False)
for preds in net_output_preds
]
for index, next_word_sample in enumerate(samples):
# If the caption is empty, or non-empty but the last word isn't EOS,
# predict another word.
if not output_captions[index] or output_captions[index][-1] != 0:
output_captions[index].append(next_word_sample)
output_probs[index].append(net_output_probs[index, next_word_sample])
if next_word_sample == 0: num_done += 1
sys.stdout.write('\r%d/%d done after word %d' %
(num_done, batch_size, caption_index))
sys.stdout.flush()
caption_index += 1
sys.stdout.write('\n')
return output_captions, output_probs
def sentence(self, vocab_indices):
sentence = ' '.join([self.vocab[i] for i in vocab_indices])
if not sentence: return sentence
sentence = sentence[0].upper() + sentence[1:]
# If sentence ends with ' <EOS>', remove and replace with '.'
# Otherwise (doesn't end with '<EOS>' -- maybe was the max length?):
# append '...'
suffix = ' ' + self.vocab[0]
if sentence.endswith(suffix):
sentence = sentence[:-len(suffix)] + '.'
else:
sentence += '...'
return sentence
def softmax(softmax_inputs, temp):
shifted_inputs = softmax_inputs - softmax_inputs.max()
exp_outputs = np.exp(temp * shifted_inputs)
exp_outputs_sum = exp_outputs.sum()
if math.isnan(exp_outputs_sum):
return exp_outputs * float('nan')
assert exp_outputs_sum > 0
if math.isinf(exp_outputs_sum):
return np.zeros_like(exp_outputs)
eps_sum = 1e-20
return exp_outputs / max(exp_outputs_sum, eps_sum)
def random_choice_from_probs(softmax_inputs, temp=1, already_softmaxed=False):
# temperature of infinity == take the max
if temp == float('inf'):
return np.argmax(softmax_inputs)
if already_softmaxed:
probs = softmax_inputs
assert temp == 1
else:
probs = softmax(softmax_inputs, temp)
r = random.random()
cum_sum = 0.
for i, p in enumerate(probs):
cum_sum += p
if cum_sum >= r: return i
return 1 # return UNK?
def gen_stats(prob, normalizer=None):
stats = {}
stats['length'] = len(prob)
stats['log_p'] = 0.0
eps = 1e-12
for p in prob:
assert 0.0 <= p <= 1.0
stats['log_p'] += math.log(max(eps, p))
stats['log_p_word'] = stats['log_p'] / stats['length']
stats['p'] = math.exp(stats['log_p'])
stats['p_word'] = math.exp(stats['log_p'])
try:
stats['perplex'] = math.exp(-stats['log_p'])
except OverflowError:
stats['perplex'] = float('inf')
try:
stats['perplex_word'] = math.exp(-stats['log_p_word'])
except OverflowError:
stats['perplex_word'] = float('inf')
if normalizer is not None:
norm_stats = gen_stats(normalizer)
stats['normed_perplex'] = stats['perplex'] / norm_stats['perplex']
stats['normed_perplex_word'] = \
stats['perplex_word'] / norm_stats['perplex_word']
return stats
| 16,658 | 40.337469 | 88 | py |
DRT | DRT-master/caffe/examples/coco_caption/retrieval_experiment.py | #!/usr/bin/env python
from collections import OrderedDict
import json
import numpy as np
import pprint
import cPickle as pickle
import string
import sys
# seed the RNG so we evaluate on the same subset each time
np.random.seed(seed=0)
from coco_to_hdf5_data import *
from captioner import Captioner
COCO_EVAL_PATH = './data/coco/coco-caption-eval'
sys.path.append(COCO_EVAL_PATH)
from pycocoevalcap.eval import COCOEvalCap
class CaptionExperiment():
# captioner is an initialized Captioner (captioner.py)
# dataset is a dict: image path -> [caption1, caption2, ...]
def __init__(self, captioner, dataset, dataset_cache_dir, cache_dir, sg):
self.captioner = captioner
self.sg = sg
self.dataset_cache_dir = dataset_cache_dir
self.cache_dir = cache_dir
for d in [dataset_cache_dir, cache_dir]:
if not os.path.exists(d): os.makedirs(d)
self.dataset = dataset
self.images = dataset.keys()
self.init_caption_list(dataset)
self.caption_scores = [None] * len(self.images)
print 'Initialized caption experiment: %d images, %d captions' % \
(len(self.images), len(self.captions))
def init_caption_list(self, dataset):
self.captions = []
for image, captions in dataset.iteritems():
for caption, _ in captions:
self.captions.append({'source_image': image, 'caption': caption})
# Sort by length for performance.
self.captions.sort(key=lambda c: len(c['caption']))
def compute_descriptors(self):
descriptor_filename = '%s/descriptors.npz' % self.dataset_cache_dir
if os.path.exists(descriptor_filename):
self.descriptors = np.load(descriptor_filename)['descriptors']
else:
self.descriptors = self.captioner.compute_descriptors(self.images)
np.savez_compressed(descriptor_filename, descriptors=self.descriptors)
def score_captions(self, image_index, output_name='probs'):
assert image_index < len(self.images)
caption_scores_dir = '%s/caption_scores' % self.cache_dir
if not os.path.exists(caption_scores_dir):
os.makedirs(caption_scores_dir)
caption_scores_filename = '%s/scores_image_%06d.pkl' % \
(caption_scores_dir, image_index)
if os.path.exists(caption_scores_filename):
with open(caption_scores_filename, 'rb') as caption_scores_file:
outputs = pickle.load(caption_scores_file)
else:
outputs = self.captioner.score_captions(self.descriptors[image_index],
self.captions, output_name=output_name, caption_source='gt',
verbose=False)
self.caption_stats(image_index, outputs)
with open(caption_scores_filename, 'wb') as caption_scores_file:
pickle.dump(outputs, caption_scores_file)
self.caption_scores[image_index] = outputs
def caption_stats(self, image_index, caption_scores):
image_path = self.images[image_index]
for caption, score in zip(self.captions, caption_scores):
assert caption['caption'] == score['caption']
score['stats'] = gen_stats(score['prob'])
score['correct'] = (image_path == caption['source_image'])
def eval_image_to_caption(self, image_index, methods=None):
scores = self.caption_scores[image_index]
return self.eval_recall(scores, methods=methods)
def eval_caption_to_image(self, caption_index, methods=None):
scores = [s[caption_index] for s in self.caption_scores]
return self.eval_recall(scores, methods=methods)
def normalize_caption_scores(self, caption_index, stats=['log_p', 'log_p_word']):
scores = [s[caption_index] for s in self.caption_scores]
for stat in stats:
log_stat_scores = np.array([score['stats'][stat] for score in scores])
stat_scores = np.exp(log_stat_scores)
mean_stat_score = np.mean(stat_scores)
log_mean_stat_score = np.log(mean_stat_score)
for log_stat_score, score in zip(log_stat_scores, scores):
score['stats']['normalized_' + stat] = log_stat_score - log_mean_stat_score
def eval_recall(self, scores, methods=None, neg_prefix='negative_'):
if methods is None:
# rank on all stats, and all their inverses
methods = scores[0]['stats'].keys()
methods += [neg_prefix + method for method in methods]
correct_ranks = {}
for method in methods:
if method.startswith(neg_prefix):
multiplier = -1
method_key = method[len(neg_prefix):]
else:
multiplier = 1
method_key = method
sort_key = lambda s: multiplier * s['stats'][method_key]
ranked_scores = sorted(scores, key=sort_key)
for index, score in enumerate(ranked_scores):
if score['correct']:
correct_ranks[method] = index
break
return correct_ranks
def recall_results(self, correct_ranks, recall_ranks=[]):
num_instances = float(len(correct_ranks))
assert num_instances > 0
methods = correct_ranks[0].keys()
results = {}
for method in methods:
method_correct_ranks = \
np.array([correct_rank[method] for correct_rank in correct_ranks])
r = OrderedDict()
r['mean'] = np.mean(method_correct_ranks)
r['median'] = np.median(method_correct_ranks)
r['mean (1-indexed)'] = r['mean'] + 1
r['median (1-indexed)'] = r['median'] + 1
for recall_rank in recall_ranks:
r['R@%d' % recall_rank] = \
np.where(method_correct_ranks < recall_rank)[0].shape[0] / num_instances
results[method] = r
return results
def print_recall_results(self, results):
for method, result in results.iteritems():
print 'Ranking method:', method
for metric_name_and_value in result.iteritems():
print ' %s: %f' % metric_name_and_value
def retrieval_experiment(self):
# Compute image descriptors.
print 'Computing image descriptors'
self.compute_descriptors()
num_images, num_captions = len(self.images), len(self.captions)
# For each image, score all captions.
for image_index in xrange(num_images):
sys.stdout.write("\rScoring captions for image %d/%d" %
(image_index, num_images))
sys.stdout.flush()
self.score_captions(image_index)
sys.stdout.write('\n')
# Compute global caption statistics for normalization.
for caption_index in xrange(num_captions):
self.normalize_caption_scores(caption_index)
recall_ranks = [1, 5, 10, 50]
eval_methods = ['negative_normalized_log_p']
# Evaluate caption-to-image retrieval task.
self.caption_to_image_ranks = [None] * num_captions
for caption_index in xrange(num_captions):
sys.stdout.write("\rCaption-to-image evaluation: "
"computing recall for caption %d/%d" %
(caption_index, num_captions))
sys.stdout.flush()
self.caption_to_image_ranks[caption_index] = \
self.eval_caption_to_image(caption_index, methods=eval_methods)
sys.stdout.write('\n')
self.caption_to_image_recall = \
self.recall_results(self.caption_to_image_ranks, recall_ranks)
print 'Caption-to-image retrieval results:'
self.print_recall_results(self.caption_to_image_recall)
# Evaluate image-to-caption retrieval task.
self.image_to_caption_ranks = [None] * num_images
for image_index in xrange(num_images):
sys.stdout.write("\rImage-to-caption evaluation: "
"computing recall for image %d/%d" %
(image_index, num_images))
sys.stdout.flush()
self.image_to_caption_ranks[image_index] = \
self.eval_image_to_caption(image_index, methods=eval_methods)
sys.stdout.write('\n')
self.image_to_caption_recall = \
self.recall_results(self.image_to_caption_ranks, recall_ranks)
print 'Image-to-caption retrieval results:'
self.print_recall_results(self.image_to_caption_recall)
def generation_experiment(self, strategy, max_batch_size=1000):
# Compute image descriptors.
print 'Computing image descriptors'
self.compute_descriptors()
do_batches = (strategy['type'] == 'beam' and strategy['beam_size'] == 1) or \
(strategy['type'] == 'sample' and
('temp' not in strategy or strategy['temp'] in (1, float('inf'))) and
('num' not in strategy or strategy['num'] == 1))
num_images = len(self.images)
batch_size = min(max_batch_size, num_images) if do_batches else 1
# Generate captions for all images.
all_captions = [None] * num_images
for image_index in xrange(0, num_images, batch_size):
batch_end_index = min(image_index + batch_size, num_images)
sys.stdout.write("\rGenerating captions for image %d/%d" %
(image_index, num_images))
sys.stdout.flush()
if do_batches:
if strategy['type'] == 'beam' or \
('temp' in strategy and strategy['temp'] == float('inf')):
temp = float('inf')
else:
temp = strategy['temp'] if 'temp' in strategy else 1
output_captions, output_probs = self.captioner.sample_captions(
self.descriptors[image_index:batch_end_index], temp=temp)
for batch_index, output in zip(range(image_index, batch_end_index),
output_captions):
all_captions[batch_index] = output
else:
for batch_image_index in xrange(image_index, batch_end_index):
captions, caption_probs = self.captioner.predict_caption(
self.descriptors[batch_image_index], strategy=strategy)
best_caption, max_log_prob = None, None
for caption, probs in zip(captions, caption_probs):
log_prob = gen_stats(probs)['log_p']
if best_caption is None or \
(best_caption is not None and log_prob > max_log_prob):
best_caption, max_log_prob = caption, log_prob
all_captions[batch_image_index] = best_caption
sys.stdout.write('\n')
# Compute the number of reference files as the maximum number of ground
# truth captions of any image in the dataset.
num_reference_files = 0
for captions in self.dataset.values():
if len(captions) > num_reference_files:
num_reference_files = len(captions)
if num_reference_files <= 0:
raise Exception('No reference captions.')
# Collect model/reference captions, formatting the model's captions and
# each set of reference captions as a list of len(self.images) strings.
exp_dir = '%s/generation' % self.cache_dir
if not os.path.exists(exp_dir):
os.makedirs(exp_dir)
# For each image, write out the highest probability caption.
model_captions = [''] * len(self.images)
reference_captions = [([''] * len(self.images)) for _ in xrange(num_reference_files)]
for image_index, image in enumerate(self.images):
caption = self.captioner.sentence(all_captions[image_index])
model_captions[image_index] = caption
for reference_index, (_, caption) in enumerate(self.dataset[image]):
caption = ' '.join(caption)
reference_captions[reference_index][image_index] = caption
coco_image_ids = [self.sg.image_path_to_id[image_path]
for image_path in self.images]
generation_result = [{
'image_id': self.sg.image_path_to_id[image_path],
'caption': model_captions[image_index]
} for (image_index, image_path) in enumerate(self.images)]
json_filename = '%s/generation_result.json' % self.cache_dir
print 'Dumping result to file: %s' % json_filename
with open(json_filename, 'w') as json_file:
json.dump(generation_result, json_file)
generation_result = self.sg.coco.loadRes(json_filename)
coco_evaluator = COCOEvalCap(self.sg.coco, generation_result)
coco_evaluator.params['image_id'] = coco_image_ids
coco_evaluator.evaluate()
def gen_stats(prob):
stats = {}
stats['length'] = len(prob)
stats['log_p'] = 0.0
eps = 1e-12
for p in prob:
assert 0.0 <= p <= 1.0
stats['log_p'] += np.log(max(eps, p))
stats['log_p_word'] = stats['log_p'] / stats['length']
try:
stats['perplex'] = np.exp(-stats['log_p'])
except OverflowError:
stats['perplex'] = float('inf')
try:
stats['perplex_word'] = np.exp(-stats['log_p_word'])
except OverflowError:
stats['perplex_word'] = float('inf')
return stats
def main():
MAX_IMAGES = -1 # -1 to use all images
TAG = 'coco_2layer_factored'
if MAX_IMAGES >= 0:
TAG += '_%dimages' % MAX_IMAGES
eval_on_test = False
if eval_on_test:
ITER = 100000
MODEL_FILENAME = 'lrcn_finetune_trainval_stepsize40k_iter_%d' % ITER
DATASET_NAME = 'test'
else: # eval on val
ITER = 50000
MODEL_FILENAME = 'lrcn_finetune_iter_%d' % ITER
DATASET_NAME = 'val'
TAG += '_%s' % DATASET_NAME
MODEL_DIR = './examples/coco_caption'
MODEL_FILE = '%s/%s.caffemodel' % (MODEL_DIR, MODEL_FILENAME)
IMAGE_NET_FILE = './models/bvlc_reference_caffenet/deploy.prototxt'
LSTM_NET_FILE = './examples/coco_caption/lrcn_word_to_preds.deploy.prototxt'
NET_TAG = '%s_%s' % (TAG, MODEL_FILENAME)
DATASET_SUBDIR = '%s/%s_ims' % (DATASET_NAME,
str(MAX_IMAGES) if MAX_IMAGES >= 0 else 'all')
DATASET_CACHE_DIR = './retrieval_cache/%s/%s' % (DATASET_SUBDIR, MODEL_FILENAME)
VOCAB_FILE = './examples/coco_caption/h5_data/buffer_100/vocabulary.txt'
DEVICE_ID = 0
with open(VOCAB_FILE, 'r') as vocab_file:
vocab = [line.strip() for line in vocab_file.readlines()]
coco = COCO(COCO_ANNO_PATH % DATASET_NAME)
image_root = COCO_IMAGE_PATTERN % DATASET_NAME
sg = CocoSequenceGenerator(coco, BUFFER_SIZE, image_root, vocab=vocab,
align=False, shuffle=False)
dataset = {}
for image_path, sentence in sg.image_sentence_pairs:
if image_path not in dataset:
dataset[image_path] = []
dataset[image_path].append((sg.line_to_stream(sentence), sentence))
print 'Original dataset contains %d images' % len(dataset.keys())
if 0 <= MAX_IMAGES < len(dataset.keys()):
all_keys = dataset.keys()
perm = np.random.permutation(len(all_keys))[:MAX_IMAGES]
chosen_keys = set([all_keys[p] for p in perm])
for key in all_keys:
if key not in chosen_keys:
del dataset[key]
print 'Reduced dataset to %d images' % len(dataset.keys())
if MAX_IMAGES < 0: MAX_IMAGES = len(dataset.keys())
captioner = Captioner(MODEL_FILE, IMAGE_NET_FILE, LSTM_NET_FILE, VOCAB_FILE,
device_id=DEVICE_ID)
beam_size = 1
generation_strategy = {'type': 'beam', 'beam_size': beam_size}
if generation_strategy['type'] == 'beam':
strategy_name = 'beam%d' % generation_strategy['beam_size']
elif generation_strategy['type'] == 'sample':
strategy_name = 'sample%f' % generation_strategy['temp']
else:
raise Exception('Unknown generation strategy type: %s' % generation_strategy['type'])
CACHE_DIR = '%s/%s' % (DATASET_CACHE_DIR, strategy_name)
experimenter = CaptionExperiment(captioner, dataset, DATASET_CACHE_DIR, CACHE_DIR, sg)
captioner.set_image_batch_size(min(100, MAX_IMAGES))
experimenter.generation_experiment(generation_strategy)
captioner.set_caption_batch_size(min(MAX_IMAGES * 5, 1000))
experimenter.retrieval_experiment()
if __name__ == "__main__":
main()
| 15,281 | 41.099174 | 89 | py |
DRT | DRT-master/caffe/python/draw_net.py | #!/usr/bin/env python
"""
Draw a graph of the net architecture.
"""
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
from google.protobuf import text_format
import caffe
import caffe.draw
from caffe.proto import caffe_pb2
def parse_args():
"""Parse input arguments
"""
parser = ArgumentParser(description=__doc__,
formatter_class=ArgumentDefaultsHelpFormatter)
parser.add_argument('input_net_proto_file',
help='Input network prototxt file')
parser.add_argument('output_image_file',
help='Output image file')
parser.add_argument('--rankdir',
help=('One of TB (top-bottom, i.e., vertical), '
'RL (right-left, i.e., horizontal), or another '
'valid dot option; see '
'http://www.graphviz.org/doc/info/'
'attrs.html#k:rankdir'),
default='LR')
args = parser.parse_args()
return args
def main():
args = parse_args()
net = caffe_pb2.NetParameter()
text_format.Merge(open(args.input_net_proto_file).read(), net)
print('Drawing net to %s' % args.output_image_file)
caffe.draw.draw_net_to_file(net, args.output_image_file, args.rankdir)
if __name__ == '__main__':
main()
| 1,389 | 29.217391 | 78 | py |
DRT | DRT-master/caffe/python/detect.py | #!/usr/bin/env python
"""
detector.py is an out-of-the-box windowed detector
callable from the command line.
By default it configures and runs the Caffe reference ImageNet model.
Note that this model was trained for image classification and not detection,
and finetuning for detection can be expected to improve results.
The selective_search_ijcv_with_python code required for the selective search
proposal mode is available at
https://github.com/sergeyk/selective_search_ijcv_with_python
TODO:
- batch up image filenames as well: don't want to load all of them into memory
- come up with a batching scheme that preserved order / keeps a unique ID
"""
import numpy as np
import pandas as pd
import os
import argparse
import time
import caffe
CROP_MODES = ['list', 'selective_search']
COORD_COLS = ['ymin', 'xmin', 'ymax', 'xmax']
def main(argv):
pycaffe_dir = os.path.dirname(__file__)
parser = argparse.ArgumentParser()
# Required arguments: input and output.
parser.add_argument(
"input_file",
help="Input txt/csv filename. If .txt, must be list of filenames.\
If .csv, must be comma-separated file with header\
'filename, xmin, ymin, xmax, ymax'"
)
parser.add_argument(
"output_file",
help="Output h5/csv filename. Format depends on extension."
)
# Optional arguments.
parser.add_argument(
"--model_def",
default=os.path.join(pycaffe_dir,
"../models/bvlc_reference_caffenet/deploy.prototxt.prototxt"),
help="Model definition file."
)
parser.add_argument(
"--pretrained_model",
default=os.path.join(pycaffe_dir,
"../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel"),
help="Trained model weights file."
)
parser.add_argument(
"--crop_mode",
default="selective_search",
choices=CROP_MODES,
help="How to generate windows for detection."
)
parser.add_argument(
"--gpu",
action='store_true',
help="Switch for gpu computation."
)
parser.add_argument(
"--mean_file",
default=os.path.join(pycaffe_dir,
'caffe/imagenet/ilsvrc_2012_mean.npy'),
help="Data set image mean of H x W x K dimensions (numpy array). " +
"Set to '' for no mean subtraction."
)
parser.add_argument(
"--input_scale",
type=float,
help="Multiply input features by this scale to finish preprocessing."
)
parser.add_argument(
"--raw_scale",
type=float,
default=255.0,
help="Multiply raw input by this scale before preprocessing."
)
parser.add_argument(
"--channel_swap",
default='2,1,0',
help="Order to permute input channels. The default converts " +
"RGB -> BGR since BGR is the Caffe default by way of OpenCV."
)
parser.add_argument(
"--context_pad",
type=int,
default='16',
help="Amount of surrounding context to collect in input window."
)
args = parser.parse_args()
mean, channel_swap = None, None
if args.mean_file:
mean = np.load(args.mean_file)
if mean.shape[1:] != (1, 1):
mean = mean.mean(1).mean(1)
if args.channel_swap:
channel_swap = [int(s) for s in args.channel_swap.split(',')]
if args.gpu:
caffe.set_mode_gpu()
print("GPU mode")
else:
caffe.set_mode_cpu()
print("CPU mode")
# Make detector.
detector = caffe.Detector(args.model_def, args.pretrained_model, mean=mean,
input_scale=args.input_scale, raw_scale=args.raw_scale,
channel_swap=channel_swap,
context_pad=args.context_pad)
# Load input.
t = time.time()
print("Loading input...")
if args.input_file.lower().endswith('txt'):
with open(args.input_file) as f:
inputs = [_.strip() for _ in f.readlines()]
elif args.input_file.lower().endswith('csv'):
inputs = pd.read_csv(args.input_file, sep=',', dtype={'filename': str})
inputs.set_index('filename', inplace=True)
else:
raise Exception("Unknown input file type: not in txt or csv.")
# Detect.
if args.crop_mode == 'list':
# Unpack sequence of (image filename, windows).
images_windows = [
(ix, inputs.iloc[np.where(inputs.index == ix)][COORD_COLS].values)
for ix in inputs.index.unique()
]
detections = detector.detect_windows(images_windows)
else:
detections = detector.detect_selective_search(inputs)
print("Processed {} windows in {:.3f} s.".format(len(detections),
time.time() - t))
# Collect into dataframe with labeled fields.
df = pd.DataFrame(detections)
df.set_index('filename', inplace=True)
df[COORD_COLS] = pd.DataFrame(
data=np.vstack(df['window']), index=df.index, columns=COORD_COLS)
del(df['window'])
# Save results.
t = time.time()
if args.output_file.lower().endswith('csv'):
# csv
# Enumerate the class probabilities.
class_cols = ['class{}'.format(x) for x in range(NUM_OUTPUT)]
df[class_cols] = pd.DataFrame(
data=np.vstack(df['feat']), index=df.index, columns=class_cols)
df.to_csv(args.output_file, cols=COORD_COLS + class_cols)
else:
# h5
df.to_hdf(args.output_file, 'df', mode='w')
print("Saved to {} in {:.3f} s.".format(args.output_file,
time.time() - t))
if __name__ == "__main__":
import sys
main(sys.argv)
| 5,743 | 32.011494 | 88 | py |
DRT | DRT-master/caffe/python/classify.py | #!/usr/bin/env python
"""
classify.py is an out-of-the-box image classifer callable from the command line.
By default it configures and runs the Caffe reference ImageNet model.
"""
import numpy as np
import os
import sys
import argparse
import glob
import time
import caffe
def main(argv):
pycaffe_dir = os.path.dirname(__file__)
parser = argparse.ArgumentParser()
# Required arguments: input and output files.
parser.add_argument(
"input_file",
help="Input image, directory, or npy."
)
parser.add_argument(
"output_file",
help="Output npy filename."
)
# Optional arguments.
parser.add_argument(
"--model_def",
default=os.path.join(pycaffe_dir,
"../models/bvlc_reference_caffenet/deploy.prototxt"),
help="Model definition file."
)
parser.add_argument(
"--pretrained_model",
default=os.path.join(pycaffe_dir,
"../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel"),
help="Trained model weights file."
)
parser.add_argument(
"--gpu",
action='store_true',
help="Switch for gpu computation."
)
parser.add_argument(
"--center_only",
action='store_true',
help="Switch for prediction from center crop alone instead of " +
"averaging predictions across crops (default)."
)
parser.add_argument(
"--images_dim",
default='256,256',
help="Canonical 'height,width' dimensions of input images."
)
parser.add_argument(
"--mean_file",
default=os.path.join(pycaffe_dir,
'caffe/imagenet/ilsvrc_2012_mean.npy'),
help="Data set image mean of [Channels x Height x Width] dimensions " +
"(numpy array). Set to '' for no mean subtraction."
)
parser.add_argument(
"--input_scale",
type=float,
help="Multiply input features by this scale to finish preprocessing."
)
parser.add_argument(
"--raw_scale",
type=float,
default=255.0,
help="Multiply raw input by this scale before preprocessing."
)
parser.add_argument(
"--channel_swap",
default='2,1,0',
help="Order to permute input channels. The default converts " +
"RGB -> BGR since BGR is the Caffe default by way of OpenCV."
)
parser.add_argument(
"--ext",
default='jpg',
help="Image file extension to take as input when a directory " +
"is given as the input file."
)
args = parser.parse_args()
image_dims = [int(s) for s in args.images_dim.split(',')]
mean, channel_swap = None, None
if args.mean_file:
mean = np.load(args.mean_file)
if args.channel_swap:
channel_swap = [int(s) for s in args.channel_swap.split(',')]
if args.gpu:
caffe.set_mode_gpu()
print("GPU mode")
else:
caffe.set_mode_cpu()
print("CPU mode")
# Make classifier.
classifier = caffe.Classifier(args.model_def, args.pretrained_model,
image_dims=image_dims, mean=mean,
input_scale=args.input_scale, raw_scale=args.raw_scale,
channel_swap=channel_swap)
# Load numpy array (.npy), directory glob (*.jpg), or image file.
args.input_file = os.path.expanduser(args.input_file)
if args.input_file.endswith('npy'):
print("Loading file: %s" % args.input_file)
inputs = np.load(args.input_file)
elif os.path.isdir(args.input_file):
print("Loading folder: %s" % args.input_file)
inputs =[caffe.io.load_image(im_f)
for im_f in glob.glob(args.input_file + '/*.' + args.ext)]
else:
print("Loading file: %s" % args.input_file)
inputs = [caffe.io.load_image(args.input_file)]
print("Classifying %d inputs." % len(inputs))
# Classify.
start = time.time()
predictions = classifier.predict(inputs, not args.center_only)
print("Done in %.2f s." % (time.time() - start))
# Save
print("Saving results into %s" % args.output_file)
np.save(args.output_file, predictions)
if __name__ == '__main__':
main(sys.argv)
| 4,262 | 29.669065 | 88 | py |
DRT | DRT-master/caffe/python/caffe/net_spec.py | """Python net specification.
This module provides a way to write nets directly in Python, using a natural,
functional style. See examples/pycaffe/caffenet.py for an example.
Currently this works as a thin wrapper around the Python protobuf interface,
with layers and parameters automatically generated for the "layers" and
"params" pseudo-modules, which are actually objects using __getattr__ magic
to generate protobuf messages.
Note that when using to_proto or Top.to_proto, names of intermediate blobs will
be automatically generated. To explicitly specify blob names, use the NetSpec
class -- assign to its attributes directly to name layers, and call
NetSpec.to_proto to serialize all assigned layers.
This interface is expected to continue to evolve as Caffe gains new capabilities
for specifying nets. In particular, the automatically generated layer names
are not guaranteed to be forward-compatible.
"""
from collections import OrderedDict, Counter
from .proto import caffe_pb2
from google import protobuf
import six
def param_name_dict():
"""Find out the correspondence between layer names and parameter names."""
layer = caffe_pb2.LayerParameter()
# get all parameter names (typically underscore case) and corresponding
# type names (typically camel case), which contain the layer names
# (note that not all parameters correspond to layers, but we'll ignore that)
param_names = [s for s in dir(layer) if s.endswith('_param')]
param_type_names = [type(getattr(layer, s)).__name__ for s in param_names]
# strip the final '_param' or 'Parameter'
param_names = [s[:-len('_param')] for s in param_names]
param_type_names = [s[:-len('Parameter')] for s in param_type_names]
return dict(zip(param_type_names, param_names))
def to_proto(*tops):
"""Generate a NetParameter that contains all layers needed to compute
all arguments."""
layers = OrderedDict()
autonames = Counter()
for top in tops:
top.fn._to_proto(layers, {}, autonames)
net = caffe_pb2.NetParameter()
net.layer.extend(layers.values())
return net
def assign_proto(proto, name, val):
"""Assign a Python object to a protobuf message, based on the Python
type (in recursive fashion). Lists become repeated fields/messages, dicts
become messages, and other types are assigned directly. For convenience,
repeated fields whose values are not lists are converted to single-element
lists; e.g., `my_repeated_int_field=3` is converted to
`my_repeated_int_field=[3]`."""
is_repeated_field = hasattr(getattr(proto, name), 'extend')
if is_repeated_field and not isinstance(val, list):
val = [val]
if isinstance(val, list):
if isinstance(val[0], dict):
for item in val:
proto_item = getattr(proto, name).add()
for k, v in six.iteritems(item):
assign_proto(proto_item, k, v)
else:
getattr(proto, name).extend(val)
elif isinstance(val, dict):
for k, v in six.iteritems(val):
assign_proto(getattr(proto, name), k, v)
else:
setattr(proto, name, val)
class Top(object):
"""A Top specifies a single output blob (which could be one of several
produced by a layer.)"""
def __init__(self, fn, n):
self.fn = fn
self.n = n
def to_proto(self):
"""Generate a NetParameter that contains all layers needed to compute
this top."""
return to_proto(self)
def _to_proto(self, layers, names, autonames):
return self.fn._to_proto(layers, names, autonames)
class Function(object):
"""A Function specifies a layer, its parameters, and its inputs (which
are Tops from other layers)."""
def __init__(self, type_name, inputs, params):
self.type_name = type_name
self.inputs = inputs
self.params = params
self.ntop = self.params.get('ntop', 1)
# use del to make sure kwargs are not double-processed as layer params
if 'ntop' in self.params:
del self.params['ntop']
self.in_place = self.params.get('in_place', False)
if 'in_place' in self.params:
del self.params['in_place']
self.tops = tuple(Top(self, n) for n in range(self.ntop))
def _get_name(self, names, autonames):
if self not in names and self.ntop > 0:
names[self] = self._get_top_name(self.tops[0], names, autonames)
elif self not in names:
autonames[self.type_name] += 1
names[self] = self.type_name + str(autonames[self.type_name])
return names[self]
def _get_top_name(self, top, names, autonames):
if top not in names:
autonames[top.fn.type_name] += 1
names[top] = top.fn.type_name + str(autonames[top.fn.type_name])
return names[top]
def _to_proto(self, layers, names, autonames):
if self in layers:
return
bottom_names = []
for inp in self.inputs:
inp._to_proto(layers, names, autonames)
bottom_names.append(layers[inp.fn].top[inp.n])
layer = caffe_pb2.LayerParameter()
layer.type = self.type_name
layer.bottom.extend(bottom_names)
if self.in_place:
layer.top.extend(layer.bottom)
else:
for top in self.tops:
layer.top.append(self._get_top_name(top, names, autonames))
layer.name = self._get_name(names, autonames)
for k, v in six.iteritems(self.params):
# special case to handle generic *params
if k.endswith('param'):
assign_proto(layer, k, v)
else:
try:
assign_proto(getattr(layer,
_param_names[self.type_name] + '_param'), k, v)
except (AttributeError, KeyError):
assign_proto(layer, k, v)
layers[self] = layer
class NetSpec(object):
"""A NetSpec contains a set of Tops (assigned directly as attributes).
Calling NetSpec.to_proto generates a NetParameter containing all of the
layers needed to produce all of the assigned Tops, using the assigned
names."""
def __init__(self):
super(NetSpec, self).__setattr__('tops', OrderedDict())
def __setattr__(self, name, value):
self.tops[name] = value
def __getattr__(self, name):
return self.tops[name]
def to_proto(self):
names = {v: k for k, v in six.iteritems(self.tops)}
autonames = Counter()
layers = OrderedDict()
for name, top in six.iteritems(self.tops):
top._to_proto(layers, names, autonames)
net = caffe_pb2.NetParameter()
net.layer.extend(layers.values())
return net
class Layers(object):
"""A Layers object is a pseudo-module which generates functions that specify
layers; e.g., Layers().Convolution(bottom, kernel_size=3) will produce a Top
specifying a 3x3 convolution applied to bottom."""
def __getattr__(self, name):
def layer_fn(*args, **kwargs):
fn = Function(name, args, kwargs)
if fn.ntop == 0:
return fn
elif fn.ntop == 1:
return fn.tops[0]
else:
return fn.tops
return layer_fn
class Parameters(object):
"""A Parameters object is a pseudo-module which generates constants used
in layer parameters; e.g., Parameters().Pooling.MAX is the value used
to specify max pooling."""
def __getattr__(self, name):
class Param:
def __getattr__(self, param_name):
return getattr(getattr(caffe_pb2, name + 'Parameter'), param_name)
return Param()
_param_names = param_name_dict()
layers = Layers()
params = Parameters()
| 7,876 | 34.642534 | 82 | py |
DRT | DRT-master/caffe/python/caffe/classifier.py | #!/usr/bin/env python
"""
Classifier is an image classifier specialization of Net.
"""
import numpy as np
import caffe
class Classifier(caffe.Net):
"""
Classifier extends Net for image class prediction
by scaling, center cropping, or oversampling.
Parameters
----------
image_dims : dimensions to scale input for cropping/sampling.
Default is to scale to net input size for whole-image crop.
mean, input_scale, raw_scale, channel_swap: params for
preprocessing options.
"""
def __init__(self, model_file, pretrained_file, image_dims=None,
mean=None, input_scale=None, raw_scale=None,
channel_swap=None):
caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST)
# configure pre-processing
in_ = self.inputs[0]
self.transformer = caffe.io.Transformer(
{in_: self.blobs[in_].data.shape})
self.transformer.set_transpose(in_, (2, 0, 1))
if mean is not None:
self.transformer.set_mean(in_, mean)
if input_scale is not None:
self.transformer.set_input_scale(in_, input_scale)
if raw_scale is not None:
self.transformer.set_raw_scale(in_, raw_scale)
if channel_swap is not None:
self.transformer.set_channel_swap(in_, channel_swap)
self.crop_dims = np.array(self.blobs[in_].data.shape[2:])
if not image_dims:
image_dims = self.crop_dims
self.image_dims = image_dims
def predict(self, inputs, oversample=True):
"""
Predict classification probabilities of inputs.
Parameters
----------
inputs : iterable of (H x W x K) input ndarrays.
oversample : boolean
average predictions across center, corners, and mirrors
when True (default). Center-only prediction when False.
Returns
-------
predictions: (N x C) ndarray of class probabilities for N images and C
classes.
"""
# Scale to standardize input dimensions.
input_ = np.zeros((len(inputs),
self.image_dims[0],
self.image_dims[1],
inputs[0].shape[2]),
dtype=np.float32)
for ix, in_ in enumerate(inputs):
input_[ix] = caffe.io.resize_image(in_, self.image_dims)
if oversample:
# Generate center, corner, and mirrored crops.
input_ = caffe.io.oversample(input_, self.crop_dims)
else:
# Take center crop.
center = np.array(self.image_dims) / 2.0
crop = np.tile(center, (1, 2))[0] + np.concatenate([
-self.crop_dims / 2.0,
self.crop_dims / 2.0
])
input_ = input_[:, crop[0]:crop[2], crop[1]:crop[3], :]
# Classify
caffe_in = np.zeros(np.array(input_.shape)[[0, 3, 1, 2]],
dtype=np.float32)
for ix, in_ in enumerate(input_):
caffe_in[ix] = self.transformer.preprocess(self.inputs[0], in_)
out = self.forward_all(**{self.inputs[0]: caffe_in})
predictions = out[self.outputs[0]]
# For oversampling, average predictions across crops.
if oversample:
predictions = predictions.reshape((len(predictions) / 10, 10, -1))
predictions = predictions.mean(1)
return predictions
| 3,501 | 34.734694 | 78 | py |
DRT | DRT-master/caffe/python/caffe/detector.py | #!/usr/bin/env python
"""
Do windowed detection by classifying a number of images/crops at once,
optionally using the selective search window proposal method.
This implementation follows ideas in
Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik.
Rich feature hierarchies for accurate object detection and semantic
segmentation.
http://arxiv.org/abs/1311.2524
The selective_search_ijcv_with_python code required for the selective search
proposal mode is available at
https://github.com/sergeyk/selective_search_ijcv_with_python
"""
import numpy as np
import os
import caffe
class Detector(caffe.Net):
"""
Detector extends Net for windowed detection by a list of crops or
selective search proposals.
Parameters
----------
mean, input_scale, raw_scale, channel_swap : params for preprocessing
options.
context_pad : amount of surrounding context to take s.t. a `context_pad`
sized border of pixels in the network input image is context, as in
R-CNN feature extraction.
"""
def __init__(self, model_file, pretrained_file, mean=None,
input_scale=None, raw_scale=None, channel_swap=None,
context_pad=None):
caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST)
# configure pre-processing
in_ = self.inputs[0]
self.transformer = caffe.io.Transformer(
{in_: self.blobs[in_].data.shape})
self.transformer.set_transpose(in_, (2, 0, 1))
if mean is not None:
self.transformer.set_mean(in_, mean)
if input_scale is not None:
self.transformer.set_input_scale(in_, input_scale)
if raw_scale is not None:
self.transformer.set_raw_scale(in_, raw_scale)
if channel_swap is not None:
self.transformer.set_channel_swap(in_, channel_swap)
self.configure_crop(context_pad)
def detect_windows(self, images_windows):
"""
Do windowed detection over given images and windows. Windows are
extracted then warped to the input dimensions of the net.
Parameters
----------
images_windows: (image filename, window list) iterable.
context_crop: size of context border to crop in pixels.
Returns
-------
detections: list of {filename: image filename, window: crop coordinates,
predictions: prediction vector} dicts.
"""
# Extract windows.
window_inputs = []
for image_fname, windows in images_windows:
image = caffe.io.load_image(image_fname).astype(np.float32)
for window in windows:
window_inputs.append(self.crop(image, window))
# Run through the net (warping windows to input dimensions).
in_ = self.inputs[0]
caffe_in = np.zeros((len(window_inputs), window_inputs[0].shape[2])
+ self.blobs[in_].data.shape[2:],
dtype=np.float32)
for ix, window_in in enumerate(window_inputs):
caffe_in[ix] = self.transformer.preprocess(in_, window_in)
out = self.forward_all(**{in_: caffe_in})
predictions = out[self.outputs[0]].squeeze(axis=(2, 3))
# Package predictions with images and windows.
detections = []
ix = 0
for image_fname, windows in images_windows:
for window in windows:
detections.append({
'window': window,
'prediction': predictions[ix],
'filename': image_fname
})
ix += 1
return detections
def detect_selective_search(self, image_fnames):
"""
Do windowed detection over Selective Search proposals by extracting
the crop and warping to the input dimensions of the net.
Parameters
----------
image_fnames: list
Returns
-------
detections: list of {filename: image filename, window: crop coordinates,
predictions: prediction vector} dicts.
"""
import selective_search_ijcv_with_python as selective_search
# Make absolute paths so MATLAB can find the files.
image_fnames = [os.path.abspath(f) for f in image_fnames]
windows_list = selective_search.get_windows(
image_fnames,
cmd='selective_search_rcnn'
)
# Run windowed detection on the selective search list.
return self.detect_windows(zip(image_fnames, windows_list))
def crop(self, im, window):
"""
Crop a window from the image for detection. Include surrounding context
according to the `context_pad` configuration.
Parameters
----------
im: H x W x K image ndarray to crop.
window: bounding box coordinates as ymin, xmin, ymax, xmax.
Returns
-------
crop: cropped window.
"""
# Crop window from the image.
crop = im[window[0]:window[2], window[1]:window[3]]
if self.context_pad:
box = window.copy()
crop_size = self.blobs[self.inputs[0]].width # assumes square
scale = crop_size / (1. * crop_size - self.context_pad * 2)
# Crop a box + surrounding context.
half_h = (box[2] - box[0] + 1) / 2.
half_w = (box[3] - box[1] + 1) / 2.
center = (box[0] + half_h, box[1] + half_w)
scaled_dims = scale * np.array((-half_h, -half_w, half_h, half_w))
box = np.round(np.tile(center, 2) + scaled_dims)
full_h = box[2] - box[0] + 1
full_w = box[3] - box[1] + 1
scale_h = crop_size / full_h
scale_w = crop_size / full_w
pad_y = round(max(0, -box[0]) * scale_h) # amount out-of-bounds
pad_x = round(max(0, -box[1]) * scale_w)
# Clip box to image dimensions.
im_h, im_w = im.shape[:2]
box = np.clip(box, 0., [im_h, im_w, im_h, im_w])
clip_h = box[2] - box[0] + 1
clip_w = box[3] - box[1] + 1
assert(clip_h > 0 and clip_w > 0)
crop_h = round(clip_h * scale_h)
crop_w = round(clip_w * scale_w)
if pad_y + crop_h > crop_size:
crop_h = crop_size - pad_y
if pad_x + crop_w > crop_size:
crop_w = crop_size - pad_x
# collect with context padding and place in input
# with mean padding
context_crop = im[box[0]:box[2], box[1]:box[3]]
context_crop = caffe.io.resize_image(context_crop, (crop_h, crop_w))
crop = np.ones(self.crop_dims, dtype=np.float32) * self.crop_mean
crop[pad_y:(pad_y + crop_h), pad_x:(pad_x + crop_w)] = context_crop
return crop
def configure_crop(self, context_pad):
"""
Configure crop dimensions and amount of context for cropping.
If context is included, make the special input mean for context padding.
Parameters
----------
context_pad : amount of context for cropping.
"""
# crop dimensions
in_ = self.inputs[0]
tpose = self.transformer.transpose[in_]
inv_tpose = [tpose[t] for t in tpose]
self.crop_dims = np.array(self.blobs[in_].data.shape[1:])[inv_tpose]
#.transpose(inv_tpose)
# context padding
self.context_pad = context_pad
if self.context_pad:
in_ = self.inputs[0]
transpose = self.transformer.transpose.get(in_)
channel_order = self.transformer.channel_swap.get(in_)
raw_scale = self.transformer.raw_scale.get(in_)
# Padding context crops needs the mean in unprocessed input space.
mean = self.transformer.mean.get(in_)
if mean is not None:
inv_transpose = [transpose[t] for t in transpose]
crop_mean = mean.copy().transpose(inv_transpose)
if channel_order is not None:
channel_order_inverse = [channel_order.index(i)
for i in range(crop_mean.shape[2])]
crop_mean = crop_mean[:, :, channel_order_inverse]
if raw_scale is not None:
crop_mean /= raw_scale
self.crop_mean = crop_mean
else:
self.crop_mean = np.zeros(self.crop_dims, dtype=np.float32)
| 8,562 | 38.460829 | 80 | py |
DRT | DRT-master/caffe/python/caffe/__init__.py | from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver
from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list
from .proto.caffe_pb2 import TRAIN, TEST
from .classifier import Classifier
from .detector import Detector
from . import io
from .net_spec import layers, params, NetSpec, to_proto
| 385 | 47.25 | 109 | py |
DRT | DRT-master/caffe/python/caffe/pycaffe.py | """
Wrap the internal caffe C++ module (_caffe.so) with a clean, Pythonic
interface.
"""
from collections import OrderedDict
try:
from itertools import izip_longest
except:
from itertools import zip_longest as izip_longest
import numpy as np
from ._caffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, \
RMSPropSolver, AdaDeltaSolver, AdamSolver
import caffe.io
# We directly update methods from Net here (rather than using composition or
# inheritance) so that nets created by caffe (e.g., by SGDSolver) will
# automatically have the improved interface.
@property
def _Net_blobs(self):
"""
An OrderedDict (bottom to top, i.e., input to output) of network
blobs indexed by name
"""
return OrderedDict(zip(self._blob_names, self._blobs))
@property
def _Net_blob_loss_weights(self):
"""
An OrderedDict (bottom to top, i.e., input to output) of network
blob loss weights indexed by name
"""
return OrderedDict(zip(self._blob_names, self._blob_loss_weights))
@property
def _Net_params(self):
"""
An OrderedDict (bottom to top, i.e., input to output) of network
parameters indexed by name; each is a list of multiple blobs (e.g.,
weights and biases)
"""
return OrderedDict([(name, lr.blobs)
for name, lr in zip(self._layer_names, self.layers)
if len(lr.blobs) > 0])
@property
def _Net_inputs(self):
return [list(self.blobs.keys())[i] for i in self._inputs]
@property
def _Net_outputs(self):
return [list(self.blobs.keys())[i] for i in self._outputs]
def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
"""
Forward pass: prepare inputs and run the net forward.
Parameters
----------
blobs : list of blobs to return in addition to output blobs.
kwargs : Keys are input blob names and values are blob ndarrays.
For formatting inputs for Caffe, see Net.preprocess().
If None, input is taken from data layers.
start : optional name of layer at which to begin the forward pass
end : optional name of layer at which to finish the forward pass
(inclusive)
Returns
-------
outs : {blob name: blob ndarray} dict.
"""
if blobs is None:
blobs = []
if start is not None:
start_ind = list(self._layer_names).index(start)
else:
start_ind = 0
if end is not None:
end_ind = list(self._layer_names).index(end)
outputs = set([end] + blobs)
else:
end_ind = len(self.layers) - 1
outputs = set(self.outputs + blobs)
if kwargs:
if set(kwargs.keys()) != set(self.inputs):
raise Exception('Input blob arguments do not match net inputs.')
# Set input according to defined shapes and make arrays single and
# C-contiguous as Caffe expects.
for in_, blob in kwargs.iteritems():
if blob.shape[0] != self.blobs[in_].num:
raise Exception('Input is not batch sized')
self.blobs[in_].data[...] = blob
self._forward(start_ind, end_ind)
# Unpack blobs to extract
return {out: self.blobs[out].data for out in outputs}
def _Net_backward(self, diffs=None, start=None, end=None, **kwargs):
"""
Backward pass: prepare diffs and run the net backward.
Parameters
----------
diffs : list of diffs to return in addition to bottom diffs.
kwargs : Keys are output blob names and values are diff ndarrays.
If None, top diffs are taken from forward loss.
start : optional name of layer at which to begin the backward pass
end : optional name of layer at which to finish the backward pass
(inclusive)
Returns
-------
outs: {blob name: diff ndarray} dict.
"""
if diffs is None:
diffs = []
if start is not None:
start_ind = list(self._layer_names).index(start)
else:
start_ind = len(self.layers) - 1
if end is not None:
end_ind = list(self._layer_names).index(end)
outputs = set([end] + diffs)
else:
end_ind = 0
outputs = set(self.inputs + diffs)
if kwargs:
if set(kwargs.keys()) != set(self.outputs):
raise Exception('Top diff arguments do not match net outputs.')
# Set top diffs according to defined shapes and make arrays single and
# C-contiguous as Caffe expects.
for top, diff in kwargs.iteritems():
if diff.ndim != 4:
raise Exception('{} diff is not 4-d'.format(top))
if diff.shape[0] != self.blobs[top].num:
raise Exception('Diff is not batch sized')
self.blobs[top].diff[...] = diff
self._backward(start_ind, end_ind)
# Unpack diffs to extract
return {out: self.blobs[out].diff for out in outputs}
def _Net_forward_all(self, blobs=None, **kwargs):
"""
Run net forward in batches.
Parameters
----------
blobs : list of blobs to extract as in forward()
kwargs : Keys are input blob names and values are blob ndarrays.
Refer to forward().
Returns
-------
all_outs : {blob name: list of blobs} dict.
"""
# Collect outputs from batches
all_outs = {out: [] for out in set(self.outputs + (blobs or []))}
for batch in self._batch(kwargs):
outs = self.forward(blobs=blobs, **batch)
for out, out_blob in outs.iteritems():
all_outs[out].extend(out_blob.copy())
# Package in ndarray.
for out in all_outs:
all_outs[out] = np.asarray(all_outs[out])
# Discard padding.
pad = len(all_outs.itervalues().next()) - len(kwargs.itervalues().next())
if pad:
for out in all_outs:
all_outs[out] = all_outs[out][:-pad]
return all_outs
def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs):
"""
Run net forward + backward in batches.
Parameters
----------
blobs: list of blobs to extract as in forward()
diffs: list of diffs to extract as in backward()
kwargs: Keys are input (for forward) and output (for backward) blob names
and values are ndarrays. Refer to forward() and backward().
Prefilled variants are called for lack of input or output blobs.
Returns
-------
all_blobs: {blob name: blob ndarray} dict.
all_diffs: {blob name: diff ndarray} dict.
"""
# Batch blobs and diffs.
all_outs = {out: [] for out in set(self.outputs + (blobs or []))}
all_diffs = {diff: [] for diff in set(self.inputs + (diffs or []))}
forward_batches = self._batch({in_: kwargs[in_]
for in_ in self.inputs if in_ in kwargs})
backward_batches = self._batch({out: kwargs[out]
for out in self.outputs if out in kwargs})
# Collect outputs from batches (and heed lack of forward/backward batches).
for fb, bb in izip_longest(forward_batches, backward_batches, fillvalue={}):
batch_blobs = self.forward(blobs=blobs, **fb)
batch_diffs = self.backward(diffs=diffs, **bb)
for out, out_blobs in batch_blobs.iteritems():
all_outs[out].extend(out_blobs)
for diff, out_diffs in batch_diffs.iteritems():
all_diffs[diff].extend(out_diffs)
# Package in ndarray.
for out, diff in zip(all_outs, all_diffs):
all_outs[out] = np.asarray(all_outs[out])
all_diffs[diff] = np.asarray(all_diffs[diff])
# Discard padding at the end and package in ndarray.
pad = len(all_outs.itervalues().next()) - len(kwargs.itervalues().next())
if pad:
for out, diff in zip(all_outs, all_diffs):
all_outs[out] = all_outs[out][:-pad]
all_diffs[diff] = all_diffs[diff][:-pad]
return all_outs, all_diffs
def _Net_set_input_arrays(self, data, labels):
"""
Set input arrays of the in-memory MemoryDataLayer.
(Note: this is only for networks declared with the memory data layer.)
"""
if labels.ndim == 1:
labels = np.ascontiguousarray(labels[:, np.newaxis, np.newaxis,
np.newaxis])
return self._set_input_arrays(data, labels)
def _Net_batch(self, blobs):
"""
Batch blob lists according to net's batch size.
Parameters
----------
blobs: Keys blob names and values are lists of blobs (of any length).
Naturally, all the lists should have the same length.
Yields
------
batch: {blob name: list of blobs} dict for a single batch.
"""
num = len(blobs.itervalues().next())
batch_size = self.blobs.itervalues().next().num
remainder = num % batch_size
num_batches = num / batch_size
# Yield full batches.
for b in range(num_batches):
i = b * batch_size
yield {name: blobs[name][i:i + batch_size] for name in blobs}
# Yield last padded batch, if any.
if remainder > 0:
padded_batch = {}
for name in blobs:
padding = np.zeros((batch_size - remainder,)
+ blobs[name].shape[1:])
padded_batch[name] = np.concatenate([blobs[name][-remainder:],
padding])
yield padded_batch
# Attach methods to Net.
Net.blobs = _Net_blobs
Net.blob_loss_weights = _Net_blob_loss_weights
Net.params = _Net_params
Net.forward = _Net_forward
Net.backward = _Net_backward
Net.forward_all = _Net_forward_all
Net.forward_backward_all = _Net_forward_backward_all
Net.set_input_arrays = _Net_set_input_arrays
Net._batch = _Net_batch
Net.inputs = _Net_inputs
Net.outputs = _Net_outputs
| 9,706 | 32.129693 | 80 | py |
DRT | DRT-master/caffe/python/caffe/draw.py | """
Caffe network visualization: draw the NetParameter protobuffer.
.. note::
This requires pydot>=1.0.2, which is not included in requirements.txt since
it requires graphviz and other prerequisites outside the scope of the
Caffe.
"""
from caffe.proto import caffe_pb2
import pydot
# Internal layer and blob styles.
LAYER_STYLE_DEFAULT = {'shape': 'record',
'fillcolor': '#6495ED',
'style': 'filled'}
NEURON_LAYER_STYLE = {'shape': 'record',
'fillcolor': '#90EE90',
'style': 'filled'}
BLOB_STYLE = {'shape': 'octagon',
'fillcolor': '#E0E0E0',
'style': 'filled'}
def get_pooling_types_dict():
"""Get dictionary mapping pooling type number to type name
"""
desc = caffe_pb2.PoolingParameter.PoolMethod.DESCRIPTOR
d = {}
for k, v in desc.values_by_name.items():
d[v.number] = k
return d
def get_edge_label(layer):
"""Define edge label based on layer type.
"""
if layer.type == 'Data':
edge_label = 'Batch ' + str(layer.data_param.batch_size)
elif layer.type == 'Convolution' or layer.type == 'Deconvolution':
edge_label = str(layer.convolution_param.num_output)
elif layer.type == 'InnerProduct':
edge_label = str(layer.inner_product_param.num_output)
else:
edge_label = '""'
return edge_label
def get_layer_label(layer, rankdir):
"""Define node label based on layer type.
Parameters
----------
layer : ?
rankdir : {'LR', 'TB', 'BT'}
Direction of graph layout.
Returns
-------
string :
A label for the current layer
"""
if rankdir in ('TB', 'BT'):
# If graph orientation is vertical, horizontal space is free and
# vertical space is not; separate words with spaces
separator = ' '
else:
# If graph orientation is horizontal, vertical space is free and
# horizontal space is not; separate words with newlines
separator = '\\n'
if layer.type == 'Convolution' or layer.type == 'Deconvolution':
# Outer double quotes needed or else colon characters don't parse
# properly
node_label = '"%s%s(%s)%skernel size: %d%sstride: %d%spad: %d"' %\
(layer.name,
separator,
layer.type,
separator,
layer.convolution_param.kernel_size,
separator,
layer.convolution_param.stride,
separator,
layer.convolution_param.pad)
elif layer.type == 'Pooling':
pooling_types_dict = get_pooling_types_dict()
node_label = '"%s%s(%s %s)%skernel size: %d%sstride: %d%spad: %d"' %\
(layer.name,
separator,
pooling_types_dict[layer.pooling_param.pool],
layer.type,
separator,
layer.pooling_param.kernel_size,
separator,
layer.pooling_param.stride,
separator,
layer.pooling_param.pad)
else:
node_label = '"%s%s(%s)"' % (layer.name, separator, layer.type)
return node_label
def choose_color_by_layertype(layertype):
"""Define colors for nodes based on the layer type.
"""
color = '#6495ED' # Default
if layertype == 'Convolution' or layertype == 'Deconvolution':
color = '#FF5050'
elif layertype == 'Pooling':
color = '#FF9900'
elif layertype == 'InnerProduct':
color = '#CC33FF'
return color
def get_pydot_graph(caffe_net, rankdir, label_edges=True):
"""Create a data structure which represents the `caffe_net`.
Parameters
----------
caffe_net : object
rankdir : {'LR', 'TB', 'BT'}
Direction of graph layout.
label_edges : boolean, optional
Label the edges (default is True).
Returns
-------
pydot graph object
"""
pydot_graph = pydot.Dot(caffe_net.name,
graph_type='digraph',
rankdir=rankdir)
pydot_nodes = {}
pydot_edges = []
for layer in caffe_net.layer:
node_label = get_layer_label(layer, rankdir)
node_name = "%s_%s" % (layer.name, layer.type)
if (len(layer.bottom) == 1 and len(layer.top) == 1 and
layer.bottom[0] == layer.top[0]):
# We have an in-place neuron layer.
pydot_nodes[node_name] = pydot.Node(node_label,
**NEURON_LAYER_STYLE)
else:
layer_style = LAYER_STYLE_DEFAULT
layer_style['fillcolor'] = choose_color_by_layertype(layer.type)
pydot_nodes[node_name] = pydot.Node(node_label, **layer_style)
for bottom_blob in layer.bottom:
pydot_nodes[bottom_blob + '_blob'] = pydot.Node('%s' % bottom_blob,
**BLOB_STYLE)
edge_label = '""'
pydot_edges.append({'src': bottom_blob + '_blob',
'dst': node_name,
'label': edge_label})
for top_blob in layer.top:
pydot_nodes[top_blob + '_blob'] = pydot.Node('%s' % (top_blob))
if label_edges:
edge_label = get_edge_label(layer)
else:
edge_label = '""'
pydot_edges.append({'src': node_name,
'dst': top_blob + '_blob',
'label': edge_label})
# Now, add the nodes and edges to the graph.
for node in pydot_nodes.values():
pydot_graph.add_node(node)
for edge in pydot_edges:
pydot_graph.add_edge(
pydot.Edge(pydot_nodes[edge['src']],
pydot_nodes[edge['dst']],
label=edge['label']))
return pydot_graph
def draw_net(caffe_net, rankdir, ext='png'):
"""Draws a caffe net and returns the image string encoded using the given
extension.
Parameters
----------
caffe_net : a caffe.proto.caffe_pb2.NetParameter protocol buffer.
ext : string, optional
The image extension (the default is 'png').
Returns
-------
string :
Postscript representation of the graph.
"""
return get_pydot_graph(caffe_net, rankdir).create(format=ext)
def draw_net_to_file(caffe_net, filename, rankdir='LR'):
"""Draws a caffe net, and saves it to file using the format given as the
file extension. Use '.raw' to output raw text that you can manually feed
to graphviz to draw graphs.
Parameters
----------
caffe_net : a caffe.proto.caffe_pb2.NetParameter protocol buffer.
filename : string
The path to a file where the networks visualization will be stored.
rankdir : {'LR', 'TB', 'BT'}
Direction of graph layout.
"""
ext = filename[filename.rfind('.')+1:]
with open(filename, 'wb') as fid:
fid.write(draw_net(caffe_net, rankdir, ext))
| 7,216 | 32.724299 | 79 | py |
DRT | DRT-master/caffe/python/caffe/io.py | import numpy as np
import skimage.io
from scipy.ndimage import zoom
from skimage.transform import resize
try:
# Python3 will most likely not be able to load protobuf
from caffe.proto import caffe_pb2
except:
import sys
if sys.version_info >= (3, 0):
print("Failed to include caffe_pb2, things might go wrong!")
else:
raise
## proto / datum / ndarray conversion
def blobproto_to_array(blob, return_diff=False):
"""
Convert a blob proto to an array. In default, we will just return the data,
unless return_diff is True, in which case we will return the diff.
"""
if return_diff:
return np.array(blob.diff).reshape(
blob.num, blob.channels, blob.height, blob.width)
else:
return np.array(blob.data).reshape(
blob.num, blob.channels, blob.height, blob.width)
def array_to_blobproto(arr, diff=None):
"""Converts a 4-dimensional array to blob proto. If diff is given, also
convert the diff. You need to make sure that arr and diff have the same
shape, and this function does not do sanity check.
"""
if arr.ndim != 4:
raise ValueError('Incorrect array shape.')
blob = caffe_pb2.BlobProto()
blob.num, blob.channels, blob.height, blob.width = arr.shape
blob.data.extend(arr.astype(float).flat)
if diff is not None:
blob.diff.extend(diff.astype(float).flat)
return blob
def arraylist_to_blobprotovecor_str(arraylist):
"""Converts a list of arrays to a serialized blobprotovec, which could be
then passed to a network for processing.
"""
vec = caffe_pb2.BlobProtoVector()
vec.blobs.extend([array_to_blobproto(arr) for arr in arraylist])
return vec.SerializeToString()
def blobprotovector_str_to_arraylist(str):
"""Converts a serialized blobprotovec to a list of arrays.
"""
vec = caffe_pb2.BlobProtoVector()
vec.ParseFromString(str)
return [blobproto_to_array(blob) for blob in vec.blobs]
def array_to_datum(arr, label=0):
"""Converts a 3-dimensional array to datum. If the array has dtype uint8,
the output data will be encoded as a string. Otherwise, the output data
will be stored in float format.
"""
if arr.ndim != 3:
raise ValueError('Incorrect array shape.')
datum = caffe_pb2.Datum()
datum.channels, datum.height, datum.width = arr.shape
if arr.dtype == np.uint8:
datum.data = arr.tostring()
else:
datum.float_data.extend(arr.flat)
datum.label = label
return datum
def datum_to_array(datum):
"""Converts a datum to an array. Note that the label is not returned,
as one can easily get it by calling datum.label.
"""
if len(datum.data):
return np.fromstring(datum.data, dtype=np.uint8).reshape(
datum.channels, datum.height, datum.width)
else:
return np.array(datum.float_data).astype(float).reshape(
datum.channels, datum.height, datum.width)
## Pre-processing
class Transformer:
"""
Transform input for feeding into a Net.
Note: this is mostly for illustrative purposes and it is likely better
to define your own input preprocessing routine for your needs.
Parameters
----------
net : a Net for which the input should be prepared
"""
def __init__(self, inputs):
self.inputs = inputs
self.transpose = {}
self.channel_swap = {}
self.raw_scale = {}
self.mean = {}
self.input_scale = {}
def __check_input(self, in_):
if in_ not in self.inputs:
raise Exception('{} is not one of the net inputs: {}'.format(
in_, self.inputs))
def preprocess(self, in_, data):
"""
Format input for Caffe:
- convert to single
- resize to input dimensions (preserving number of channels)
- transpose dimensions to K x H x W
- reorder channels (for instance color to BGR)
- scale raw input (e.g. from [0, 1] to [0, 255] for ImageNet models)
- subtract mean
- scale feature
Parameters
----------
in_ : name of input blob to preprocess for
data : (H' x W' x K) ndarray
Returns
-------
caffe_in : (K x H x W) ndarray for input to a Net
"""
self.__check_input(in_)
caffe_in = data.astype(np.float32, copy=False)
transpose = self.transpose.get(in_)
channel_swap = self.channel_swap.get(in_)
raw_scale = self.raw_scale.get(in_)
mean = self.mean.get(in_)
input_scale = self.input_scale.get(in_)
in_dims = self.inputs[in_][2:]
if caffe_in.shape[:2] != in_dims:
caffe_in = resize_image(caffe_in, in_dims)
if transpose is not None:
caffe_in = caffe_in.transpose(transpose)
if channel_swap is not None:
caffe_in = caffe_in[channel_swap, :, :]
if raw_scale is not None:
caffe_in *= raw_scale
if mean is not None:
caffe_in -= mean
if input_scale is not None:
caffe_in *= input_scale
return caffe_in
def deprocess(self, in_, data):
"""
Invert Caffe formatting; see preprocess().
"""
self.__check_input(in_)
decaf_in = data.copy().squeeze()
transpose = self.transpose.get(in_)
channel_swap = self.channel_swap.get(in_)
raw_scale = self.raw_scale.get(in_)
mean = self.mean.get(in_)
input_scale = self.input_scale.get(in_)
if input_scale is not None:
decaf_in /= input_scale
if mean is not None:
decaf_in += mean
if raw_scale is not None:
decaf_in /= raw_scale
if channel_swap is not None:
decaf_in = decaf_in[channel_swap, :, :]
if transpose is not None:
decaf_in = decaf_in.transpose([transpose[t] for t in transpose])
return decaf_in
def set_transpose(self, in_, order):
"""
Set the input channel order for e.g. RGB to BGR conversion
as needed for the reference ImageNet model.
Parameters
----------
in_ : which input to assign this channel order
order : the order to transpose the dimensions
"""
self.__check_input(in_)
if len(order) != len(self.inputs[in_]) - 1:
raise Exception('Transpose order needs to have the same number of '
'dimensions as the input.')
self.transpose[in_] = order
def set_channel_swap(self, in_, order):
"""
Set the input channel order for e.g. RGB to BGR conversion
as needed for the reference ImageNet model.
N.B. this assumes the channels are the first dimension AFTER transpose.
Parameters
----------
in_ : which input to assign this channel order
order : the order to take the channels.
(2,1,0) maps RGB to BGR for example.
"""
self.__check_input(in_)
if len(order) != self.inputs[in_][1]:
raise Exception('Channel swap needs to have the same number of '
'dimensions as the input channels.')
self.channel_swap[in_] = order
def set_raw_scale(self, in_, scale):
"""
Set the scale of raw features s.t. the input blob = input * scale.
While Python represents images in [0, 1], certain Caffe models
like CaffeNet and AlexNet represent images in [0, 255] so the raw_scale
of these models must be 255.
Parameters
----------
in_ : which input to assign this scale factor
scale : scale coefficient
"""
self.__check_input(in_)
self.raw_scale[in_] = scale
def set_mean(self, in_, mean):
"""
Set the mean to subtract for centering the data.
Parameters
----------
in_ : which input to assign this mean.
mean : mean ndarray (input dimensional or broadcastable)
"""
self.__check_input(in_)
ms = mean.shape
if mean.ndim == 1:
# broadcast channels
if ms[0] != self.inputs[in_][1]:
raise ValueError('Mean channels incompatible with input.')
mean = mean[:, np.newaxis, np.newaxis]
else:
# elementwise mean
if len(ms) == 2:
ms = (1,) + ms
if len(ms) != 3:
raise ValueError('Mean shape invalid')
if ms != self.inputs[in_][1:]:
raise ValueError('Mean shape incompatible with input shape.')
self.mean[in_] = mean
def set_input_scale(self, in_, scale):
"""
Set the scale of preprocessed inputs s.t. the blob = blob * scale.
N.B. input_scale is done AFTER mean subtraction and other preprocessing
while raw_scale is done BEFORE.
Parameters
----------
in_ : which input to assign this scale factor
scale : scale coefficient
"""
self.__check_input(in_)
self.input_scale[in_] = scale
## Image IO
def load_image(filename, color=True):
"""
Load an image converting from grayscale or alpha as needed.
Parameters
----------
filename : string
color : boolean
flag for color format. True (default) loads as RGB while False
loads as intensity (if image is already grayscale).
Returns
-------
image : an image with type np.float32 in range [0, 1]
of size (H x W x 3) in RGB or
of size (H x W x 1) in grayscale.
"""
img = skimage.img_as_float(skimage.io.imread(filename)).astype(np.float32)
if img.ndim == 2:
img = img[:, :, np.newaxis]
if color:
img = np.tile(img, (1, 1, 3))
elif img.shape[2] == 4:
img = img[:, :, :3]
return img
def resize_image(im, new_dims, interp_order=1):
"""
Resize an image array with interpolation.
Parameters
----------
im : (H x W x K) ndarray
new_dims : (height, width) tuple of new dimensions.
interp_order : interpolation order, default is linear.
Returns
-------
im : resized ndarray with shape (new_dims[0], new_dims[1], K)
"""
if im.shape[-1] == 1 or im.shape[-1] == 3:
im_min, im_max = im.min(), im.max()
if im_max > im_min:
# skimage is fast but only understands {1,3} channel images
# in [0, 1].
im_std = (im - im_min) / (im_max - im_min)
resized_std = resize(im_std, new_dims, order=interp_order)
resized_im = resized_std * (im_max - im_min) + im_min
else:
# the image is a constant -- avoid divide by 0
ret = np.empty((new_dims[0], new_dims[1], im.shape[-1]),
dtype=np.float32)
ret.fill(im_min)
return ret
else:
# ndimage interpolates anything but more slowly.
scale = tuple(np.array(new_dims) / np.array(im.shape[:2]))
resized_im = zoom(im, scale + (1,), order=interp_order)
return resized_im.astype(np.float32)
def oversample(images, crop_dims):
"""
Crop images into the four corners, center, and their mirrored versions.
Parameters
----------
image : iterable of (H x W x K) ndarrays
crop_dims : (height, width) tuple for the crops.
Returns
-------
crops : (10*N x H x W x K) ndarray of crops for number of inputs N.
"""
# Dimensions and center.
im_shape = np.array(images[0].shape)
crop_dims = np.array(crop_dims)
im_center = im_shape[:2] / 2.0
# Make crop coordinates
h_indices = (0, im_shape[0] - crop_dims[0])
w_indices = (0, im_shape[1] - crop_dims[1])
crops_ix = np.empty((5, 4), dtype=int)
curr = 0
for i in h_indices:
for j in w_indices:
crops_ix[curr] = (i, j, i + crop_dims[0], j + crop_dims[1])
curr += 1
crops_ix[4] = np.tile(im_center, (1, 2)) + np.concatenate([
-crop_dims / 2.0,
crop_dims / 2.0
])
crops_ix = np.tile(crops_ix, (2, 1))
# Extract crops
crops = np.empty((10 * len(images), crop_dims[0], crop_dims[1],
im_shape[-1]), dtype=np.float32)
ix = 0
for im in images:
for crop in crops_ix:
crops[ix] = im[crop[0]:crop[2], crop[1]:crop[3], :]
ix += 1
crops[ix-5:ix] = crops[ix-5:ix, :, ::-1, :] # flip for mirrors
return crops
| 12,575 | 32.094737 | 79 | py |
DRT | DRT-master/caffe/python/caffe/test/test_python_layer_with_param_str.py | import unittest
import tempfile
import os
import six
import caffe
class SimpleParamLayer(caffe.Layer):
"""A layer that just multiplies by the numeric value of its param string"""
def setup(self, bottom, top):
try:
self.value = float(self.param_str)
except ValueError:
raise ValueError("Parameter string must be a legible float")
def reshape(self, bottom, top):
top[0].reshape(*bottom[0].data.shape)
def forward(self, bottom, top):
top[0].data[...] = self.value * bottom[0].data
def backward(self, top, propagate_down, bottom):
bottom[0].diff[...] = self.value * top[0].diff
def python_param_net_file():
with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
f.write("""name: 'pythonnet' force_backward: true
input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
layer { type: 'Python' name: 'mul10' bottom: 'data' top: 'mul10'
python_param { module: 'test_python_layer_with_param_str'
layer: 'SimpleParamLayer' param_str: '10' } }
layer { type: 'Python' name: 'mul2' bottom: 'mul10' top: 'mul2'
python_param { module: 'test_python_layer_with_param_str'
layer: 'SimpleParamLayer' param_str: '2' } }""")
return f.name
class TestLayerWithParam(unittest.TestCase):
def setUp(self):
net_file = python_param_net_file()
self.net = caffe.Net(net_file, caffe.TRAIN)
os.remove(net_file)
def test_forward(self):
x = 8
self.net.blobs['data'].data[...] = x
self.net.forward()
for y in self.net.blobs['mul2'].data.flat:
self.assertEqual(y, 2 * 10 * x)
def test_backward(self):
x = 7
self.net.blobs['mul2'].diff[...] = x
self.net.backward()
for y in self.net.blobs['data'].diff.flat:
self.assertEqual(y, 2 * 10 * x)
| 1,925 | 31.1 | 79 | py |
DRT | DRT-master/caffe/python/caffe/test/test_solver.py | import unittest
import tempfile
import os
import numpy as np
import six
import caffe
from test_net import simple_net_file
class TestSolver(unittest.TestCase):
def setUp(self):
self.num_output = 13
net_f = simple_net_file(self.num_output)
f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
f.write("""net: '""" + net_f + """'
test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9
weight_decay: 0.0005 lr_policy: 'inv' gamma: 0.0001 power: 0.75
display: 100 max_iter: 100 snapshot_after_train: false""")
f.close()
self.solver = caffe.SGDSolver(f.name)
# also make sure get_solver runs
caffe.get_solver(f.name)
caffe.set_mode_cpu()
# fill in valid labels
self.solver.net.blobs['label'].data[...] = \
np.random.randint(self.num_output,
size=self.solver.net.blobs['label'].data.shape)
self.solver.test_nets[0].blobs['label'].data[...] = \
np.random.randint(self.num_output,
size=self.solver.test_nets[0].blobs['label'].data.shape)
os.remove(f.name)
os.remove(net_f)
def test_solve(self):
self.assertEqual(self.solver.iter, 0)
self.solver.solve()
self.assertEqual(self.solver.iter, 100)
def test_net_memory(self):
"""Check that nets survive after the solver is destroyed."""
nets = [self.solver.net] + list(self.solver.test_nets)
self.assertEqual(len(nets), 2)
del self.solver
total = 0
for net in nets:
for ps in six.itervalues(net.params):
for p in ps:
total += p.data.sum() + p.diff.sum()
for bl in six.itervalues(net.blobs):
total += bl.data.sum() + bl.diff.sum()
| 1,849 | 33.259259 | 76 | py |
DRT | DRT-master/caffe/python/caffe/test/test_layer_type_list.py | import unittest
import caffe
class TestLayerTypeList(unittest.TestCase):
def test_standard_types(self):
for type_name in ['Data', 'Convolution', 'InnerProduct']:
self.assertIn(type_name, caffe.layer_type_list(),
'%s not in layer_type_list()' % type_name)
| 302 | 26.545455 | 65 | py |
DRT | DRT-master/caffe/python/caffe/test/test_net.py | import unittest
import tempfile
import os
import numpy as np
import six
import caffe
def simple_net_file(num_output):
"""Make a simple net prototxt, based on test_net.cpp, returning the name
of the (temporary) file."""
f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
f.write("""name: 'testnet' force_backward: true
layer { type: 'DummyData' name: 'data' top: 'data' top: 'label'
dummy_data_param { num: 5 channels: 2 height: 3 width: 4
num: 5 channels: 1 height: 1 width: 1
data_filler { type: 'gaussian' std: 1 }
data_filler { type: 'constant' } } }
layer { type: 'Convolution' name: 'conv' bottom: 'data' top: 'conv'
convolution_param { num_output: 11 kernel_size: 2 pad: 3
weight_filler { type: 'gaussian' std: 1 }
bias_filler { type: 'constant' value: 2 } }
param { decay_mult: 1 } param { decay_mult: 0 }
}
layer { type: 'InnerProduct' name: 'ip' bottom: 'conv' top: 'ip'
inner_product_param { num_output: """ + str(num_output) + """
weight_filler { type: 'gaussian' std: 2.5 }
bias_filler { type: 'constant' value: -3 } } }
layer { type: 'SoftmaxWithLoss' name: 'loss' bottom: 'ip' bottom: 'label'
top: 'loss' }""")
f.close()
return f.name
class TestNet(unittest.TestCase):
def setUp(self):
self.num_output = 13
net_file = simple_net_file(self.num_output)
self.net = caffe.Net(net_file, caffe.TRAIN)
# fill in valid labels
self.net.blobs['label'].data[...] = \
np.random.randint(self.num_output,
size=self.net.blobs['label'].data.shape)
os.remove(net_file)
def test_memory(self):
"""Check that holding onto blob data beyond the life of a Net is OK"""
params = sum(map(list, six.itervalues(self.net.params)), [])
blobs = self.net.blobs.values()
del self.net
# now sum everything (forcing all memory to be read)
total = 0
for p in params:
total += p.data.sum() + p.diff.sum()
for bl in blobs:
total += bl.data.sum() + bl.diff.sum()
def test_forward_backward(self):
self.net.forward()
self.net.backward()
def test_inputs_outputs(self):
self.assertEqual(self.net.inputs, [])
self.assertEqual(self.net.outputs, ['loss'])
def test_save_and_read(self):
f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
f.close()
self.net.save(f.name)
net_file = simple_net_file(self.num_output)
net2 = caffe.Net(net_file, f.name, caffe.TRAIN)
os.remove(net_file)
os.remove(f.name)
for name in self.net.params:
for i in range(len(self.net.params[name])):
self.assertEqual(abs(self.net.params[name][i].data
- net2.params[name][i].data).sum(), 0)
| 2,927 | 34.707317 | 78 | py |
DRT | DRT-master/caffe/python/caffe/test/test_net_spec.py | import unittest
import tempfile
import caffe
from caffe import layers as L
from caffe import params as P
def lenet(batch_size):
n = caffe.NetSpec()
n.data, n.label = L.DummyData(shape=[dict(dim=[batch_size, 1, 28, 28]),
dict(dim=[batch_size, 1, 1, 1])],
transform_param=dict(scale=1./255), ntop=2)
n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20,
weight_filler=dict(type='xavier'))
n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)
n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50,
weight_filler=dict(type='xavier'))
n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)
n.ip1 = L.InnerProduct(n.pool2, num_output=500,
weight_filler=dict(type='xavier'))
n.relu1 = L.ReLU(n.ip1, in_place=True)
n.ip2 = L.InnerProduct(n.relu1, num_output=10,
weight_filler=dict(type='xavier'))
n.loss = L.SoftmaxWithLoss(n.ip2, n.label)
return n.to_proto()
def anon_lenet(batch_size):
data, label = L.DummyData(shape=[dict(dim=[batch_size, 1, 28, 28]),
dict(dim=[batch_size, 1, 1, 1])],
transform_param=dict(scale=1./255), ntop=2)
conv1 = L.Convolution(data, kernel_size=5, num_output=20,
weight_filler=dict(type='xavier'))
pool1 = L.Pooling(conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)
conv2 = L.Convolution(pool1, kernel_size=5, num_output=50,
weight_filler=dict(type='xavier'))
pool2 = L.Pooling(conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)
ip1 = L.InnerProduct(pool2, num_output=500,
weight_filler=dict(type='xavier'))
relu1 = L.ReLU(ip1, in_place=True)
ip2 = L.InnerProduct(relu1, num_output=10,
weight_filler=dict(type='xavier'))
loss = L.SoftmaxWithLoss(ip2, label)
return loss.to_proto()
def silent_net():
n = caffe.NetSpec()
n.data, n.data2 = L.DummyData(shape=dict(dim=3), ntop=2)
n.silence_data = L.Silence(n.data, ntop=0)
n.silence_data2 = L.Silence(n.data2, ntop=0)
return n.to_proto()
class TestNetSpec(unittest.TestCase):
def load_net(self, net_proto):
f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
f.write(str(net_proto))
f.close()
return caffe.Net(f.name, caffe.TEST)
def test_lenet(self):
"""Construct and build the Caffe version of LeNet."""
net_proto = lenet(50)
# check that relu is in-place
self.assertEqual(net_proto.layer[6].bottom,
net_proto.layer[6].top)
net = self.load_net(net_proto)
# check that all layers are present
self.assertEqual(len(net.layers), 9)
# now the check the version with automatically-generated layer names
net_proto = anon_lenet(50)
self.assertEqual(net_proto.layer[6].bottom,
net_proto.layer[6].top)
net = self.load_net(net_proto)
self.assertEqual(len(net.layers), 9)
def test_zero_tops(self):
"""Test net construction for top-less layers."""
net_proto = silent_net()
net = self.load_net(net_proto)
self.assertEqual(len(net.forward()), 0)
| 3,287 | 39.097561 | 77 | py |
DRT | DRT-master/caffe/python/caffe/test/test_python_layer.py | import unittest
import tempfile
import os
import six
import caffe
class SimpleLayer(caffe.Layer):
"""A layer that just multiplies by ten"""
def setup(self, bottom, top):
pass
def reshape(self, bottom, top):
top[0].reshape(*bottom[0].data.shape)
def forward(self, bottom, top):
top[0].data[...] = 10 * bottom[0].data
def backward(self, top, propagate_down, bottom):
bottom[0].diff[...] = 10 * top[0].diff
class ExceptionLayer(caffe.Layer):
"""A layer for checking exceptions from Python"""
def setup(self, bottom, top):
raise RuntimeError
class ParameterLayer(caffe.Layer):
"""A layer that just multiplies by ten"""
def setup(self, bottom, top):
self.blobs.add_blob(1)
self.blobs[0].data[0] = 0
def reshape(self, bottom, top):
top[0].reshape(*bottom[0].data.shape)
def forward(self, bottom, top):
pass
def backward(self, top, propagate_down, bottom):
self.blobs[0].diff[0] = 1
def python_net_file():
with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
f.write("""name: 'pythonnet' force_backward: true
input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
layer { type: 'Python' name: 'one' bottom: 'data' top: 'one'
python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }
layer { type: 'Python' name: 'two' bottom: 'one' top: 'two'
python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }
layer { type: 'Python' name: 'three' bottom: 'two' top: 'three'
python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }""")
return f.name
def exception_net_file():
with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
f.write("""name: 'pythonnet' force_backward: true
input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
layer { type: 'Python' name: 'layer' bottom: 'data' top: 'top'
python_param { module: 'test_python_layer' layer: 'ExceptionLayer' } }
""")
return f.name
def parameter_net_file():
with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
f.write("""name: 'pythonnet' force_backward: true
input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
layer { type: 'Python' name: 'layer' bottom: 'data' top: 'top'
python_param { module: 'test_python_layer' layer: 'ParameterLayer' } }
""")
return f.name
class TestPythonLayer(unittest.TestCase):
def setUp(self):
net_file = python_net_file()
self.net = caffe.Net(net_file, caffe.TRAIN)
os.remove(net_file)
def test_forward(self):
x = 8
self.net.blobs['data'].data[...] = x
self.net.forward()
for y in self.net.blobs['three'].data.flat:
self.assertEqual(y, 10**3 * x)
def test_backward(self):
x = 7
self.net.blobs['three'].diff[...] = x
self.net.backward()
for y in self.net.blobs['data'].diff.flat:
self.assertEqual(y, 10**3 * x)
def test_reshape(self):
s = 4
self.net.blobs['data'].reshape(s, s, s, s)
self.net.forward()
for blob in six.itervalues(self.net.blobs):
for d in blob.data.shape:
self.assertEqual(s, d)
def test_exception(self):
net_file = exception_net_file()
self.assertRaises(RuntimeError, caffe.Net, net_file, caffe.TEST)
os.remove(net_file)
def test_parameter(self):
net_file = parameter_net_file()
net = caffe.Net(net_file, caffe.TRAIN)
# Test forward and backward
net.forward()
net.backward()
layer = net.layers[list(net._layer_names).index('layer')]
self.assertEqual(layer.blobs[0].data[0], 0)
self.assertEqual(layer.blobs[0].diff[0], 1)
layer.blobs[0].data[0] += layer.blobs[0].diff[0]
self.assertEqual(layer.blobs[0].data[0], 1)
# Test saving and loading
h, caffemodel_file = tempfile.mkstemp()
net.save(caffemodel_file)
layer.blobs[0].data[0] = -1
self.assertEqual(layer.blobs[0].data[0], -1)
net.copy_from(caffemodel_file)
self.assertEqual(layer.blobs[0].data[0], 1)
os.remove(caffemodel_file)
# Test weight sharing
net2 = caffe.Net(net_file, caffe.TRAIN)
net2.share_with(net)
layer = net.layers[list(net2._layer_names).index('layer')]
self.assertEqual(layer.blobs[0].data[0], 1)
os.remove(net_file)
| 4,604 | 31.659574 | 81 | py |
DRT | DRT-master/caffe/scripts/cpp_lint.py | #!/usr/bin/python2
#
# Copyright (c) 2009 Google Inc. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following disclaimer
# in the documentation and/or other materials provided with the
# distribution.
# * Neither the name of Google Inc. nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Does google-lint on c++ files.
The goal of this script is to identify places in the code that *may*
be in non-compliance with google style. It does not attempt to fix
up these problems -- the point is to educate. It does also not
attempt to find all problems, or to ensure that everything it does
find is legitimately a problem.
In particular, we can get very confused by /* and // inside strings!
We do a small hack, which is to ignore //'s with "'s after them on the
same line, but it is far from perfect (in either direction).
"""
import codecs
import copy
import getopt
import math # for log
import os
import re
import sre_compile
import string
import sys
import unicodedata
_USAGE = """
Syntax: cpp_lint.py [--verbose=#] [--output=vs7] [--filter=-x,+y,...]
[--counting=total|toplevel|detailed] [--root=subdir]
[--linelength=digits]
<file> [file] ...
The style guidelines this tries to follow are those in
http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml
Every problem is given a confidence score from 1-5, with 5 meaning we are
certain of the problem, and 1 meaning it could be a legitimate construct.
This will miss some errors, and is not a substitute for a code review.
To suppress false-positive errors of a certain category, add a
'NOLINT(category)' comment to the line. NOLINT or NOLINT(*)
suppresses errors of all categories on that line.
The files passed in will be linted; at least one file must be provided.
Default linted extensions are .cc, .cpp, .cu, .cuh and .h. Change the
extensions with the --extensions flag.
Flags:
output=vs7
By default, the output is formatted to ease emacs parsing. Visual Studio
compatible output (vs7) may also be used. Other formats are unsupported.
verbose=#
Specify a number 0-5 to restrict errors to certain verbosity levels.
filter=-x,+y,...
Specify a comma-separated list of category-filters to apply: only
error messages whose category names pass the filters will be printed.
(Category names are printed with the message and look like
"[whitespace/indent]".) Filters are evaluated left to right.
"-FOO" and "FOO" means "do not print categories that start with FOO".
"+FOO" means "do print categories that start with FOO".
Examples: --filter=-whitespace,+whitespace/braces
--filter=whitespace,runtime/printf,+runtime/printf_format
--filter=-,+build/include_what_you_use
To see a list of all the categories used in cpplint, pass no arg:
--filter=
counting=total|toplevel|detailed
The total number of errors found is always printed. If
'toplevel' is provided, then the count of errors in each of
the top-level categories like 'build' and 'whitespace' will
also be printed. If 'detailed' is provided, then a count
is provided for each category like 'build/class'.
root=subdir
The root directory used for deriving header guard CPP variable.
By default, the header guard CPP variable is calculated as the relative
path to the directory that contains .git, .hg, or .svn. When this flag
is specified, the relative path is calculated from the specified
directory. If the specified directory does not exist, this flag is
ignored.
Examples:
Assuing that src/.git exists, the header guard CPP variables for
src/chrome/browser/ui/browser.h are:
No flag => CHROME_BROWSER_UI_BROWSER_H_
--root=chrome => BROWSER_UI_BROWSER_H_
--root=chrome/browser => UI_BROWSER_H_
linelength=digits
This is the allowed line length for the project. The default value is
80 characters.
Examples:
--linelength=120
extensions=extension,extension,...
The allowed file extensions that cpplint will check
Examples:
--extensions=hpp,cpp
"""
# We categorize each error message we print. Here are the categories.
# We want an explicit list so we can list them all in cpplint --filter=.
# If you add a new error message with a new category, add it to the list
# here! cpplint_unittest.py should tell you if you forget to do this.
_ERROR_CATEGORIES = [
'build/class',
'build/deprecated',
'build/endif_comment',
'build/explicit_make_pair',
'build/forward_decl',
'build/header_guard',
'build/include',
'build/include_alpha',
'build/include_dir',
'build/include_order',
'build/include_what_you_use',
'build/namespaces',
'build/printf_format',
'build/storage_class',
'caffe/alt_fn',
'caffe/data_layer_setup',
'caffe/random_fn',
'legal/copyright',
'readability/alt_tokens',
'readability/braces',
'readability/casting',
'readability/check',
'readability/constructors',
'readability/fn_size',
'readability/function',
'readability/multiline_comment',
'readability/multiline_string',
'readability/namespace',
'readability/nolint',
'readability/nul',
'readability/streams',
'readability/todo',
'readability/utf8',
'runtime/arrays',
'runtime/casting',
'runtime/explicit',
'runtime/int',
'runtime/init',
'runtime/invalid_increment',
'runtime/member_string_references',
'runtime/memset',
'runtime/operator',
'runtime/printf',
'runtime/printf_format',
'runtime/references',
'runtime/string',
'runtime/threadsafe_fn',
'runtime/vlog',
'whitespace/blank_line',
'whitespace/braces',
'whitespace/comma',
'whitespace/comments',
'whitespace/empty_conditional_body',
'whitespace/empty_loop_body',
'whitespace/end_of_line',
'whitespace/ending_newline',
'whitespace/forcolon',
'whitespace/indent',
'whitespace/line_length',
'whitespace/newline',
'whitespace/operators',
'whitespace/parens',
'whitespace/semicolon',
'whitespace/tab',
'whitespace/todo'
]
# The default state of the category filter. This is overrided by the --filter=
# flag. By default all errors are on, so only add here categories that should be
# off by default (i.e., categories that must be enabled by the --filter= flags).
# All entries here should start with a '-' or '+', as in the --filter= flag.
_DEFAULT_FILTERS = [
'-build/include_dir',
'-readability/todo',
]
# We used to check for high-bit characters, but after much discussion we
# decided those were OK, as long as they were in UTF-8 and didn't represent
# hard-coded international strings, which belong in a separate i18n file.
# C++ headers
_CPP_HEADERS = frozenset([
# Legacy
'algobase.h',
'algo.h',
'alloc.h',
'builtinbuf.h',
'bvector.h',
'complex.h',
'defalloc.h',
'deque.h',
'editbuf.h',
'fstream.h',
'function.h',
'hash_map',
'hash_map.h',
'hash_set',
'hash_set.h',
'hashtable.h',
'heap.h',
'indstream.h',
'iomanip.h',
'iostream.h',
'istream.h',
'iterator.h',
'list.h',
'map.h',
'multimap.h',
'multiset.h',
'ostream.h',
'pair.h',
'parsestream.h',
'pfstream.h',
'procbuf.h',
'pthread_alloc',
'pthread_alloc.h',
'rope',
'rope.h',
'ropeimpl.h',
'set.h',
'slist',
'slist.h',
'stack.h',
'stdiostream.h',
'stl_alloc.h',
'stl_relops.h',
'streambuf.h',
'stream.h',
'strfile.h',
'strstream.h',
'tempbuf.h',
'tree.h',
'type_traits.h',
'vector.h',
# 17.6.1.2 C++ library headers
'algorithm',
'array',
'atomic',
'bitset',
'chrono',
'codecvt',
'complex',
'condition_variable',
'deque',
'exception',
'forward_list',
'fstream',
'functional',
'future',
'initializer_list',
'iomanip',
'ios',
'iosfwd',
'iostream',
'istream',
'iterator',
'limits',
'list',
'locale',
'map',
'memory',
'mutex',
'new',
'numeric',
'ostream',
'queue',
'random',
'ratio',
'regex',
'set',
'sstream',
'stack',
'stdexcept',
'streambuf',
'string',
'strstream',
'system_error',
'thread',
'tuple',
'typeindex',
'typeinfo',
'type_traits',
'unordered_map',
'unordered_set',
'utility',
'valarray',
'vector',
# 17.6.1.2 C++ headers for C library facilities
'cassert',
'ccomplex',
'cctype',
'cerrno',
'cfenv',
'cfloat',
'cinttypes',
'ciso646',
'climits',
'clocale',
'cmath',
'csetjmp',
'csignal',
'cstdalign',
'cstdarg',
'cstdbool',
'cstddef',
'cstdint',
'cstdio',
'cstdlib',
'cstring',
'ctgmath',
'ctime',
'cuchar',
'cwchar',
'cwctype',
])
# Assertion macros. These are defined in base/logging.h and
# testing/base/gunit.h. Note that the _M versions need to come first
# for substring matching to work.
_CHECK_MACROS = [
'DCHECK', 'CHECK',
'EXPECT_TRUE_M', 'EXPECT_TRUE',
'ASSERT_TRUE_M', 'ASSERT_TRUE',
'EXPECT_FALSE_M', 'EXPECT_FALSE',
'ASSERT_FALSE_M', 'ASSERT_FALSE',
]
# Replacement macros for CHECK/DCHECK/EXPECT_TRUE/EXPECT_FALSE
_CHECK_REPLACEMENT = dict([(m, {}) for m in _CHECK_MACROS])
for op, replacement in [('==', 'EQ'), ('!=', 'NE'),
('>=', 'GE'), ('>', 'GT'),
('<=', 'LE'), ('<', 'LT')]:
_CHECK_REPLACEMENT['DCHECK'][op] = 'DCHECK_%s' % replacement
_CHECK_REPLACEMENT['CHECK'][op] = 'CHECK_%s' % replacement
_CHECK_REPLACEMENT['EXPECT_TRUE'][op] = 'EXPECT_%s' % replacement
_CHECK_REPLACEMENT['ASSERT_TRUE'][op] = 'ASSERT_%s' % replacement
_CHECK_REPLACEMENT['EXPECT_TRUE_M'][op] = 'EXPECT_%s_M' % replacement
_CHECK_REPLACEMENT['ASSERT_TRUE_M'][op] = 'ASSERT_%s_M' % replacement
for op, inv_replacement in [('==', 'NE'), ('!=', 'EQ'),
('>=', 'LT'), ('>', 'LE'),
('<=', 'GT'), ('<', 'GE')]:
_CHECK_REPLACEMENT['EXPECT_FALSE'][op] = 'EXPECT_%s' % inv_replacement
_CHECK_REPLACEMENT['ASSERT_FALSE'][op] = 'ASSERT_%s' % inv_replacement
_CHECK_REPLACEMENT['EXPECT_FALSE_M'][op] = 'EXPECT_%s_M' % inv_replacement
_CHECK_REPLACEMENT['ASSERT_FALSE_M'][op] = 'ASSERT_%s_M' % inv_replacement
# Alternative tokens and their replacements. For full list, see section 2.5
# Alternative tokens [lex.digraph] in the C++ standard.
#
# Digraphs (such as '%:') are not included here since it's a mess to
# match those on a word boundary.
_ALT_TOKEN_REPLACEMENT = {
'and': '&&',
'bitor': '|',
'or': '||',
'xor': '^',
'compl': '~',
'bitand': '&',
'and_eq': '&=',
'or_eq': '|=',
'xor_eq': '^=',
'not': '!',
'not_eq': '!='
}
# Compile regular expression that matches all the above keywords. The "[ =()]"
# bit is meant to avoid matching these keywords outside of boolean expressions.
#
# False positives include C-style multi-line comments and multi-line strings
# but those have always been troublesome for cpplint.
_ALT_TOKEN_REPLACEMENT_PATTERN = re.compile(
r'[ =()](' + ('|'.join(_ALT_TOKEN_REPLACEMENT.keys())) + r')(?=[ (]|$)')
# These constants define types of headers for use with
# _IncludeState.CheckNextIncludeOrder().
_C_SYS_HEADER = 1
_CPP_SYS_HEADER = 2
_LIKELY_MY_HEADER = 3
_POSSIBLE_MY_HEADER = 4
_OTHER_HEADER = 5
# These constants define the current inline assembly state
_NO_ASM = 0 # Outside of inline assembly block
_INSIDE_ASM = 1 # Inside inline assembly block
_END_ASM = 2 # Last line of inline assembly block
_BLOCK_ASM = 3 # The whole block is an inline assembly block
# Match start of assembly blocks
_MATCH_ASM = re.compile(r'^\s*(?:asm|_asm|__asm|__asm__)'
r'(?:\s+(volatile|__volatile__))?'
r'\s*[{(]')
_regexp_compile_cache = {}
# Finds occurrences of NOLINT[_NEXT_LINE] or NOLINT[_NEXT_LINE](...).
_RE_SUPPRESSION = re.compile(r'\bNOLINT(_NEXT_LINE)?\b(\([^)]*\))?')
# {str, set(int)}: a map from error categories to sets of linenumbers
# on which those errors are expected and should be suppressed.
_error_suppressions = {}
# Finds Copyright.
_RE_COPYRIGHT = re.compile(r'Copyright')
# The root directory used for deriving header guard CPP variable.
# This is set by --root flag.
_root = None
# The allowed line length of files.
# This is set by --linelength flag.
_line_length = 80
# The allowed extensions for file names
# This is set by --extensions flag.
_valid_extensions = set(['cc', 'h', 'cpp', 'hpp', 'cu', 'cuh'])
def ParseNolintSuppressions(filename, raw_line, linenum, error):
"""Updates the global list of error-suppressions.
Parses any NOLINT comments on the current line, updating the global
error_suppressions store. Reports an error if the NOLINT comment
was malformed.
Args:
filename: str, the name of the input file.
raw_line: str, the line of input text, with comments.
linenum: int, the number of the current line.
error: function, an error handler.
"""
# FIXME(adonovan): "NOLINT(" is misparsed as NOLINT(*).
matched = _RE_SUPPRESSION.search(raw_line)
if matched:
if matched.group(1) == '_NEXT_LINE':
linenum += 1
category = matched.group(2)
if category in (None, '(*)'): # => "suppress all"
_error_suppressions.setdefault(None, set()).add(linenum)
else:
if category.startswith('(') and category.endswith(')'):
category = category[1:-1]
if category in _ERROR_CATEGORIES:
_error_suppressions.setdefault(category, set()).add(linenum)
else:
error(filename, linenum, 'readability/nolint', 5,
'Unknown NOLINT error category: %s' % category)
def ResetNolintSuppressions():
"Resets the set of NOLINT suppressions to empty."
_error_suppressions.clear()
def IsErrorSuppressedByNolint(category, linenum):
"""Returns true if the specified error category is suppressed on this line.
Consults the global error_suppressions map populated by
ParseNolintSuppressions/ResetNolintSuppressions.
Args:
category: str, the category of the error.
linenum: int, the current line number.
Returns:
bool, True iff the error should be suppressed due to a NOLINT comment.
"""
return (linenum in _error_suppressions.get(category, set()) or
linenum in _error_suppressions.get(None, set()))
def Match(pattern, s):
"""Matches the string with the pattern, caching the compiled regexp."""
# The regexp compilation caching is inlined in both Match and Search for
# performance reasons; factoring it out into a separate function turns out
# to be noticeably expensive.
if pattern not in _regexp_compile_cache:
_regexp_compile_cache[pattern] = sre_compile.compile(pattern)
return _regexp_compile_cache[pattern].match(s)
def ReplaceAll(pattern, rep, s):
"""Replaces instances of pattern in a string with a replacement.
The compiled regex is kept in a cache shared by Match and Search.
Args:
pattern: regex pattern
rep: replacement text
s: search string
Returns:
string with replacements made (or original string if no replacements)
"""
if pattern not in _regexp_compile_cache:
_regexp_compile_cache[pattern] = sre_compile.compile(pattern)
return _regexp_compile_cache[pattern].sub(rep, s)
def Search(pattern, s):
"""Searches the string for the pattern, caching the compiled regexp."""
if pattern not in _regexp_compile_cache:
_regexp_compile_cache[pattern] = sre_compile.compile(pattern)
return _regexp_compile_cache[pattern].search(s)
class _IncludeState(dict):
"""Tracks line numbers for includes, and the order in which includes appear.
As a dict, an _IncludeState object serves as a mapping between include
filename and line number on which that file was included.
Call CheckNextIncludeOrder() once for each header in the file, passing
in the type constants defined above. Calls in an illegal order will
raise an _IncludeError with an appropriate error message.
"""
# self._section will move monotonically through this set. If it ever
# needs to move backwards, CheckNextIncludeOrder will raise an error.
_INITIAL_SECTION = 0
_MY_H_SECTION = 1
_C_SECTION = 2
_CPP_SECTION = 3
_OTHER_H_SECTION = 4
_TYPE_NAMES = {
_C_SYS_HEADER: 'C system header',
_CPP_SYS_HEADER: 'C++ system header',
_LIKELY_MY_HEADER: 'header this file implements',
_POSSIBLE_MY_HEADER: 'header this file may implement',
_OTHER_HEADER: 'other header',
}
_SECTION_NAMES = {
_INITIAL_SECTION: "... nothing. (This can't be an error.)",
_MY_H_SECTION: 'a header this file implements',
_C_SECTION: 'C system header',
_CPP_SECTION: 'C++ system header',
_OTHER_H_SECTION: 'other header',
}
def __init__(self):
dict.__init__(self)
self.ResetSection()
def ResetSection(self):
# The name of the current section.
self._section = self._INITIAL_SECTION
# The path of last found header.
self._last_header = ''
def SetLastHeader(self, header_path):
self._last_header = header_path
def CanonicalizeAlphabeticalOrder(self, header_path):
"""Returns a path canonicalized for alphabetical comparison.
- replaces "-" with "_" so they both cmp the same.
- removes '-inl' since we don't require them to be after the main header.
- lowercase everything, just in case.
Args:
header_path: Path to be canonicalized.
Returns:
Canonicalized path.
"""
return header_path.replace('-inl.h', '.h').replace('-', '_').lower()
def IsInAlphabeticalOrder(self, clean_lines, linenum, header_path):
"""Check if a header is in alphabetical order with the previous header.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
header_path: Canonicalized header to be checked.
Returns:
Returns true if the header is in alphabetical order.
"""
# If previous section is different from current section, _last_header will
# be reset to empty string, so it's always less than current header.
#
# If previous line was a blank line, assume that the headers are
# intentionally sorted the way they are.
if (self._last_header > header_path and
not Match(r'^\s*$', clean_lines.elided[linenum - 1])):
return False
return True
def CheckNextIncludeOrder(self, header_type):
"""Returns a non-empty error message if the next header is out of order.
This function also updates the internal state to be ready to check
the next include.
Args:
header_type: One of the _XXX_HEADER constants defined above.
Returns:
The empty string if the header is in the right order, or an
error message describing what's wrong.
"""
error_message = ('Found %s after %s' %
(self._TYPE_NAMES[header_type],
self._SECTION_NAMES[self._section]))
last_section = self._section
if header_type == _C_SYS_HEADER:
if self._section <= self._C_SECTION:
self._section = self._C_SECTION
else:
self._last_header = ''
return error_message
elif header_type == _CPP_SYS_HEADER:
if self._section <= self._CPP_SECTION:
self._section = self._CPP_SECTION
else:
self._last_header = ''
return error_message
elif header_type == _LIKELY_MY_HEADER:
if self._section <= self._MY_H_SECTION:
self._section = self._MY_H_SECTION
else:
self._section = self._OTHER_H_SECTION
elif header_type == _POSSIBLE_MY_HEADER:
if self._section <= self._MY_H_SECTION:
self._section = self._MY_H_SECTION
else:
# This will always be the fallback because we're not sure
# enough that the header is associated with this file.
self._section = self._OTHER_H_SECTION
else:
assert header_type == _OTHER_HEADER
self._section = self._OTHER_H_SECTION
if last_section != self._section:
self._last_header = ''
return ''
class _CppLintState(object):
"""Maintains module-wide state.."""
def __init__(self):
self.verbose_level = 1 # global setting.
self.error_count = 0 # global count of reported errors
# filters to apply when emitting error messages
self.filters = _DEFAULT_FILTERS[:]
self.counting = 'total' # In what way are we counting errors?
self.errors_by_category = {} # string to int dict storing error counts
# output format:
# "emacs" - format that emacs can parse (default)
# "vs7" - format that Microsoft Visual Studio 7 can parse
self.output_format = 'emacs'
def SetOutputFormat(self, output_format):
"""Sets the output format for errors."""
self.output_format = output_format
def SetVerboseLevel(self, level):
"""Sets the module's verbosity, and returns the previous setting."""
last_verbose_level = self.verbose_level
self.verbose_level = level
return last_verbose_level
def SetCountingStyle(self, counting_style):
"""Sets the module's counting options."""
self.counting = counting_style
def SetFilters(self, filters):
"""Sets the error-message filters.
These filters are applied when deciding whether to emit a given
error message.
Args:
filters: A string of comma-separated filters (eg "+whitespace/indent").
Each filter should start with + or -; else we die.
Raises:
ValueError: The comma-separated filters did not all start with '+' or '-'.
E.g. "-,+whitespace,-whitespace/indent,whitespace/badfilter"
"""
# Default filters always have less priority than the flag ones.
self.filters = _DEFAULT_FILTERS[:]
for filt in filters.split(','):
clean_filt = filt.strip()
if clean_filt:
self.filters.append(clean_filt)
for filt in self.filters:
if not (filt.startswith('+') or filt.startswith('-')):
raise ValueError('Every filter in --filters must start with + or -'
' (%s does not)' % filt)
def ResetErrorCounts(self):
"""Sets the module's error statistic back to zero."""
self.error_count = 0
self.errors_by_category = {}
def IncrementErrorCount(self, category):
"""Bumps the module's error statistic."""
self.error_count += 1
if self.counting in ('toplevel', 'detailed'):
if self.counting != 'detailed':
category = category.split('/')[0]
if category not in self.errors_by_category:
self.errors_by_category[category] = 0
self.errors_by_category[category] += 1
def PrintErrorCounts(self):
"""Print a summary of errors by category, and the total."""
for category, count in self.errors_by_category.iteritems():
sys.stderr.write('Category \'%s\' errors found: %d\n' %
(category, count))
sys.stderr.write('Total errors found: %d\n' % self.error_count)
_cpplint_state = _CppLintState()
def _OutputFormat():
"""Gets the module's output format."""
return _cpplint_state.output_format
def _SetOutputFormat(output_format):
"""Sets the module's output format."""
_cpplint_state.SetOutputFormat(output_format)
def _VerboseLevel():
"""Returns the module's verbosity setting."""
return _cpplint_state.verbose_level
def _SetVerboseLevel(level):
"""Sets the module's verbosity, and returns the previous setting."""
return _cpplint_state.SetVerboseLevel(level)
def _SetCountingStyle(level):
"""Sets the module's counting options."""
_cpplint_state.SetCountingStyle(level)
def _Filters():
"""Returns the module's list of output filters, as a list."""
return _cpplint_state.filters
def _SetFilters(filters):
"""Sets the module's error-message filters.
These filters are applied when deciding whether to emit a given
error message.
Args:
filters: A string of comma-separated filters (eg "whitespace/indent").
Each filter should start with + or -; else we die.
"""
_cpplint_state.SetFilters(filters)
class _FunctionState(object):
"""Tracks current function name and the number of lines in its body."""
_NORMAL_TRIGGER = 250 # for --v=0, 500 for --v=1, etc.
_TEST_TRIGGER = 400 # about 50% more than _NORMAL_TRIGGER.
def __init__(self):
self.in_a_function = False
self.lines_in_function = 0
self.current_function = ''
def Begin(self, function_name):
"""Start analyzing function body.
Args:
function_name: The name of the function being tracked.
"""
self.in_a_function = True
self.lines_in_function = 0
self.current_function = function_name
def Count(self):
"""Count line in current function body."""
if self.in_a_function:
self.lines_in_function += 1
def Check(self, error, filename, linenum):
"""Report if too many lines in function body.
Args:
error: The function to call with any errors found.
filename: The name of the current file.
linenum: The number of the line to check.
"""
if Match(r'T(EST|est)', self.current_function):
base_trigger = self._TEST_TRIGGER
else:
base_trigger = self._NORMAL_TRIGGER
trigger = base_trigger * 2**_VerboseLevel()
if self.lines_in_function > trigger:
error_level = int(math.log(self.lines_in_function / base_trigger, 2))
# 50 => 0, 100 => 1, 200 => 2, 400 => 3, 800 => 4, 1600 => 5, ...
if error_level > 5:
error_level = 5
error(filename, linenum, 'readability/fn_size', error_level,
'Small and focused functions are preferred:'
' %s has %d non-comment lines'
' (error triggered by exceeding %d lines).' % (
self.current_function, self.lines_in_function, trigger))
def End(self):
"""Stop analyzing function body."""
self.in_a_function = False
class _IncludeError(Exception):
"""Indicates a problem with the include order in a file."""
pass
class FileInfo:
"""Provides utility functions for filenames.
FileInfo provides easy access to the components of a file's path
relative to the project root.
"""
def __init__(self, filename):
self._filename = filename
def FullName(self):
"""Make Windows paths like Unix."""
return os.path.abspath(self._filename).replace('\\', '/')
def RepositoryName(self):
"""FullName after removing the local path to the repository.
If we have a real absolute path name here we can try to do something smart:
detecting the root of the checkout and truncating /path/to/checkout from
the name so that we get header guards that don't include things like
"C:\Documents and Settings\..." or "/home/username/..." in them and thus
people on different computers who have checked the source out to different
locations won't see bogus errors.
"""
fullname = self.FullName()
if os.path.exists(fullname):
project_dir = os.path.dirname(fullname)
if os.path.exists(os.path.join(project_dir, ".svn")):
# If there's a .svn file in the current directory, we recursively look
# up the directory tree for the top of the SVN checkout
root_dir = project_dir
one_up_dir = os.path.dirname(root_dir)
while os.path.exists(os.path.join(one_up_dir, ".svn")):
root_dir = os.path.dirname(root_dir)
one_up_dir = os.path.dirname(one_up_dir)
prefix = os.path.commonprefix([root_dir, project_dir])
return fullname[len(prefix) + 1:]
# Not SVN <= 1.6? Try to find a git, hg, or svn top level directory by
# searching up from the current path.
root_dir = os.path.dirname(fullname)
while (root_dir != os.path.dirname(root_dir) and
not os.path.exists(os.path.join(root_dir, ".git")) and
not os.path.exists(os.path.join(root_dir, ".hg")) and
not os.path.exists(os.path.join(root_dir, ".svn"))):
root_dir = os.path.dirname(root_dir)
if (os.path.exists(os.path.join(root_dir, ".git")) or
os.path.exists(os.path.join(root_dir, ".hg")) or
os.path.exists(os.path.join(root_dir, ".svn"))):
prefix = os.path.commonprefix([root_dir, project_dir])
return fullname[len(prefix) + 1:]
# Don't know what to do; header guard warnings may be wrong...
return fullname
def Split(self):
"""Splits the file into the directory, basename, and extension.
For 'chrome/browser/browser.cc', Split() would
return ('chrome/browser', 'browser', '.cc')
Returns:
A tuple of (directory, basename, extension).
"""
googlename = self.RepositoryName()
project, rest = os.path.split(googlename)
return (project,) + os.path.splitext(rest)
def BaseName(self):
"""File base name - text after the final slash, before the final period."""
return self.Split()[1]
def Extension(self):
"""File extension - text following the final period."""
return self.Split()[2]
def NoExtension(self):
"""File has no source file extension."""
return '/'.join(self.Split()[0:2])
def IsSource(self):
"""File has a source file extension."""
return self.Extension()[1:] in ('c', 'cc', 'cpp', 'cxx')
def _ShouldPrintError(category, confidence, linenum):
"""If confidence >= verbose, category passes filter and is not suppressed."""
# There are three ways we might decide not to print an error message:
# a "NOLINT(category)" comment appears in the source,
# the verbosity level isn't high enough, or the filters filter it out.
if IsErrorSuppressedByNolint(category, linenum):
return False
if confidence < _cpplint_state.verbose_level:
return False
is_filtered = False
for one_filter in _Filters():
if one_filter.startswith('-'):
if category.startswith(one_filter[1:]):
is_filtered = True
elif one_filter.startswith('+'):
if category.startswith(one_filter[1:]):
is_filtered = False
else:
assert False # should have been checked for in SetFilter.
if is_filtered:
return False
return True
def Error(filename, linenum, category, confidence, message):
"""Logs the fact we've found a lint error.
We log where the error was found, and also our confidence in the error,
that is, how certain we are this is a legitimate style regression, and
not a misidentification or a use that's sometimes justified.
False positives can be suppressed by the use of
"cpplint(category)" comments on the offending line. These are
parsed into _error_suppressions.
Args:
filename: The name of the file containing the error.
linenum: The number of the line containing the error.
category: A string used to describe the "category" this bug
falls under: "whitespace", say, or "runtime". Categories
may have a hierarchy separated by slashes: "whitespace/indent".
confidence: A number from 1-5 representing a confidence score for
the error, with 5 meaning that we are certain of the problem,
and 1 meaning that it could be a legitimate construct.
message: The error message.
"""
if _ShouldPrintError(category, confidence, linenum):
_cpplint_state.IncrementErrorCount(category)
if _cpplint_state.output_format == 'vs7':
sys.stderr.write('%s(%s): %s [%s] [%d]\n' % (
filename, linenum, message, category, confidence))
elif _cpplint_state.output_format == 'eclipse':
sys.stderr.write('%s:%s: warning: %s [%s] [%d]\n' % (
filename, linenum, message, category, confidence))
else:
sys.stderr.write('%s:%s: %s [%s] [%d]\n' % (
filename, linenum, message, category, confidence))
# Matches standard C++ escape sequences per 2.13.2.3 of the C++ standard.
_RE_PATTERN_CLEANSE_LINE_ESCAPES = re.compile(
r'\\([abfnrtv?"\\\']|\d+|x[0-9a-fA-F]+)')
# Matches strings. Escape codes should already be removed by ESCAPES.
_RE_PATTERN_CLEANSE_LINE_DOUBLE_QUOTES = re.compile(r'"[^"]*"')
# Matches characters. Escape codes should already be removed by ESCAPES.
_RE_PATTERN_CLEANSE_LINE_SINGLE_QUOTES = re.compile(r"'.'")
# Matches multi-line C++ comments.
# This RE is a little bit more complicated than one might expect, because we
# have to take care of space removals tools so we can handle comments inside
# statements better.
# The current rule is: We only clear spaces from both sides when we're at the
# end of the line. Otherwise, we try to remove spaces from the right side,
# if this doesn't work we try on left side but only if there's a non-character
# on the right.
_RE_PATTERN_CLEANSE_LINE_C_COMMENTS = re.compile(
r"""(\s*/\*.*\*/\s*$|
/\*.*\*/\s+|
\s+/\*.*\*/(?=\W)|
/\*.*\*/)""", re.VERBOSE)
def IsCppString(line):
"""Does line terminate so, that the next symbol is in string constant.
This function does not consider single-line nor multi-line comments.
Args:
line: is a partial line of code starting from the 0..n.
Returns:
True, if next character appended to 'line' is inside a
string constant.
"""
line = line.replace(r'\\', 'XX') # after this, \\" does not match to \"
return ((line.count('"') - line.count(r'\"') - line.count("'\"'")) & 1) == 1
def CleanseRawStrings(raw_lines):
"""Removes C++11 raw strings from lines.
Before:
static const char kData[] = R"(
multi-line string
)";
After:
static const char kData[] = ""
(replaced by blank line)
"";
Args:
raw_lines: list of raw lines.
Returns:
list of lines with C++11 raw strings replaced by empty strings.
"""
delimiter = None
lines_without_raw_strings = []
for line in raw_lines:
if delimiter:
# Inside a raw string, look for the end
end = line.find(delimiter)
if end >= 0:
# Found the end of the string, match leading space for this
# line and resume copying the original lines, and also insert
# a "" on the last line.
leading_space = Match(r'^(\s*)\S', line)
line = leading_space.group(1) + '""' + line[end + len(delimiter):]
delimiter = None
else:
# Haven't found the end yet, append a blank line.
line = ''
else:
# Look for beginning of a raw string.
# See 2.14.15 [lex.string] for syntax.
matched = Match(r'^(.*)\b(?:R|u8R|uR|UR|LR)"([^\s\\()]*)\((.*)$', line)
if matched:
delimiter = ')' + matched.group(2) + '"'
end = matched.group(3).find(delimiter)
if end >= 0:
# Raw string ended on same line
line = (matched.group(1) + '""' +
matched.group(3)[end + len(delimiter):])
delimiter = None
else:
# Start of a multi-line raw string
line = matched.group(1) + '""'
lines_without_raw_strings.append(line)
# TODO(unknown): if delimiter is not None here, we might want to
# emit a warning for unterminated string.
return lines_without_raw_strings
def FindNextMultiLineCommentStart(lines, lineix):
"""Find the beginning marker for a multiline comment."""
while lineix < len(lines):
if lines[lineix].strip().startswith('/*'):
# Only return this marker if the comment goes beyond this line
if lines[lineix].strip().find('*/', 2) < 0:
return lineix
lineix += 1
return len(lines)
def FindNextMultiLineCommentEnd(lines, lineix):
"""We are inside a comment, find the end marker."""
while lineix < len(lines):
if lines[lineix].strip().endswith('*/'):
return lineix
lineix += 1
return len(lines)
def RemoveMultiLineCommentsFromRange(lines, begin, end):
"""Clears a range of lines for multi-line comments."""
# Having // dummy comments makes the lines non-empty, so we will not get
# unnecessary blank line warnings later in the code.
for i in range(begin, end):
lines[i] = '// dummy'
def RemoveMultiLineComments(filename, lines, error):
"""Removes multiline (c-style) comments from lines."""
lineix = 0
while lineix < len(lines):
lineix_begin = FindNextMultiLineCommentStart(lines, lineix)
if lineix_begin >= len(lines):
return
lineix_end = FindNextMultiLineCommentEnd(lines, lineix_begin)
if lineix_end >= len(lines):
error(filename, lineix_begin + 1, 'readability/multiline_comment', 5,
'Could not find end of multi-line comment')
return
RemoveMultiLineCommentsFromRange(lines, lineix_begin, lineix_end + 1)
lineix = lineix_end + 1
def CleanseComments(line):
"""Removes //-comments and single-line C-style /* */ comments.
Args:
line: A line of C++ source.
Returns:
The line with single-line comments removed.
"""
commentpos = line.find('//')
if commentpos != -1 and not IsCppString(line[:commentpos]):
line = line[:commentpos].rstrip()
# get rid of /* ... */
return _RE_PATTERN_CLEANSE_LINE_C_COMMENTS.sub('', line)
class CleansedLines(object):
"""Holds 3 copies of all lines with different preprocessing applied to them.
1) elided member contains lines without strings and comments,
2) lines member contains lines without comments, and
3) raw_lines member contains all the lines without processing.
All these three members are of <type 'list'>, and of the same length.
"""
def __init__(self, lines):
self.elided = []
self.lines = []
self.raw_lines = lines
self.num_lines = len(lines)
self.lines_without_raw_strings = CleanseRawStrings(lines)
for linenum in range(len(self.lines_without_raw_strings)):
self.lines.append(CleanseComments(
self.lines_without_raw_strings[linenum]))
elided = self._CollapseStrings(self.lines_without_raw_strings[linenum])
self.elided.append(CleanseComments(elided))
def NumLines(self):
"""Returns the number of lines represented."""
return self.num_lines
@staticmethod
def _CollapseStrings(elided):
"""Collapses strings and chars on a line to simple "" or '' blocks.
We nix strings first so we're not fooled by text like '"http://"'
Args:
elided: The line being processed.
Returns:
The line with collapsed strings.
"""
if not _RE_PATTERN_INCLUDE.match(elided):
# Remove escaped characters first to make quote/single quote collapsing
# basic. Things that look like escaped characters shouldn't occur
# outside of strings and chars.
elided = _RE_PATTERN_CLEANSE_LINE_ESCAPES.sub('', elided)
elided = _RE_PATTERN_CLEANSE_LINE_SINGLE_QUOTES.sub("''", elided)
elided = _RE_PATTERN_CLEANSE_LINE_DOUBLE_QUOTES.sub('""', elided)
return elided
def FindEndOfExpressionInLine(line, startpos, depth, startchar, endchar):
"""Find the position just after the matching endchar.
Args:
line: a CleansedLines line.
startpos: start searching at this position.
depth: nesting level at startpos.
startchar: expression opening character.
endchar: expression closing character.
Returns:
On finding matching endchar: (index just after matching endchar, 0)
Otherwise: (-1, new depth at end of this line)
"""
for i in xrange(startpos, len(line)):
if line[i] == startchar:
depth += 1
elif line[i] == endchar:
depth -= 1
if depth == 0:
return (i + 1, 0)
return (-1, depth)
def CloseExpression(clean_lines, linenum, pos):
"""If input points to ( or { or [ or <, finds the position that closes it.
If lines[linenum][pos] points to a '(' or '{' or '[' or '<', finds the
linenum/pos that correspond to the closing of the expression.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: A position on the line.
Returns:
A tuple (line, linenum, pos) pointer *past* the closing brace, or
(line, len(lines), -1) if we never find a close. Note we ignore
strings and comments when matching; and the line we return is the
'cleansed' line at linenum.
"""
line = clean_lines.elided[linenum]
startchar = line[pos]
if startchar not in '({[<':
return (line, clean_lines.NumLines(), -1)
if startchar == '(': endchar = ')'
if startchar == '[': endchar = ']'
if startchar == '{': endchar = '}'
if startchar == '<': endchar = '>'
# Check first line
(end_pos, num_open) = FindEndOfExpressionInLine(
line, pos, 0, startchar, endchar)
if end_pos > -1:
return (line, linenum, end_pos)
# Continue scanning forward
while linenum < clean_lines.NumLines() - 1:
linenum += 1
line = clean_lines.elided[linenum]
(end_pos, num_open) = FindEndOfExpressionInLine(
line, 0, num_open, startchar, endchar)
if end_pos > -1:
return (line, linenum, end_pos)
# Did not find endchar before end of file, give up
return (line, clean_lines.NumLines(), -1)
def FindStartOfExpressionInLine(line, endpos, depth, startchar, endchar):
"""Find position at the matching startchar.
This is almost the reverse of FindEndOfExpressionInLine, but note
that the input position and returned position differs by 1.
Args:
line: a CleansedLines line.
endpos: start searching at this position.
depth: nesting level at endpos.
startchar: expression opening character.
endchar: expression closing character.
Returns:
On finding matching startchar: (index at matching startchar, 0)
Otherwise: (-1, new depth at beginning of this line)
"""
for i in xrange(endpos, -1, -1):
if line[i] == endchar:
depth += 1
elif line[i] == startchar:
depth -= 1
if depth == 0:
return (i, 0)
return (-1, depth)
def ReverseCloseExpression(clean_lines, linenum, pos):
"""If input points to ) or } or ] or >, finds the position that opens it.
If lines[linenum][pos] points to a ')' or '}' or ']' or '>', finds the
linenum/pos that correspond to the opening of the expression.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: A position on the line.
Returns:
A tuple (line, linenum, pos) pointer *at* the opening brace, or
(line, 0, -1) if we never find the matching opening brace. Note
we ignore strings and comments when matching; and the line we
return is the 'cleansed' line at linenum.
"""
line = clean_lines.elided[linenum]
endchar = line[pos]
if endchar not in ')}]>':
return (line, 0, -1)
if endchar == ')': startchar = '('
if endchar == ']': startchar = '['
if endchar == '}': startchar = '{'
if endchar == '>': startchar = '<'
# Check last line
(start_pos, num_open) = FindStartOfExpressionInLine(
line, pos, 0, startchar, endchar)
if start_pos > -1:
return (line, linenum, start_pos)
# Continue scanning backward
while linenum > 0:
linenum -= 1
line = clean_lines.elided[linenum]
(start_pos, num_open) = FindStartOfExpressionInLine(
line, len(line) - 1, num_open, startchar, endchar)
if start_pos > -1:
return (line, linenum, start_pos)
# Did not find startchar before beginning of file, give up
return (line, 0, -1)
def CheckForCopyright(filename, lines, error):
"""Logs an error if a Copyright message appears at the top of the file."""
# We'll check up to line 10. Don't forget there's a
# dummy line at the front.
for line in xrange(1, min(len(lines), 11)):
if _RE_COPYRIGHT.search(lines[line], re.I):
error(filename, 0, 'legal/copyright', 5,
'Copyright message found. '
'You should not include a copyright line.')
def GetHeaderGuardCPPVariable(filename):
"""Returns the CPP variable that should be used as a header guard.
Args:
filename: The name of a C++ header file.
Returns:
The CPP variable that should be used as a header guard in the
named file.
"""
# Restores original filename in case that cpplint is invoked from Emacs's
# flymake.
filename = re.sub(r'_flymake\.h$', '.h', filename)
filename = re.sub(r'/\.flymake/([^/]*)$', r'/\1', filename)
fileinfo = FileInfo(filename)
file_path_from_root = fileinfo.RepositoryName()
if _root:
file_path_from_root = re.sub('^' + _root + os.sep, '', file_path_from_root)
return re.sub(r'[-./\s]', '_', file_path_from_root).upper() + '_'
def CheckForHeaderGuard(filename, lines, error):
"""Checks that the file contains a header guard.
Logs an error if no #ifndef header guard is present. For other
headers, checks that the full pathname is used.
Args:
filename: The name of the C++ header file.
lines: An array of strings, each representing a line of the file.
error: The function to call with any errors found.
"""
cppvar = GetHeaderGuardCPPVariable(filename)
ifndef = None
ifndef_linenum = 0
define = None
endif = None
endif_linenum = 0
for linenum, line in enumerate(lines):
linesplit = line.split()
if len(linesplit) >= 2:
# find the first occurrence of #ifndef and #define, save arg
if not ifndef and linesplit[0] == '#ifndef':
# set ifndef to the header guard presented on the #ifndef line.
ifndef = linesplit[1]
ifndef_linenum = linenum
if not define and linesplit[0] == '#define':
define = linesplit[1]
# find the last occurrence of #endif, save entire line
if line.startswith('#endif'):
endif = line
endif_linenum = linenum
if not ifndef:
error(filename, 0, 'build/header_guard', 5,
'No #ifndef header guard found, suggested CPP variable is: %s' %
cppvar)
return
if not define:
error(filename, 0, 'build/header_guard', 5,
'No #define header guard found, suggested CPP variable is: %s' %
cppvar)
return
# The guard should be PATH_FILE_H_, but we also allow PATH_FILE_H__
# for backward compatibility.
if ifndef != cppvar:
error_level = 0
if ifndef != cppvar + '_':
error_level = 5
ParseNolintSuppressions(filename, lines[ifndef_linenum], ifndef_linenum,
error)
error(filename, ifndef_linenum, 'build/header_guard', error_level,
'#ifndef header guard has wrong style, please use: %s' % cppvar)
if define != ifndef:
error(filename, 0, 'build/header_guard', 5,
'#ifndef and #define don\'t match, suggested CPP variable is: %s' %
cppvar)
return
if endif != ('#endif // %s' % cppvar):
error_level = 0
if endif != ('#endif // %s' % (cppvar + '_')):
error_level = 5
ParseNolintSuppressions(filename, lines[endif_linenum], endif_linenum,
error)
error(filename, endif_linenum, 'build/header_guard', error_level,
'#endif line should be "#endif // %s"' % cppvar)
def CheckForBadCharacters(filename, lines, error):
"""Logs an error for each line containing bad characters.
Two kinds of bad characters:
1. Unicode replacement characters: These indicate that either the file
contained invalid UTF-8 (likely) or Unicode replacement characters (which
it shouldn't). Note that it's possible for this to throw off line
numbering if the invalid UTF-8 occurred adjacent to a newline.
2. NUL bytes. These are problematic for some tools.
Args:
filename: The name of the current file.
lines: An array of strings, each representing a line of the file.
error: The function to call with any errors found.
"""
for linenum, line in enumerate(lines):
if u'\ufffd' in line:
error(filename, linenum, 'readability/utf8', 5,
'Line contains invalid UTF-8 (or Unicode replacement character).')
if '\0' in line:
error(filename, linenum, 'readability/nul', 5, 'Line contains NUL byte.')
def CheckForNewlineAtEOF(filename, lines, error):
"""Logs an error if there is no newline char at the end of the file.
Args:
filename: The name of the current file.
lines: An array of strings, each representing a line of the file.
error: The function to call with any errors found.
"""
# The array lines() was created by adding two newlines to the
# original file (go figure), then splitting on \n.
# To verify that the file ends in \n, we just have to make sure the
# last-but-two element of lines() exists and is empty.
if len(lines) < 3 or lines[-2]:
error(filename, len(lines) - 2, 'whitespace/ending_newline', 5,
'Could not find a newline character at the end of the file.')
def CheckForMultilineCommentsAndStrings(filename, clean_lines, linenum, error):
"""Logs an error if we see /* ... */ or "..." that extend past one line.
/* ... */ comments are legit inside macros, for one line.
Otherwise, we prefer // comments, so it's ok to warn about the
other. Likewise, it's ok for strings to extend across multiple
lines, as long as a line continuation character (backslash)
terminates each line. Although not currently prohibited by the C++
style guide, it's ugly and unnecessary. We don't do well with either
in this lint program, so we warn about both.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# Remove all \\ (escaped backslashes) from the line. They are OK, and the
# second (escaped) slash may trigger later \" detection erroneously.
line = line.replace('\\\\', '')
if line.count('/*') > line.count('*/'):
error(filename, linenum, 'readability/multiline_comment', 5,
'Complex multi-line /*...*/-style comment found. '
'Lint may give bogus warnings. '
'Consider replacing these with //-style comments, '
'with #if 0...#endif, '
'or with more clearly structured multi-line comments.')
if (line.count('"') - line.count('\\"')) % 2:
error(filename, linenum, 'readability/multiline_string', 5,
'Multi-line string ("...") found. This lint script doesn\'t '
'do well with such strings, and may give bogus warnings. '
'Use C++11 raw strings or concatenation instead.')
caffe_alt_function_list = (
('memset', ['caffe_set', 'caffe_memset']),
('cudaMemset', ['caffe_gpu_set', 'caffe_gpu_memset']),
('memcpy', ['caffe_copy', 'caffe_memcpy']),
('cudaMemcpy', ['caffe_copy', 'caffe_gpu_memcpy']),
)
def CheckCaffeAlternatives(filename, clean_lines, linenum, error):
"""Checks for C(++) functions for which a Caffe substitute should be used.
For certain native C functions (memset, memcpy), there is a Caffe alternative
which should be used instead.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
for function, alts in caffe_alt_function_list:
ix = line.find(function + '(')
if ix >= 0 and (ix == 0 or (not line[ix - 1].isalnum() and
line[ix - 1] not in ('_', '.', '>'))):
disp_alts = ['%s(...)' % alt for alt in alts]
error(filename, linenum, 'caffe/alt_fn', 2,
'Use Caffe function %s instead of %s(...).' %
(' or '.join(disp_alts), function))
def CheckCaffeDataLayerSetUp(filename, clean_lines, linenum, error):
"""Except the base classes, Caffe DataLayer should define DataLayerSetUp
instead of LayerSetUp.
The base DataLayers define common SetUp steps, the subclasses should
not override them.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
ix = line.find('DataLayer<Dtype>::LayerSetUp')
if ix >= 0 and (
line.find('void DataLayer<Dtype>::LayerSetUp') != -1 or
line.find('void ImageDataLayer<Dtype>::LayerSetUp') != -1 or
line.find('void MemoryDataLayer<Dtype>::LayerSetUp') != -1 or
line.find('void WindowDataLayer<Dtype>::LayerSetUp') != -1):
error(filename, linenum, 'caffe/data_layer_setup', 2,
'Except the base classes, Caffe DataLayer should define'
+ ' DataLayerSetUp instead of LayerSetUp. The base DataLayers'
+ ' define common SetUp steps, the subclasses should'
+ ' not override them.')
ix = line.find('DataLayer<Dtype>::DataLayerSetUp')
if ix >= 0 and (
line.find('void Base') == -1 and
line.find('void DataLayer<Dtype>::DataLayerSetUp') == -1 and
line.find('void ImageDataLayer<Dtype>::DataLayerSetUp') == -1 and
line.find('void MemoryDataLayer<Dtype>::DataLayerSetUp') == -1 and
line.find('void WindowDataLayer<Dtype>::DataLayerSetUp') == -1):
error(filename, linenum, 'caffe/data_layer_setup', 2,
'Except the base classes, Caffe DataLayer should define'
+ ' DataLayerSetUp instead of LayerSetUp. The base DataLayers'
+ ' define common SetUp steps, the subclasses should'
+ ' not override them.')
c_random_function_list = (
'rand(',
'rand_r(',
'random(',
)
def CheckCaffeRandom(filename, clean_lines, linenum, error):
"""Checks for calls to C random functions (rand, rand_r, random, ...).
Caffe code should (almost) always use the caffe_rng_* functions rather
than these, as the internal state of these C functions is independent of the
native Caffe RNG system which should produce deterministic results for a
fixed Caffe seed set using Caffe::set_random_seed(...).
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
for function in c_random_function_list:
ix = line.find(function)
# Comparisons made explicit for clarity -- pylint: disable=g-explicit-bool-comparison
if ix >= 0 and (ix == 0 or (not line[ix - 1].isalnum() and
line[ix - 1] not in ('_', '.', '>'))):
error(filename, linenum, 'caffe/random_fn', 2,
'Use caffe_rng_rand() (or other caffe_rng_* function) instead of '
+ function +
') to ensure results are deterministic for a fixed Caffe seed.')
threading_list = (
('asctime(', 'asctime_r('),
('ctime(', 'ctime_r('),
('getgrgid(', 'getgrgid_r('),
('getgrnam(', 'getgrnam_r('),
('getlogin(', 'getlogin_r('),
('getpwnam(', 'getpwnam_r('),
('getpwuid(', 'getpwuid_r('),
('gmtime(', 'gmtime_r('),
('localtime(', 'localtime_r('),
('strtok(', 'strtok_r('),
('ttyname(', 'ttyname_r('),
)
def CheckPosixThreading(filename, clean_lines, linenum, error):
"""Checks for calls to thread-unsafe functions.
Much code has been originally written without consideration of
multi-threading. Also, engineers are relying on their old experience;
they have learned posix before threading extensions were added. These
tests guide the engineers to use thread-safe functions (when using
posix directly).
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
for single_thread_function, multithread_safe_function in threading_list:
ix = line.find(single_thread_function)
# Comparisons made explicit for clarity -- pylint: disable=g-explicit-bool-comparison
if ix >= 0 and (ix == 0 or (not line[ix - 1].isalnum() and
line[ix - 1] not in ('_', '.', '>'))):
error(filename, linenum, 'runtime/threadsafe_fn', 2,
'Consider using ' + multithread_safe_function +
'...) instead of ' + single_thread_function +
'...) for improved thread safety.')
def CheckVlogArguments(filename, clean_lines, linenum, error):
"""Checks that VLOG() is only used for defining a logging level.
For example, VLOG(2) is correct. VLOG(INFO), VLOG(WARNING), VLOG(ERROR), and
VLOG(FATAL) are not.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
if Search(r'\bVLOG\((INFO|ERROR|WARNING|DFATAL|FATAL)\)', line):
error(filename, linenum, 'runtime/vlog', 5,
'VLOG() should be used with numeric verbosity level. '
'Use LOG() if you want symbolic severity levels.')
# Matches invalid increment: *count++, which moves pointer instead of
# incrementing a value.
_RE_PATTERN_INVALID_INCREMENT = re.compile(
r'^\s*\*\w+(\+\+|--);')
def CheckInvalidIncrement(filename, clean_lines, linenum, error):
"""Checks for invalid increment *count++.
For example following function:
void increment_counter(int* count) {
*count++;
}
is invalid, because it effectively does count++, moving pointer, and should
be replaced with ++*count, (*count)++ or *count += 1.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
if _RE_PATTERN_INVALID_INCREMENT.match(line):
error(filename, linenum, 'runtime/invalid_increment', 5,
'Changing pointer instead of value (or unused value of operator*).')
class _BlockInfo(object):
"""Stores information about a generic block of code."""
def __init__(self, seen_open_brace):
self.seen_open_brace = seen_open_brace
self.open_parentheses = 0
self.inline_asm = _NO_ASM
def CheckBegin(self, filename, clean_lines, linenum, error):
"""Run checks that applies to text up to the opening brace.
This is mostly for checking the text after the class identifier
and the "{", usually where the base class is specified. For other
blocks, there isn't much to check, so we always pass.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
pass
def CheckEnd(self, filename, clean_lines, linenum, error):
"""Run checks that applies to text after the closing brace.
This is mostly used for checking end of namespace comments.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
pass
class _ClassInfo(_BlockInfo):
"""Stores information about a class."""
def __init__(self, name, class_or_struct, clean_lines, linenum):
_BlockInfo.__init__(self, False)
self.name = name
self.starting_linenum = linenum
self.is_derived = False
if class_or_struct == 'struct':
self.access = 'public'
self.is_struct = True
else:
self.access = 'private'
self.is_struct = False
# Remember initial indentation level for this class. Using raw_lines here
# instead of elided to account for leading comments.
initial_indent = Match(r'^( *)\S', clean_lines.raw_lines[linenum])
if initial_indent:
self.class_indent = len(initial_indent.group(1))
else:
self.class_indent = 0
# Try to find the end of the class. This will be confused by things like:
# class A {
# } *x = { ...
#
# But it's still good enough for CheckSectionSpacing.
self.last_line = 0
depth = 0
for i in range(linenum, clean_lines.NumLines()):
line = clean_lines.elided[i]
depth += line.count('{') - line.count('}')
if not depth:
self.last_line = i
break
def CheckBegin(self, filename, clean_lines, linenum, error):
# Look for a bare ':'
if Search('(^|[^:]):($|[^:])', clean_lines.elided[linenum]):
self.is_derived = True
def CheckEnd(self, filename, clean_lines, linenum, error):
# Check that closing brace is aligned with beginning of the class.
# Only do this if the closing brace is indented by only whitespaces.
# This means we will not check single-line class definitions.
indent = Match(r'^( *)\}', clean_lines.elided[linenum])
if indent and len(indent.group(1)) != self.class_indent:
if self.is_struct:
parent = 'struct ' + self.name
else:
parent = 'class ' + self.name
error(filename, linenum, 'whitespace/indent', 3,
'Closing brace should be aligned with beginning of %s' % parent)
class _NamespaceInfo(_BlockInfo):
"""Stores information about a namespace."""
def __init__(self, name, linenum):
_BlockInfo.__init__(self, False)
self.name = name or ''
self.starting_linenum = linenum
def CheckEnd(self, filename, clean_lines, linenum, error):
"""Check end of namespace comments."""
line = clean_lines.raw_lines[linenum]
# Check how many lines is enclosed in this namespace. Don't issue
# warning for missing namespace comments if there aren't enough
# lines. However, do apply checks if there is already an end of
# namespace comment and it's incorrect.
#
# TODO(unknown): We always want to check end of namespace comments
# if a namespace is large, but sometimes we also want to apply the
# check if a short namespace contained nontrivial things (something
# other than forward declarations). There is currently no logic on
# deciding what these nontrivial things are, so this check is
# triggered by namespace size only, which works most of the time.
if (linenum - self.starting_linenum < 10
and not Match(r'};*\s*(//|/\*).*\bnamespace\b', line)):
return
# Look for matching comment at end of namespace.
#
# Note that we accept C style "/* */" comments for terminating
# namespaces, so that code that terminate namespaces inside
# preprocessor macros can be cpplint clean.
#
# We also accept stuff like "// end of namespace <name>." with the
# period at the end.
#
# Besides these, we don't accept anything else, otherwise we might
# get false negatives when existing comment is a substring of the
# expected namespace.
if self.name:
# Named namespace
if not Match((r'};*\s*(//|/\*).*\bnamespace\s+' + re.escape(self.name) +
r'[\*/\.\\\s]*$'),
line):
error(filename, linenum, 'readability/namespace', 5,
'Namespace should be terminated with "// namespace %s"' %
self.name)
else:
# Anonymous namespace
if not Match(r'};*\s*(//|/\*).*\bnamespace[\*/\.\\\s]*$', line):
error(filename, linenum, 'readability/namespace', 5,
'Namespace should be terminated with "// namespace"')
class _PreprocessorInfo(object):
"""Stores checkpoints of nesting stacks when #if/#else is seen."""
def __init__(self, stack_before_if):
# The entire nesting stack before #if
self.stack_before_if = stack_before_if
# The entire nesting stack up to #else
self.stack_before_else = []
# Whether we have already seen #else or #elif
self.seen_else = False
class _NestingState(object):
"""Holds states related to parsing braces."""
def __init__(self):
# Stack for tracking all braces. An object is pushed whenever we
# see a "{", and popped when we see a "}". Only 3 types of
# objects are possible:
# - _ClassInfo: a class or struct.
# - _NamespaceInfo: a namespace.
# - _BlockInfo: some other type of block.
self.stack = []
# Stack of _PreprocessorInfo objects.
self.pp_stack = []
def SeenOpenBrace(self):
"""Check if we have seen the opening brace for the innermost block.
Returns:
True if we have seen the opening brace, False if the innermost
block is still expecting an opening brace.
"""
return (not self.stack) or self.stack[-1].seen_open_brace
def InNamespaceBody(self):
"""Check if we are currently one level inside a namespace body.
Returns:
True if top of the stack is a namespace block, False otherwise.
"""
return self.stack and isinstance(self.stack[-1], _NamespaceInfo)
def UpdatePreprocessor(self, line):
"""Update preprocessor stack.
We need to handle preprocessors due to classes like this:
#ifdef SWIG
struct ResultDetailsPageElementExtensionPoint {
#else
struct ResultDetailsPageElementExtensionPoint : public Extension {
#endif
We make the following assumptions (good enough for most files):
- Preprocessor condition evaluates to true from #if up to first
#else/#elif/#endif.
- Preprocessor condition evaluates to false from #else/#elif up
to #endif. We still perform lint checks on these lines, but
these do not affect nesting stack.
Args:
line: current line to check.
"""
if Match(r'^\s*#\s*(if|ifdef|ifndef)\b', line):
# Beginning of #if block, save the nesting stack here. The saved
# stack will allow us to restore the parsing state in the #else case.
self.pp_stack.append(_PreprocessorInfo(copy.deepcopy(self.stack)))
elif Match(r'^\s*#\s*(else|elif)\b', line):
# Beginning of #else block
if self.pp_stack:
if not self.pp_stack[-1].seen_else:
# This is the first #else or #elif block. Remember the
# whole nesting stack up to this point. This is what we
# keep after the #endif.
self.pp_stack[-1].seen_else = True
self.pp_stack[-1].stack_before_else = copy.deepcopy(self.stack)
# Restore the stack to how it was before the #if
self.stack = copy.deepcopy(self.pp_stack[-1].stack_before_if)
else:
# TODO(unknown): unexpected #else, issue warning?
pass
elif Match(r'^\s*#\s*endif\b', line):
# End of #if or #else blocks.
if self.pp_stack:
# If we saw an #else, we will need to restore the nesting
# stack to its former state before the #else, otherwise we
# will just continue from where we left off.
if self.pp_stack[-1].seen_else:
# Here we can just use a shallow copy since we are the last
# reference to it.
self.stack = self.pp_stack[-1].stack_before_else
# Drop the corresponding #if
self.pp_stack.pop()
else:
# TODO(unknown): unexpected #endif, issue warning?
pass
def Update(self, filename, clean_lines, linenum, error):
"""Update nesting state with current line.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# Update pp_stack first
self.UpdatePreprocessor(line)
# Count parentheses. This is to avoid adding struct arguments to
# the nesting stack.
if self.stack:
inner_block = self.stack[-1]
depth_change = line.count('(') - line.count(')')
inner_block.open_parentheses += depth_change
# Also check if we are starting or ending an inline assembly block.
if inner_block.inline_asm in (_NO_ASM, _END_ASM):
if (depth_change != 0 and
inner_block.open_parentheses == 1 and
_MATCH_ASM.match(line)):
# Enter assembly block
inner_block.inline_asm = _INSIDE_ASM
else:
# Not entering assembly block. If previous line was _END_ASM,
# we will now shift to _NO_ASM state.
inner_block.inline_asm = _NO_ASM
elif (inner_block.inline_asm == _INSIDE_ASM and
inner_block.open_parentheses == 0):
# Exit assembly block
inner_block.inline_asm = _END_ASM
# Consume namespace declaration at the beginning of the line. Do
# this in a loop so that we catch same line declarations like this:
# namespace proto2 { namespace bridge { class MessageSet; } }
while True:
# Match start of namespace. The "\b\s*" below catches namespace
# declarations even if it weren't followed by a whitespace, this
# is so that we don't confuse our namespace checker. The
# missing spaces will be flagged by CheckSpacing.
namespace_decl_match = Match(r'^\s*namespace\b\s*([:\w]+)?(.*)$', line)
if not namespace_decl_match:
break
new_namespace = _NamespaceInfo(namespace_decl_match.group(1), linenum)
self.stack.append(new_namespace)
line = namespace_decl_match.group(2)
if line.find('{') != -1:
new_namespace.seen_open_brace = True
line = line[line.find('{') + 1:]
# Look for a class declaration in whatever is left of the line
# after parsing namespaces. The regexp accounts for decorated classes
# such as in:
# class LOCKABLE API Object {
# };
#
# Templates with class arguments may confuse the parser, for example:
# template <class T
# class Comparator = less<T>,
# class Vector = vector<T> >
# class HeapQueue {
#
# Because this parser has no nesting state about templates, by the
# time it saw "class Comparator", it may think that it's a new class.
# Nested templates have a similar problem:
# template <
# typename ExportedType,
# typename TupleType,
# template <typename, typename> class ImplTemplate>
#
# To avoid these cases, we ignore classes that are followed by '=' or '>'
class_decl_match = Match(
r'\s*(template\s*<[\w\s<>,:]*>\s*)?'
r'(class|struct)\s+([A-Z_]+\s+)*(\w+(?:::\w+)*)'
r'(([^=>]|<[^<>]*>|<[^<>]*<[^<>]*>\s*>)*)$', line)
if (class_decl_match and
(not self.stack or self.stack[-1].open_parentheses == 0)):
self.stack.append(_ClassInfo(
class_decl_match.group(4), class_decl_match.group(2),
clean_lines, linenum))
line = class_decl_match.group(5)
# If we have not yet seen the opening brace for the innermost block,
# run checks here.
if not self.SeenOpenBrace():
self.stack[-1].CheckBegin(filename, clean_lines, linenum, error)
# Update access control if we are inside a class/struct
if self.stack and isinstance(self.stack[-1], _ClassInfo):
classinfo = self.stack[-1]
access_match = Match(
r'^(.*)\b(public|private|protected|signals)(\s+(?:slots\s*)?)?'
r':(?:[^:]|$)',
line)
if access_match:
classinfo.access = access_match.group(2)
# Check that access keywords are indented +1 space. Skip this
# check if the keywords are not preceded by whitespaces.
indent = access_match.group(1)
if (len(indent) != classinfo.class_indent + 1 and
Match(r'^\s*$', indent)):
if classinfo.is_struct:
parent = 'struct ' + classinfo.name
else:
parent = 'class ' + classinfo.name
slots = ''
if access_match.group(3):
slots = access_match.group(3)
error(filename, linenum, 'whitespace/indent', 3,
'%s%s: should be indented +1 space inside %s' % (
access_match.group(2), slots, parent))
# Consume braces or semicolons from what's left of the line
while True:
# Match first brace, semicolon, or closed parenthesis.
matched = Match(r'^[^{;)}]*([{;)}])(.*)$', line)
if not matched:
break
token = matched.group(1)
if token == '{':
# If namespace or class hasn't seen a opening brace yet, mark
# namespace/class head as complete. Push a new block onto the
# stack otherwise.
if not self.SeenOpenBrace():
self.stack[-1].seen_open_brace = True
else:
self.stack.append(_BlockInfo(True))
if _MATCH_ASM.match(line):
self.stack[-1].inline_asm = _BLOCK_ASM
elif token == ';' or token == ')':
# If we haven't seen an opening brace yet, but we already saw
# a semicolon, this is probably a forward declaration. Pop
# the stack for these.
#
# Similarly, if we haven't seen an opening brace yet, but we
# already saw a closing parenthesis, then these are probably
# function arguments with extra "class" or "struct" keywords.
# Also pop these stack for these.
if not self.SeenOpenBrace():
self.stack.pop()
else: # token == '}'
# Perform end of block checks and pop the stack.
if self.stack:
self.stack[-1].CheckEnd(filename, clean_lines, linenum, error)
self.stack.pop()
line = matched.group(2)
def InnermostClass(self):
"""Get class info on the top of the stack.
Returns:
A _ClassInfo object if we are inside a class, or None otherwise.
"""
for i in range(len(self.stack), 0, -1):
classinfo = self.stack[i - 1]
if isinstance(classinfo, _ClassInfo):
return classinfo
return None
def CheckCompletedBlocks(self, filename, error):
"""Checks that all classes and namespaces have been completely parsed.
Call this when all lines in a file have been processed.
Args:
filename: The name of the current file.
error: The function to call with any errors found.
"""
# Note: This test can result in false positives if #ifdef constructs
# get in the way of brace matching. See the testBuildClass test in
# cpplint_unittest.py for an example of this.
for obj in self.stack:
if isinstance(obj, _ClassInfo):
error(filename, obj.starting_linenum, 'build/class', 5,
'Failed to find complete declaration of class %s' %
obj.name)
elif isinstance(obj, _NamespaceInfo):
error(filename, obj.starting_linenum, 'build/namespaces', 5,
'Failed to find complete declaration of namespace %s' %
obj.name)
def CheckForNonStandardConstructs(filename, clean_lines, linenum,
nesting_state, error):
r"""Logs an error if we see certain non-ANSI constructs ignored by gcc-2.
Complain about several constructs which gcc-2 accepts, but which are
not standard C++. Warning about these in lint is one way to ease the
transition to new compilers.
- put storage class first (e.g. "static const" instead of "const static").
- "%lld" instead of %qd" in printf-type functions.
- "%1$d" is non-standard in printf-type functions.
- "\%" is an undefined character escape sequence.
- text after #endif is not allowed.
- invalid inner-style forward declaration.
- >? and <? operators, and their >?= and <?= cousins.
Additionally, check for constructor/destructor style violations and reference
members, as it is very convenient to do so while checking for
gcc-2 compliance.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: A callable to which errors are reported, which takes 4 arguments:
filename, line number, error level, and message
"""
# Remove comments from the line, but leave in strings for now.
line = clean_lines.lines[linenum]
if Search(r'printf\s*\(.*".*%[-+ ]?\d*q', line):
error(filename, linenum, 'runtime/printf_format', 3,
'%q in format strings is deprecated. Use %ll instead.')
if Search(r'printf\s*\(.*".*%\d+\$', line):
error(filename, linenum, 'runtime/printf_format', 2,
'%N$ formats are unconventional. Try rewriting to avoid them.')
# Remove escaped backslashes before looking for undefined escapes.
line = line.replace('\\\\', '')
if Search(r'("|\').*\\(%|\[|\(|{)', line):
error(filename, linenum, 'build/printf_format', 3,
'%, [, (, and { are undefined character escapes. Unescape them.')
# For the rest, work with both comments and strings removed.
line = clean_lines.elided[linenum]
if Search(r'\b(const|volatile|void|char|short|int|long'
r'|float|double|signed|unsigned'
r'|schar|u?int8|u?int16|u?int32|u?int64)'
r'\s+(register|static|extern|typedef)\b',
line):
error(filename, linenum, 'build/storage_class', 5,
'Storage class (static, extern, typedef, etc) should be first.')
if Match(r'\s*#\s*endif\s*[^/\s]+', line):
error(filename, linenum, 'build/endif_comment', 5,
'Uncommented text after #endif is non-standard. Use a comment.')
if Match(r'\s*class\s+(\w+\s*::\s*)+\w+\s*;', line):
error(filename, linenum, 'build/forward_decl', 5,
'Inner-style forward declarations are invalid. Remove this line.')
if Search(r'(\w+|[+-]?\d+(\.\d*)?)\s*(<|>)\?=?\s*(\w+|[+-]?\d+)(\.\d*)?',
line):
error(filename, linenum, 'build/deprecated', 3,
'>? and <? (max and min) operators are non-standard and deprecated.')
if Search(r'^\s*const\s*string\s*&\s*\w+\s*;', line):
# TODO(unknown): Could it be expanded safely to arbitrary references,
# without triggering too many false positives? The first
# attempt triggered 5 warnings for mostly benign code in the regtest, hence
# the restriction.
# Here's the original regexp, for the reference:
# type_name = r'\w+((\s*::\s*\w+)|(\s*<\s*\w+?\s*>))?'
# r'\s*const\s*' + type_name + '\s*&\s*\w+\s*;'
error(filename, linenum, 'runtime/member_string_references', 2,
'const string& members are dangerous. It is much better to use '
'alternatives, such as pointers or simple constants.')
# Everything else in this function operates on class declarations.
# Return early if the top of the nesting stack is not a class, or if
# the class head is not completed yet.
classinfo = nesting_state.InnermostClass()
if not classinfo or not classinfo.seen_open_brace:
return
# The class may have been declared with namespace or classname qualifiers.
# The constructor and destructor will not have those qualifiers.
base_classname = classinfo.name.split('::')[-1]
# Look for single-argument constructors that aren't marked explicit.
# Technically a valid construct, but against style.
args = Match(r'\s+(?:inline\s+)?%s\s*\(([^,()]+)\)'
% re.escape(base_classname),
line)
if (args and
args.group(1) != 'void' and
not Match(r'(const\s+)?%s(\s+const)?\s*(?:<\w+>\s*)?&'
% re.escape(base_classname), args.group(1).strip())):
error(filename, linenum, 'runtime/explicit', 5,
'Single-argument constructors should be marked explicit.')
def CheckSpacingForFunctionCall(filename, line, linenum, error):
"""Checks for the correctness of various spacing around function calls.
Args:
filename: The name of the current file.
line: The text of the line to check.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
# Since function calls often occur inside if/for/while/switch
# expressions - which have their own, more liberal conventions - we
# first see if we should be looking inside such an expression for a
# function call, to which we can apply more strict standards.
fncall = line # if there's no control flow construct, look at whole line
for pattern in (r'\bif\s*\((.*)\)\s*{',
r'\bfor\s*\((.*)\)\s*{',
r'\bwhile\s*\((.*)\)\s*[{;]',
r'\bswitch\s*\((.*)\)\s*{'):
match = Search(pattern, line)
if match:
fncall = match.group(1) # look inside the parens for function calls
break
# Except in if/for/while/switch, there should never be space
# immediately inside parens (eg "f( 3, 4 )"). We make an exception
# for nested parens ( (a+b) + c ). Likewise, there should never be
# a space before a ( when it's a function argument. I assume it's a
# function argument when the char before the whitespace is legal in
# a function name (alnum + _) and we're not starting a macro. Also ignore
# pointers and references to arrays and functions coz they're too tricky:
# we use a very simple way to recognize these:
# " (something)(maybe-something)" or
# " (something)(maybe-something," or
# " (something)[something]"
# Note that we assume the contents of [] to be short enough that
# they'll never need to wrap.
if ( # Ignore control structures.
not Search(r'\b(if|for|while|switch|return|new|delete|catch|sizeof)\b',
fncall) and
# Ignore pointers/references to functions.
not Search(r' \([^)]+\)\([^)]*(\)|,$)', fncall) and
# Ignore pointers/references to arrays.
not Search(r' \([^)]+\)\[[^\]]+\]', fncall)):
if Search(r'\w\s*\(\s(?!\s*\\$)', fncall): # a ( used for a fn call
error(filename, linenum, 'whitespace/parens', 4,
'Extra space after ( in function call')
elif Search(r'\(\s+(?!(\s*\\)|\()', fncall):
error(filename, linenum, 'whitespace/parens', 2,
'Extra space after (')
if (Search(r'\w\s+\(', fncall) and
not Search(r'#\s*define|typedef', fncall) and
not Search(r'\w\s+\((\w+::)*\*\w+\)\(', fncall)):
error(filename, linenum, 'whitespace/parens', 4,
'Extra space before ( in function call')
# If the ) is followed only by a newline or a { + newline, assume it's
# part of a control statement (if/while/etc), and don't complain
if Search(r'[^)]\s+\)\s*[^{\s]', fncall):
# If the closing parenthesis is preceded by only whitespaces,
# try to give a more descriptive error message.
if Search(r'^\s+\)', fncall):
error(filename, linenum, 'whitespace/parens', 2,
'Closing ) should be moved to the previous line')
else:
error(filename, linenum, 'whitespace/parens', 2,
'Extra space before )')
def IsBlankLine(line):
"""Returns true if the given line is blank.
We consider a line to be blank if the line is empty or consists of
only white spaces.
Args:
line: A line of a string.
Returns:
True, if the given line is blank.
"""
return not line or line.isspace()
def CheckForFunctionLengths(filename, clean_lines, linenum,
function_state, error):
"""Reports for long function bodies.
For an overview why this is done, see:
http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml#Write_Short_Functions
Uses a simplistic algorithm assuming other style guidelines
(especially spacing) are followed.
Only checks unindented functions, so class members are unchecked.
Trivial bodies are unchecked, so constructors with huge initializer lists
may be missed.
Blank/comment lines are not counted so as to avoid encouraging the removal
of vertical space and comments just to get through a lint check.
NOLINT *on the last line of a function* disables this check.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
function_state: Current function name and lines in body so far.
error: The function to call with any errors found.
"""
lines = clean_lines.lines
line = lines[linenum]
raw = clean_lines.raw_lines
raw_line = raw[linenum]
joined_line = ''
starting_func = False
regexp = r'(\w(\w|::|\*|\&|\s)*)\(' # decls * & space::name( ...
match_result = Match(regexp, line)
if match_result:
# If the name is all caps and underscores, figure it's a macro and
# ignore it, unless it's TEST or TEST_F.
function_name = match_result.group(1).split()[-1]
if function_name == 'TEST' or function_name == 'TEST_F' or (
not Match(r'[A-Z_]+$', function_name)):
starting_func = True
if starting_func:
body_found = False
for start_linenum in xrange(linenum, clean_lines.NumLines()):
start_line = lines[start_linenum]
joined_line += ' ' + start_line.lstrip()
if Search(r'(;|})', start_line): # Declarations and trivial functions
body_found = True
break # ... ignore
elif Search(r'{', start_line):
body_found = True
function = Search(r'((\w|:)*)\(', line).group(1)
if Match(r'TEST', function): # Handle TEST... macros
parameter_regexp = Search(r'(\(.*\))', joined_line)
if parameter_regexp: # Ignore bad syntax
function += parameter_regexp.group(1)
else:
function += '()'
function_state.Begin(function)
break
if not body_found:
# No body for the function (or evidence of a non-function) was found.
error(filename, linenum, 'readability/fn_size', 5,
'Lint failed to find start of function body.')
elif Match(r'^\}\s*$', line): # function end
function_state.Check(error, filename, linenum)
function_state.End()
elif not Match(r'^\s*$', line):
function_state.Count() # Count non-blank/non-comment lines.
_RE_PATTERN_TODO = re.compile(r'^//(\s*)TODO(\(.+?\))?:?(\s|$)?')
def CheckComment(comment, filename, linenum, error):
"""Checks for common mistakes in TODO comments.
Args:
comment: The text of the comment from the line in question.
filename: The name of the current file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
match = _RE_PATTERN_TODO.match(comment)
if match:
# One whitespace is correct; zero whitespace is handled elsewhere.
leading_whitespace = match.group(1)
if len(leading_whitespace) > 1:
error(filename, linenum, 'whitespace/todo', 2,
'Too many spaces before TODO')
username = match.group(2)
if not username:
error(filename, linenum, 'readability/todo', 2,
'Missing username in TODO; it should look like '
'"// TODO(my_username): Stuff."')
middle_whitespace = match.group(3)
# Comparisons made explicit for correctness -- pylint: disable=g-explicit-bool-comparison
if middle_whitespace != ' ' and middle_whitespace != '':
error(filename, linenum, 'whitespace/todo', 2,
'TODO(my_username) should be followed by a space')
def CheckAccess(filename, clean_lines, linenum, nesting_state, error):
"""Checks for improper use of DISALLOW* macros.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum] # get rid of comments and strings
matched = Match((r'\s*(DISALLOW_COPY_AND_ASSIGN|'
r'DISALLOW_EVIL_CONSTRUCTORS|'
r'DISALLOW_IMPLICIT_CONSTRUCTORS)'), line)
if not matched:
return
if nesting_state.stack and isinstance(nesting_state.stack[-1], _ClassInfo):
if nesting_state.stack[-1].access != 'private':
error(filename, linenum, 'readability/constructors', 3,
'%s must be in the private: section' % matched.group(1))
else:
# Found DISALLOW* macro outside a class declaration, or perhaps it
# was used inside a function when it should have been part of the
# class declaration. We could issue a warning here, but it
# probably resulted in a compiler error already.
pass
def FindNextMatchingAngleBracket(clean_lines, linenum, init_suffix):
"""Find the corresponding > to close a template.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: Current line number.
init_suffix: Remainder of the current line after the initial <.
Returns:
True if a matching bracket exists.
"""
line = init_suffix
nesting_stack = ['<']
while True:
# Find the next operator that can tell us whether < is used as an
# opening bracket or as a less-than operator. We only want to
# warn on the latter case.
#
# We could also check all other operators and terminate the search
# early, e.g. if we got something like this "a<b+c", the "<" is
# most likely a less-than operator, but then we will get false
# positives for default arguments and other template expressions.
match = Search(r'^[^<>(),;\[\]]*([<>(),;\[\]])(.*)$', line)
if match:
# Found an operator, update nesting stack
operator = match.group(1)
line = match.group(2)
if nesting_stack[-1] == '<':
# Expecting closing angle bracket
if operator in ('<', '(', '['):
nesting_stack.append(operator)
elif operator == '>':
nesting_stack.pop()
if not nesting_stack:
# Found matching angle bracket
return True
elif operator == ',':
# Got a comma after a bracket, this is most likely a template
# argument. We have not seen a closing angle bracket yet, but
# it's probably a few lines later if we look for it, so just
# return early here.
return True
else:
# Got some other operator.
return False
else:
# Expecting closing parenthesis or closing bracket
if operator in ('<', '(', '['):
nesting_stack.append(operator)
elif operator in (')', ']'):
# We don't bother checking for matching () or []. If we got
# something like (] or [), it would have been a syntax error.
nesting_stack.pop()
else:
# Scan the next line
linenum += 1
if linenum >= len(clean_lines.elided):
break
line = clean_lines.elided[linenum]
# Exhausted all remaining lines and still no matching angle bracket.
# Most likely the input was incomplete, otherwise we should have
# seen a semicolon and returned early.
return True
def FindPreviousMatchingAngleBracket(clean_lines, linenum, init_prefix):
"""Find the corresponding < that started a template.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: Current line number.
init_prefix: Part of the current line before the initial >.
Returns:
True if a matching bracket exists.
"""
line = init_prefix
nesting_stack = ['>']
while True:
# Find the previous operator
match = Search(r'^(.*)([<>(),;\[\]])[^<>(),;\[\]]*$', line)
if match:
# Found an operator, update nesting stack
operator = match.group(2)
line = match.group(1)
if nesting_stack[-1] == '>':
# Expecting opening angle bracket
if operator in ('>', ')', ']'):
nesting_stack.append(operator)
elif operator == '<':
nesting_stack.pop()
if not nesting_stack:
# Found matching angle bracket
return True
elif operator == ',':
# Got a comma before a bracket, this is most likely a
# template argument. The opening angle bracket is probably
# there if we look for it, so just return early here.
return True
else:
# Got some other operator.
return False
else:
# Expecting opening parenthesis or opening bracket
if operator in ('>', ')', ']'):
nesting_stack.append(operator)
elif operator in ('(', '['):
nesting_stack.pop()
else:
# Scan the previous line
linenum -= 1
if linenum < 0:
break
line = clean_lines.elided[linenum]
# Exhausted all earlier lines and still no matching angle bracket.
return False
def CheckSpacing(filename, clean_lines, linenum, nesting_state, error):
"""Checks for the correctness of various spacing issues in the code.
Things we check for: spaces around operators, spaces after
if/for/while/switch, no spaces around parens in function calls, two
spaces between code and comment, don't start a block with a blank
line, don't end a function with a blank line, don't add a blank line
after public/protected/private, don't have too many blank lines in a row.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found.
"""
# Don't use "elided" lines here, otherwise we can't check commented lines.
# Don't want to use "raw" either, because we don't want to check inside C++11
# raw strings,
raw = clean_lines.lines_without_raw_strings
line = raw[linenum]
# Before nixing comments, check if the line is blank for no good
# reason. This includes the first line after a block is opened, and
# blank lines at the end of a function (ie, right before a line like '}'
#
# Skip all the blank line checks if we are immediately inside a
# namespace body. In other words, don't issue blank line warnings
# for this block:
# namespace {
#
# }
#
# A warning about missing end of namespace comments will be issued instead.
if IsBlankLine(line) and not nesting_state.InNamespaceBody():
elided = clean_lines.elided
prev_line = elided[linenum - 1]
prevbrace = prev_line.rfind('{')
# TODO(unknown): Don't complain if line before blank line, and line after,
# both start with alnums and are indented the same amount.
# This ignores whitespace at the start of a namespace block
# because those are not usually indented.
if prevbrace != -1 and prev_line[prevbrace:].find('}') == -1:
# OK, we have a blank line at the start of a code block. Before we
# complain, we check if it is an exception to the rule: The previous
# non-empty line has the parameters of a function header that are indented
# 4 spaces (because they did not fit in a 80 column line when placed on
# the same line as the function name). We also check for the case where
# the previous line is indented 6 spaces, which may happen when the
# initializers of a constructor do not fit into a 80 column line.
exception = False
if Match(r' {6}\w', prev_line): # Initializer list?
# We are looking for the opening column of initializer list, which
# should be indented 4 spaces to cause 6 space indentation afterwards.
search_position = linenum-2
while (search_position >= 0
and Match(r' {6}\w', elided[search_position])):
search_position -= 1
exception = (search_position >= 0
and elided[search_position][:5] == ' :')
else:
# Search for the function arguments or an initializer list. We use a
# simple heuristic here: If the line is indented 4 spaces; and we have a
# closing paren, without the opening paren, followed by an opening brace
# or colon (for initializer lists) we assume that it is the last line of
# a function header. If we have a colon indented 4 spaces, it is an
# initializer list.
exception = (Match(r' {4}\w[^\(]*\)\s*(const\s*)?(\{\s*$|:)',
prev_line)
or Match(r' {4}:', prev_line))
if not exception:
error(filename, linenum, 'whitespace/blank_line', 2,
'Redundant blank line at the start of a code block '
'should be deleted.')
# Ignore blank lines at the end of a block in a long if-else
# chain, like this:
# if (condition1) {
# // Something followed by a blank line
#
# } else if (condition2) {
# // Something else
# }
if linenum + 1 < clean_lines.NumLines():
next_line = raw[linenum + 1]
if (next_line
and Match(r'\s*}', next_line)
and next_line.find('} else ') == -1):
error(filename, linenum, 'whitespace/blank_line', 3,
'Redundant blank line at the end of a code block '
'should be deleted.')
matched = Match(r'\s*(public|protected|private):', prev_line)
if matched:
error(filename, linenum, 'whitespace/blank_line', 3,
'Do not leave a blank line after "%s:"' % matched.group(1))
# Next, we complain if there's a comment too near the text
commentpos = line.find('//')
if commentpos != -1:
# Check if the // may be in quotes. If so, ignore it
# Comparisons made explicit for clarity -- pylint: disable=g-explicit-bool-comparison
if (line.count('"', 0, commentpos) -
line.count('\\"', 0, commentpos)) % 2 == 0: # not in quotes
# Allow one space for new scopes, two spaces otherwise:
if (not Match(r'^\s*{ //', line) and
((commentpos >= 1 and
line[commentpos-1] not in string.whitespace) or
(commentpos >= 2 and
line[commentpos-2] not in string.whitespace))):
error(filename, linenum, 'whitespace/comments', 2,
'At least two spaces is best between code and comments')
# There should always be a space between the // and the comment
commentend = commentpos + 2
if commentend < len(line) and not line[commentend] == ' ':
# but some lines are exceptions -- e.g. if they're big
# comment delimiters like:
# //----------------------------------------------------------
# or are an empty C++ style Doxygen comment, like:
# ///
# or C++ style Doxygen comments placed after the variable:
# ///< Header comment
# //!< Header comment
# or they begin with multiple slashes followed by a space:
# //////// Header comment
match = (Search(r'[=/-]{4,}\s*$', line[commentend:]) or
Search(r'^/$', line[commentend:]) or
Search(r'^!< ', line[commentend:]) or
Search(r'^/< ', line[commentend:]) or
Search(r'^/+ ', line[commentend:]))
if not match:
error(filename, linenum, 'whitespace/comments', 4,
'Should have a space between // and comment')
CheckComment(line[commentpos:], filename, linenum, error)
line = clean_lines.elided[linenum] # get rid of comments and strings
# Don't try to do spacing checks for operator methods
line = re.sub(r'operator(==|!=|<|<<|<=|>=|>>|>)\(', 'operator\(', line)
# We allow no-spaces around = within an if: "if ( (a=Foo()) == 0 )".
# Otherwise not. Note we only check for non-spaces on *both* sides;
# sometimes people put non-spaces on one side when aligning ='s among
# many lines (not that this is behavior that I approve of...)
if Search(r'[\w.]=[\w.]', line) and not Search(r'\b(if|while) ', line):
error(filename, linenum, 'whitespace/operators', 4,
'Missing spaces around =')
# It's ok not to have spaces around binary operators like + - * /, but if
# there's too little whitespace, we get concerned. It's hard to tell,
# though, so we punt on this one for now. TODO.
# You should always have whitespace around binary operators.
#
# Check <= and >= first to avoid false positives with < and >, then
# check non-include lines for spacing around < and >.
match = Search(r'[^<>=!\s](==|!=|<=|>=)[^<>=!\s]', line)
if match:
error(filename, linenum, 'whitespace/operators', 3,
'Missing spaces around %s' % match.group(1))
# We allow no-spaces around << when used like this: 10<<20, but
# not otherwise (particularly, not when used as streams)
# Also ignore using ns::operator<<;
match = Search(r'(operator|\S)(?:L|UL|ULL|l|ul|ull)?<<(\S)', line)
if (match and
not (match.group(1).isdigit() and match.group(2).isdigit()) and
not (match.group(1) == 'operator' and match.group(2) == ';')):
error(filename, linenum, 'whitespace/operators', 3,
'Missing spaces around <<')
elif not Match(r'#.*include', line):
# Avoid false positives on ->
reduced_line = line.replace('->', '')
# Look for < that is not surrounded by spaces. This is only
# triggered if both sides are missing spaces, even though
# technically should should flag if at least one side is missing a
# space. This is done to avoid some false positives with shifts.
match = Search(r'[^\s<]<([^\s=<].*)', reduced_line)
if (match and
not FindNextMatchingAngleBracket(clean_lines, linenum, match.group(1))):
error(filename, linenum, 'whitespace/operators', 3,
'Missing spaces around <')
# Look for > that is not surrounded by spaces. Similar to the
# above, we only trigger if both sides are missing spaces to avoid
# false positives with shifts.
match = Search(r'^(.*[^\s>])>[^\s=>]', reduced_line)
if (match and
not FindPreviousMatchingAngleBracket(clean_lines, linenum,
match.group(1))):
error(filename, linenum, 'whitespace/operators', 3,
'Missing spaces around >')
# We allow no-spaces around >> for almost anything. This is because
# C++11 allows ">>" to close nested templates, which accounts for
# most cases when ">>" is not followed by a space.
#
# We still warn on ">>" followed by alpha character, because that is
# likely due to ">>" being used for right shifts, e.g.:
# value >> alpha
#
# When ">>" is used to close templates, the alphanumeric letter that
# follows would be part of an identifier, and there should still be
# a space separating the template type and the identifier.
# type<type<type>> alpha
match = Search(r'>>[a-zA-Z_]', line)
if match:
error(filename, linenum, 'whitespace/operators', 3,
'Missing spaces around >>')
# There shouldn't be space around unary operators
match = Search(r'(!\s|~\s|[\s]--[\s;]|[\s]\+\+[\s;])', line)
if match:
error(filename, linenum, 'whitespace/operators', 4,
'Extra space for operator %s' % match.group(1))
# A pet peeve of mine: no spaces after an if, while, switch, or for
match = Search(r' (if\(|for\(|while\(|switch\()', line)
if match:
error(filename, linenum, 'whitespace/parens', 5,
'Missing space before ( in %s' % match.group(1))
# For if/for/while/switch, the left and right parens should be
# consistent about how many spaces are inside the parens, and
# there should either be zero or one spaces inside the parens.
# We don't want: "if ( foo)" or "if ( foo )".
# Exception: "for ( ; foo; bar)" and "for (foo; bar; )" are allowed.
match = Search(r'\b(if|for|while|switch)\s*'
r'\(([ ]*)(.).*[^ ]+([ ]*)\)\s*{\s*$',
line)
if match:
if len(match.group(2)) != len(match.group(4)):
if not (match.group(3) == ';' and
len(match.group(2)) == 1 + len(match.group(4)) or
not match.group(2) and Search(r'\bfor\s*\(.*; \)', line)):
error(filename, linenum, 'whitespace/parens', 5,
'Mismatching spaces inside () in %s' % match.group(1))
if len(match.group(2)) not in [0, 1]:
error(filename, linenum, 'whitespace/parens', 5,
'Should have zero or one spaces inside ( and ) in %s' %
match.group(1))
# You should always have a space after a comma (either as fn arg or operator)
#
# This does not apply when the non-space character following the
# comma is another comma, since the only time when that happens is
# for empty macro arguments.
#
# We run this check in two passes: first pass on elided lines to
# verify that lines contain missing whitespaces, second pass on raw
# lines to confirm that those missing whitespaces are not due to
# elided comments.
if Search(r',[^,\s]', line) and Search(r',[^,\s]', raw[linenum]):
error(filename, linenum, 'whitespace/comma', 3,
'Missing space after ,')
# You should always have a space after a semicolon
# except for few corner cases
# TODO(unknown): clarify if 'if (1) { return 1;}' is requires one more
# space after ;
if Search(r';[^\s};\\)/]', line):
error(filename, linenum, 'whitespace/semicolon', 3,
'Missing space after ;')
# Next we will look for issues with function calls.
CheckSpacingForFunctionCall(filename, line, linenum, error)
# Except after an opening paren, or after another opening brace (in case of
# an initializer list, for instance), you should have spaces before your
# braces. And since you should never have braces at the beginning of a line,
# this is an easy test.
match = Match(r'^(.*[^ ({]){', line)
if match:
# Try a bit harder to check for brace initialization. This
# happens in one of the following forms:
# Constructor() : initializer_list_{} { ... }
# Constructor{}.MemberFunction()
# Type variable{};
# FunctionCall(type{}, ...);
# LastArgument(..., type{});
# LOG(INFO) << type{} << " ...";
# map_of_type[{...}] = ...;
#
# We check for the character following the closing brace, and
# silence the warning if it's one of those listed above, i.e.
# "{.;,)<]".
#
# To account for nested initializer list, we allow any number of
# closing braces up to "{;,)<". We can't simply silence the
# warning on first sight of closing brace, because that would
# cause false negatives for things that are not initializer lists.
# Silence this: But not this:
# Outer{ if (...) {
# Inner{...} if (...){ // Missing space before {
# }; }
#
# There is a false negative with this approach if people inserted
# spurious semicolons, e.g. "if (cond){};", but we will catch the
# spurious semicolon with a separate check.
(endline, endlinenum, endpos) = CloseExpression(
clean_lines, linenum, len(match.group(1)))
trailing_text = ''
if endpos > -1:
trailing_text = endline[endpos:]
for offset in xrange(endlinenum + 1,
min(endlinenum + 3, clean_lines.NumLines() - 1)):
trailing_text += clean_lines.elided[offset]
if not Match(r'^[\s}]*[{.;,)<\]]', trailing_text):
error(filename, linenum, 'whitespace/braces', 5,
'Missing space before {')
# Make sure '} else {' has spaces.
if Search(r'}else', line):
error(filename, linenum, 'whitespace/braces', 5,
'Missing space before else')
# You shouldn't have spaces before your brackets, except maybe after
# 'delete []' or 'new char * []'.
if Search(r'\w\s+\[', line) and not Search(r'delete\s+\[', line):
error(filename, linenum, 'whitespace/braces', 5,
'Extra space before [')
# You shouldn't have a space before a semicolon at the end of the line.
# There's a special case for "for" since the style guide allows space before
# the semicolon there.
if Search(r':\s*;\s*$', line):
error(filename, linenum, 'whitespace/semicolon', 5,
'Semicolon defining empty statement. Use {} instead.')
elif Search(r'^\s*;\s*$', line):
error(filename, linenum, 'whitespace/semicolon', 5,
'Line contains only semicolon. If this should be an empty statement, '
'use {} instead.')
elif (Search(r'\s+;\s*$', line) and
not Search(r'\bfor\b', line)):
error(filename, linenum, 'whitespace/semicolon', 5,
'Extra space before last semicolon. If this should be an empty '
'statement, use {} instead.')
# In range-based for, we wanted spaces before and after the colon, but
# not around "::" tokens that might appear.
if (Search('for *\(.*[^:]:[^: ]', line) or
Search('for *\(.*[^: ]:[^:]', line)):
error(filename, linenum, 'whitespace/forcolon', 2,
'Missing space around colon in range-based for loop')
def CheckSectionSpacing(filename, clean_lines, class_info, linenum, error):
"""Checks for additional blank line issues related to sections.
Currently the only thing checked here is blank line before protected/private.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
class_info: A _ClassInfo objects.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
# Skip checks if the class is small, where small means 25 lines or less.
# 25 lines seems like a good cutoff since that's the usual height of
# terminals, and any class that can't fit in one screen can't really
# be considered "small".
#
# Also skip checks if we are on the first line. This accounts for
# classes that look like
# class Foo { public: ... };
#
# If we didn't find the end of the class, last_line would be zero,
# and the check will be skipped by the first condition.
if (class_info.last_line - class_info.starting_linenum <= 24 or
linenum <= class_info.starting_linenum):
return
matched = Match(r'\s*(public|protected|private):', clean_lines.lines[linenum])
if matched:
# Issue warning if the line before public/protected/private was
# not a blank line, but don't do this if the previous line contains
# "class" or "struct". This can happen two ways:
# - We are at the beginning of the class.
# - We are forward-declaring an inner class that is semantically
# private, but needed to be public for implementation reasons.
# Also ignores cases where the previous line ends with a backslash as can be
# common when defining classes in C macros.
prev_line = clean_lines.lines[linenum - 1]
if (not IsBlankLine(prev_line) and
not Search(r'\b(class|struct)\b', prev_line) and
not Search(r'\\$', prev_line)):
# Try a bit harder to find the beginning of the class. This is to
# account for multi-line base-specifier lists, e.g.:
# class Derived
# : public Base {
end_class_head = class_info.starting_linenum
for i in range(class_info.starting_linenum, linenum):
if Search(r'\{\s*$', clean_lines.lines[i]):
end_class_head = i
break
if end_class_head < linenum - 1:
error(filename, linenum, 'whitespace/blank_line', 3,
'"%s:" should be preceded by a blank line' % matched.group(1))
def GetPreviousNonBlankLine(clean_lines, linenum):
"""Return the most recent non-blank line and its line number.
Args:
clean_lines: A CleansedLines instance containing the file contents.
linenum: The number of the line to check.
Returns:
A tuple with two elements. The first element is the contents of the last
non-blank line before the current line, or the empty string if this is the
first non-blank line. The second is the line number of that line, or -1
if this is the first non-blank line.
"""
prevlinenum = linenum - 1
while prevlinenum >= 0:
prevline = clean_lines.elided[prevlinenum]
if not IsBlankLine(prevline): # if not a blank line...
return (prevline, prevlinenum)
prevlinenum -= 1
return ('', -1)
def CheckBraces(filename, clean_lines, linenum, error):
"""Looks for misplaced braces (e.g. at the end of line).
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum] # get rid of comments and strings
if Match(r'\s*{\s*$', line):
# We allow an open brace to start a line in the case where someone is using
# braces in a block to explicitly create a new scope, which is commonly used
# to control the lifetime of stack-allocated variables. Braces are also
# used for brace initializers inside function calls. We don't detect this
# perfectly: we just don't complain if the last non-whitespace character on
# the previous non-blank line is ',', ';', ':', '(', '{', or '}', or if the
# previous line starts a preprocessor block.
prevline = GetPreviousNonBlankLine(clean_lines, linenum)[0]
if (not Search(r'[,;:}{(]\s*$', prevline) and
not Match(r'\s*#', prevline)):
error(filename, linenum, 'whitespace/braces', 4,
'{ should almost always be at the end of the previous line')
# An else clause should be on the same line as the preceding closing brace.
if Match(r'\s*else\s*', line):
prevline = GetPreviousNonBlankLine(clean_lines, linenum)[0]
if Match(r'\s*}\s*$', prevline):
error(filename, linenum, 'whitespace/newline', 4,
'An else should appear on the same line as the preceding }')
# If braces come on one side of an else, they should be on both.
# However, we have to worry about "else if" that spans multiple lines!
if Search(r'}\s*else[^{]*$', line) or Match(r'[^}]*else\s*{', line):
if Search(r'}\s*else if([^{]*)$', line): # could be multi-line if
# find the ( after the if
pos = line.find('else if')
pos = line.find('(', pos)
if pos > 0:
(endline, _, endpos) = CloseExpression(clean_lines, linenum, pos)
if endline[endpos:].find('{') == -1: # must be brace after if
error(filename, linenum, 'readability/braces', 5,
'If an else has a brace on one side, it should have it on both')
else: # common case: else not followed by a multi-line if
error(filename, linenum, 'readability/braces', 5,
'If an else has a brace on one side, it should have it on both')
# Likewise, an else should never have the else clause on the same line
if Search(r'\belse [^\s{]', line) and not Search(r'\belse if\b', line):
error(filename, linenum, 'whitespace/newline', 4,
'Else clause should never be on same line as else (use 2 lines)')
# In the same way, a do/while should never be on one line
if Match(r'\s*do [^\s{]', line):
error(filename, linenum, 'whitespace/newline', 4,
'do/while clauses should not be on a single line')
# Block bodies should not be followed by a semicolon. Due to C++11
# brace initialization, there are more places where semicolons are
# required than not, so we use a whitelist approach to check these
# rather than a blacklist. These are the places where "};" should
# be replaced by just "}":
# 1. Some flavor of block following closing parenthesis:
# for (;;) {};
# while (...) {};
# switch (...) {};
# Function(...) {};
# if (...) {};
# if (...) else if (...) {};
#
# 2. else block:
# if (...) else {};
#
# 3. const member function:
# Function(...) const {};
#
# 4. Block following some statement:
# x = 42;
# {};
#
# 5. Block at the beginning of a function:
# Function(...) {
# {};
# }
#
# Note that naively checking for the preceding "{" will also match
# braces inside multi-dimensional arrays, but this is fine since
# that expression will not contain semicolons.
#
# 6. Block following another block:
# while (true) {}
# {};
#
# 7. End of namespaces:
# namespace {};
#
# These semicolons seems far more common than other kinds of
# redundant semicolons, possibly due to people converting classes
# to namespaces. For now we do not warn for this case.
#
# Try matching case 1 first.
match = Match(r'^(.*\)\s*)\{', line)
if match:
# Matched closing parenthesis (case 1). Check the token before the
# matching opening parenthesis, and don't warn if it looks like a
# macro. This avoids these false positives:
# - macro that defines a base class
# - multi-line macro that defines a base class
# - macro that defines the whole class-head
#
# But we still issue warnings for macros that we know are safe to
# warn, specifically:
# - TEST, TEST_F, TEST_P, MATCHER, MATCHER_P
# - TYPED_TEST
# - INTERFACE_DEF
# - EXCLUSIVE_LOCKS_REQUIRED, SHARED_LOCKS_REQUIRED, LOCKS_EXCLUDED:
#
# We implement a whitelist of safe macros instead of a blacklist of
# unsafe macros, even though the latter appears less frequently in
# google code and would have been easier to implement. This is because
# the downside for getting the whitelist wrong means some extra
# semicolons, while the downside for getting the blacklist wrong
# would result in compile errors.
#
# In addition to macros, we also don't want to warn on compound
# literals.
closing_brace_pos = match.group(1).rfind(')')
opening_parenthesis = ReverseCloseExpression(
clean_lines, linenum, closing_brace_pos)
if opening_parenthesis[2] > -1:
line_prefix = opening_parenthesis[0][0:opening_parenthesis[2]]
macro = Search(r'\b([A-Z_]+)\s*$', line_prefix)
if ((macro and
macro.group(1) not in (
'TEST', 'TEST_F', 'MATCHER', 'MATCHER_P', 'TYPED_TEST',
'EXCLUSIVE_LOCKS_REQUIRED', 'SHARED_LOCKS_REQUIRED',
'LOCKS_EXCLUDED', 'INTERFACE_DEF')) or
Search(r'\s+=\s*$', line_prefix)):
match = None
else:
# Try matching cases 2-3.
match = Match(r'^(.*(?:else|\)\s*const)\s*)\{', line)
if not match:
# Try matching cases 4-6. These are always matched on separate lines.
#
# Note that we can't simply concatenate the previous line to the
# current line and do a single match, otherwise we may output
# duplicate warnings for the blank line case:
# if (cond) {
# // blank line
# }
prevline = GetPreviousNonBlankLine(clean_lines, linenum)[0]
if prevline and Search(r'[;{}]\s*$', prevline):
match = Match(r'^(\s*)\{', line)
# Check matching closing brace
if match:
(endline, endlinenum, endpos) = CloseExpression(
clean_lines, linenum, len(match.group(1)))
if endpos > -1 and Match(r'^\s*;', endline[endpos:]):
# Current {} pair is eligible for semicolon check, and we have found
# the redundant semicolon, output warning here.
#
# Note: because we are scanning forward for opening braces, and
# outputting warnings for the matching closing brace, if there are
# nested blocks with trailing semicolons, we will get the error
# messages in reversed order.
error(filename, endlinenum, 'readability/braces', 4,
"You don't need a ; after a }")
def CheckEmptyBlockBody(filename, clean_lines, linenum, error):
"""Look for empty loop/conditional body with only a single semicolon.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
# Search for loop keywords at the beginning of the line. Because only
# whitespaces are allowed before the keywords, this will also ignore most
# do-while-loops, since those lines should start with closing brace.
#
# We also check "if" blocks here, since an empty conditional block
# is likely an error.
line = clean_lines.elided[linenum]
matched = Match(r'\s*(for|while|if)\s*\(', line)
if matched:
# Find the end of the conditional expression
(end_line, end_linenum, end_pos) = CloseExpression(
clean_lines, linenum, line.find('('))
# Output warning if what follows the condition expression is a semicolon.
# No warning for all other cases, including whitespace or newline, since we
# have a separate check for semicolons preceded by whitespace.
if end_pos >= 0 and Match(r';', end_line[end_pos:]):
if matched.group(1) == 'if':
error(filename, end_linenum, 'whitespace/empty_conditional_body', 5,
'Empty conditional bodies should use {}')
else:
error(filename, end_linenum, 'whitespace/empty_loop_body', 5,
'Empty loop bodies should use {} or continue')
def CheckCheck(filename, clean_lines, linenum, error):
"""Checks the use of CHECK and EXPECT macros.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
# Decide the set of replacement macros that should be suggested
lines = clean_lines.elided
check_macro = None
start_pos = -1
for macro in _CHECK_MACROS:
i = lines[linenum].find(macro)
if i >= 0:
check_macro = macro
# Find opening parenthesis. Do a regular expression match here
# to make sure that we are matching the expected CHECK macro, as
# opposed to some other macro that happens to contain the CHECK
# substring.
matched = Match(r'^(.*\b' + check_macro + r'\s*)\(', lines[linenum])
if not matched:
continue
start_pos = len(matched.group(1))
break
if not check_macro or start_pos < 0:
# Don't waste time here if line doesn't contain 'CHECK' or 'EXPECT'
return
# Find end of the boolean expression by matching parentheses
(last_line, end_line, end_pos) = CloseExpression(
clean_lines, linenum, start_pos)
if end_pos < 0:
return
if linenum == end_line:
expression = lines[linenum][start_pos + 1:end_pos - 1]
else:
expression = lines[linenum][start_pos + 1:]
for i in xrange(linenum + 1, end_line):
expression += lines[i]
expression += last_line[0:end_pos - 1]
# Parse expression so that we can take parentheses into account.
# This avoids false positives for inputs like "CHECK((a < 4) == b)",
# which is not replaceable by CHECK_LE.
lhs = ''
rhs = ''
operator = None
while expression:
matched = Match(r'^\s*(<<|<<=|>>|>>=|->\*|->|&&|\|\||'
r'==|!=|>=|>|<=|<|\()(.*)$', expression)
if matched:
token = matched.group(1)
if token == '(':
# Parenthesized operand
expression = matched.group(2)
(end, _) = FindEndOfExpressionInLine(expression, 0, 1, '(', ')')
if end < 0:
return # Unmatched parenthesis
lhs += '(' + expression[0:end]
expression = expression[end:]
elif token in ('&&', '||'):
# Logical and/or operators. This means the expression
# contains more than one term, for example:
# CHECK(42 < a && a < b);
#
# These are not replaceable with CHECK_LE, so bail out early.
return
elif token in ('<<', '<<=', '>>', '>>=', '->*', '->'):
# Non-relational operator
lhs += token
expression = matched.group(2)
else:
# Relational operator
operator = token
rhs = matched.group(2)
break
else:
# Unparenthesized operand. Instead of appending to lhs one character
# at a time, we do another regular expression match to consume several
# characters at once if possible. Trivial benchmark shows that this
# is more efficient when the operands are longer than a single
# character, which is generally the case.
matched = Match(r'^([^-=!<>()&|]+)(.*)$', expression)
if not matched:
matched = Match(r'^(\s*\S)(.*)$', expression)
if not matched:
break
lhs += matched.group(1)
expression = matched.group(2)
# Only apply checks if we got all parts of the boolean expression
if not (lhs and operator and rhs):
return
# Check that rhs do not contain logical operators. We already know
# that lhs is fine since the loop above parses out && and ||.
if rhs.find('&&') > -1 or rhs.find('||') > -1:
return
# At least one of the operands must be a constant literal. This is
# to avoid suggesting replacements for unprintable things like
# CHECK(variable != iterator)
#
# The following pattern matches decimal, hex integers, strings, and
# characters (in that order).
lhs = lhs.strip()
rhs = rhs.strip()
match_constant = r'^([-+]?(\d+|0[xX][0-9a-fA-F]+)[lLuU]{0,3}|".*"|\'.*\')$'
if Match(match_constant, lhs) or Match(match_constant, rhs):
# Note: since we know both lhs and rhs, we can provide a more
# descriptive error message like:
# Consider using CHECK_EQ(x, 42) instead of CHECK(x == 42)
# Instead of:
# Consider using CHECK_EQ instead of CHECK(a == b)
#
# We are still keeping the less descriptive message because if lhs
# or rhs gets long, the error message might become unreadable.
error(filename, linenum, 'readability/check', 2,
'Consider using %s instead of %s(a %s b)' % (
_CHECK_REPLACEMENT[check_macro][operator],
check_macro, operator))
def CheckAltTokens(filename, clean_lines, linenum, error):
"""Check alternative keywords being used in boolean expressions.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# Avoid preprocessor lines
if Match(r'^\s*#', line):
return
# Last ditch effort to avoid multi-line comments. This will not help
# if the comment started before the current line or ended after the
# current line, but it catches most of the false positives. At least,
# it provides a way to workaround this warning for people who use
# multi-line comments in preprocessor macros.
#
# TODO(unknown): remove this once cpplint has better support for
# multi-line comments.
if line.find('/*') >= 0 or line.find('*/') >= 0:
return
for match in _ALT_TOKEN_REPLACEMENT_PATTERN.finditer(line):
error(filename, linenum, 'readability/alt_tokens', 2,
'Use operator %s instead of %s' % (
_ALT_TOKEN_REPLACEMENT[match.group(1)], match.group(1)))
def GetLineWidth(line):
"""Determines the width of the line in column positions.
Args:
line: A string, which may be a Unicode string.
Returns:
The width of the line in column positions, accounting for Unicode
combining characters and wide characters.
"""
if isinstance(line, unicode):
width = 0
for uc in unicodedata.normalize('NFC', line):
if unicodedata.east_asian_width(uc) in ('W', 'F'):
width += 2
elif not unicodedata.combining(uc):
width += 1
return width
else:
return len(line)
def CheckStyle(filename, clean_lines, linenum, file_extension, nesting_state,
error):
"""Checks rules from the 'C++ style rules' section of cppguide.html.
Most of these rules are hard to test (naming, comment style), but we
do what we can. In particular we check for 2-space indents, line lengths,
tab usage, spaces inside code, etc.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
file_extension: The extension (without the dot) of the filename.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found.
"""
# Don't use "elided" lines here, otherwise we can't check commented lines.
# Don't want to use "raw" either, because we don't want to check inside C++11
# raw strings,
raw_lines = clean_lines.lines_without_raw_strings
line = raw_lines[linenum]
if line.find('\t') != -1:
error(filename, linenum, 'whitespace/tab', 1,
'Tab found; better to use spaces')
# One or three blank spaces at the beginning of the line is weird; it's
# hard to reconcile that with 2-space indents.
# NOTE: here are the conditions rob pike used for his tests. Mine aren't
# as sophisticated, but it may be worth becoming so: RLENGTH==initial_spaces
# if(RLENGTH > 20) complain = 0;
# if(match($0, " +(error|private|public|protected):")) complain = 0;
# if(match(prev, "&& *$")) complain = 0;
# if(match(prev, "\\|\\| *$")) complain = 0;
# if(match(prev, "[\",=><] *$")) complain = 0;
# if(match($0, " <<")) complain = 0;
# if(match(prev, " +for \\(")) complain = 0;
# if(prevodd && match(prevprev, " +for \\(")) complain = 0;
initial_spaces = 0
cleansed_line = clean_lines.elided[linenum]
while initial_spaces < len(line) and line[initial_spaces] == ' ':
initial_spaces += 1
if line and line[-1].isspace():
error(filename, linenum, 'whitespace/end_of_line', 4,
'Line ends in whitespace. Consider deleting these extra spaces.')
# There are certain situations we allow one space, notably for section labels
elif ((initial_spaces == 1 or initial_spaces == 3) and
not Match(r'\s*\w+\s*:\s*$', cleansed_line)):
error(filename, linenum, 'whitespace/indent', 3,
'Weird number of spaces at line-start. '
'Are you using a 2-space indent?')
# Check if the line is a header guard.
is_header_guard = False
if file_extension == 'h':
cppvar = GetHeaderGuardCPPVariable(filename)
if (line.startswith('#ifndef %s' % cppvar) or
line.startswith('#define %s' % cppvar) or
line.startswith('#endif // %s' % cppvar)):
is_header_guard = True
# #include lines and header guards can be long, since there's no clean way to
# split them.
#
# URLs can be long too. It's possible to split these, but it makes them
# harder to cut&paste.
#
# The "$Id:...$" comment may also get very long without it being the
# developers fault.
if (not line.startswith('#include') and not is_header_guard and
not Match(r'^\s*//.*http(s?)://\S*$', line) and
not Match(r'^// \$Id:.*#[0-9]+ \$$', line)):
line_width = GetLineWidth(line)
extended_length = int((_line_length * 1.25))
if line_width > extended_length:
error(filename, linenum, 'whitespace/line_length', 4,
'Lines should very rarely be longer than %i characters' %
extended_length)
elif line_width > _line_length:
error(filename, linenum, 'whitespace/line_length', 2,
'Lines should be <= %i characters long' % _line_length)
if (cleansed_line.count(';') > 1 and
# for loops are allowed two ;'s (and may run over two lines).
cleansed_line.find('for') == -1 and
(GetPreviousNonBlankLine(clean_lines, linenum)[0].find('for') == -1 or
GetPreviousNonBlankLine(clean_lines, linenum)[0].find(';') != -1) and
# It's ok to have many commands in a switch case that fits in 1 line
not ((cleansed_line.find('case ') != -1 or
cleansed_line.find('default:') != -1) and
cleansed_line.find('break;') != -1)):
error(filename, linenum, 'whitespace/newline', 0,
'More than one command on the same line')
# Some more style checks
CheckBraces(filename, clean_lines, linenum, error)
CheckEmptyBlockBody(filename, clean_lines, linenum, error)
CheckAccess(filename, clean_lines, linenum, nesting_state, error)
CheckSpacing(filename, clean_lines, linenum, nesting_state, error)
CheckCheck(filename, clean_lines, linenum, error)
CheckAltTokens(filename, clean_lines, linenum, error)
classinfo = nesting_state.InnermostClass()
if classinfo:
CheckSectionSpacing(filename, clean_lines, classinfo, linenum, error)
_RE_PATTERN_INCLUDE_NEW_STYLE = re.compile(r'#include +"[^/]+\.h"')
_RE_PATTERN_INCLUDE = re.compile(r'^\s*#\s*include\s*([<"])([^>"]*)[>"].*$')
# Matches the first component of a filename delimited by -s and _s. That is:
# _RE_FIRST_COMPONENT.match('foo').group(0) == 'foo'
# _RE_FIRST_COMPONENT.match('foo.cc').group(0) == 'foo'
# _RE_FIRST_COMPONENT.match('foo-bar_baz.cc').group(0) == 'foo'
# _RE_FIRST_COMPONENT.match('foo_bar-baz.cc').group(0) == 'foo'
_RE_FIRST_COMPONENT = re.compile(r'^[^-_.]+')
def _DropCommonSuffixes(filename):
"""Drops common suffixes like _test.cc or -inl.h from filename.
For example:
>>> _DropCommonSuffixes('foo/foo-inl.h')
'foo/foo'
>>> _DropCommonSuffixes('foo/bar/foo.cc')
'foo/bar/foo'
>>> _DropCommonSuffixes('foo/foo_internal.h')
'foo/foo'
>>> _DropCommonSuffixes('foo/foo_unusualinternal.h')
'foo/foo_unusualinternal'
Args:
filename: The input filename.
Returns:
The filename with the common suffix removed.
"""
for suffix in ('test.cc', 'regtest.cc', 'unittest.cc',
'inl.h', 'impl.h', 'internal.h'):
if (filename.endswith(suffix) and len(filename) > len(suffix) and
filename[-len(suffix) - 1] in ('-', '_')):
return filename[:-len(suffix) - 1]
return os.path.splitext(filename)[0]
def _IsTestFilename(filename):
"""Determines if the given filename has a suffix that identifies it as a test.
Args:
filename: The input filename.
Returns:
True if 'filename' looks like a test, False otherwise.
"""
if (filename.endswith('_test.cc') or
filename.endswith('_unittest.cc') or
filename.endswith('_regtest.cc')):
return True
else:
return False
def _ClassifyInclude(fileinfo, include, is_system):
"""Figures out what kind of header 'include' is.
Args:
fileinfo: The current file cpplint is running over. A FileInfo instance.
include: The path to a #included file.
is_system: True if the #include used <> rather than "".
Returns:
One of the _XXX_HEADER constants.
For example:
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'stdio.h', True)
_C_SYS_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'string', True)
_CPP_SYS_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/foo.h', False)
_LIKELY_MY_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo_unknown_extension.cc'),
... 'bar/foo_other_ext.h', False)
_POSSIBLE_MY_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/bar.h', False)
_OTHER_HEADER
"""
# This is a list of all standard c++ header files, except
# those already checked for above.
is_cpp_h = include in _CPP_HEADERS
if is_system:
if is_cpp_h:
return _CPP_SYS_HEADER
else:
return _C_SYS_HEADER
# If the target file and the include we're checking share a
# basename when we drop common extensions, and the include
# lives in . , then it's likely to be owned by the target file.
target_dir, target_base = (
os.path.split(_DropCommonSuffixes(fileinfo.RepositoryName())))
include_dir, include_base = os.path.split(_DropCommonSuffixes(include))
if target_base == include_base and (
include_dir == target_dir or
include_dir == os.path.normpath(target_dir + '/../public')):
return _LIKELY_MY_HEADER
# If the target and include share some initial basename
# component, it's possible the target is implementing the
# include, so it's allowed to be first, but we'll never
# complain if it's not there.
target_first_component = _RE_FIRST_COMPONENT.match(target_base)
include_first_component = _RE_FIRST_COMPONENT.match(include_base)
if (target_first_component and include_first_component and
target_first_component.group(0) ==
include_first_component.group(0)):
return _POSSIBLE_MY_HEADER
return _OTHER_HEADER
def CheckIncludeLine(filename, clean_lines, linenum, include_state, error):
"""Check rules that are applicable to #include lines.
Strings on #include lines are NOT removed from elided line, to make
certain tasks easier. However, to prevent false positives, checks
applicable to #include lines in CheckLanguage must be put here.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
include_state: An _IncludeState instance in which the headers are inserted.
error: The function to call with any errors found.
"""
fileinfo = FileInfo(filename)
line = clean_lines.lines[linenum]
# "include" should use the new style "foo/bar.h" instead of just "bar.h"
if _RE_PATTERN_INCLUDE_NEW_STYLE.search(line):
error(filename, linenum, 'build/include_dir', 4,
'Include the directory when naming .h files')
# we shouldn't include a file more than once. actually, there are a
# handful of instances where doing so is okay, but in general it's
# not.
match = _RE_PATTERN_INCLUDE.search(line)
if match:
include = match.group(2)
is_system = (match.group(1) == '<')
if include in include_state:
error(filename, linenum, 'build/include', 4,
'"%s" already included at %s:%s' %
(include, filename, include_state[include]))
else:
include_state[include] = linenum
# We want to ensure that headers appear in the right order:
# 1) for foo.cc, foo.h (preferred location)
# 2) c system files
# 3) cpp system files
# 4) for foo.cc, foo.h (deprecated location)
# 5) other google headers
#
# We classify each include statement as one of those 5 types
# using a number of techniques. The include_state object keeps
# track of the highest type seen, and complains if we see a
# lower type after that.
error_message = include_state.CheckNextIncludeOrder(
_ClassifyInclude(fileinfo, include, is_system))
if error_message:
error(filename, linenum, 'build/include_order', 4,
'%s. Should be: %s.h, c system, c++ system, other.' %
(error_message, fileinfo.BaseName()))
canonical_include = include_state.CanonicalizeAlphabeticalOrder(include)
if not include_state.IsInAlphabeticalOrder(
clean_lines, linenum, canonical_include):
error(filename, linenum, 'build/include_alpha', 4,
'Include "%s" not in alphabetical order' % include)
include_state.SetLastHeader(canonical_include)
# Look for any of the stream classes that are part of standard C++.
match = _RE_PATTERN_INCLUDE.match(line)
if match:
include = match.group(2)
if Match(r'(f|ind|io|i|o|parse|pf|stdio|str|)?stream$', include):
# Many unit tests use cout, so we exempt them.
if not _IsTestFilename(filename):
error(filename, linenum, 'readability/streams', 3,
'Streams are highly discouraged.')
def _GetTextInside(text, start_pattern):
r"""Retrieves all the text between matching open and close parentheses.
Given a string of lines and a regular expression string, retrieve all the text
following the expression and between opening punctuation symbols like
(, [, or {, and the matching close-punctuation symbol. This properly nested
occurrences of the punctuations, so for the text like
printf(a(), b(c()));
a call to _GetTextInside(text, r'printf\(') will return 'a(), b(c())'.
start_pattern must match string having an open punctuation symbol at the end.
Args:
text: The lines to extract text. Its comments and strings must be elided.
It can be single line and can span multiple lines.
start_pattern: The regexp string indicating where to start extracting
the text.
Returns:
The extracted text.
None if either the opening string or ending punctuation could not be found.
"""
# TODO(sugawarayu): Audit cpplint.py to see what places could be profitably
# rewritten to use _GetTextInside (and use inferior regexp matching today).
# Give opening punctuations to get the matching close-punctuations.
matching_punctuation = {'(': ')', '{': '}', '[': ']'}
closing_punctuation = set(matching_punctuation.itervalues())
# Find the position to start extracting text.
match = re.search(start_pattern, text, re.M)
if not match: # start_pattern not found in text.
return None
start_position = match.end(0)
assert start_position > 0, (
'start_pattern must ends with an opening punctuation.')
assert text[start_position - 1] in matching_punctuation, (
'start_pattern must ends with an opening punctuation.')
# Stack of closing punctuations we expect to have in text after position.
punctuation_stack = [matching_punctuation[text[start_position - 1]]]
position = start_position
while punctuation_stack and position < len(text):
if text[position] == punctuation_stack[-1]:
punctuation_stack.pop()
elif text[position] in closing_punctuation:
# A closing punctuation without matching opening punctuations.
return None
elif text[position] in matching_punctuation:
punctuation_stack.append(matching_punctuation[text[position]])
position += 1
if punctuation_stack:
# Opening punctuations left without matching close-punctuations.
return None
# punctuations match.
return text[start_position:position - 1]
# Patterns for matching call-by-reference parameters.
#
# Supports nested templates up to 2 levels deep using this messy pattern:
# < (?: < (?: < [^<>]*
# >
# | [^<>] )*
# >
# | [^<>] )*
# >
_RE_PATTERN_IDENT = r'[_a-zA-Z]\w*' # =~ [[:alpha:]][[:alnum:]]*
_RE_PATTERN_TYPE = (
r'(?:const\s+)?(?:typename\s+|class\s+|struct\s+|union\s+|enum\s+)?'
r'(?:\w|'
r'\s*<(?:<(?:<[^<>]*>|[^<>])*>|[^<>])*>|'
r'::)+')
# A call-by-reference parameter ends with '& identifier'.
_RE_PATTERN_REF_PARAM = re.compile(
r'(' + _RE_PATTERN_TYPE + r'(?:\s*(?:\bconst\b|[*]))*\s*'
r'&\s*' + _RE_PATTERN_IDENT + r')\s*(?:=[^,()]+)?[,)]')
# A call-by-const-reference parameter either ends with 'const& identifier'
# or looks like 'const type& identifier' when 'type' is atomic.
_RE_PATTERN_CONST_REF_PARAM = (
r'(?:.*\s*\bconst\s*&\s*' + _RE_PATTERN_IDENT +
r'|const\s+' + _RE_PATTERN_TYPE + r'\s*&\s*' + _RE_PATTERN_IDENT + r')')
def CheckLanguage(filename, clean_lines, linenum, file_extension,
include_state, nesting_state, error):
"""Checks rules from the 'C++ language rules' section of cppguide.html.
Some of these rules are hard to test (function overloading, using
uint32 inappropriately), but we do the best we can.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
file_extension: The extension (without the dot) of the filename.
include_state: An _IncludeState instance in which the headers are inserted.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found.
"""
# If the line is empty or consists of entirely a comment, no need to
# check it.
line = clean_lines.elided[linenum]
if not line:
return
match = _RE_PATTERN_INCLUDE.search(line)
if match:
CheckIncludeLine(filename, clean_lines, linenum, include_state, error)
return
# Reset include state across preprocessor directives. This is meant
# to silence warnings for conditional includes.
if Match(r'^\s*#\s*(?:ifdef|elif|else|endif)\b', line):
include_state.ResetSection()
# Make Windows paths like Unix.
fullname = os.path.abspath(filename).replace('\\', '/')
# TODO(unknown): figure out if they're using default arguments in fn proto.
# Check to see if they're using an conversion function cast.
# I just try to capture the most common basic types, though there are more.
# Parameterless conversion functions, such as bool(), are allowed as they are
# probably a member operator declaration or default constructor.
match = Search(
r'(\bnew\s+)?\b' # Grab 'new' operator, if it's there
r'(int|float|double|bool|char|int32|uint32|int64|uint64)'
r'(\([^)].*)', line)
if match:
matched_new = match.group(1)
matched_type = match.group(2)
matched_funcptr = match.group(3)
# gMock methods are defined using some variant of MOCK_METHODx(name, type)
# where type may be float(), int(string), etc. Without context they are
# virtually indistinguishable from int(x) casts. Likewise, gMock's
# MockCallback takes a template parameter of the form return_type(arg_type),
# which looks much like the cast we're trying to detect.
#
# std::function<> wrapper has a similar problem.
#
# Return types for function pointers also look like casts if they
# don't have an extra space.
if (matched_new is None and # If new operator, then this isn't a cast
not (Match(r'^\s*MOCK_(CONST_)?METHOD\d+(_T)?\(', line) or
Search(r'\bMockCallback<.*>', line) or
Search(r'\bstd::function<.*>', line)) and
not (matched_funcptr and
Match(r'\((?:[^() ]+::\s*\*\s*)?[^() ]+\)\s*\(',
matched_funcptr))):
# Try a bit harder to catch gmock lines: the only place where
# something looks like an old-style cast is where we declare the
# return type of the mocked method, and the only time when we
# are missing context is if MOCK_METHOD was split across
# multiple lines. The missing MOCK_METHOD is usually one or two
# lines back, so scan back one or two lines.
#
# It's not possible for gmock macros to appear in the first 2
# lines, since the class head + section name takes up 2 lines.
if (linenum < 2 or
not (Match(r'^\s*MOCK_(?:CONST_)?METHOD\d+(?:_T)?\((?:\S+,)?\s*$',
clean_lines.elided[linenum - 1]) or
Match(r'^\s*MOCK_(?:CONST_)?METHOD\d+(?:_T)?\(\s*$',
clean_lines.elided[linenum - 2]))):
error(filename, linenum, 'readability/casting', 4,
'Using deprecated casting style. '
'Use static_cast<%s>(...) instead' %
matched_type)
CheckCStyleCast(filename, linenum, line, clean_lines.raw_lines[linenum],
'static_cast',
r'\((int|float|double|bool|char|u?int(16|32|64))\)', error)
# This doesn't catch all cases. Consider (const char * const)"hello".
#
# (char *) "foo" should always be a const_cast (reinterpret_cast won't
# compile).
if CheckCStyleCast(filename, linenum, line, clean_lines.raw_lines[linenum],
'const_cast', r'\((char\s?\*+\s?)\)\s*"', error):
pass
else:
# Check pointer casts for other than string constants
CheckCStyleCast(filename, linenum, line, clean_lines.raw_lines[linenum],
'reinterpret_cast', r'\((\w+\s?\*+\s?)\)', error)
# In addition, we look for people taking the address of a cast. This
# is dangerous -- casts can assign to temporaries, so the pointer doesn't
# point where you think.
match = Search(
r'(?:&\(([^)]+)\)[\w(])|'
r'(?:&(static|dynamic|down|reinterpret)_cast\b)', line)
if match and match.group(1) != '*':
error(filename, linenum, 'runtime/casting', 4,
('Are you taking an address of a cast? '
'This is dangerous: could be a temp var. '
'Take the address before doing the cast, rather than after'))
# Create an extended_line, which is the concatenation of the current and
# next lines, for more effective checking of code that may span more than one
# line.
if linenum + 1 < clean_lines.NumLines():
extended_line = line + clean_lines.elided[linenum + 1]
else:
extended_line = line
# Check for people declaring static/global STL strings at the top level.
# This is dangerous because the C++ language does not guarantee that
# globals with constructors are initialized before the first access.
match = Match(
r'((?:|static +)(?:|const +))string +([a-zA-Z0-9_:]+)\b(.*)',
line)
# Make sure it's not a function.
# Function template specialization looks like: "string foo<Type>(...".
# Class template definitions look like: "string Foo<Type>::Method(...".
#
# Also ignore things that look like operators. These are matched separately
# because operator names cross non-word boundaries. If we change the pattern
# above, we would decrease the accuracy of matching identifiers.
if (match and
not Search(r'\boperator\W', line) and
not Match(r'\s*(<.*>)?(::[a-zA-Z0-9_]+)?\s*\(([^"]|$)', match.group(3))):
error(filename, linenum, 'runtime/string', 4,
'For a static/global string constant, use a C style string instead: '
'"%schar %s[]".' %
(match.group(1), match.group(2)))
if Search(r'\b([A-Za-z0-9_]*_)\(\1\)', line):
error(filename, linenum, 'runtime/init', 4,
'You seem to be initializing a member variable with itself.')
if file_extension == 'h':
# TODO(unknown): check that 1-arg constructors are explicit.
# How to tell it's a constructor?
# (handled in CheckForNonStandardConstructs for now)
# TODO(unknown): check that classes have DISALLOW_EVIL_CONSTRUCTORS
# (level 1 error)
pass
# Check if people are using the verboten C basic types. The only exception
# we regularly allow is "unsigned short port" for port.
if Search(r'\bshort port\b', line):
if not Search(r'\bunsigned short port\b', line):
error(filename, linenum, 'runtime/int', 4,
'Use "unsigned short" for ports, not "short"')
else:
match = Search(r'\b(short|long(?! +double)|long long)\b', line)
if match:
error(filename, linenum, 'runtime/int', 4,
'Use int16/int64/etc, rather than the C type %s' % match.group(1))
# When snprintf is used, the second argument shouldn't be a literal.
match = Search(r'snprintf\s*\(([^,]*),\s*([0-9]*)\s*,', line)
if match and match.group(2) != '0':
# If 2nd arg is zero, snprintf is used to calculate size.
error(filename, linenum, 'runtime/printf', 3,
'If you can, use sizeof(%s) instead of %s as the 2nd arg '
'to snprintf.' % (match.group(1), match.group(2)))
# Check if some verboten C functions are being used.
if Search(r'\bsprintf\b', line):
error(filename, linenum, 'runtime/printf', 5,
'Never use sprintf. Use snprintf instead.')
match = Search(r'\b(strcpy|strcat)\b', line)
if match:
error(filename, linenum, 'runtime/printf', 4,
'Almost always, snprintf is better than %s' % match.group(1))
# Check if some verboten operator overloading is going on
# TODO(unknown): catch out-of-line unary operator&:
# class X {};
# int operator&(const X& x) { return 42; } // unary operator&
# The trick is it's hard to tell apart from binary operator&:
# class Y { int operator&(const Y& x) { return 23; } }; // binary operator&
if Search(r'\boperator\s*&\s*\(\s*\)', line):
error(filename, linenum, 'runtime/operator', 4,
'Unary operator& is dangerous. Do not use it.')
# Check for suspicious usage of "if" like
# } if (a == b) {
if Search(r'\}\s*if\s*\(', line):
error(filename, linenum, 'readability/braces', 4,
'Did you mean "else if"? If not, start a new line for "if".')
# Check for potential format string bugs like printf(foo).
# We constrain the pattern not to pick things like DocidForPrintf(foo).
# Not perfect but it can catch printf(foo.c_str()) and printf(foo->c_str())
# TODO(sugawarayu): Catch the following case. Need to change the calling
# convention of the whole function to process multiple line to handle it.
# printf(
# boy_this_is_a_really_long_variable_that_cannot_fit_on_the_prev_line);
printf_args = _GetTextInside(line, r'(?i)\b(string)?printf\s*\(')
if printf_args:
match = Match(r'([\w.\->()]+)$', printf_args)
if match and match.group(1) != '__VA_ARGS__':
function_name = re.search(r'\b((?:string)?printf)\s*\(',
line, re.I).group(1)
error(filename, linenum, 'runtime/printf', 4,
'Potential format string bug. Do %s("%%s", %s) instead.'
% (function_name, match.group(1)))
# Check for potential memset bugs like memset(buf, sizeof(buf), 0).
match = Search(r'memset\s*\(([^,]*),\s*([^,]*),\s*0\s*\)', line)
if match and not Match(r"^''|-?[0-9]+|0x[0-9A-Fa-f]$", match.group(2)):
error(filename, linenum, 'runtime/memset', 4,
'Did you mean "memset(%s, 0, %s)"?'
% (match.group(1), match.group(2)))
if Search(r'\busing namespace\b', line):
error(filename, linenum, 'build/namespaces', 5,
'Do not use namespace using-directives. '
'Use using-declarations instead.')
# Detect variable-length arrays.
match = Match(r'\s*(.+::)?(\w+) [a-z]\w*\[(.+)];', line)
if (match and match.group(2) != 'return' and match.group(2) != 'delete' and
match.group(3).find(']') == -1):
# Split the size using space and arithmetic operators as delimiters.
# If any of the resulting tokens are not compile time constants then
# report the error.
tokens = re.split(r'\s|\+|\-|\*|\/|<<|>>]', match.group(3))
is_const = True
skip_next = False
for tok in tokens:
if skip_next:
skip_next = False
continue
if Search(r'sizeof\(.+\)', tok): continue
if Search(r'arraysize\(\w+\)', tok): continue
tok = tok.lstrip('(')
tok = tok.rstrip(')')
if not tok: continue
if Match(r'\d+', tok): continue
if Match(r'0[xX][0-9a-fA-F]+', tok): continue
if Match(r'k[A-Z0-9]\w*', tok): continue
if Match(r'(.+::)?k[A-Z0-9]\w*', tok): continue
if Match(r'(.+::)?[A-Z][A-Z0-9_]*', tok): continue
# A catch all for tricky sizeof cases, including 'sizeof expression',
# 'sizeof(*type)', 'sizeof(const type)', 'sizeof(struct StructName)'
# requires skipping the next token because we split on ' ' and '*'.
if tok.startswith('sizeof'):
skip_next = True
continue
is_const = False
break
if not is_const:
error(filename, linenum, 'runtime/arrays', 1,
'Do not use variable-length arrays. Use an appropriately named '
"('k' followed by CamelCase) compile-time constant for the size.")
# If DISALLOW_EVIL_CONSTRUCTORS, DISALLOW_COPY_AND_ASSIGN, or
# DISALLOW_IMPLICIT_CONSTRUCTORS is present, then it should be the last thing
# in the class declaration.
match = Match(
(r'\s*'
r'(DISALLOW_(EVIL_CONSTRUCTORS|COPY_AND_ASSIGN|IMPLICIT_CONSTRUCTORS))'
r'\(.*\);$'),
line)
if match and linenum + 1 < clean_lines.NumLines():
next_line = clean_lines.elided[linenum + 1]
# We allow some, but not all, declarations of variables to be present
# in the statement that defines the class. The [\w\*,\s]* fragment of
# the regular expression below allows users to declare instances of
# the class or pointers to instances, but not less common types such
# as function pointers or arrays. It's a tradeoff between allowing
# reasonable code and avoiding trying to parse more C++ using regexps.
if not Search(r'^\s*}[\w\*,\s]*;', next_line):
error(filename, linenum, 'readability/constructors', 3,
match.group(1) + ' should be the last thing in the class')
# Check for use of unnamed namespaces in header files. Registration
# macros are typically OK, so we allow use of "namespace {" on lines
# that end with backslashes.
if (file_extension == 'h'
and Search(r'\bnamespace\s*{', line)
and line[-1] != '\\'):
error(filename, linenum, 'build/namespaces', 4,
'Do not use unnamed namespaces in header files. See '
'http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml#Namespaces'
' for more information.')
def CheckForNonConstReference(filename, clean_lines, linenum,
nesting_state, error):
"""Check for non-const references.
Separate from CheckLanguage since it scans backwards from current
line, instead of scanning forward.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found.
"""
# Do nothing if there is no '&' on current line.
line = clean_lines.elided[linenum]
if '&' not in line:
return
# Long type names may be broken across multiple lines, usually in one
# of these forms:
# LongType
# ::LongTypeContinued &identifier
# LongType::
# LongTypeContinued &identifier
# LongType<
# ...>::LongTypeContinued &identifier
#
# If we detected a type split across two lines, join the previous
# line to current line so that we can match const references
# accordingly.
#
# Note that this only scans back one line, since scanning back
# arbitrary number of lines would be expensive. If you have a type
# that spans more than 2 lines, please use a typedef.
if linenum > 1:
previous = None
if Match(r'\s*::(?:[\w<>]|::)+\s*&\s*\S', line):
# previous_line\n + ::current_line
previous = Search(r'\b((?:const\s*)?(?:[\w<>]|::)+[\w<>])\s*$',
clean_lines.elided[linenum - 1])
elif Match(r'\s*[a-zA-Z_]([\w<>]|::)+\s*&\s*\S', line):
# previous_line::\n + current_line
previous = Search(r'\b((?:const\s*)?(?:[\w<>]|::)+::)\s*$',
clean_lines.elided[linenum - 1])
if previous:
line = previous.group(1) + line.lstrip()
else:
# Check for templated parameter that is split across multiple lines
endpos = line.rfind('>')
if endpos > -1:
(_, startline, startpos) = ReverseCloseExpression(
clean_lines, linenum, endpos)
if startpos > -1 and startline < linenum:
# Found the matching < on an earlier line, collect all
# pieces up to current line.
line = ''
for i in xrange(startline, linenum + 1):
line += clean_lines.elided[i].strip()
# Check for non-const references in function parameters. A single '&' may
# found in the following places:
# inside expression: binary & for bitwise AND
# inside expression: unary & for taking the address of something
# inside declarators: reference parameter
# We will exclude the first two cases by checking that we are not inside a
# function body, including one that was just introduced by a trailing '{'.
# TODO(unknwon): Doesn't account for preprocessor directives.
# TODO(unknown): Doesn't account for 'catch(Exception& e)' [rare].
check_params = False
if not nesting_state.stack:
check_params = True # top level
elif (isinstance(nesting_state.stack[-1], _ClassInfo) or
isinstance(nesting_state.stack[-1], _NamespaceInfo)):
check_params = True # within class or namespace
elif Match(r'.*{\s*$', line):
if (len(nesting_state.stack) == 1 or
isinstance(nesting_state.stack[-2], _ClassInfo) or
isinstance(nesting_state.stack[-2], _NamespaceInfo)):
check_params = True # just opened global/class/namespace block
# We allow non-const references in a few standard places, like functions
# called "swap()" or iostream operators like "<<" or ">>". Do not check
# those function parameters.
#
# We also accept & in static_assert, which looks like a function but
# it's actually a declaration expression.
whitelisted_functions = (r'(?:[sS]wap(?:<\w:+>)?|'
r'operator\s*[<>][<>]|'
r'static_assert|COMPILE_ASSERT'
r')\s*\(')
if Search(whitelisted_functions, line):
check_params = False
elif not Search(r'\S+\([^)]*$', line):
# Don't see a whitelisted function on this line. Actually we
# didn't see any function name on this line, so this is likely a
# multi-line parameter list. Try a bit harder to catch this case.
for i in xrange(2):
if (linenum > i and
Search(whitelisted_functions, clean_lines.elided[linenum - i - 1])):
check_params = False
break
if check_params:
decls = ReplaceAll(r'{[^}]*}', ' ', line) # exclude function body
for parameter in re.findall(_RE_PATTERN_REF_PARAM, decls):
if not Match(_RE_PATTERN_CONST_REF_PARAM, parameter):
error(filename, linenum, 'runtime/references', 2,
'Is this a non-const reference? '
'If so, make const or use a pointer: ' +
ReplaceAll(' *<', '<', parameter))
def CheckCStyleCast(filename, linenum, line, raw_line, cast_type, pattern,
error):
"""Checks for a C-style cast by looking for the pattern.
Args:
filename: The name of the current file.
linenum: The number of the line to check.
line: The line of code to check.
raw_line: The raw line of code to check, with comments.
cast_type: The string for the C++ cast to recommend. This is either
reinterpret_cast, static_cast, or const_cast, depending.
pattern: The regular expression used to find C-style casts.
error: The function to call with any errors found.
Returns:
True if an error was emitted.
False otherwise.
"""
match = Search(pattern, line)
if not match:
return False
# Exclude lines with sizeof, since sizeof looks like a cast.
sizeof_match = Match(r'.*sizeof\s*$', line[0:match.start(1) - 1])
if sizeof_match:
return False
# operator++(int) and operator--(int)
if (line[0:match.start(1) - 1].endswith(' operator++') or
line[0:match.start(1) - 1].endswith(' operator--')):
return False
# A single unnamed argument for a function tends to look like old
# style cast. If we see those, don't issue warnings for deprecated
# casts, instead issue warnings for unnamed arguments where
# appropriate.
#
# These are things that we want warnings for, since the style guide
# explicitly require all parameters to be named:
# Function(int);
# Function(int) {
# ConstMember(int) const;
# ConstMember(int) const {
# ExceptionMember(int) throw (...);
# ExceptionMember(int) throw (...) {
# PureVirtual(int) = 0;
#
# These are functions of some sort, where the compiler would be fine
# if they had named parameters, but people often omit those
# identifiers to reduce clutter:
# (FunctionPointer)(int);
# (FunctionPointer)(int) = value;
# Function((function_pointer_arg)(int))
# <TemplateArgument(int)>;
# <(FunctionPointerTemplateArgument)(int)>;
remainder = line[match.end(0):]
if Match(r'^\s*(?:;|const\b|throw\b|=|>|\{|\))', remainder):
# Looks like an unnamed parameter.
# Don't warn on any kind of template arguments.
if Match(r'^\s*>', remainder):
return False
# Don't warn on assignments to function pointers, but keep warnings for
# unnamed parameters to pure virtual functions. Note that this pattern
# will also pass on assignments of "0" to function pointers, but the
# preferred values for those would be "nullptr" or "NULL".
matched_zero = Match(r'^\s=\s*(\S+)\s*;', remainder)
if matched_zero and matched_zero.group(1) != '0':
return False
# Don't warn on function pointer declarations. For this we need
# to check what came before the "(type)" string.
if Match(r'.*\)\s*$', line[0:match.start(0)]):
return False
# Don't warn if the parameter is named with block comments, e.g.:
# Function(int /*unused_param*/);
if '/*' in raw_line:
return False
# Passed all filters, issue warning here.
error(filename, linenum, 'readability/function', 3,
'All parameters should be named in a function')
return True
# At this point, all that should be left is actual casts.
error(filename, linenum, 'readability/casting', 4,
'Using C-style cast. Use %s<%s>(...) instead' %
(cast_type, match.group(1)))
return True
_HEADERS_CONTAINING_TEMPLATES = (
('<deque>', ('deque',)),
('<functional>', ('unary_function', 'binary_function',
'plus', 'minus', 'multiplies', 'divides', 'modulus',
'negate',
'equal_to', 'not_equal_to', 'greater', 'less',
'greater_equal', 'less_equal',
'logical_and', 'logical_or', 'logical_not',
'unary_negate', 'not1', 'binary_negate', 'not2',
'bind1st', 'bind2nd',
'pointer_to_unary_function',
'pointer_to_binary_function',
'ptr_fun',
'mem_fun_t', 'mem_fun', 'mem_fun1_t', 'mem_fun1_ref_t',
'mem_fun_ref_t',
'const_mem_fun_t', 'const_mem_fun1_t',
'const_mem_fun_ref_t', 'const_mem_fun1_ref_t',
'mem_fun_ref',
)),
('<limits>', ('numeric_limits',)),
('<list>', ('list',)),
('<map>', ('map', 'multimap',)),
('<memory>', ('allocator',)),
('<queue>', ('queue', 'priority_queue',)),
('<set>', ('set', 'multiset',)),
('<stack>', ('stack',)),
('<string>', ('char_traits', 'basic_string',)),
('<utility>', ('pair',)),
('<vector>', ('vector',)),
# gcc extensions.
# Note: std::hash is their hash, ::hash is our hash
('<hash_map>', ('hash_map', 'hash_multimap',)),
('<hash_set>', ('hash_set', 'hash_multiset',)),
('<slist>', ('slist',)),
)
_RE_PATTERN_STRING = re.compile(r'\bstring\b')
_re_pattern_algorithm_header = []
for _template in ('copy', 'max', 'min', 'min_element', 'sort', 'swap',
'transform'):
# Match max<type>(..., ...), max(..., ...), but not foo->max, foo.max or
# type::max().
_re_pattern_algorithm_header.append(
(re.compile(r'[^>.]\b' + _template + r'(<.*?>)?\([^\)]'),
_template,
'<algorithm>'))
_re_pattern_templates = []
for _header, _templates in _HEADERS_CONTAINING_TEMPLATES:
for _template in _templates:
_re_pattern_templates.append(
(re.compile(r'(\<|\b)' + _template + r'\s*\<'),
_template + '<>',
_header))
def FilesBelongToSameModule(filename_cc, filename_h):
"""Check if these two filenames belong to the same module.
The concept of a 'module' here is a as follows:
foo.h, foo-inl.h, foo.cc, foo_test.cc and foo_unittest.cc belong to the
same 'module' if they are in the same directory.
some/path/public/xyzzy and some/path/internal/xyzzy are also considered
to belong to the same module here.
If the filename_cc contains a longer path than the filename_h, for example,
'/absolute/path/to/base/sysinfo.cc', and this file would include
'base/sysinfo.h', this function also produces the prefix needed to open the
header. This is used by the caller of this function to more robustly open the
header file. We don't have access to the real include paths in this context,
so we need this guesswork here.
Known bugs: tools/base/bar.cc and base/bar.h belong to the same module
according to this implementation. Because of this, this function gives
some false positives. This should be sufficiently rare in practice.
Args:
filename_cc: is the path for the .cc file
filename_h: is the path for the header path
Returns:
Tuple with a bool and a string:
bool: True if filename_cc and filename_h belong to the same module.
string: the additional prefix needed to open the header file.
"""
if not filename_cc.endswith('.cc'):
return (False, '')
filename_cc = filename_cc[:-len('.cc')]
if filename_cc.endswith('_unittest'):
filename_cc = filename_cc[:-len('_unittest')]
elif filename_cc.endswith('_test'):
filename_cc = filename_cc[:-len('_test')]
filename_cc = filename_cc.replace('/public/', '/')
filename_cc = filename_cc.replace('/internal/', '/')
if not filename_h.endswith('.h'):
return (False, '')
filename_h = filename_h[:-len('.h')]
if filename_h.endswith('-inl'):
filename_h = filename_h[:-len('-inl')]
filename_h = filename_h.replace('/public/', '/')
filename_h = filename_h.replace('/internal/', '/')
files_belong_to_same_module = filename_cc.endswith(filename_h)
common_path = ''
if files_belong_to_same_module:
common_path = filename_cc[:-len(filename_h)]
return files_belong_to_same_module, common_path
def UpdateIncludeState(filename, include_state, io=codecs):
"""Fill up the include_state with new includes found from the file.
Args:
filename: the name of the header to read.
include_state: an _IncludeState instance in which the headers are inserted.
io: The io factory to use to read the file. Provided for testability.
Returns:
True if a header was succesfully added. False otherwise.
"""
headerfile = None
try:
headerfile = io.open(filename, 'r', 'utf8', 'replace')
except IOError:
return False
linenum = 0
for line in headerfile:
linenum += 1
clean_line = CleanseComments(line)
match = _RE_PATTERN_INCLUDE.search(clean_line)
if match:
include = match.group(2)
# The value formatting is cute, but not really used right now.
# What matters here is that the key is in include_state.
include_state.setdefault(include, '%s:%d' % (filename, linenum))
return True
def CheckForIncludeWhatYouUse(filename, clean_lines, include_state, error,
io=codecs):
"""Reports for missing stl includes.
This function will output warnings to make sure you are including the headers
necessary for the stl containers and functions that you use. We only give one
reason to include a header. For example, if you use both equal_to<> and
less<> in a .h file, only one (the latter in the file) of these will be
reported as a reason to include the <functional>.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
include_state: An _IncludeState instance.
error: The function to call with any errors found.
io: The IO factory to use to read the header file. Provided for unittest
injection.
"""
required = {} # A map of header name to linenumber and the template entity.
# Example of required: { '<functional>': (1219, 'less<>') }
for linenum in xrange(clean_lines.NumLines()):
line = clean_lines.elided[linenum]
if not line or line[0] == '#':
continue
# String is special -- it is a non-templatized type in STL.
matched = _RE_PATTERN_STRING.search(line)
if matched:
# Don't warn about strings in non-STL namespaces:
# (We check only the first match per line; good enough.)
prefix = line[:matched.start()]
if prefix.endswith('std::') or not prefix.endswith('::'):
required['<string>'] = (linenum, 'string')
for pattern, template, header in _re_pattern_algorithm_header:
if pattern.search(line):
required[header] = (linenum, template)
# The following function is just a speed up, no semantics are changed.
if not '<' in line: # Reduces the cpu time usage by skipping lines.
continue
for pattern, template, header in _re_pattern_templates:
if pattern.search(line):
required[header] = (linenum, template)
# The policy is that if you #include something in foo.h you don't need to
# include it again in foo.cc. Here, we will look at possible includes.
# Let's copy the include_state so it is only messed up within this function.
include_state = include_state.copy()
# Did we find the header for this file (if any) and succesfully load it?
header_found = False
# Use the absolute path so that matching works properly.
abs_filename = FileInfo(filename).FullName()
# For Emacs's flymake.
# If cpplint is invoked from Emacs's flymake, a temporary file is generated
# by flymake and that file name might end with '_flymake.cc'. In that case,
# restore original file name here so that the corresponding header file can be
# found.
# e.g. If the file name is 'foo_flymake.cc', we should search for 'foo.h'
# instead of 'foo_flymake.h'
abs_filename = re.sub(r'_flymake\.cc$', '.cc', abs_filename)
# include_state is modified during iteration, so we iterate over a copy of
# the keys.
header_keys = include_state.keys()
for header in header_keys:
(same_module, common_path) = FilesBelongToSameModule(abs_filename, header)
fullpath = common_path + header
if same_module and UpdateIncludeState(fullpath, include_state, io):
header_found = True
# If we can't find the header file for a .cc, assume it's because we don't
# know where to look. In that case we'll give up as we're not sure they
# didn't include it in the .h file.
# TODO(unknown): Do a better job of finding .h files so we are confident that
# not having the .h file means there isn't one.
if filename.endswith('.cc') and not header_found:
return
# All the lines have been processed, report the errors found.
for required_header_unstripped in required:
template = required[required_header_unstripped][1]
if required_header_unstripped.strip('<>"') not in include_state:
error(filename, required[required_header_unstripped][0],
'build/include_what_you_use', 4,
'Add #include ' + required_header_unstripped + ' for ' + template)
_RE_PATTERN_EXPLICIT_MAKEPAIR = re.compile(r'\bmake_pair\s*<')
def CheckMakePairUsesDeduction(filename, clean_lines, linenum, error):
"""Check that make_pair's template arguments are deduced.
G++ 4.6 in C++0x mode fails badly if make_pair's template arguments are
specified explicitly, and such use isn't intended in any case.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
match = _RE_PATTERN_EXPLICIT_MAKEPAIR.search(line)
if match:
error(filename, linenum, 'build/explicit_make_pair',
4, # 4 = high confidence
'For C++11-compatibility, omit template arguments from make_pair'
' OR use pair directly OR if appropriate, construct a pair directly')
def ProcessLine(filename, file_extension, clean_lines, line,
include_state, function_state, nesting_state, error,
extra_check_functions=[]):
"""Processes a single line in the file.
Args:
filename: Filename of the file that is being processed.
file_extension: The extension (dot not included) of the file.
clean_lines: An array of strings, each representing a line of the file,
with comments stripped.
line: Number of line being processed.
include_state: An _IncludeState instance in which the headers are inserted.
function_state: A _FunctionState instance which counts function lines, etc.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: A callable to which errors are reported, which takes 4 arguments:
filename, line number, error level, and message
extra_check_functions: An array of additional check functions that will be
run on each source line. Each function takes 4
arguments: filename, clean_lines, line, error
"""
raw_lines = clean_lines.raw_lines
ParseNolintSuppressions(filename, raw_lines[line], line, error)
nesting_state.Update(filename, clean_lines, line, error)
if nesting_state.stack and nesting_state.stack[-1].inline_asm != _NO_ASM:
return
CheckForFunctionLengths(filename, clean_lines, line, function_state, error)
CheckForMultilineCommentsAndStrings(filename, clean_lines, line, error)
CheckStyle(filename, clean_lines, line, file_extension, nesting_state, error)
CheckLanguage(filename, clean_lines, line, file_extension, include_state,
nesting_state, error)
CheckForNonConstReference(filename, clean_lines, line, nesting_state, error)
CheckForNonStandardConstructs(filename, clean_lines, line,
nesting_state, error)
CheckVlogArguments(filename, clean_lines, line, error)
CheckCaffeAlternatives(filename, clean_lines, line, error)
CheckCaffeDataLayerSetUp(filename, clean_lines, line, error)
CheckCaffeRandom(filename, clean_lines, line, error)
CheckPosixThreading(filename, clean_lines, line, error)
CheckInvalidIncrement(filename, clean_lines, line, error)
CheckMakePairUsesDeduction(filename, clean_lines, line, error)
for check_fn in extra_check_functions:
check_fn(filename, clean_lines, line, error)
def ProcessFileData(filename, file_extension, lines, error,
extra_check_functions=[]):
"""Performs lint checks and reports any errors to the given error function.
Args:
filename: Filename of the file that is being processed.
file_extension: The extension (dot not included) of the file.
lines: An array of strings, each representing a line of the file, with the
last element being empty if the file is terminated with a newline.
error: A callable to which errors are reported, which takes 4 arguments:
filename, line number, error level, and message
extra_check_functions: An array of additional check functions that will be
run on each source line. Each function takes 4
arguments: filename, clean_lines, line, error
"""
lines = (['// marker so line numbers and indices both start at 1'] + lines +
['// marker so line numbers end in a known way'])
include_state = _IncludeState()
function_state = _FunctionState()
nesting_state = _NestingState()
ResetNolintSuppressions()
CheckForCopyright(filename, lines, error)
if file_extension == 'h':
CheckForHeaderGuard(filename, lines, error)
RemoveMultiLineComments(filename, lines, error)
clean_lines = CleansedLines(lines)
for line in xrange(clean_lines.NumLines()):
ProcessLine(filename, file_extension, clean_lines, line,
include_state, function_state, nesting_state, error,
extra_check_functions)
nesting_state.CheckCompletedBlocks(filename, error)
CheckForIncludeWhatYouUse(filename, clean_lines, include_state, error)
# We check here rather than inside ProcessLine so that we see raw
# lines rather than "cleaned" lines.
CheckForBadCharacters(filename, lines, error)
CheckForNewlineAtEOF(filename, lines, error)
def ProcessFile(filename, vlevel, extra_check_functions=[]):
"""Does google-lint on a single file.
Args:
filename: The name of the file to parse.
vlevel: The level of errors to report. Every error of confidence
>= verbose_level will be reported. 0 is a good default.
extra_check_functions: An array of additional check functions that will be
run on each source line. Each function takes 4
arguments: filename, clean_lines, line, error
"""
_SetVerboseLevel(vlevel)
try:
# Support the UNIX convention of using "-" for stdin. Note that
# we are not opening the file with universal newline support
# (which codecs doesn't support anyway), so the resulting lines do
# contain trailing '\r' characters if we are reading a file that
# has CRLF endings.
# If after the split a trailing '\r' is present, it is removed
# below. If it is not expected to be present (i.e. os.linesep !=
# '\r\n' as in Windows), a warning is issued below if this file
# is processed.
if filename == '-':
lines = codecs.StreamReaderWriter(sys.stdin,
codecs.getreader('utf8'),
codecs.getwriter('utf8'),
'replace').read().split('\n')
else:
lines = codecs.open(filename, 'r', 'utf8', 'replace').read().split('\n')
carriage_return_found = False
# Remove trailing '\r'.
for linenum in range(len(lines)):
if lines[linenum].endswith('\r'):
lines[linenum] = lines[linenum].rstrip('\r')
carriage_return_found = True
except IOError:
sys.stderr.write(
"Skipping input '%s': Can't open for reading\n" % filename)
return
# Note, if no dot is found, this will give the entire filename as the ext.
file_extension = filename[filename.rfind('.') + 1:]
# When reading from stdin, the extension is unknown, so no cpplint tests
# should rely on the extension.
if filename != '-' and file_extension not in _valid_extensions:
sys.stderr.write('Ignoring %s; not a valid file name '
'(%s)\n' % (filename, ', '.join(_valid_extensions)))
else:
ProcessFileData(filename, file_extension, lines, Error,
extra_check_functions)
if carriage_return_found and os.linesep != '\r\n':
# Use 0 for linenum since outputting only one error for potentially
# several lines.
Error(filename, 0, 'whitespace/newline', 1,
'One or more unexpected \\r (^M) found;'
'better to use only a \\n')
sys.stderr.write('Done processing %s\n' % filename)
def PrintUsage(message):
"""Prints a brief usage string and exits, optionally with an error message.
Args:
message: The optional error message.
"""
sys.stderr.write(_USAGE)
if message:
sys.exit('\nFATAL ERROR: ' + message)
else:
sys.exit(1)
def PrintCategories():
"""Prints a list of all the error-categories used by error messages.
These are the categories used to filter messages via --filter.
"""
sys.stderr.write(''.join(' %s\n' % cat for cat in _ERROR_CATEGORIES))
sys.exit(0)
def ParseArguments(args):
"""Parses the command line arguments.
This may set the output format and verbosity level as side-effects.
Args:
args: The command line arguments:
Returns:
The list of filenames to lint.
"""
try:
(opts, filenames) = getopt.getopt(args, '', ['help', 'output=', 'verbose=',
'counting=',
'filter=',
'root=',
'linelength=',
'extensions='])
except getopt.GetoptError:
PrintUsage('Invalid arguments.')
verbosity = _VerboseLevel()
output_format = _OutputFormat()
filters = ''
counting_style = ''
for (opt, val) in opts:
if opt == '--help':
PrintUsage(None)
elif opt == '--output':
if val not in ('emacs', 'vs7', 'eclipse'):
PrintUsage('The only allowed output formats are emacs, vs7 and eclipse.')
output_format = val
elif opt == '--verbose':
verbosity = int(val)
elif opt == '--filter':
filters = val
if not filters:
PrintCategories()
elif opt == '--counting':
if val not in ('total', 'toplevel', 'detailed'):
PrintUsage('Valid counting options are total, toplevel, and detailed')
counting_style = val
elif opt == '--root':
global _root
_root = val
elif opt == '--linelength':
global _line_length
try:
_line_length = int(val)
except ValueError:
PrintUsage('Line length must be digits.')
elif opt == '--extensions':
global _valid_extensions
try:
_valid_extensions = set(val.split(','))
except ValueError:
PrintUsage('Extensions must be comma seperated list.')
if not filenames:
PrintUsage('No files were specified.')
_SetOutputFormat(output_format)
_SetVerboseLevel(verbosity)
_SetFilters(filters)
_SetCountingStyle(counting_style)
return filenames
def main():
filenames = ParseArguments(sys.argv[1:])
# Change stderr to write with replacement characters so we don't die
# if we try to print something containing non-ASCII characters.
sys.stderr = codecs.StreamReaderWriter(sys.stderr,
codecs.getreader('utf8'),
codecs.getwriter('utf8'),
'replace')
_cpplint_state.ResetErrorCounts()
for filename in filenames:
ProcessFile(filename, _cpplint_state.verbose_level)
_cpplint_state.PrintErrorCounts()
sys.exit(_cpplint_state.error_count > 0)
if __name__ == '__main__':
main()
| 187,464 | 37.501746 | 93 | py |
DRT | DRT-master/caffe/scripts/download_model_binary.py | #!/usr/bin/env python
import os
import sys
import time
import yaml
import urllib
import hashlib
import argparse
required_keys = ['caffemodel', 'caffemodel_url', 'sha1']
def reporthook(count, block_size, total_size):
"""
From http://blog.moleculea.com/2012/10/04/urlretrieve-progres-indicator/
"""
global start_time
if count == 0:
start_time = time.time()
return
duration = time.time() - start_time
progress_size = int(count * block_size)
speed = int(progress_size / (1024 * duration))
percent = int(count * block_size * 100 / total_size)
sys.stdout.write("\r...%d%%, %d MB, %d KB/s, %d seconds passed" %
(percent, progress_size / (1024 * 1024), speed, duration))
sys.stdout.flush()
def parse_readme_frontmatter(dirname):
readme_filename = os.path.join(dirname, 'readme.md')
with open(readme_filename) as f:
lines = [line.strip() for line in f.readlines()]
top = lines.index('---')
bottom = lines.index('---', top + 1)
frontmatter = yaml.load('\n'.join(lines[top + 1:bottom]))
assert all(key in frontmatter for key in required_keys)
return dirname, frontmatter
def valid_dirname(dirname):
try:
return parse_readme_frontmatter(dirname)
except Exception as e:
print('ERROR: {}'.format(e))
raise argparse.ArgumentTypeError(
'Must be valid Caffe model directory with a correct readme.md')
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Download trained model binary.')
parser.add_argument('dirname', type=valid_dirname)
args = parser.parse_args()
# A tiny hack: the dirname validator also returns readme YAML frontmatter.
dirname = args.dirname[0]
frontmatter = args.dirname[1]
model_filename = os.path.join(dirname, frontmatter['caffemodel'])
# Closure-d function for checking SHA1.
def model_checks_out(filename=model_filename, sha1=frontmatter['sha1']):
with open(filename, 'r') as f:
return hashlib.sha1(f.read()).hexdigest() == sha1
# Check if model exists.
if os.path.exists(model_filename) and model_checks_out():
print("Model already exists.")
sys.exit(0)
# Download and verify model.
urllib.urlretrieve(
frontmatter['caffemodel_url'], model_filename, reporthook)
if not model_checks_out():
print('ERROR: model did not download correctly! Run this again.')
sys.exit(1)
| 2,496 | 31.428571 | 78 | py |
TraceLinkExplanation | TraceLinkExplanation-master/sentence_classifier/predict.py | from collections import defaultdict
import os
import sys
sys.path.append(".")
sys.path.append("..")
from torch import nn
from tqdm import tqdm
from transformers.models.auto.tokenization_auto import AutoTokenizer
from transformers import AutoModelForSequenceClassification
from torch.utils.data import DataLoader
import jsonlines
import pandas as pd
import torch
import argparse
key_dict = {
"acronym": ("short", "long"),
"definition": ("concept", "definition"),
"context": ("concept", "context"),
}
# FIXME import error if import it from train.py
class DMDataset(torch.utils.data.Dataset):
def __init__(self, encodings, labels=None):
self.encodings = encodings
self.labels = labels
def __getitem__(self, idx):
item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
if self.labels is not None:
item["labels"] = torch.tensor(self.labels[idx])
return item
def __len__(self):
return len(self.encodings["input_ids"])
def write_res_for_manual_evaluation(concepts, sents, predicts, res_file):
df = pd.DataFrame(columns=["concept", "sentences", "predicts"])
df["concept"] = concepts
df["sentences"] = sents
df["predicts"] = predicts
df.to_csv(res_file)
def select_sentence(concepts, sents, predicts, res_file, type, thrd=0.5):
index = defaultdict(set)
for c, s, p in zip(concepts, sents, predicts):
if p > thrd:
index[c].add((s, p))
with jsonlines.open(res_file, "w") as fout:
for c in index:
k1, k2 = key_dict[type]
index[c] = [
x[0] for x in sorted(index[c], key=lambda x: x[1], reverse=True)
]
fout.write({k1: c, k2: index[c]})
def test(outptu_dir, model, tokenizer, type):
infile = os.path.join(outptu_dir, f"{type}.jsonl")
eval_file = os.path.join(outptu_dir, f"{type}_eval.csv")
sel_file = os.path.join(outptu_dir, f"{type}_sel.jsonl")
k1, k2 = key_dict[type]
concepts, sents = [], []
with jsonlines.open(infile) as fout:
for item in fout:
for s in item[k2]:
concepts.append(item[k1])
sents.append(s)
preds = run_prediction(sents, model, tokenizer)
write_res_for_manual_evaluation(concepts, sents, preds, eval_file)
select_sentence(concepts, sents, preds, sel_file, type)
def run_prediction(sents, model, tokenizer):
encodings = tokenizer(
sents,
truncation=True,
padding=True,
max_length=128,
)
dataset = DMDataset(encodings, None)
eval_dataloader = DataLoader(dataset, batch_size=8)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model.to(device)
model.eval()
m = nn.Softmax(dim=-1)
preds = []
with torch.no_grad():
for batch in tqdm(eval_dataloader):
batch = {k: v.to(device) for k, v in batch.items()}
outputs = model(**batch)
logits = outputs.logits
scores = m(logits)[:, 1].tolist()
preds.extend(scores)
return preds
def run(proj_name, model_path):
tokenizer = AutoTokenizer.from_pretrained("allenai/scibert_scivocab_uncased")
model = AutoModelForSequenceClassification.from_pretrained(model_path)
for direction in ["bot_up", "top_down"]:
output_dir = os.path.join("./output", proj_name, direction)
test(output_dir, model, tokenizer, "acronym")
test(output_dir, model, tokenizer, "definition")
test(output_dir, model, tokenizer, "context")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--proj_name")
parser.add_argument("--model_path")
args = parser.parse_args()
run(args.proj_name, args.model_path) | 3,821 | 31.389831 | 87 | py |
TraceLinkExplanation | TraceLinkExplanation-master/sentence_classifier/train.py | from transformers import TrainingArguments, AutoTokenizer
from transformers import AutoModelForSequenceClassification, Trainer
import torch
from datasets import load_metric
import numpy as np
import sys
sys.path.append("..")
sys.path.append(".")
from evaluation import utils
from nltk.tokenize import sent_tokenize
import os
from sklearn.model_selection import train_test_split
import argparse
metric = load_metric("f1")
lm_name = "allenai/scibert_scivocab_uncased"
def read_training_data(project_name):
def get_sent_for_proj(dir_path):
s_art, t_art, link_dict, concept_set = utils.read_project(dir_path)
sents = set()
for sid in s_art:
sents.update(sent_tokenize(s_art[sid]))
for tid in t_art:
sents.update(sent_tokenize(t_art[tid]))
return sents
proj_root = "./data/projects"
sents, labels = [], []
for pname in ["CCHIT", "CM1", "PTC"]:
dir_path = os.path.join(proj_root, pname)
proj_sents = get_sent_for_proj(dir_path)
sents.extend(proj_sents)
labels.extend([1 if pname == project_name else 0] * len(proj_sents))
train_texts, val_texts, train_labels, val_labels = train_test_split(
sents, labels, test_size=0.2
)
return {
"train": (train_texts, train_labels),
"val": (val_texts, val_labels),
}
def run(proj_name):
raw_datas = read_training_data(proj_name)
tokenizer = AutoTokenizer.from_pretrained(lm_name)
datasets = dict()
for part in raw_datas.keys():
texts, labels = raw_datas[part]
encodings = tokenizer(
texts,
truncation=True,
padding=True,
max_length=128,
)
datasets[part] = DMDataset(encodings=encodings, labels=labels)
train(proj_name, datasets["train"], datasets["val"])
class DMDataset(torch.utils.data.Dataset):
def __init__(self, encodings, labels=None):
self.encodings = encodings
self.labels = labels
def __getitem__(self, idx):
item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
if self.labels is not None:
item["labels"] = torch.tensor(self.labels[idx])
return item
def __len__(self):
return len(self.encodings["input_ids"])
def compute_metrics(eval_pred):
logits, labels = eval_pred
predictions = np.argmax(logits, axis=-1)
return metric.compute(predictions=predictions, references=labels)
def train(proj_name, train_data, eval_data):
training_args = TrainingArguments(
output_dir=f"./sentence_classifier/{proj_name}",
report_to="tensorboard",
num_train_epochs=10,
per_device_train_batch_size=16,
per_device_eval_batch_size=64,
logging_dir="./logs",
load_best_model_at_end=True,
save_strategy="epoch",
logging_strategy="epoch",
evaluation_strategy="epoch",
save_total_limit=3,
)
model = AutoModelForSequenceClassification.from_pretrained(lm_name, num_labels=2)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_data,
eval_dataset=eval_data,
compute_metrics=compute_metrics,
)
trainer.train()
trainer.evaluate()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--proj_name")
args = parser.parse_args()
run(args.proj_name) | 3,452 | 28.512821 | 85 | py |
Opportunistic | Opportunistic-master/src/utils.py | import gc
import numpy as np
import torch
class ExperienceBuffer():
def __init__(self, buffer_size):
self.buffer = []
self.buffer_size = buffer_size
def push(self,experience):
if len(self.buffer) + 1 >= self.buffer_size:
self.buffer[0:(1+len(self.buffer))-self.buffer_size] = []
self.buffer.append(experience)
def sample(self,size):
inds = np.random.choice(len(self.buffer),size)
return [self.buffer[i] for i in inds]
def clear(self, buffer_size_new=None):
self.buffer = []
if buffer_size_new:
self.buffer_size = buffer_size_new
def mem_report():
'''Report the memory usage of the tensor.storage in pytorch
Both on CPUs and GPUs are reported'''
def _mem_report(tensors, mem_type):
'''Print the selected tensors of type
There are two major storage types in our major concern:
- GPU: tensors transferred to CUDA devices
- CPU: tensors remaining on the system memory (usually unimportant)
Args:
- tensors: the tensors of specified type
- mem_type: 'CPU' or 'GPU' in current implementation '''
print('Storage on %s' %(mem_type))
print('-'*LEN)
total_numel = 0
total_mem = 0
visited_data = []
for tensor in tensors:
if tensor.is_sparse:
continue
# a data_ptr indicates a memory block allocated
data_ptr = tensor.storage().data_ptr()
if data_ptr in visited_data:
continue
visited_data.append(data_ptr)
numel = tensor.storage().size()
total_numel += numel
element_size = tensor.storage().element_size()
mem = numel*element_size /1024/1024 # 32bit=4Byte, MByte
total_mem += mem
element_type = type(tensor).__name__
size = tuple(tensor.size())
print('%s\t\t%s\t\t%.2f' % (
element_type,
size,
mem) )
print('-'*LEN)
print('Total Tensors: %d \tUsed Memory Space: %.2f MBytes' % (total_numel, total_mem) )
print('-'*LEN)
LEN = 65
print('='*LEN)
objects = gc.get_objects()
print('%s\t%s\t\t\t%s' %('Element type', 'Size', 'Used MEM(MBytes)') )
tensors = [obj for obj in objects if torch.is_tensor(obj)]
cuda_tensors = [t for t in tensors if t.is_cuda]
host_tensors = [t for t in tensors if not t.is_cuda]
_mem_report(cuda_tensors, 'GPU')
_mem_report(host_tensors, 'CPU')
| 2,645 | 33.363636 | 95 | py |
DROO | DROO-master/memoryPyTorch.py | # #################################################################
# This file contains the main DROO operations, including building DNN,
# Storing data sample, Training DNN, and generating quantized binary offloading decisions.
# version 1.0 -- February 2020. Written based on Tensorflow 2 by Weijian Pan and
# Liang Huang (lianghuang AT zjut.edu.cn)
# ###################################################################
from __future__ import print_function
import torch
import torch.optim as optim
import torch.nn as nn
import numpy as np
print(torch.__version__)
# DNN network for memory
class MemoryDNN:
def __init__(
self,
net,
learning_rate = 0.01,
training_interval=10,
batch_size=100,
memory_size=1000,
output_graph=False
):
self.net = net
self.training_interval = training_interval # learn every #training_interval
self.lr = learning_rate
self.batch_size = batch_size
self.memory_size = memory_size
# store all binary actions
self.enumerate_actions = []
# stored # memory entry
self.memory_counter = 1
# store training cost
self.cost_his = []
# initialize zero memory [h, m]
self.memory = np.zeros((self.memory_size, self.net[0] + self.net[-1]))
# construct memory network
self._build_net()
def _build_net(self):
self.model = nn.Sequential(
nn.Linear(self.net[0], self.net[1]),
nn.ReLU(),
nn.Linear(self.net[1], self.net[2]),
nn.ReLU(),
nn.Linear(self.net[2], self.net[3]),
nn.Sigmoid()
)
def remember(self, h, m):
# replace the old memory with new memory
idx = self.memory_counter % self.memory_size
self.memory[idx, :] = np.hstack((h, m))
self.memory_counter += 1
def encode(self, h, m):
# encoding the entry
self.remember(h, m)
# train the DNN every 10 step
# if self.memory_counter> self.memory_size / 2 and self.memory_counter % self.training_interval == 0:
if self.memory_counter % self.training_interval == 0:
self.learn()
def learn(self):
# sample batch memory from all memory
if self.memory_counter > self.memory_size:
sample_index = np.random.choice(self.memory_size, size=self.batch_size)
else:
sample_index = np.random.choice(self.memory_counter, size=self.batch_size)
batch_memory = self.memory[sample_index, :]
h_train = torch.Tensor(batch_memory[:, 0: self.net[0]])
m_train = torch.Tensor(batch_memory[:, self.net[0]:])
# train the DNN
optimizer = optim.Adam(self.model.parameters(), lr=self.lr,betas = (0.09,0.999),weight_decay=0.0001)
criterion = nn.BCELoss()
self.model.train()
optimizer.zero_grad()
predict = self.model(h_train)
loss = criterion(predict, m_train)
loss.backward()
optimizer.step()
self.cost = loss.item()
assert(self.cost > 0)
self.cost_his.append(self.cost)
def decode(self, h, k = 1, mode = 'OP'):
# to have batch dimension when feed into Tensor
h = torch.Tensor(h[np.newaxis, :])
self.model.eval()
m_pred = self.model(h)
m_pred = m_pred.detach().numpy()
if mode is 'OP':
return self.knm(m_pred[0], k)
elif mode is 'KNN':
return self.knn(m_pred[0], k)
else:
print("The action selection must be 'OP' or 'KNN'")
def knm(self, m, k = 1):
# return k order-preserving binary actions
m_list = []
# generate the first binary offloading decision with respect to equation (8)
m_list.append(1*(m>0.5))
if k > 1:
# generate the remaining K-1 binary offloading decisions with respect to equation (9)
m_abs = abs(m-0.5)
idx_list = np.argsort(m_abs)[:k-1]
for i in range(k-1):
if m[idx_list[i]] >0.5:
# set the \hat{x}_{t,(k-1)} to 0
m_list.append(1*(m - m[idx_list[i]] > 0))
else:
# set the \hat{x}_{t,(k-1)} to 1
m_list.append(1*(m - m[idx_list[i]] >= 0))
return m_list
def knn(self, m, k = 1):
# list all 2^N binary offloading actions
if len(self.enumerate_actions) is 0:
import itertools
self.enumerate_actions = np.array(list(map(list, itertools.product([0, 1], repeat=self.net[0]))))
# the 2-norm
sqd = ((self.enumerate_actions - m)**2).sum(1)
idx = np.argsort(sqd)
return self.enumerate_actions[idx[:k]]
def plot_cost(self):
import matplotlib.pyplot as plt
plt.plot(np.arange(len(self.cost_his))*self.training_interval, self.cost_his)
plt.ylabel('Training Loss')
plt.xlabel('Time Frames')
plt.show()
| 5,082 | 31.583333 | 109 | py |
DROO | DROO-master/memoryTF2.py | # #################################################################
# This file contains the main DROO operations, including building DNN,
# Storing data sample, Training DNN, and generating quantized binary offloading decisions.
# version 1.0 -- January 2020. Written based on Tensorflow 2 by Weijian Pan and
# Liang Huang (lianghuang AT zjut.edu.cn)
# #################################################################
from __future__ import print_function
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np
print(tf.__version__)
print(tf.keras.__version__)
# DNN network for memory
class MemoryDNN:
def __init__(
self,
net,
learning_rate = 0.01,
training_interval=10,
batch_size=100,
memory_size=1000,
output_graph=False
):
self.net = net # the size of the DNN
self.training_interval = training_interval # learn every #training_interval
self.lr = learning_rate
self.batch_size = batch_size
self.memory_size = memory_size
# store all binary actions
self.enumerate_actions = []
# stored # memory entry
self.memory_counter = 1
# store training cost
self.cost_his = []
# initialize zero memory [h, m]
self.memory = np.zeros((self.memory_size, self.net[0] + self.net[-1]))
# construct memory network
self._build_net()
def _build_net(self):
self.model = keras.Sequential([
layers.Dense(self.net[1], activation='relu'), # the first hidden layer
layers.Dense(self.net[2], activation='relu'), # the second hidden layer
layers.Dense(self.net[-1], activation='sigmoid') # the output layer
])
self.model.compile(optimizer=keras.optimizers.Adam(lr=self.lr), loss=tf.losses.binary_crossentropy, metrics=['accuracy'])
def remember(self, h, m):
# replace the old memory with new memory
idx = self.memory_counter % self.memory_size
self.memory[idx, :] = np.hstack((h, m))
self.memory_counter += 1
def encode(self, h, m):
# encoding the entry
self.remember(h, m)
# train the DNN every 10 step
# if self.memory_counter> self.memory_size / 2 and self.memory_counter % self.training_interval == 0:
if self.memory_counter % self.training_interval == 0:
self.learn()
def learn(self):
# sample batch memory from all memory
if self.memory_counter > self.memory_size:
sample_index = np.random.choice(self.memory_size, size=self.batch_size)
else:
sample_index = np.random.choice(self.memory_counter, size=self.batch_size)
batch_memory = self.memory[sample_index, :]
h_train = batch_memory[:, 0: self.net[0]]
m_train = batch_memory[:, self.net[0]:]
# print(h_train) # (128, 10)
# print(m_train) # (128, 10)
# train the DNN
hist = self.model.fit(h_train, m_train, verbose=0)
self.cost = hist.history['loss'][0]
assert(self.cost > 0)
self.cost_his.append(self.cost)
def decode(self, h, k = 1, mode = 'OP'):
# to have batch dimension when feed into tf placeholder
h = h[np.newaxis, :]
m_pred = self.model.predict(h)
if mode is 'OP':
return self.knm(m_pred[0], k)
elif mode is 'KNN':
return self.knn(m_pred[0], k)
else:
print("The action selection must be 'OP' or 'KNN'")
def knm(self, m, k = 1):
# return k order-preserving binary actions
m_list = []
# generate the first binary offloading decision with respect to equation (8)
m_list.append(1*(m>0.5))
if k > 1:
# generate the remaining K-1 binary offloading decisions with respect to equation (9)
m_abs = abs(m-0.5)
idx_list = np.argsort(m_abs)[:k-1]
for i in range(k-1):
if m[idx_list[i]] >0.5:
# set the \hat{x}_{t,(k-1)} to 0
m_list.append(1*(m - m[idx_list[i]] > 0))
else:
# set the \hat{x}_{t,(k-1)} to 1
m_list.append(1*(m - m[idx_list[i]] >= 0))
return m_list
def knn(self, m, k = 1):
# list all 2^N binary offloading actions
if len(self.enumerate_actions) is 0:
import itertools
self.enumerate_actions = np.array(list(map(list, itertools.product([0, 1], repeat=self.net[0]))))
# the 2-norm
sqd = ((self.enumerate_actions - m)**2).sum(1)
idx = np.argsort(sqd)
return self.enumerate_actions[idx[:k]]
def plot_cost(self):
import matplotlib.pyplot as plt
plt.plot(np.arange(len(self.cost_his))*self.training_interval, self.cost_his)
plt.ylabel('Training Loss')
plt.xlabel('Time Frames')
plt.show()
| 5,117 | 33.816327 | 129 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/exps/der_womask/cifar100/b0/10steps/main.py | '''
@Author : Yan Shipeng, Xie Jiangwei
@Contact: yanshp@shanghaitech.edu.cn, xiejw@shanghaitech.edu.cn
'''
import sys
import os
import os.path as osp
import copy
import time
import shutil
import cProfile
import logging
from pathlib import Path
import numpy as np
import random
from easydict import EasyDict as edict
from tensorboardX import SummaryWriter
repo_name = 'DER-ClassIL.pytorch'
base_dir = osp.realpath(".")[:osp.realpath(".").index(repo_name) + len(repo_name)]
sys.path.insert(0, base_dir)
from sacred import Experiment
ex = Experiment(base_dir=base_dir)
# Save which files
# ex.add_source_file(osp.join(base_dir, "inclearn/models/icarl.py"))
# ex.add_source_file(osp.join(base_dir, "inclearn/lib/data.py"))
# ex.add_source_file(osp.join(base_dir, "inclearn/lib/network.py"))
# ex.add_source_file(osp.join(base_dir, "inclearn/convnet/resnet.py"))
# ex.add_source_file(osp.join(os.getcwd(), "icarl.py"))
# ex.add_source_file(osp.join(os.getcwd(), "network.py"))
# ex.add_source_file(osp.join(os.getcwd(), "resnet.py"))
# MongoDB Observer
# ex.observers.append(MongoObserver.create(url='xx.xx.xx.xx:port', db_name='classil'))
import torch
from inclearn.tools import factory, results_utils, utils
from inclearn.learn.pretrain import pretrain
from inclearn.tools.metrics import IncConfusionMeter
def initialization(config, seed, mode, exp_id):
# Add it if your input size is fixed
# ref: https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936
torch.backends.cudnn.benchmark = True # This will result in non-deterministic results.
# ex.captured_out_filter = lambda text: 'Output capturing turned off.'
cfg = edict(config)
utils.set_seed(cfg['seed'])
if exp_id is None:
exp_id = -1
cfg.exp.savedir = "./logs"
logger = utils.make_logger(f"exp{exp_id}_{cfg.exp.name}_{mode}", savedir=cfg.exp.savedir)
# Tensorboard
exp_name = f'{exp_id}_{cfg["exp"]["name"]}' if exp_id is not None else f'../inbox/{cfg["exp"]["name"]}'
tensorboard_dir = cfg["exp"]["tensorboard_dir"] + f"/{exp_name}"
# If not only save latest tensorboard log.
# if Path(tensorboard_dir).exists():
# shutil.move(tensorboard_dir, cfg["exp"]["tensorboard_dir"] + f"/../inbox/{time.time()}_{exp_name}")
tensorboard = SummaryWriter(tensorboard_dir)
return cfg, logger, tensorboard
@ex.command
def train(_run, _rnd, _seed):
cfg, ex.logger, tensorboard = initialization(_run.config, _seed, "train", _run._id)
ex.logger.info(cfg)
cfg.data_folder = osp.join(base_dir, "data")
start_time = time.time()
_train(cfg, _run, ex, tensorboard)
ex.logger.info("Training finished in {}s.".format(int(time.time() - start_time)))
def _train(cfg, _run, ex, tensorboard):
device = factory.set_device(cfg)
trial_i = cfg['trial']
inc_dataset = factory.get_data(cfg, trial_i)
ex.logger.info("classes_order")
ex.logger.info(inc_dataset.class_order)
model = factory.get_model(cfg, trial_i, _run, ex, tensorboard, inc_dataset)
if _run.meta_info["options"]["--file_storage"] is not None:
_save_dir = osp.join(_run.meta_info["options"]["--file_storage"], str(_run._id))
else:
_save_dir = cfg["exp"]["ckptdir"]
results = results_utils.get_template_results(cfg)
for task_i in range(inc_dataset.n_tasks):
task_info, train_loader, val_loader, test_loader = inc_dataset.new_task()
model.set_task_info(
task=task_info["task"],
total_n_classes=task_info["max_class"],
increment=task_info["increment"],
n_train_data=task_info["n_train_data"],
n_test_data=task_info["n_test_data"],
n_tasks=inc_dataset.n_tasks,
)
model.before_task(task_i, inc_dataset)
# TODO: Move to incmodel.py
if 'min_class' in task_info:
ex.logger.info("Train on {}->{}.".format(task_info["min_class"], task_info["max_class"]))
# Pretraining at step0 if needed
if task_i == 0 and cfg["start_class"] > 0:
do_pretrain(cfg, ex, model, device, train_loader, test_loader)
inc_dataset.shared_data_inc = train_loader.dataset.share_memory
elif task_i < cfg['start_task']:
state_dict = torch.load(f'./ckpts/step{task_i}.ckpt')
model._parallel_network.load_state_dict(state_dict)
inc_dataset.shared_data_inc = train_loader.dataset.share_memory
else:
model.train_task(train_loader, val_loader)
model.after_task(task_i, inc_dataset)
ex.logger.info("Eval on {}->{}.".format(0, task_info["max_class"]))
ypred, ytrue = model.eval_task(test_loader)
acc_stats = utils.compute_accuracy(ypred, ytrue, increments=model._increments, n_classes=model._n_classes)
#Logging
model._tensorboard.add_scalar(f"taskaccu/trial{trial_i}", acc_stats["top1"]["total"], task_i)
_run.log_scalar(f"trial{trial_i}_taskaccu", acc_stats["top1"]["total"], task_i)
_run.log_scalar(f"trial{trial_i}_task_top5_accu", acc_stats["top5"]["total"], task_i)
ex.logger.info(f"top1:{acc_stats['top1']}")
ex.logger.info(f"top5:{acc_stats['top5']}")
results["results"].append(acc_stats)
top1_avg_acc, top5_avg_acc = results_utils.compute_avg_inc_acc(results["results"])
_run.info[f"trial{trial_i}"][f"avg_incremental_accu_top1"] = top1_avg_acc
_run.info[f"trial{trial_i}"][f"avg_incremental_accu_top5"] = top5_avg_acc
ex.logger.info("Average Incremental Accuracy Top 1: {} Top 5: {}.".format(
_run.info[f"trial{trial_i}"][f"avg_incremental_accu_top1"],
_run.info[f"trial{trial_i}"][f"avg_incremental_accu_top5"],
))
if cfg["exp"]["name"]:
results_utils.save_results(results, cfg["exp"]["name"])
def do_pretrain(cfg, ex, model, device, train_loader, test_loader):
if not os.path.exists(osp.join(ex.base_dir, 'pretrain/')):
os.makedirs(osp.join(ex.base_dir, 'pretrain/'))
model_path = osp.join(
ex.base_dir,
"pretrain/{}_{}_cosine_{}_multi_{}_aux{}_nplus1_{}_{}_trial_{}_{}_seed_{}_start_{}_epoch_{}.pth".format(
cfg["model"],
cfg["convnet"],
cfg["weight_normalization"],
cfg["der"],
cfg["use_aux_cls"],
cfg["aux_n+1"],
cfg["dataset"],
cfg["trial"],
cfg["train_head"],
cfg['seed'],
cfg["start_class"],
cfg["pretrain"]["epochs"],
),
)
if osp.exists(model_path):
print("Load pretrain model")
if hasattr(model._network, "module"):
model._network.module.load_state_dict(torch.load(model_path))
else:
model._network.load_state_dict(torch.load(model_path))
else:
pretrain(cfg, ex, model, device, train_loader, test_loader, model_path)
@ex.command
def test(_run, _rnd, _seed):
cfg, ex.logger, tensorboard = initialization(_run.config, _seed, "test", _run._id)
ex.logger.info(cfg)
trial_i = cfg['trial']
cfg.data_folder = osp.join(base_dir, "data")
inc_dataset = factory.get_data(cfg, trial_i)
# inc_dataset._current_task = taski
# train_loader = inc_dataset._get_loader(inc_dataset.data_cur, inc_dataset.targets_cur)
model = factory.get_model(cfg, trial_i, _run, ex, tensorboard, inc_dataset)
model._network.task_size = cfg.increment
test_results = results_utils.get_template_results(cfg)
for taski in range(inc_dataset.n_tasks):
task_info, train_loader, _, test_loader = inc_dataset.new_task()
model.set_task_info(
task=task_info["task"],
total_n_classes=task_info["max_class"],
increment=task_info["increment"],
n_train_data=task_info["n_train_data"],
n_test_data=task_info["n_test_data"],
n_tasks=task_info["max_task"]
)
model.before_task(taski, inc_dataset)
state_dict = torch.load(f'./ckpts/step{taski}.ckpt')
model._parallel_network.load_state_dict(state_dict)
model.eval()
#Build exemplars
model.after_task(taski, inc_dataset)
ypred, ytrue = model.eval_task(test_loader)
test_acc_stats = utils.compute_accuracy(ypred, ytrue, increments=model._increments, n_classes=model._n_classes)
test_results['results'].append(test_acc_stats)
ex.logger.info(f"task{taski} test top1acc:{test_acc_stats['top1']}")
avg_test_acc = results_utils.compute_avg_inc_acc(test_results['results'])
ex.logger.info(f"Test Average Incremental Accuracy: {avg_test_acc}")
if __name__ == "__main__":
# ex.add_config('./codes/base/configs/default.yaml')
ex.add_config("./configs/default.yaml")
ex.run_commandline()
| 8,825 | 37.710526 | 119 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/exps/der_womask/cifar100/b50/10steps/main.py | '''
@Author : Yan Shipeng, Xie Jiangwei
@Contact: yanshp@shanghaitech.edu.cn, xiejw@shanghaitech.edu.cn
'''
import sys
import os
import os.path as osp
import copy
import time
import shutil
import cProfile
import logging
from pathlib import Path
import numpy as np
import random
from easydict import EasyDict as edict
from tensorboardX import SummaryWriter
repo_name = 'DER-ClassIL.pytorch'
base_dir = osp.realpath(".")[:osp.realpath(".").index(repo_name) + len(repo_name)]
sys.path.insert(0, base_dir)
from sacred import Experiment
ex = Experiment(base_dir=base_dir)
# Save which files
# ex.add_source_file(osp.join(base_dir, "inclearn/models/icarl.py"))
# ex.add_source_file(osp.join(base_dir, "inclearn/lib/data.py"))
# ex.add_source_file(osp.join(base_dir, "inclearn/lib/network.py"))
# ex.add_source_file(osp.join(base_dir, "inclearn/convnet/resnet.py"))
# ex.add_source_file(osp.join(os.getcwd(), "icarl.py"))
# ex.add_source_file(osp.join(os.getcwd(), "network.py"))
# ex.add_source_file(osp.join(os.getcwd(), "resnet.py"))
# MongoDB Observer
# ex.observers.append(MongoObserver.create(url='xx.xx.xx.xx:port', db_name='classil'))
import torch
from inclearn.tools import factory, results_utils, utils
from inclearn.learn.pretrain import pretrain
from inclearn.tools.metrics import IncConfusionMeter
def initialization(config, seed, mode, exp_id):
# Add it if your input size is fixed
# ref: https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936
torch.backends.cudnn.benchmark = True # This will result in non-deterministic results.
# ex.captured_out_filter = lambda text: 'Output capturing turned off.'
cfg = edict(config)
utils.set_seed(cfg['seed'])
if exp_id is None:
exp_id = -1
cfg.exp.savedir = "./logs"
logger = utils.make_logger(f"exp{exp_id}_{cfg.exp.name}_{mode}", savedir=cfg.exp.savedir)
# Tensorboard
exp_name = f'{exp_id}_{cfg["exp"]["name"]}' if exp_id is not None else f'../inbox/{cfg["exp"]["name"]}'
tensorboard_dir = cfg["exp"]["tensorboard_dir"] + f"/{exp_name}"
# If not only save latest tensorboard log.
# if Path(tensorboard_dir).exists():
# shutil.move(tensorboard_dir, cfg["exp"]["tensorboard_dir"] + f"/../inbox/{time.time()}_{exp_name}")
tensorboard = SummaryWriter(tensorboard_dir)
return cfg, logger, tensorboard
@ex.command
def train(_run, _rnd, _seed):
cfg, ex.logger, tensorboard = initialization(_run.config, _seed, "train", _run._id)
ex.logger.info(cfg)
cfg.data_folder = osp.join(base_dir, "data")
start_time = time.time()
_train(cfg, _run, ex, tensorboard)
ex.logger.info("Training finished in {}s.".format(int(time.time() - start_time)))
def _train(cfg, _run, ex, tensorboard):
device = factory.set_device(cfg)
trial_i = cfg['trial']
inc_dataset = factory.get_data(cfg, trial_i)
ex.logger.info("classes_order")
ex.logger.info(inc_dataset.class_order)
model = factory.get_model(cfg, trial_i, _run, ex, tensorboard, inc_dataset)
if _run.meta_info["options"]["--file_storage"] is not None:
_save_dir = osp.join(_run.meta_info["options"]["--file_storage"], str(_run._id))
else:
_save_dir = cfg["exp"]["ckptdir"]
results = results_utils.get_template_results(cfg)
for task_i in range(inc_dataset.n_tasks):
task_info, train_loader, val_loader, test_loader = inc_dataset.new_task()
model.set_task_info(
task=task_info["task"],
total_n_classes=task_info["max_class"],
increment=task_info["increment"],
n_train_data=task_info["n_train_data"],
n_test_data=task_info["n_test_data"],
n_tasks=inc_dataset.n_tasks,
)
model.before_task(task_i, inc_dataset)
# TODO: Move to incmodel.py
if 'min_class' in task_info:
ex.logger.info("Train on {}->{}.".format(task_info["min_class"], task_info["max_class"]))
# Pretraining at step0 if needed
if task_i == 0 and cfg["start_class"] > 0:
do_pretrain(cfg, ex, model, device, train_loader, test_loader)
inc_dataset.shared_data_inc = train_loader.dataset.share_memory
elif task_i < cfg['start_task']:
state_dict = torch.load(f'./ckpts/step{task_i}.ckpt')
model._parallel_network.load_state_dict(state_dict)
inc_dataset.shared_data_inc = train_loader.dataset.share_memory
else:
model.train_task(train_loader, val_loader)
model.after_task(task_i, inc_dataset)
ex.logger.info("Eval on {}->{}.".format(0, task_info["max_class"]))
ypred, ytrue = model.eval_task(test_loader)
acc_stats = utils.compute_accuracy(ypred, ytrue, increments=model._increments, n_classes=model._n_classes)
#Logging
model._tensorboard.add_scalar(f"taskaccu/trial{trial_i}", acc_stats["top1"]["total"], task_i)
_run.log_scalar(f"trial{trial_i}_taskaccu", acc_stats["top1"]["total"], task_i)
_run.log_scalar(f"trial{trial_i}_task_top5_accu", acc_stats["top5"]["total"], task_i)
ex.logger.info(f"top1:{acc_stats['top1']}")
ex.logger.info(f"top5:{acc_stats['top5']}")
results["results"].append(acc_stats)
top1_avg_acc, top5_avg_acc = results_utils.compute_avg_inc_acc(results["results"])
_run.info[f"trial{trial_i}"][f"avg_incremental_accu_top1"] = top1_avg_acc
_run.info[f"trial{trial_i}"][f"avg_incremental_accu_top5"] = top5_avg_acc
ex.logger.info("Average Incremental Accuracy Top 1: {} Top 5: {}.".format(
_run.info[f"trial{trial_i}"][f"avg_incremental_accu_top1"],
_run.info[f"trial{trial_i}"][f"avg_incremental_accu_top5"],
))
if cfg["exp"]["name"]:
results_utils.save_results(results, cfg["exp"]["name"])
def do_pretrain(cfg, ex, model, device, train_loader, test_loader):
if not os.path.exists(osp.join(ex.base_dir, 'pretrain/')):
os.makedirs(osp.join(ex.base_dir, 'pretrain/'))
model_path = osp.join(
ex.base_dir,
"pretrain/{}_{}_cosine_{}_multi_{}_aux{}_nplus1_{}_{}_trial_{}_{}_seed_{}_start_{}_epoch_{}.pth".format(
cfg["model"],
cfg["convnet"],
cfg["weight_normalization"],
cfg["der"],
cfg["use_aux_cls"],
cfg["aux_n+1"],
cfg["dataset"],
cfg["trial"],
cfg["train_head"],
cfg['seed'],
cfg["start_class"],
cfg["pretrain"]["epochs"],
),
)
if osp.exists(model_path):
print("Load pretrain model")
if hasattr(model._network, "module"):
model._network.module.load_state_dict(torch.load(model_path))
else:
model._network.load_state_dict(torch.load(model_path))
else:
pretrain(cfg, ex, model, device, train_loader, test_loader, model_path)
@ex.command
def test(_run, _rnd, _seed):
cfg, ex.logger, tensorboard = initialization(_run.config, _seed, "test", _run._id)
ex.logger.info(cfg)
trial_i = cfg['trial']
cfg.data_folder = osp.join(base_dir, "data")
inc_dataset = factory.get_data(cfg, trial_i)
# inc_dataset._current_task = taski
# train_loader = inc_dataset._get_loader(inc_dataset.data_cur, inc_dataset.targets_cur)
model = factory.get_model(cfg, trial_i, _run, ex, tensorboard, inc_dataset)
model._network.task_size = cfg.increment
test_results = results_utils.get_template_results(cfg)
for taski in range(inc_dataset.n_tasks):
task_info, train_loader, _, test_loader = inc_dataset.new_task()
model.set_task_info(
task=task_info["task"],
total_n_classes=task_info["max_class"],
increment=task_info["increment"],
n_train_data=task_info["n_train_data"],
n_test_data=task_info["n_test_data"],
n_tasks=task_info["max_task"]
)
model.before_task(taski, inc_dataset)
state_dict = torch.load(f'./ckpts/step{taski}.ckpt')
model._parallel_network.load_state_dict(state_dict)
model.eval()
#Build exemplars
model.after_task(taski, inc_dataset)
ypred, ytrue = model.eval_task(test_loader)
test_acc_stats = utils.compute_accuracy(ypred, ytrue, increments=model._increments, n_classes=model._n_classes)
test_results['results'].append(test_acc_stats)
ex.logger.info(f"task{taski} test top1acc:{test_acc_stats['top1']}")
avg_test_acc = results_utils.compute_avg_inc_acc(test_results['results'])
ex.logger.info(f"Test Average Incremental Accuracy: {avg_test_acc}")
if __name__ == "__main__":
# ex.add_config('./codes/base/configs/default.yaml')
ex.add_config("./configs/default.yaml")
ex.run_commandline()
| 8,825 | 37.710526 | 119 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/exps/der_womask/imagenet-100/b0_10s/main.py | '''
@Author : Yan Shipeng, Xie Jiangwei
@Contact: yanshp@shanghaitech.edu.cn, xiejw@shanghaitech.edu.cn
'''
import sys
import os
import os.path as osp
import copy
import time
import shutil
import cProfile
import logging
from pathlib import Path
import numpy as np
import random
from easydict import EasyDict as edict
from tensorboardX import SummaryWriter
repo_name = 'DER-ClassIL.pytorch'
base_dir = osp.realpath(".")[:osp.realpath(".").index(repo_name) + len(repo_name)]
sys.path.insert(0, base_dir)
from sacred import Experiment
ex = Experiment(base_dir=base_dir)
# Save which files
# ex.add_source_file(osp.join(base_dir, "inclearn/models/icarl.py"))
# ex.add_source_file(osp.join(base_dir, "inclearn/lib/data.py"))
# ex.add_source_file(osp.join(base_dir, "inclearn/lib/network.py"))
# ex.add_source_file(osp.join(base_dir, "inclearn/convnet/resnet.py"))
# ex.add_source_file(osp.join(os.getcwd(), "icarl.py"))
# ex.add_source_file(osp.join(os.getcwd(), "network.py"))
# ex.add_source_file(osp.join(os.getcwd(), "resnet.py"))
# MongoDB Observer
# ex.observers.append(MongoObserver.create(url='xx.xx.xx.xx:port', db_name='classil'))
import torch
from inclearn.tools import factory, results_utils, utils
from inclearn.learn.pretrain import pretrain
from inclearn.tools.metrics import IncConfusionMeter
def initialization(config, seed, mode, exp_id):
# Add it if your input size is fixed
# ref: https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936
torch.backends.cudnn.benchmark = True # This will result in non-deterministic results.
# ex.captured_out_filter = lambda text: 'Output capturing turned off.'
cfg = edict(config)
utils.set_seed(cfg['seed'])
if exp_id is None:
exp_id = -1
cfg.exp.savedir = "./logs"
logger = utils.make_logger(f"exp{exp_id}_{cfg.exp.name}_{mode}", savedir=cfg.exp.savedir)
# Tensorboard
exp_name = f'{exp_id}_{cfg["exp"]["name"]}' if exp_id is not None else f'../inbox/{cfg["exp"]["name"]}'
tensorboard_dir = cfg["exp"]["tensorboard_dir"] + f"/{exp_name}"
# If not only save latest tensorboard log.
# if Path(tensorboard_dir).exists():
# shutil.move(tensorboard_dir, cfg["exp"]["tensorboard_dir"] + f"/../inbox/{time.time()}_{exp_name}")
tensorboard = SummaryWriter(tensorboard_dir)
return cfg, logger, tensorboard
@ex.command
def train(_run, _rnd, _seed):
cfg, ex.logger, tensorboard = initialization(_run.config, _seed, "train", _run._id)
cfg.data_folder = osp.join(base_dir, "data")
start_time = time.time()
_train(cfg, _run, ex, tensorboard)
ex.logger.info("Training finished in {}s.".format(int(time.time() - start_time)))
def _train(cfg, _run, ex, tensorboard):
device = factory.set_device(cfg)
trial_i = cfg['trial']
inc_dataset = factory.get_data(cfg, trial_i)
ex.logger.info("classes_order")
ex.logger.info(inc_dataset.class_order)
model = factory.get_model(cfg, trial_i, _run, ex, tensorboard, inc_dataset)
if _run.meta_info["options"]["--file_storage"] is not None:
_save_dir = osp.join(_run.meta_info["options"]["--file_storage"], str(_run._id))
else:
_save_dir = cfg["exp"]["ckptdir"]
results = results_utils.get_template_results(cfg)
for task_i in range(inc_dataset.n_tasks):
task_info, train_loader, val_loader, test_loader = inc_dataset.new_task()
model.set_task_info(
task=task_info["task"],
total_n_classes=task_info["max_class"],
increment=task_info["increment"],
n_train_data=task_info["n_train_data"],
n_test_data=task_info["n_test_data"],
n_tasks=inc_dataset.n_tasks,
)
model.before_task(task_i, inc_dataset)
# TODO: Move to incmodel.py
if 'min_class' in task_info:
ex.logger.info("Train on {}->{}.".format(task_info["min_class"], task_info["max_class"]))
# Pretraining at step0 if needed
if task_i == 0 and cfg["start_class"] > 0:
do_pretrain(cfg, ex, model, device, train_loader, test_loader)
inc_dataset.shared_data_inc = train_loader.dataset.share_memory
elif task_i < cfg['start_task']:
state_dict = torch.load(f'./ckpts/step{task_i}.ckpt')
model._parallel_network.load_state_dict(state_dict)
inc_dataset.shared_data_inc = train_loader.dataset.share_memory
else:
model.train_task(train_loader, val_loader)
model.after_task(task_i, inc_dataset)
ex.logger.info("Eval on {}->{}.".format(0, task_info["max_class"]))
ypred, ytrue = model.eval_task(test_loader)
acc_stats = utils.compute_accuracy(ypred, ytrue, increments=model._increments, n_classes=model._n_classes)
#Logging
model._tensorboard.add_scalar(f"taskaccu/trial{trial_i}", acc_stats["top1"]["total"], task_i)
_run.log_scalar(f"trial{trial_i}_taskaccu", acc_stats["top1"]["total"], task_i)
_run.log_scalar(f"trial{trial_i}_task_top5_accu", acc_stats["top5"]["total"], task_i)
ex.logger.info(f"top1:{acc_stats['top1']}")
ex.logger.info(f"top5:{acc_stats['top5']}")
results["results"].append(acc_stats)
top1_avg_acc, top5_avg_acc = results_utils.compute_avg_inc_acc(results["results"])
_run.info[f"trial{trial_i}"][f"avg_incremental_accu_top1"] = top1_avg_acc
_run.info[f"trial{trial_i}"][f"avg_incremental_accu_top5"] = top5_avg_acc
ex.logger.info("Average Incremental Accuracy Top 1: {} Top 5: {}.".format(
_run.info[f"trial{trial_i}"][f"avg_incremental_accu_top1"],
_run.info[f"trial{trial_i}"][f"avg_incremental_accu_top5"],
))
if cfg["exp"]["name"]:
results_utils.save_results(results, cfg["exp"]["name"])
def do_pretrain(cfg, ex, model, device, train_loader, test_loader):
if not os.path.exists(osp.join(ex.base_dir, 'pretrain/')):
os.makedirs(osp.join(ex.base_dir, 'pretrain/'))
model_path = osp.join(
ex.base_dir,
"pretrain/{}_{}_cosine_{}_multi_{}_aux{}_nplus1_{}_{}_trial_{}_{}_seed_{}_start_{}_epoch_{}.pth".format(
cfg["model"],
cfg["convnet"],
cfg["weight_normalization"],
cfg["der"],
cfg["use_aux_cls"],
cfg["aux_n+1"],
cfg["dataset"],
cfg["trial"],
cfg["train_head"],
cfg['seed'],
cfg["start_class"],
cfg["pretrain"]["epochs"],
),
)
if osp.exists(model_path):
print("Load pretrain model")
if hasattr(model._network, "module"):
model._network.module.load_state_dict(torch.load(model_path))
else:
model._network.load_state_dict(torch.load(model_path))
else:
pretrain(cfg, ex, model, device, train_loader, test_loader, model_path)
@ex.command
def test(_run, _rnd, _seed):
cfg, ex.logger, tensorboard = initialization(_run.config, _seed, "test", _run._id)
trial_i = cfg['trial']
cfg.data_folder = osp.join(base_dir, "data")
inc_dataset = factory.get_data(cfg, trial_i)
# inc_dataset._current_task = taski
# train_loader = inc_dataset._get_loader(inc_dataset.data_cur, inc_dataset.targets_cur)
model = factory.get_model(cfg, trial_i, _run, ex, tensorboard, inc_dataset)
model._network.task_size = cfg.increment
test_results = results_utils.get_template_results(cfg)
for taski in range(inc_dataset.n_tasks):
task_info, train_loader, _, test_loader = inc_dataset.new_task()
model.set_task_info(
task=task_info["task"],
total_n_classes=task_info["max_class"],
increment=task_info["increment"],
n_train_data=task_info["n_train_data"],
n_test_data=task_info["n_test_data"],
n_tasks=task_info["max_task"]
)
model.before_task(taski, inc_dataset)
state_dict = torch.load(f'./ckpts/step{taski}.ckpt')
model._parallel_network.load_state_dict(state_dict)
model.eval()
#Build exemplars
model.after_task(taski, inc_dataset)
ypred, ytrue = model.eval_task(test_loader)
test_acc_stats = utils.compute_accuracy(ypred, ytrue, increments=model._increments, n_classes=model._n_classes)
test_results['results'].append(test_acc_stats)
ex.logger.info(f"task{taski} test top1acc:{test_acc_stats['top1']}")
avg_test_acc = results_utils.compute_avg_inc_acc(test_results['results'])
ex.logger.info(f"Test Average Incremental Accuracy: {avg_test_acc}")
if __name__ == "__main__":
# ex.add_config('./codes/base/configs/default.yaml')
ex.add_config("./configs/default.yaml")
ex.run_commandline()
| 8,777 | 37.840708 | 119 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/exps/weight_align/cifar100/b0/10steps/main.py | '''
@Author : Yan Shipeng, Xie Jiangwei
@Contact: yanshp@shanghaitech.edu.cn, xiejw@shanghaitech.edu.cn
'''
import sys
import os
import os.path as osp
import copy
import time
import shutil
import cProfile
import logging
from pathlib import Path
import numpy as np
import random
from easydict import EasyDict as edict
from tensorboardX import SummaryWriter
repo_name = 'DER-ClassIL.pytorch'
base_dir = osp.realpath(".")[:osp.realpath(".").index(repo_name) + len(repo_name)]
sys.path.insert(0, base_dir)
from sacred import Experiment
ex = Experiment(base_dir=base_dir)
# Save which files
# ex.add_source_file(osp.join(base_dir, "inclearn/models/icarl.py"))
# ex.add_source_file(osp.join(base_dir, "inclearn/lib/data.py"))
# ex.add_source_file(osp.join(base_dir, "inclearn/lib/network.py"))
# ex.add_source_file(osp.join(base_dir, "inclearn/convnet/resnet.py"))
# ex.add_source_file(osp.join(os.getcwd(), "icarl.py"))
# ex.add_source_file(osp.join(os.getcwd(), "network.py"))
# ex.add_source_file(osp.join(os.getcwd(), "resnet.py"))
# MongoDB Observer
# ex.observers.append(MongoObserver.create(url='xx.xx.xx.xx:port', db_name='classil'))
import torch
from inclearn.tools import factory, results_utils, utils
from inclearn.learn.pretrain import pretrain
from inclearn.tools.metrics import IncConfusionMeter
def initialization(config, seed, mode, exp_id):
# Add it if your input size is fixed
# ref: https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936
torch.backends.cudnn.benchmark = True # This will result in non-deterministic results.
# ex.captured_out_filter = lambda text: 'Output capturing turned off.'
cfg = edict(config)
utils.set_seed(cfg['seed'])
if exp_id is None:
exp_id = -1
cfg.exp.savedir = "./logs"
logger = utils.make_logger(f"exp{exp_id}_{cfg.exp.name}_{mode}", savedir=cfg.exp.savedir)
# Tensorboard
exp_name = f'{exp_id}_{cfg["exp"]["name"]}' if exp_id is not None else f'../inbox/{cfg["exp"]["name"]}'
tensorboard_dir = cfg["exp"]["tensorboard_dir"] + f"/{exp_name}"
# If not only save latest tensorboard log.
# if Path(tensorboard_dir).exists():
# shutil.move(tensorboard_dir, cfg["exp"]["tensorboard_dir"] + f"/../inbox/{time.time()}_{exp_name}")
tensorboard = SummaryWriter(tensorboard_dir)
return cfg, logger, tensorboard
@ex.command
def train(_run, _rnd, _seed):
cfg, ex.logger, tensorboard = initialization(_run.config, _seed, "train", _run._id)
ex.logger.info(cfg)
cfg.data_folder = osp.join(base_dir, "data")
start_time = time.time()
_train(cfg, _run, ex, tensorboard)
ex.logger.info("Training finished in {}s.".format(int(time.time() - start_time)))
def _train(cfg, _run, ex, tensorboard):
device = factory.set_device(cfg)
trial_i = cfg['trial']
inc_dataset = factory.get_data(cfg, trial_i)
ex.logger.info("classes_order")
ex.logger.info(inc_dataset.class_order)
model = factory.get_model(cfg, trial_i, _run, ex, tensorboard, inc_dataset)
if _run.meta_info["options"]["--file_storage"] is not None:
_save_dir = osp.join(_run.meta_info["options"]["--file_storage"], str(_run._id))
else:
_save_dir = cfg["exp"]["ckptdir"]
results = results_utils.get_template_results(cfg)
for task_i in range(inc_dataset.n_tasks):
task_info, train_loader, val_loader, test_loader = inc_dataset.new_task()
model.set_task_info(
task=task_info["task"],
total_n_classes=task_info["max_class"],
increment=task_info["increment"],
n_train_data=task_info["n_train_data"],
n_test_data=task_info["n_test_data"],
n_tasks=inc_dataset.n_tasks,
)
model.before_task(task_i, inc_dataset)
# TODO: Move to incmodel.py
if 'min_class' in task_info:
ex.logger.info("Train on {}->{}.".format(task_info["min_class"], task_info["max_class"]))
# Pretraining at step0 if needed
if task_i == 0 and cfg["start_class"] > 0:
do_pretrain(cfg, ex, model, device, train_loader, test_loader)
inc_dataset.shared_data_inc = train_loader.dataset.share_memory
elif task_i < cfg['start_task']:
state_dict = torch.load(f'./ckpts/step{task_i}.ckpt')
model._parallel_network.load_state_dict(state_dict)
inc_dataset.shared_data_inc = train_loader.dataset.share_memory
else:
model.train_task(train_loader, val_loader)
model.after_task(task_i, inc_dataset)
ex.logger.info("Eval on {}->{}.".format(0, task_info["max_class"]))
ypred, ytrue = model.eval_task(test_loader)
acc_stats = utils.compute_accuracy(ypred, ytrue, increments=model._increments, n_classes=model._n_classes)
#Logging
model._tensorboard.add_scalar(f"taskaccu/trial{trial_i}", acc_stats["top1"]["total"], task_i)
_run.log_scalar(f"trial{trial_i}_taskaccu", acc_stats["top1"]["total"], task_i)
_run.log_scalar(f"trial{trial_i}_task_top5_accu", acc_stats["top5"]["total"], task_i)
ex.logger.info(f"top1:{acc_stats['top1']}")
ex.logger.info(f"top5:{acc_stats['top5']}")
results["results"].append(acc_stats)
top1_avg_acc, top5_avg_acc = results_utils.compute_avg_inc_acc(results["results"])
_run.info[f"trial{trial_i}"][f"avg_incremental_accu_top1"] = top1_avg_acc
_run.info[f"trial{trial_i}"][f"avg_incremental_accu_top5"] = top5_avg_acc
ex.logger.info("Average Incremental Accuracy Top 1: {} Top 5: {}.".format(
_run.info[f"trial{trial_i}"][f"avg_incremental_accu_top1"],
_run.info[f"trial{trial_i}"][f"avg_incremental_accu_top5"],
))
if cfg["exp"]["name"]:
results_utils.save_results(results, cfg["exp"]["name"])
def do_pretrain(cfg, ex, model, device, train_loader, test_loader):
if not os.path.exists(osp.join(ex.base_dir, 'pretrain/')):
os.makedirs(osp.join(ex.base_dir, 'pretrain/'))
model_path = osp.join(
ex.base_dir,
"pretrain/{}_{}_cosine_{}_multi_{}_aux{}_nplus1_{}_{}_trial_{}_{}_seed_{}_start_{}_epoch_{}.pth".format(
cfg["model"],
cfg["convnet"],
cfg["weight_normalization"],
cfg["der"],
cfg["use_aux_cls"],
cfg["aux_n+1"],
cfg["dataset"],
cfg["trial"],
cfg["train_head"],
cfg['seed'],
cfg["start_class"],
cfg["pretrain"]["epochs"],
),
)
if osp.exists(model_path):
print("Load pretrain model")
if hasattr(model._network, "module"):
model._network.module.load_state_dict(torch.load(model_path))
else:
model._network.load_state_dict(torch.load(model_path))
else:
pretrain(cfg, ex, model, device, train_loader, test_loader, model_path)
@ex.command
def test(_run, _rnd, _seed):
cfg, ex.logger, tensorboard = initialization(_run.config, _seed, "test", _run._id)
ex.logger.info(cfg)
trial_i = cfg['trial']
cfg.data_folder = osp.join(base_dir, "data")
inc_dataset = factory.get_data(cfg, trial_i)
# inc_dataset._current_task = taski
# train_loader = inc_dataset._get_loader(inc_dataset.data_cur, inc_dataset.targets_cur)
model = factory.get_model(cfg, trial_i, _run, ex, tensorboard, inc_dataset)
model._network.task_size = cfg.increment
test_results = results_utils.get_template_results(cfg)
for taski in range(inc_dataset.n_tasks):
task_info, train_loader, _, test_loader = inc_dataset.new_task()
model.set_task_info(
task=task_info["task"],
total_n_classes=task_info["max_class"],
increment=task_info["increment"],
n_train_data=task_info["n_train_data"],
n_test_data=task_info["n_test_data"],
n_tasks=task_info["max_task"]
)
model.before_task(taski, inc_dataset)
state_dict = torch.load(f'./ckpts/step{taski}.ckpt')
model._parallel_network.load_state_dict(state_dict)
model.eval()
#Build exemplars
model.after_task(taski, inc_dataset)
ypred, ytrue = model.eval_task(test_loader)
test_acc_stats = utils.compute_accuracy(ypred, ytrue, increments=model._increments, n_classes=model._n_classes)
test_results['results'].append(test_acc_stats)
ex.logger.info(f"task{taski} test top1acc:{test_acc_stats['top1']}")
avg_test_acc = results_utils.compute_avg_inc_acc(test_results['results'])
ex.logger.info(f"Test Average Incremental Accuracy: {avg_test_acc}")
if __name__ == "__main__":
# ex.add_config('./codes/base/configs/default.yaml')
ex.add_config("./configs/default.yaml")
ex.run_commandline()
| 8,825 | 37.710526 | 119 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/learn/pretrain.py | import os.path as osp
import torch
import torch.nn.functional as F
from inclearn.tools import factory, utils
from inclearn.tools.metrics import ClassErrorMeter, AverageValueMeter
# import line_profiler
# import atexit
# profile = line_profiler.LineProfiler()
# atexit.register(profile.print_stats)
def _compute_loss(cfg, logits, targets, device):
if cfg["train_head"] == "sigmoid":
n_classes = cfg["start_class"]
onehot_targets = utils.to_onehot(targets, n_classes).to(device)
loss = F.binary_cross_entropy_with_logits(logits, onehot_targets)
elif cfg["train_head"] == "softmax":
loss = F.cross_entropy(logits, targets)
else:
raise ValueError()
return loss
def train(cfg, model, optimizer, device, train_loader):
_loss = 0.0
accu = ClassErrorMeter(accuracy=True)
accu.reset()
model.train()
for i, (inputs, targets) in enumerate(train_loader, start=1):
# assert torch.isnan(inputs).sum().item() == 0
optimizer.zero_grad()
inputs, targets = inputs.to(device), targets.to(device)
logits = model._parallel_network(inputs)['logit']
if accu is not None:
accu.add(logits.detach(), targets)
loss = _compute_loss(cfg, logits, targets, device)
if torch.isnan(loss):
import pdb
pdb.set_trace()
loss.backward()
optimizer.step()
_loss += loss
return (
round(_loss.item() / i, 3),
round(accu.value()[0], 3),
)
def test(cfg, model, device, test_loader):
_loss = 0.0
accu = ClassErrorMeter(accuracy=True)
accu.reset()
model.eval()
with torch.no_grad():
for i, (inputs, targets) in enumerate(test_loader, start=1):
# assert torch.isnan(inputs).sum().item() == 0
inputs, targets = inputs.to(device), targets.to(device)
logits = model._parallel_network(inputs)['logit']
if accu is not None:
accu.add(logits.detach(), targets)
loss = _compute_loss(cfg, logits, targets, device)
if torch.isnan(loss):
import pdb
pdb.set_trace()
_loss = _loss + loss
return round(_loss.item() / i, 3), round(accu.value()[0], 3)
def pretrain(cfg, ex, model, device, train_loader, test_loader, model_path):
ex.logger.info(f"nb Train {len(train_loader.dataset)} Eval {len(test_loader.dataset)}")
optimizer = torch.optim.SGD(model._network.parameters(),
lr=cfg["pretrain"]["lr"],
momentum=0.9,
weight_decay=cfg["pretrain"]["weight_decay"])
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
cfg["pretrain"]["scheduling"],
gamma=cfg["pretrain"]["lr_decay"])
test_loss, test_acc = float("nan"), float("nan")
for e in range(cfg["pretrain"]["epochs"]):
train_loss, train_acc = train(cfg, model, optimizer, device, train_loader)
if e % 5 == 0:
test_loss, test_acc = test(cfg, model, device, test_loader)
ex.logger.info(
"Pretrain Class {}, Epoch {}/{} => Clf Train loss: {}, Accu {} | Eval loss: {}, Accu {}".format(
cfg["start_class"], e + 1, cfg["pretrain"]["epochs"], train_loss, train_acc, test_loss, test_acc))
else:
ex.logger.info("Pretrain Class {}, Epoch {}/{} => Clf Train loss: {}, Accu {} ".format(
cfg["start_class"], e + 1, cfg["pretrain"]["epochs"], train_loss, train_acc))
scheduler.step()
if hasattr(model._network, "module"):
torch.save(model._network.module.state_dict(), model_path)
else:
torch.save(model._network.state_dict(), model_path)
| 3,886 | 36.019048 | 118 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/tools/memory.py | import numpy as np
from copy import deepcopy
import torch
from torch.nn import functional as F
from inclearn.tools.utils import get_class_loss
from inclearn.convnet.utils import extract_features
class MemorySize:
def __init__(self, mode, inc_dataset, total_memory=None, fixed_memory_per_cls=None):
self.mode = mode
assert mode.lower() in ["uniform_fixed_per_cls", "uniform_fixed_total_mem", "dynamic_fixed_per_cls"]
self.total_memory = total_memory
self.fixed_memory_per_cls = fixed_memory_per_cls
self._n_classes = 0
self.mem_per_cls = []
self._inc_dataset = inc_dataset
def update_n_classes(self, n_classes):
self._n_classes = n_classes
def update_memory_per_cls_uniform(self, n_classes):
if "fixed_per_cls" in self.mode:
self.mem_per_cls = [self.fixed_memory_per_cls for i in range(n_classes)]
elif "fixed_total_mem" in self.mode:
self.mem_per_cls = [self.total_memory // n_classes for i in range(n_classes)]
return self.mem_per_cls
def update_memory_per_cls(self, network, n_classes, task_size):
if "uniform" in self.mode:
self.update_memory_per_cls_uniform(n_classes)
else:
if n_classes == task_size:
self.update_memory_per_cls_uniform(n_classes)
@property
def memsize(self):
if self.mode == "fixed_total_mem":
return self.total_memory
elif self.mode == "fixed_per_cls":
return self.fixed_memory_per_cls * self._n_classes
def compute_examplar_mean(feat_norm, feat_flip, herding_mat, nb_max):
EPSILON = 1e-8
D = feat_norm.T
D = D / (np.linalg.norm(D, axis=0) + EPSILON)
D2 = feat_flip.T
D2 = D2 / (np.linalg.norm(D2, axis=0) + EPSILON)
alph = herding_mat
alph = (alph > 0) * (alph < nb_max + 1) * 1.0
alph_mean = alph / np.sum(alph)
mean = (np.dot(D, alph_mean) + np.dot(D2, alph_mean)) / 2
# mean = np.dot(D, alph_mean)
mean /= np.linalg.norm(mean) + EPSILON
return mean, alph
def select_examplars(features, nb_max):
EPSILON = 1e-8
D = features.T
D = D / (np.linalg.norm(D, axis=0) + EPSILON)
mu = np.mean(D, axis=1)
herding_matrix = np.zeros((features.shape[0], ))
idxes = []
w_t = mu
iter_herding, iter_herding_eff = 0, 0
while not (np.sum(herding_matrix != 0) == min(nb_max, features.shape[0])) and iter_herding_eff < 1000:
tmp_t = np.dot(w_t, D)
# tmp_t = -np.linalg.norm(w_t[:,np.newaxis]-D, axis=0)
# tmp_t = np.linalg.norm(w_t[:,np.newaxis]-D, axis=0)
ind_max = np.argmax(tmp_t)
iter_herding_eff += 1
if herding_matrix[ind_max] == 0:
herding_matrix[ind_max] = 1 + iter_herding
idxes.append(ind_max)
iter_herding += 1
w_t = w_t + mu - D[:, ind_max]
return herding_matrix, idxes
def random_selection(n_classes, task_size, network, logger, inc_dataset, memory_per_class: list):
# TODO: Move data_memroy,targets_memory into IncDataset
logger.info("Building & updating memory.(Random Selection)")
tmp_data_memory, tmp_targets_memory = [], []
assert len(memory_per_class) == n_classes
for class_idx in range(n_classes):
if class_idx < n_classes - task_size:
inputs, targets, loader = inc_dataset.get_custom_loader_from_memory([class_idx])
else:
inputs, targets, loader = inc_dataset.get_custom_loader(class_idx, mode="test")
memory_this_cls = min(memory_per_class[class_idx], inputs.shape[0])
idxs = np.random.choice(inputs.shape[0], memory_this_cls, replace=False)
tmp_data_memory.append(inputs[idxs])
tmp_targets_memory.append(targets[idxs])
tmp_data_memory = np.concatenate(tmp_data_memory)
tmp_targets_memory = np.concatenate(tmp_targets_memory)
return tmp_data_memory, tmp_targets_memory
def herding(n_classes, task_size, network, herding_matrix, inc_dataset, shared_data_inc, memory_per_class: list,
logger):
"""Herding matrix: list
"""
logger.info("Building & updating memory.(iCaRL)")
tmp_data_memory, tmp_targets_memory = [], []
for class_idx in range(n_classes):
inputs = inc_dataset.data_train[inc_dataset.targets_train == class_idx]
targets = inc_dataset.targets_train[inc_dataset.targets_train == class_idx]
if class_idx >= n_classes - task_size:
if len(shared_data_inc) > len(inc_dataset.targets_inc):
share_memory = [shared_data_inc[i] for i in np.where(inc_dataset.targets_inc == class_idx)[0].tolist()]
else:
share_memory = []
for i in np.where(inc_dataset.targets_inc == class_idx)[0].tolist():
if i < len(shared_data_inc):
share_memory.append(shared_data_inc[i])
# share_memory = [shared_data_inc[i] for i in np.where(inc_dataset.targets_inc == class_idx)[0].tolist()]
loader = inc_dataset._get_loader(inc_dataset.data_inc[inc_dataset.targets_inc == class_idx],
inc_dataset.targets_inc[inc_dataset.targets_inc == class_idx],
share_memory=share_memory,
batch_size=1024,
shuffle=False,
mode="test")
features, _ = extract_features(network, loader)
# features_flipped, _ = extract_features(network, inc_dataset.get_custom_loader(class_idx, mode="flip")[-1])
herding_matrix.append(select_examplars(features, memory_per_class[class_idx])[0])
alph = herding_matrix[class_idx]
alph = (alph > 0) * (alph < memory_per_class[class_idx] + 1) * 1.0
# examplar_mean, alph = compute_examplar_mean(features, features_flipped, herding_matrix[class_idx],
# memory_per_class[class_idx])
tmp_data_memory.append(inputs[np.where(alph == 1)[0]])
tmp_targets_memory.append(targets[np.where(alph == 1)[0]])
tmp_data_memory = np.concatenate(tmp_data_memory)
tmp_targets_memory = np.concatenate(tmp_targets_memory)
return tmp_data_memory, tmp_targets_memory, herding_matrix
| 6,375 | 41.791946 | 120 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/tools/utils.py | import random
from copy import deepcopy
import numpy as np
import datetime
import torch
from inclearn.tools.metrics import ClassErrorMeter
def get_date():
return datetime.datetime.now().strftime("%Y%m%d")
def to_onehot(targets, n_classes):
if not hasattr(targets, "device"):
targets = torch.from_numpy(targets)
onehot = torch.zeros(targets.shape[0], n_classes).to(targets.device)
onehot.scatter_(dim=1, index=targets.long().view(-1, 1), value=1.0)
return onehot
def get_class_loss(network, cur_n_cls, loader):
class_loss = torch.zeros(cur_n_cls)
n_cls_data = torch.zeros(cur_n_cls) # the num of imgs for cls i.
EPS = 1e-10
task_size = 10
network.eval()
for x, y in loader:
x, y = x.cuda(), y.cuda()
preds = network(x)['logit'].softmax(dim=1)
# preds[:,-task_size:] = preds[:,-task_size:].softmax(dim=1)
for i, lbl in enumerate(y):
class_loss[lbl] = class_loss[lbl] - (preds[i, lbl] + EPS).detach().log().cpu()
n_cls_data[lbl] += 1
class_loss = class_loss / n_cls_data
return class_loss
def get_featnorm_grouped_by_class(network, cur_n_cls, loader):
"""
Ret: feat_norms: list of list
feat_norms[idx] is the list of feature norm of the images for class idx.
"""
feats = [[] for i in range(cur_n_cls)]
feat_norms = np.zeros(cur_n_cls)
network.eval()
with torch.no_grad():
for x, y in loader:
x = x.cuda()
feat = network(x)['feature'].cpu()
for i, lbl in enumerate(y):
feats[lbl].append(feat[y == lbl])
for i in range(len(feats)):
if len(feats[i]) != 0:
feat_cls = torch.cat((feats[i]))
feat_norms[i] = torch.norm(feat_cls, p=2, dim=1).mean().data.numpy()
return feat_norms
def set_seed(seed):
print("Set seed", seed)
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True # This will slow down training.
torch.backends.cudnn.benchmark = False
def display_weight_norm(logger, network, increments, tag):
weight_norms = [[] for _ in range(len(increments))]
increments = np.cumsum(np.array(increments))
for idx in range(network.module.classifier.weight.shape[0]):
norm = torch.norm(network.module.classifier.weight[idx].data, p=2).item()
for i in range(len(weight_norms)):
if idx < increments[i]:
break
weight_norms[i].append(round(norm, 3))
avg_weight_norm = []
for idx in range(len(weight_norms)):
avg_weight_norm.append(round(np.array(weight_norms[idx]).mean(), 3))
logger.info("%s: Weight norm per class %s" % (tag, str(avg_weight_norm)))
def display_feature_norm(logger, network, loader, n_classes, increments, tag, return_norm=False):
avg_feat_norm_per_cls = get_featnorm_grouped_by_class(network, n_classes, loader)
feature_norms = [[] for _ in range(len(increments))]
increments = np.cumsum(np.array(increments))
for idx in range(len(avg_feat_norm_per_cls)):
for i in range(len(feature_norms)):
if idx < increments[i]: #Find the mapping from class idx to step i.
break
feature_norms[i].append(round(avg_feat_norm_per_cls[idx], 3))
avg_feature_norm = []
for idx in range(len(feature_norms)):
avg_feature_norm.append(round(np.array(feature_norms[idx]).mean(), 3))
logger.info("%s: Feature norm per class %s" % (tag, str(avg_feature_norm)))
if return_norm:
return avg_feature_norm
else:
return
def check_loss(loss):
return not bool(torch.isnan(loss).item()) and bool((loss >= 0.0).item())
def compute_accuracy(ypred, ytrue, increments, n_classes):
all_acc = {"top1": {}, "top5": {}}
topk = 5 if n_classes >= 5 else n_classes
ncls = np.unique(ytrue).shape[0]
if topk > ncls:
topk = ncls
all_acc_meter = ClassErrorMeter(topk=[1, topk], accuracy=True)
all_acc_meter.add(ypred, ytrue)
all_acc["top1"]["total"] = round(all_acc_meter.value()[0], 3)
all_acc["top5"]["total"] = round(all_acc_meter.value()[1], 3)
# all_acc["total"] = round((ypred == ytrue).sum() / len(ytrue), 3)
# for class_id in range(0, np.max(ytrue), task_size):
start, end = 0, 0
for i in range(len(increments)):
if increments[i] <= 0:
pass
else:
start = end
end += increments[i]
idxes = np.where(np.logical_and(ytrue >= start, ytrue < end))[0]
topk_ = 5 if increments[i] >= 5 else increments[i]
ncls = np.unique(ytrue[idxes]).shape[0]
if topk_ > ncls:
topk_ = ncls
cur_acc_meter = ClassErrorMeter(topk=[1, topk_], accuracy=True)
cur_acc_meter.add(ypred[idxes], ytrue[idxes])
top1_acc = (ypred[idxes].argmax(1) == ytrue[idxes]).sum() / idxes.shape[0] * 100
if start < end:
label = "{}-{}".format(str(start).rjust(2, "0"), str(end - 1).rjust(2, "0"))
else:
label = "{}-{}".format(str(start).rjust(2, "0"), str(end).rjust(2, "0"))
all_acc["top1"][label] = round(top1_acc, 3)
all_acc["top5"][label] = round(cur_acc_meter.value()[1], 3)
# all_acc[label] = round((ypred[idxes] == ytrue[idxes]).sum() / len(idxes), 3)
return all_acc
def make_logger(log_name, savedir='.logs/'):
"""Set up the logger for saving log file on the disk
Args:
cfg: configuration dict
Return:
logger: a logger for record essential information
"""
import logging
import os
from logging.config import dictConfig
import time
logging_config = dict(
version=1,
formatters={'f_t': {
'format': '\n %(asctime)s | %(levelname)s | %(name)s \t %(message)s'
}},
handlers={
'stream_handler': {
'class': 'logging.StreamHandler',
'formatter': 'f_t',
'level': logging.INFO
},
'file_handler': {
'class': 'logging.FileHandler',
'formatter': 'f_t',
'level': logging.INFO,
'filename': None,
}
},
root={
'handlers': ['stream_handler', 'file_handler'],
'level': logging.DEBUG,
},
)
# set up logger
log_file = '{}.log'.format(log_name)
# if folder not exist,create it
if not os.path.exists(savedir):
os.makedirs(savedir)
log_file_path = os.path.join(savedir, log_file)
logging_config['handlers']['file_handler']['filename'] = log_file_path
open(log_file_path, 'w').close() # Clear the content of logfile
# get logger from dictConfig
dictConfig(logging_config)
logger = logging.getLogger()
return logger | 6,969 | 33.85 | 97 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/tools/scheduler.py | import math
from torch.optim.lr_scheduler import _LRScheduler
from torch.optim.lr_scheduler import ReduceLROnPlateau
class ConstantTaskLR:
def __init__(self, lr):
self._lr = lr
def get_lr(self, task_i):
return self._lr
class CosineAnnealTaskLR:
def __init__(self, lr_max, lr_min, task_max):
self._lr_max = lr_max
self._lr_min = lr_min
self._task_max = task_max
def get_lr(self, task_i):
return self._lr_min + (self._lr_max - self._lr_min) * (1 + math.cos(math.pi * task_i / self._task_max)) / 2
class GradualWarmupScheduler(_LRScheduler):
""" Gradually warm-up(increasing) learning rate in optimizer.
https://github.com/ildoonet/pytorch-gradual-warmup-lr
Proposed in 'Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour'.
Args:
optimizer (Optimizer): Wrapped optimizer.
multiplier: target learning rate = base lr * multiplier if multiplier > 1.0. if multiplier = 1.0, lr starts from 0 and ends up with the base_lr.
total_epoch: target learning rate is reached at total_epoch, gradually
after_scheduler: after target_epoch, use this scheduler(eg. ReduceLROnPlateau)
"""
def __init__(self, optimizer, multiplier, total_epoch, after_scheduler=None):
self.multiplier = multiplier
if self.multiplier < 1.:
raise ValueError('multiplier should be greater thant or equal to 1.')
self.total_epoch = total_epoch
self.after_scheduler = after_scheduler
self.finished = False
super(GradualWarmupScheduler, self).__init__(optimizer)
def get_lr(self):
if self.last_epoch > self.total_epoch:
if self.after_scheduler:
if not self.finished:
self.after_scheduler.base_lrs = [base_lr * self.multiplier for base_lr in self.base_lrs]
self.finished = True
return self.after_scheduler.get_last_lr()
return [base_lr * self.multiplier for base_lr in self.base_lrs]
if self.multiplier == 1.0:
return [base_lr * (float(self.last_epoch) / self.total_epoch) for base_lr in self.base_lrs]
else:
return [
base_lr * ((self.multiplier - 1.) * self.last_epoch / self.total_epoch + 1.)
for base_lr in self.base_lrs
]
def step_ReduceLROnPlateau(self, metrics, epoch=None):
if epoch is None:
epoch = self.last_epoch + 1
self.last_epoch = epoch if epoch != 0 else 1 # ReduceLROnPlateau is called at the end of epoch, whereas others are called at beginning
if self.last_epoch <= self.total_epoch:
warmup_lr = [
base_lr * ((self.multiplier - 1.) * self.last_epoch / self.total_epoch + 1.)
for base_lr in self.base_lrs
]
for param_group, lr in zip(self.optimizer.param_groups, warmup_lr):
param_group['lr'] = lr
else:
if epoch is None:
self.after_scheduler.step(metrics, None)
else:
self.after_scheduler.step(metrics, epoch - self.total_epoch)
def step(self, epoch=None, metrics=None):
if type(self.after_scheduler) != ReduceLROnPlateau:
if self.finished and self.after_scheduler:
if epoch is None:
self.after_scheduler.step(None)
else:
self.after_scheduler.step(epoch - self.total_epoch)
self._last_lr = self.after_scheduler.get_last_lr()
else:
return super(GradualWarmupScheduler, self).step(epoch)
else:
self.step_ReduceLROnPlateau(metrics, epoch)
| 3,749 | 41.134831 | 152 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/tools/factory.py | import torch
from torch import nn
from torch import optim
from inclearn import models
from inclearn.convnet import resnet, cifar_resnet, modified_resnet_cifar, preact_resnet
from inclearn.datasets import data
def get_optimizer(params, optimizer, lr, weight_decay=0.0):
if optimizer == "adam":
return optim.Adam(params, lr=lr, weight_decay=weight_decay, betas=(0.9, 0.999))
elif optimizer == "sgd":
return optim.SGD(params, lr=lr, weight_decay=weight_decay, momentum=0.9)
else:
raise NotImplementedError
def get_convnet(convnet_type, **kwargs):
if convnet_type == "resnet18":
return resnet.resnet18(**kwargs)
elif convnet_type == "resnet32":
return cifar_resnet.resnet32()
elif convnet_type == "modified_resnet32":
return modified_resnet_cifar.resnet32(**kwargs)
elif convnet_type == "preact_resnet18":
return preact_resnet.PreActResNet18(**kwargs)
else:
raise NotImplementedError("Unknwon convnet type {}.".format(convnet_type))
def get_model(cfg, trial_i, _run, ex, tensorboard, inc_dataset):
if cfg["model"] == "incmodel":
return models.IncModel(cfg, trial_i, _run, ex, tensorboard, inc_dataset)
if cfg["model"] == "weight_align":
return models.Weight_Align(cfg, trial_i, _run, ex, tensorboard, inc_dataset)
if cfg["model"] == "bic":
return models.BiC(cfg, trial_i, _run, ex, tensorboard, inc_dataset)
else:
raise NotImplementedError(cfg["model"])
def get_data(cfg, trial_i):
return data.IncrementalDataset(
trial_i=trial_i,
dataset_name=cfg["dataset"],
random_order=cfg["random_classes"],
shuffle=True,
batch_size=cfg["batch_size"],
workers=cfg["workers"],
validation_split=cfg["validation"],
resampling=cfg["resampling"],
increment=cfg["increment"],
data_folder=cfg["data_folder"],
start_class=cfg["start_class"],
)
def set_device(cfg):
device_type = cfg["device"]
if device_type == -1:
device = torch.device("cpu")
else:
device = torch.device("cuda:{}".format(device_type))
cfg["device"] = device
return device
| 2,205 | 30.971014 | 87 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/tools/metrics.py | import numpy as np
import torch
import numbers
import math
class IncConfusionMeter:
"""Maintains a confusion matrix for a given calssification problem.
The ConfusionMeter constructs a confusion matrix for a multi-class
classification problems. It does not support multi-label, multi-class problems:
for such problems, please use MultiLabelConfusionMeter.
Args:
k (int): number of classes in the classification problem
normalized (boolean): Determines whether or not the confusion matrix
is normalized or not
"""
def __init__(self, k, increments, normalized=False):
self.conf = np.ndarray((k, k), dtype=np.int32)
self.normalized = normalized
self.increments = increments
self.cum_increments = [0] + [sum(increments[:i + 1]) for i in range(len(increments))]
self.k = k
self.reset()
def reset(self):
self.conf.fill(0)
def add(self, predicted, target):
"""Computes the confusion matrix of K x K size where K is no of classes
Args:
predicted (tensor): Can be an N x K tensor of predicted scores obtained from
the model for N examples and K classes or an N-tensor of
integer values between 0 and K-1.
target (tensor): Can be a N-tensor of integer values assumed to be integer
values between 0 and K-1 or N x K tensor, where targets are
assumed to be provided as one-hot vectors
"""
if isinstance(predicted, torch.Tensor):
predicted = predicted.cpu().numpy()
if isinstance(target, torch.Tensor):
target = target.cpu().numpy()
assert predicted.shape[0] == target.shape[0], \
'number of targets and predicted outputs do not match'
if np.ndim(predicted) != 1:
assert predicted.shape[1] == self.k, \
'number of predictions does not match size of confusion matrix'
predicted = np.argmax(predicted, 1)
else:
assert (predicted.max() < self.k) and (predicted.min() >= 0), \
'predicted values are not between 1 and k'
onehot_target = np.ndim(target) != 1
if onehot_target:
assert target.shape[1] == self.k, \
'Onehot target does not match size of confusion matrix'
assert (target >= 0).all() and (target <= 1).all(), \
'in one-hot encoding, target values should be 0 or 1'
assert (target.sum(1) == 1).all(), \
'multi-label setting is not supported'
target = np.argmax(target, 1)
else:
assert (predicted.max() < self.k) and (predicted.min() >= 0), \
'predicted values are not between 0 and k-1'
# hack for bincounting 2 arrays together
x = predicted + self.k * target
bincount_2d = np.bincount(x.astype(np.int32), minlength=self.k**2)
assert bincount_2d.size == self.k**2
conf = bincount_2d.reshape((self.k, self.k))
self.conf += conf
def value(self):
"""
Returns:
Confustion matrix of K rows and K columns, where rows corresponds
to ground-truth targets and columns corresponds to predicted
targets.
"""
conf = self.conf.astype(np.float32)
new_conf = np.zeros([len(self.increments), len(self.increments) + 2])
for i in range(len(self.increments)):
idxs = range(self.cum_increments[i], self.cum_increments[i + 1])
new_conf[i, 0] = conf[idxs, idxs].sum()
new_conf[i, 1] = conf[self.cum_increments[i]:self.cum_increments[i + 1],
self.cum_increments[i]:self.cum_increments[i + 1]].sum() - new_conf[i, 0]
for j in range(len(self.increments)):
new_conf[i, j + 2] = conf[self.cum_increments[i]:self.cum_increments[i + 1],
self.cum_increments[j]:self.cum_increments[j + 1]].sum()
conf = new_conf
if self.normalized:
return conf / conf[:, 2:].sum(1).clip(min=1e-12)[:, None]
else:
return conf
class ClassErrorMeter:
def __init__(self, topk=[1], accuracy=False):
super(ClassErrorMeter, self).__init__()
self.topk = np.sort(topk)
self.accuracy = accuracy
self.reset()
def reset(self):
self.sum = {v: 0 for v in self.topk}
self.n = 0
def add(self, output, target):
if isinstance(output, np.ndarray):
output = torch.Tensor(output)
if isinstance(target, np.ndarray):
target = torch.Tensor(target)
# if torch.is_tensor(output):
# output = output.cpu().squeeze().numpy()
# if torch.is_tensor(target):
# target = target.cpu().squeeze().numpy()
# elif isinstance(target, numbers.Number):
# target = np.asarray([target])
# if np.ndim(output) == 1:
# output = output[np.newaxis]
# else:
# assert np.ndim(output) == 2, \
# 'wrong output size (1D or 2D expected)'
# assert np.ndim(target) == 1, \
# 'target and output do not match'
# assert target.shape[0] == output.shape[0], \
# 'target and output do not match'
topk = self.topk
maxk = int(topk[-1]) # seems like Python3 wants int and not np.int64
no = output.shape[0]
pred = output.topk(maxk, 1, True, True)[1]
correct = pred == target.unsqueeze(1).repeat(1, pred.shape[1])
# pred = torch.from_numpy(output).topk(maxk, 1, True, True)[1].numpy()
# correct = pred == target[:, np.newaxis].repeat(pred.shape[1], 1)
for k in topk:
self.sum[k] += no - correct[:, 0:k].sum()
self.n += no
def value(self, k=-1):
if k != -1:
assert k in self.sum.keys(), \
'invalid k (this k was not provided at construction time)'
if self.n == 0:
return float('nan')
if self.accuracy:
return (1. - float(self.sum[k]) / self.n) * 100.0
else:
return float(self.sum[k]) / self.n * 100.0
else:
return [self.value(k_) for k_ in self.topk]
class AverageValueMeter:
def __init__(self):
super(AverageValueMeter, self).__init__()
self.reset()
self.val = 0
def add(self, value, n=1):
self.val = value
self.sum += value
self.var += value * value
self.n += n
if self.n == 0:
self.mean, self.std = np.nan, np.nan
elif self.n == 1:
self.mean, self.std = self.sum, np.inf
self.mean_old = self.mean
self.m_s = 0.0
else:
self.mean = self.mean_old + (value - n * self.mean_old) / float(self.n)
self.m_s += (value - self.mean_old) * (value - self.mean)
self.mean_old = self.mean
self.std = math.sqrt(self.m_s / (self.n - 1.0))
def value(self):
return self.mean, self.std
def reset(self):
self.n = 0
self.sum = 0.0
self.var = 0.0
self.val = 0.0
self.mean = np.nan
self.mean_old = 0.0
self.m_s = 0.0
self.std = np.nan | 7,415 | 37.625 | 107 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/resnet.py | """Taken & slightly modified from:
* https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
"""
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torch.nn import functional as F
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, remove_last_relu=False):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
self.remove_last_relu = remove_last_relu
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
if not self.remove_last_relu:
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = conv1x1(inplanes, planes)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = conv3x3(planes, planes, stride)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = conv1x1(planes, planes * self.expansion)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self,
block,
layers,
nf=64,
zero_init_residual=True,
dataset='cifar',
start_class=0,
remove_last_relu=False):
super(ResNet, self).__init__()
self.remove_last_relu = remove_last_relu
self.inplanes = nf
if 'cifar' in dataset:
self.conv1 = nn.Sequential(nn.Conv2d(3, nf, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(nf), nn.ReLU(inplace=True))
elif 'imagenet' in dataset:
if start_class == 0:
self.conv1 = nn.Sequential(
nn.Conv2d(3, nf, kernel_size=7, stride=2, padding=3, bias=False),
nn.BatchNorm2d(nf),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
)
else:
# Following PODNET implmentation
self.conv1 = nn.Sequential(
nn.Conv2d(3, nf, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(nf),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
)
self.layer1 = self._make_layer(block, 1 * nf, layers[0])
self.layer2 = self._make_layer(block, 2 * nf, layers[1], stride=2)
self.layer3 = self._make_layer(block, 4 * nf, layers[2], stride=2)
self.layer4 = self._make_layer(block, 8 * nf, layers[3], stride=2, remove_last_relu=remove_last_relu)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.out_dim = 8 * nf * block.expansion
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, blocks, remove_last_relu=False, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
if remove_last_relu:
for i in range(1, blocks - 1):
layers.append(block(self.inplanes, planes))
layers.append(block(self.inplanes, planes, remove_last_relu=True))
else:
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def reset_bn(self):
for m in self.modules():
if isinstance(m, nn.BatchNorm2d):
m.reset_running_stats()
def forward(self, x):
x = self.conv1(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
return x
def resnet18(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
return model
def resnet34(pretrained=False, **kwargs):
"""Constructs a ResNet-34 model.
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
return model
def resnet50(pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
return model
def resnet101(pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
return model
def resnet152(pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
"""
model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
return model
| 8,130 | 32.460905 | 109 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/network.py | import copy
import pdb
import torch
from torch import nn
import torch.nn.functional as F
from inclearn.tools import factory
from inclearn.convnet.imbalance import BiC, WA
from inclearn.convnet.classifier import CosineClassifier
class BasicNet(nn.Module):
def __init__(
self,
convnet_type,
cfg,
nf=64,
use_bias=False,
init="kaiming",
device=None,
dataset="cifar100",
):
super(BasicNet, self).__init__()
self.nf = nf
self.init = init
self.convnet_type = convnet_type
self.dataset = dataset
self.start_class = cfg['start_class']
self.weight_normalization = cfg['weight_normalization']
self.remove_last_relu = True if self.weight_normalization else False
self.use_bias = use_bias if not self.weight_normalization else False
self.der = cfg['der']
self.aux_nplus1 = cfg['aux_n+1']
self.reuse_oldfc = cfg['reuse_oldfc']
if self.der:
print("Enable dynamical reprensetation expansion!")
self.convnets = nn.ModuleList()
self.convnets.append(
factory.get_convnet(convnet_type,
nf=nf,
dataset=dataset,
start_class=self.start_class,
remove_last_relu=self.remove_last_relu))
self.out_dim = self.convnets[0].out_dim
else:
self.convnet = factory.get_convnet(convnet_type,
nf=nf,
dataset=dataset,
remove_last_relu=self.remove_last_relu)
self.out_dim = self.convnet.out_dim
self.classifier = None
self.aux_classifier = None
self.n_classes = 0
self.ntask = 0
self.device = device
if cfg['postprocessor']['enable']:
if cfg['postprocessor']['type'].lower() == "bic":
self.postprocessor = BiC(cfg['postprocessor']["lr"], cfg['postprocessor']["scheduling"],
cfg['postprocessor']["lr_decay_factor"], cfg['postprocessor']["weight_decay"],
cfg['postprocessor']["batch_size"], cfg['postprocessor']["epochs"])
elif cfg['postprocessor']['type'].lower() == "wa":
self.postprocessor = WA()
else:
self.postprocessor = None
self.to(self.device)
def forward(self, x):
if self.classifier is None:
raise Exception("Add some classes before training.")
if self.der:
features = [convnet(x) for convnet in self.convnets]
features = torch.cat(features, 1)
else:
features = self.convnet(x)
logits = self.classifier(features)
aux_logits = self.aux_classifier(features[:, -self.out_dim:]) if features.shape[1] > self.out_dim else None
return {'feature': features, 'logit': logits, 'aux_logit': aux_logits}
@property
def features_dim(self):
if self.der:
return self.out_dim * len(self.convnets)
else:
return self.out_dim
def freeze(self):
for param in self.parameters():
param.requires_grad = False
self.eval()
return self
def copy(self):
return copy.deepcopy(self)
def add_classes(self, n_classes):
self.ntask += 1
if self.der:
self._add_classes_multi_fc(n_classes)
else:
self._add_classes_single_fc(n_classes)
self.n_classes += n_classes
def _add_classes_multi_fc(self, n_classes):
if self.ntask > 1:
new_clf = factory.get_convnet(self.convnet_type,
nf=self.nf,
dataset=self.dataset,
start_class=self.start_class,
remove_last_relu=self.remove_last_relu).to(self.device)
new_clf.load_state_dict(self.convnets[-1].state_dict())
self.convnets.append(new_clf)
if self.classifier is not None:
weight = copy.deepcopy(self.classifier.weight.data)
fc = self._gen_classifier(self.out_dim * len(self.convnets), self.n_classes + n_classes)
if self.classifier is not None and self.reuse_oldfc:
fc.weight.data[:self.n_classes, :self.out_dim * (len(self.convnets) - 1)] = weight
del self.classifier
self.classifier = fc
if self.aux_nplus1:
aux_fc = self._gen_classifier(self.out_dim, n_classes + 1)
else:
aux_fc = self._gen_classifier(self.out_dim, self.n_classes + n_classes)
del self.aux_classifier
self.aux_classifier = aux_fc
def _add_classes_single_fc(self, n_classes):
if self.classifier is not None:
weight = copy.deepcopy(self.classifier.weight.data)
if self.use_bias:
bias = copy.deepcopy(self.classifier.bias.data)
classifier = self._gen_classifier(self.features_dim, self.n_classes + n_classes)
if self.classifier is not None and self.reuse_oldfc:
classifier.weight.data[:self.n_classes] = weight
if self.use_bias:
classifier.bias.data[:self.n_classes] = bias
del self.classifier
self.classifier = classifier
def _gen_classifier(self, in_features, n_classes):
if self.weight_normalization:
classifier = CosineClassifier(in_features, n_classes).to(self.device)
else:
classifier = nn.Linear(in_features, n_classes, bias=self.use_bias).to(self.device)
if self.init == "kaiming":
nn.init.kaiming_normal_(classifier.weight, nonlinearity="linear")
if self.use_bias:
nn.init.constant_(classifier.bias, 0.0)
return classifier
| 6,100 | 35.532934 | 119 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/imbalance.py | import torch
import torch.nn.functional as F
from torch import nn
import numpy as np
from torch.optim.lr_scheduler import CosineAnnealingLR
class BiC(nn.Module):
def __init__(self, lr, scheduling, lr_decay_factor, weight_decay, batch_size, epochs):
super(BiC, self).__init__()
self.beta = torch.nn.Parameter(torch.ones(1)) #.cuda()
self.gamma = torch.nn.Parameter(torch.zeros(1)) #.cuda()
self.lr = lr
self.scheduling = scheduling
self.lr_decay_factor = lr_decay_factor
self.weight_decay = weight_decay
self.class_specific = False
self.batch_size = batch_size
self.epochs = epochs
self.bic_flag = False
def reset(self, lr=None, scheduling=None, lr_decay_factor=None, weight_decay=None, n_classes=-1):
with torch.no_grad():
if lr is None:
lr = self.lr
if scheduling is None:
scheduling = self.scheduling
if lr_decay_factor is None:
lr_decay_factor = self.lr_decay_factor
if weight_decay is None:
weight_decay = self.weight_decay
if self.class_specific:
assert n_classes != -1
self.beta = torch.nn.Parameter(torch.ones(n_classes).cuda())
self.gamma = torch.nn.Parameter(torch.zeros(n_classes).cuda())
else:
self.beta = torch.nn.Parameter(torch.ones(1).cuda())
self.gamma = torch.nn.Parameter(torch.zeros(1).cuda())
self.optimizer = torch.optim.SGD([self.beta, self.gamma], lr=lr, momentum=0.9, weight_decay=weight_decay)
# self.scheduler = CosineAnnealingLR(self.optimizer, 10)
self.scheduler = torch.optim.lr_scheduler.MultiStepLR(self.optimizer, scheduling, gamma=lr_decay_factor)
def extract_preds_and_targets(self, model, loader):
preds, targets = [], []
with torch.no_grad():
for (x, y) in loader:
preds.append(model(x.cuda())['logit'])
targets.append(y.cuda())
return torch.cat((preds)), torch.cat((targets))
def update(self, logger, task_size, model, loader, loss_criterion=None):
if task_size == 0:
logger.info("no new task for BiC!")
return
if loss_criterion is None:
loss_criterion = F.cross_entropy
self.bic_flag = True
logger.info("Begin BiC ...")
model.eval()
for epoch in range(self.epochs):
preds_, targets_ = self.extract_preds_and_targets(model, loader)
order = np.arange(preds_.shape[0])
np.random.shuffle(order)
preds, targets = preds_.clone(), targets_.clone()
preds, targets = preds[order], targets[order]
_loss = 0.0
_correct = 0
_count = 0
for start in range(0, preds.shape[0], self.batch_size):
if start + self.batch_size < preds.shape[0]:
out = preds[start:start + self.batch_size, :].clone()
lbls = targets[start:start + self.batch_size]
else:
out = preds[start:, :].clone()
lbls = targets[start:]
if self.class_specific is False:
out1 = out[:, :-task_size].clone()
out2 = out[:, -task_size:].clone()
outputs = torch.cat((out1, out2 * self.beta + self.gamma), 1)
else:
outputs = out * self.beta + self.gamma
loss = loss_criterion(outputs, lbls)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
_, pred = outputs.max(1)
_correct += (pred == lbls).sum()
_count += lbls.size(0)
_loss += loss.item() * outputs.shape[0]
logger.info("epoch {} loss {:4f} acc {:4f}".format(epoch, _loss / preds.shape[0], _correct / _count))
self.scheduler.step()
logger.info("beta {:.4f} gamma {:.4f}".format(self.beta.cpu().item(), self.gamma.cpu().item()))
@torch.no_grad()
def post_process(self, preds, task_size):
if self.class_specific is False:
if task_size != 0:
preds[:, -task_size:] = preds[:, -task_size:] * self.beta + self.gamma
else:
preds = preds * self.beta + self.gamma
return preds
class WA(object):
def __init__(self):
self.gamma = None
@torch.no_grad()
def update(self, classifier, task_size):
old_weight_norm = torch.norm(classifier.weight[:-task_size], p=2, dim=1)
new_weight_norm = torch.norm(classifier.weight[-task_size:], p=2, dim=1)
self.gamma = old_weight_norm.mean() / new_weight_norm.mean()
print(self.gamma.cpu().item())
@torch.no_grad()
def post_process(self, logits, task_size):
logits[:, -task_size:] = logits[:, -task_size:] * self.gamma
return logits
| 5,074 | 40.260163 | 117 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/utils.py | import numpy as np
import torch
from torch import nn
from torch.optim import SGD
import torch.nn.functional as F
from inclearn.tools.metrics import ClassErrorMeter, AverageValueMeter
def finetune_last_layer(
logger,
network,
loader,
n_class,
nepoch=30,
lr=0.1,
scheduling=[15, 35],
lr_decay=0.1,
weight_decay=5e-4,
loss_type="ce",
temperature=5.0,
test_loader=None,
):
network.eval()
#if hasattr(network.module, "convnets"):
# for net in network.module.convnets:
# net.eval()
#else:
# network.module.convnet.eval()
optim = SGD(network.module.classifier.parameters(), lr=lr, momentum=0.9, weight_decay=weight_decay)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optim, scheduling, gamma=lr_decay)
if loss_type == "ce":
criterion = nn.CrossEntropyLoss()
else:
criterion = nn.BCEWithLogitsLoss()
logger.info("Begin finetuning last layer")
for i in range(nepoch):
total_loss = 0.0
total_correct = 0.0
total_count = 0
# print(f"dataset loader length {len(loader.dataset)}")
for inputs, targets in loader:
inputs, targets = inputs.cuda(), targets.cuda()
if loss_type == "bce":
targets = to_onehot(targets, n_class)
outputs = network(inputs)['logit']
_, preds = outputs.max(1)
optim.zero_grad()
loss = criterion(outputs / temperature, targets)
loss.backward()
optim.step()
total_loss += loss * inputs.size(0)
total_correct += (preds == targets).sum()
total_count += inputs.size(0)
if test_loader is not None:
test_correct = 0.0
test_count = 0.0
with torch.no_grad():
for inputs, targets in test_loader:
outputs = network(inputs.cuda())['logit']
_, preds = outputs.max(1)
test_correct += (preds.cpu() == targets).sum().item()
test_count += inputs.size(0)
scheduler.step()
if test_loader is not None:
logger.info(
"Epoch %d finetuning loss %.3f acc %.3f Eval %.3f" %
(i, total_loss.item() / total_count, total_correct.item() / total_count, test_correct / test_count))
else:
logger.info("Epoch %d finetuning loss %.3f acc %.3f" %
(i, total_loss.item() / total_count, total_correct.item() / total_count))
return network
def extract_features(model, loader):
targets, features = [], []
model.eval()
with torch.no_grad():
for _inputs, _targets in loader:
_inputs = _inputs.cuda()
_targets = _targets.numpy()
_features = model(_inputs)['feature'].detach().cpu().numpy()
features.append(_features)
targets.append(_targets)
return np.concatenate(features), np.concatenate(targets)
def calc_class_mean(network, loader, class_idx, metric):
EPSILON = 1e-8
features, targets = extract_features(network, loader)
# norm_feats = features/(np.linalg.norm(features, axis=1)[:,np.newaxis]+EPSILON)
# examplar_mean = norm_feats.mean(axis=0)
examplar_mean = features.mean(axis=0)
if metric == "cosine" or metric == "weight":
examplar_mean /= (np.linalg.norm(examplar_mean) + EPSILON)
return examplar_mean
def update_classes_mean(network, inc_dataset, n_classes, task_size, share_memory=None, metric="cosine", EPSILON=1e-8):
loader = inc_dataset._get_loader(inc_dataset.data_inc,
inc_dataset.targets_inc,
shuffle=False,
share_memory=share_memory,
mode="test")
class_means = np.zeros((n_classes, network.module.features_dim))
count = np.zeros(n_classes)
network.eval()
with torch.no_grad():
for x, y in loader:
feat = network(x.cuda())['feature']
for lbl in torch.unique(y):
class_means[lbl] += feat[y == lbl].sum(0).cpu().numpy()
count[lbl] += feat[y == lbl].shape[0]
for i in range(n_classes):
class_means[i] /= count[i]
if metric == "cosine" or metric == "weight":
class_means[i] /= (np.linalg.norm(class_means) + EPSILON)
return class_means
| 4,496 | 35.266129 | 118 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/classifier.py | import math
import torch
from torch.nn.parameter import Parameter
from torch.nn import functional as F
from torch.nn import Module
class CosineClassifier(Module):
def __init__(self, in_features, n_classes, sigma=True):
super(CosineClassifier, self).__init__()
self.in_features = in_features
self.out_features = n_classes
self.weight = Parameter(torch.Tensor(n_classes, in_features))
if sigma:
self.sigma = Parameter(torch.Tensor(1))
else:
self.register_parameter('sigma', None)
self.reset_parameters()
def reset_parameters(self):
stdv = 1. / math.sqrt(self.weight.size(1))
self.weight.data.uniform_(-stdv, stdv)
if self.sigma is not None:
self.sigma.data.fill_(1) #for initializaiton of sigma
def forward(self, input):
out = F.linear(F.normalize(input, p=2, dim=1), F.normalize(self.weight, p=2, dim=1))
if self.sigma is not None:
out = self.sigma * out
return out
| 1,035 | 31.375 | 92 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/preact_resnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class PreActBlock(nn.Module):
'''Pre-activation version of the BasicBlock.'''
expansion = 1
def __init__(self, in_planes, planes, stride=1, remove_last_relu=False):
super(PreActBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes)
self.remove_last_relu = remove_last_relu
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False))
def forward(self, x):
out = F.relu(self.bn1(x))
shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
out = self.conv1(out)
out = self.conv2(F.relu(self.bn2(out)))
out += shortcut
out = self.bn3(out)
if not self.remove_last_relu:
out = F.relu(out)
return out
class PreActBottleneck(nn.Module):
'''Pre-activation version of the original Bottleneck module.'''
expansion = 4
def __init__(self, in_planes, planes, stride=1):
super(PreActBottleneck, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False)
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False))
def forward(self, x):
out = F.relu(self.bn1(x))
shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
out = self.conv1(out)
out = self.conv2(F.relu(self.bn2(out)))
out = self.conv3(F.relu(self.bn3(out)))
out += shortcut
return out
class PreActResNet(nn.Module):
def __init__(self,
block,
num_blocks,
nf=64,
zero_init_residual=True,
dataset="cifar",
start_class=0,
remove_last_relu=False):
super(PreActResNet, self).__init__()
self.in_planes = nf
self.dataset = dataset
self.remove_last_relu = remove_last_relu
if 'cifar' in dataset:
self.conv1 = nn.Conv2d(3, nf, kernel_size=3, stride=1, padding=1, bias=False)
else:
self.conv1 = nn.Sequential(nn.Conv2d(3, nf, kernel_size=7, stride=2, padding=3, bias=False),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
self.layer1 = self._make_layer(block, 1 * nf, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 2 * nf, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 4 * nf, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 8 * nf, num_blocks[3], stride=2, remove_last_relu=remove_last_relu)
self.out_dim = 8 * nf
if 'cifar' in dataset:
self.avgpool = nn.AvgPool2d(4)
elif 'imagenet' in dataset:
self.avgpool = nn.AvgPool2d(7)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# ---------------------------------------------
# if zero_init_residual:
# for m in self.modules():
# if isinstance(m, PreActBlock):
# nn.init.constant_(m.bn2.weight, 0)
# elif isinstance(m, PreActBottleneck):
# nn.init.constant_(m.bn3.weight, 0)
# ---------------------------------------------
def _make_layer(self, block, planes, num_blocks, stride, remove_last_relu=False):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
if remove_last_relu:
for i in range(len(strides) - 1):
layers.append(block(self.in_planes, planes, strides[i]))
self.in_planes = planes * block.expansion
layers.append(block(self.in_planes, planes, strides[-1], remove_last_relu=True))
self.in_planes = planes * block.expansion
else:
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
out = self.conv1(x)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.avgpool(out)
out = out.view(out.size(0), -1)
return out
def PreActResNet18(**kwargs):
return PreActResNet(PreActBlock, [2, 2, 2, 2], **kwargs)
def PreActResNet34(**kwargs):
return PreActResNet(PreActBlock, [3, 4, 6, 3], **kwargs)
def PreActResNet50(**kwargs):
return PreActResNet(PreActBottleneck, [3, 4, 6, 3], **kwargs)
def PreActResNet101(**kwargs):
return PreActResNet(PreActBottleneck, [3, 4, 23, 3], **kwargs)
def PreActResNet152(**kwargs):
return PreActResNet(PreActBottleneck, [3, 8, 36, 3], **kwargs)
| 5,859 | 37.552632 | 113 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/cifar_resnet.py | ''' Incremental-Classifier Learning
Authors : Khurram Javed, Muhammad Talha Paracha
Maintainer : Khurram Javed
Lab : TUKL-SEECS R&D Lab
Email : 14besekjaved@seecs.edu.pk '''
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
class DownsampleA(nn.Module):
def __init__(self, nIn, nOut, stride):
super(DownsampleA, self).__init__()
assert stride == 2
self.avg = nn.AvgPool2d(kernel_size=1, stride=stride)
def forward(self, x):
x = self.avg(x)
return torch.cat((x, x.mul(0)), 1)
class ResNetBasicblock(nn.Module):
expansion = 1
"""
RexNet basicblock (https://github.com/facebook/fb.resnet.torch/blob/master/models/resnet.lua)
"""
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(ResNetBasicblock, self).__init__()
self.conv_a = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn_a = nn.BatchNorm2d(planes)
self.conv_b = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn_b = nn.BatchNorm2d(planes)
self.downsample = downsample
self.featureSize = 64
def forward(self, x):
residual = x
basicblock = self.conv_a(x)
basicblock = self.bn_a(basicblock)
basicblock = F.relu(basicblock, inplace=True)
basicblock = self.conv_b(basicblock)
basicblock = self.bn_b(basicblock)
if self.downsample is not None:
residual = self.downsample(x)
return F.relu(residual + basicblock, inplace=True)
class CifarResNet(nn.Module):
"""
ResNet optimized for the Cifar Dataset, as specified in
https://arxiv.org/abs/1512.03385.pdf
"""
def __init__(self, block, depth, num_classes, channels=3):
""" Constructor
Args:
depth: number of layers.
num_classes: number of classes
base_width: base width
"""
super(CifarResNet, self).__init__()
self.featureSize = 64
# Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
assert (depth - 2) % 6 == 0, 'depth should be one of 20, 32, 44, 56, 110'
layer_blocks = (depth - 2) // 6
self.num_classes = num_classes
self.conv_1_3x3 = nn.Conv2d(channels, 16, kernel_size=3, stride=1, padding=1, bias=False)
self.bn_1 = nn.BatchNorm2d(16)
self.inplanes = 16
self.stage_1 = self._make_layer(block, 16, layer_blocks, 1)
self.stage_2 = self._make_layer(block, 32, layer_blocks, 2)
self.stage_3 = self._make_layer(block, 64, layer_blocks, 2)
self.avgpool = nn.AvgPool2d(8)
self.out_dim = 64 * block.expansion
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
# m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
init.kaiming_normal(m.weight)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = DownsampleA(self.inplanes, planes * block.expansion, stride)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x, feature=False, T=1, labels=False, scale=None, keep=None):
x = self.conv_1_3x3(x)
x = F.relu(self.bn_1(x), inplace=True)
x = self.stage_1(x)
x = self.stage_2(x)
x = self.stage_3(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
return x
def forwardFeature(self, x):
pass
def resnet20(num_classes=10):
"""Constructs a ResNet-20 model for CIFAR-10 (by default)
Args:
num_classes (uint): number of classes
"""
model = CifarResNet(ResNetBasicblock, 20, num_classes)
return model
def resnet10mnist(num_classes=10):
"""Constructs a ResNet-20 model for CIFAR-10 (by default)
Args:
num_classes (uint): number of classes
"""
model = CifarResNet(ResNetBasicblock, 10, num_classes, 1)
return model
def resnet20mnist(num_classes=10):
"""Constructs a ResNet-20 model for CIFAR-10 (by default)
Args:
num_classes (uint): number of classes
"""
model = CifarResNet(ResNetBasicblock, 20, num_classes, 1)
return model
def resnet32mnist(num_classes=10, channels=1):
model = CifarResNet(ResNetBasicblock, 32, num_classes, channels)
return model
def resnet32(num_classes=10):
"""Constructs a ResNet-32 model for CIFAR-10 (by default)
Args:
num_classes (uint): number of classes
"""
model = CifarResNet(ResNetBasicblock, 32, num_classes)
return model
def resnet44(num_classes=10):
"""Constructs a ResNet-44 model for CIFAR-10 (by default)
Args:
num_classes (uint): number of classes
"""
model = CifarResNet(ResNetBasicblock, 44, num_classes)
return model
def resnet56(num_classes=10):
"""Constructs a ResNet-56 model for CIFAR-10 (by default)
Args:
num_classes (uint): number of classes
"""
model = CifarResNet(ResNetBasicblock, 56, num_classes)
return model
def resnet110(num_classes=10):
"""Constructs a ResNet-110 model for CIFAR-10 (by default)
Args:
num_classes (uint): number of classes
"""
model = CifarResNet(ResNetBasicblock, 110, num_classes)
return model
| 5,944 | 29.331633 | 102 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/modified_resnet_cifar.py | """Taken & slightly modified from:
* https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
"""
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torch.nn import functional as F
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, remove_last_relu=False):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
self.remove_last_relu = remove_last_relu
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
if not self.remove_last_relu:
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, nf=16, dataset='cifar', start_class=0, remove_last_relu=False):
super(ResNet, self).__init__()
self.inplanes = nf
self.conv1 = nn.Conv2d(3, nf, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(nf)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(block, 1 * nf, layers[0])
self.layer2 = self._make_layer(block, 2 * nf, layers[1], stride=2)
self.layer3 = self._make_layer(block, 4 * nf, layers[2], stride=2)
self.avgpool = nn.AvgPool2d(8, stride=1)
self.out_dim = 4 * nf * block.expansion
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def _make_layer(self, block, planes, blocks, stride=1, remove_last_relu=False):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
if remove_last_relu:
for i in range(1, blocks - 1):
layers.append(block(self.inplanes, planes))
layers.append(block(self.inplanes, planes, remove_last_relu=True))
else:
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def reset_bn(self):
for m in self.modules():
if isinstance(m, nn.BatchNorm2d):
m.reset_running_stats()
def forward(self, x, pool=True):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
return x
def resnet20(pretrained=False, **kwargs):
n = 3
model = ResNet(BasicBlock, [n, n, n], **kwargs)
return model
def resnet32(pretrained=False, **kwargs):
n = 5
model = ResNet(BasicBlock, [n, n, n], **kwargs)
return model
| 4,519 | 32.731343 | 109 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/models/base.py | import abc
import logging
import torch
import torch.nn.functional as F
import numpy as np
from inclearn.tools.metrics import ClassErrorMeter
LOGGER = logging.Logger("IncLearn", level="INFO")
class IncrementalLearner(abc.ABC):
"""Base incremental learner.
Methods are called in this order (& repeated for each new task):
1. set_task_info
2. before_task
3. train_task
4. after_task
5. eval_task
"""
def __init__(self, *args, **kwargs):
self._increments = []
self._seen_classes = []
def set_task_info(self, task, total_n_classes, increment, n_train_data, n_test_data, n_tasks):
self._task = task
self._task_size = increment
self._increments.append(self._task_size)
self._total_n_classes = total_n_classes
self._n_train_data = n_train_data
self._n_test_data = n_test_data
self._n_tasks = n_tasks
def before_task(self, taski, inc_dataset):
LOGGER.info("Before task")
self.eval()
self._before_task(taski, inc_dataset)
def train_task(self, train_loader, val_loader):
LOGGER.info("train task")
self.train()
self._train_task(train_loader, val_loader)
def after_task(self, taski, inc_dataset):
LOGGER.info("after task")
self.eval()
self._after_task(taski, inc_dataset)
def eval_task(self, data_loader):
LOGGER.info("eval task")
self.eval()
return self._eval_task(data_loader)
def get_memory(self):
return None
def eval(self):
raise NotImplementedError
def train(self):
raise NotImplementedError
def _before_task(self, data_loader):
pass
def _train_task(self, train_loader, val_loader):
raise NotImplementedError
def _after_task(self, data_loader):
pass
def _eval_task(self, data_loader):
raise NotImplementedError
@property
def _new_task_index(self):
return self._task * self._task_size
@property
def _memory_per_class(self):
"""Returns the number of examplars per class."""
return self._memory_size.mem_per_cls
def _after_epoch(self, epoch, avg_loss, train_new_accu, train_old_accu, accu):
self._run.log_scalar(f"train_loss_trial{self._trial_i}_task{self._task}", avg_loss, epoch + 1)
self._tensorboard.add_scalar(f"trial{self._trial_i}_task{self._task}/train_loss", avg_loss, epoch + 1)
# self._run.log_scalar(f"train_new_accu_trial{self._trial_i}_task{self._task}",
# train_new_accu.value()[0], epoch + 1)
# self._tensorboard.add_scalar(f"trial{self._trial_i}_task{self._task}/train_new_accu",
# train_new_accu.value()[0], epoch + 1)
# if self._task != 0:
# self._run.log_scalar(f"train_old_accu_trial{self._trial_i}_task{self._task}",
# train_old_accu.value()[0], epoch + 1)
# self._tensorboard.add_scalar(f"trial{self._trial_i}_task{self._task}/train_old_accu",
# train_old_accu.value()[0], epoch + 1)
self._run.log_scalar(f"train_accu_trial{self._trial_i}_task{self._task}", accu.value()[0], epoch + 1)
self._tensorboard.add_scalar(f"trial{self._trial_i}_task{self._task}/train_accu", accu.value()[0], epoch + 1)
# self._tensorboard.close()
self._tensorboard.flush()
def _validation(self, val_loader, epoch):
topk = 5 if self._n_classes >= 5 else self._n_classes
if self._val_per_n_epoch != -1 and epoch % self._val_per_n_epoch == 0:
_val_loss = 0
_val_accu = ClassErrorMeter(accuracy=True, topk=[1, topk])
_val_new_accu = ClassErrorMeter(accuracy=True)
_val_old_accu = ClassErrorMeter(accuracy=True)
self._parallel_network.eval()
with torch.no_grad():
for i, (inputs, targets) in enumerate(val_loader, 1):
old_classes = targets < (self._n_classes - self._task_size)
new_classes = targets >= (self._n_classes - self._task_size)
val_loss, _ = self._forward_loss(
inputs,
targets,
old_classes,
new_classes,
accu=_val_accu,
old_accu=_val_old_accu,
new_accu=_val_new_accu,
)
_val_loss += val_loss.item()
self._ex.logger.info(
f"epoch{epoch} val acc:{_val_accu.value()[0]:.2f}, val top5acc:{_val_accu.value()[1]:.2f}")
# Test accu
self._run.log_scalar(f"test_accu_trial{self._trial_i}_task{self._task}", _val_accu.value()[0], epoch + 1)
self._run.log_scalar(f"test_5accu_trial{self._trial_i}_task{self._task}", _val_accu.value()[1], epoch + 1)
self._tensorboard.add_scalar(f"trial{self._trial_i}_task{self._task}/test_accu",
_val_accu.value()[0], epoch + 1)
self._tensorboard.add_scalar(f"trial{self._trial_i}_task{self._task}/test_5accu",
_val_accu.value()[1], epoch + 1)
# Test new accu
self._run.log_scalar(f"test_new_accu_trial{self._trial_i}_task{self._task}",
_val_new_accu.value()[0], epoch + 1)
self._tensorboard.add_scalar(f"trial{self._trial_i}_task{self._task}/test_new_accu",
_val_new_accu.value()[0], epoch + 1)
# Test old accu
if self._task != 0:
self._run.log_scalar(f"test_old_accu_trial{self._trial_i}_task{self._task}",
_val_old_accu.value()[0], epoch + 1)
self._tensorboard.add_scalar(f"trial{self._trial_i}_task{self._task}/test_old_accu",
_val_old_accu.value()[0], epoch + 1)
# Test loss
self._run.log_scalar(f"test_loss_trial{self._trial_i}_task{self._task}", round(_val_loss / i, 3), epoch + 1)
self._tensorboard.add_scalar(f"trial{self._trial_i}_task{self._task}/test_loss", round(_val_loss / i, 3),
epoch + 1)
self._tensorboard.close() | 6,449 | 40.883117 | 120 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/models/align.py | import numpy as np
import random
import time
import math
import os
from copy import deepcopy
from scipy.spatial.distance import cdist
import torch
from torch.nn import DataParallel
from torch.nn import functional as F
from inclearn.convnet import network
from inclearn.models.base import IncrementalLearner
from inclearn.tools import factory, utils
from inclearn.tools.metrics import ClassErrorMeter
from inclearn.tools.memory import MemorySize
from inclearn.tools.scheduler import GradualWarmupScheduler
from inclearn.convnet.utils import extract_features, update_classes_mean
EPSILON = 1e-8
class Weight_Align(IncrementalLearner):
def __init__(self, cfg, trial_i, _run, ex, tensorboard, inc_dataset):
super().__init__()
self._cfg = cfg
self._device = cfg['device']
self._ex = ex
self._run = _run # the sacred _run object.
self._inc_dataset = inc_dataset
self._n_classes = 0
self._trial_i = trial_i # the No. of current run.
self._opt_name = cfg["optimizer"]
self._warmup = cfg['warmup']
self._lr = cfg["lr"]
self._weight_decay = cfg["weight_decay"]
self._n_epochs = cfg["epochs"]
self._scheduling = cfg["scheduling"]
self._lr_decay = cfg["lr_decay"]
self._tensorboard = tensorboard
if f"trial{self._trial_i}" not in self._run.info:
self._run.info[f"trial{self._trial_i}"] = {}
self._val_per_n_epoch = cfg["val_per_n_epoch"]
self._network = network.BasicNet(
cfg["convnet"],
cfg=cfg,
nf=cfg["channel"],
device=self._device,
use_bias=cfg["use_bias"],
dataset=cfg["dataset"],
)
self._parallel_network = DataParallel(self._network)
self._train_head = cfg["train_head"]
self._infer_head = cfg["infer_head"]
self._old_model = None
self._temperature = cfg["temperature"]
self._distillation = cfg["distillation"]
# Memory
self._memory_size = MemorySize(cfg["mem_size_mode"], inc_dataset, cfg["memory_size"],
cfg["fixed_memory_per_cls"])
self._herding_matrix = []
self._coreset_strategy = cfg["coreset_strategy"]
if self._cfg["save_ckpt"]:
save_path = os.path.join(os.getcwd(), "ckpts")
if not os.path.exists(save_path):
os.mkdir(save_path)
if self._cfg["save_mem"]:
save_path = os.path.join(os.getcwd(), "ckpts/mem")
if not os.path.exists(save_path):
os.mkdir(save_path)
def eval(self):
self._parallel_network.eval()
def train(self):
self._parallel_network.train()
# ----------
# Public API
# ----------
def _before_task(self, taski, inc_dataset):
self._task = taski
self._n_classes += self._task_size
self._memory_size.update_n_classes(self._n_classes)
self._memory_size.update_memory_per_cls(self._network, self._n_classes, self._task_size)
self._ex.logger.info("Now {} examplars per class.".format(self._memory_per_class))
self._network.add_classes(self._task_size)
self._network.task_size = self._task_size
self.set_optimizer()
def set_optimizer(self, lr=None):
if lr is None:
lr = self._lr
if self._cfg["dynamic_weight_decay"]:
# used in BiC official implementation
weight_decay = self._weight_decay * self._cfg["task_max"] / (self._task + 1)
else:
weight_decay = self._weight_decay
self._ex.logger.info("Step {} weight decay {:.5f}".format(self._task, weight_decay))
self._optimizer = factory.get_optimizer(filter(lambda p: p.requires_grad, self._network.parameters()),
self._opt_name, lr, weight_decay)
if "cos" in self._cfg["scheduler"]:
self._scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self._optimizer, self._n_epochs)
else:
self._scheduler = torch.optim.lr_scheduler.MultiStepLR(self._optimizer,
self._scheduling,
gamma=self._lr_decay)
if self._warmup:
print("warmup")
self._warmup_scheduler = GradualWarmupScheduler(self._optimizer,
multiplier=1,
total_epoch=self._cfg['warmup_epochs'],
after_scheduler=self._scheduler)
def _train_task(self, train_loader, val_loader):
self._ex.logger.info(f"nb {len(train_loader.dataset)}")
topk = 5 if self._n_classes > 5 else self._task_size
accu = ClassErrorMeter(accuracy=True, topk=[1, topk])
train_new_accu = ClassErrorMeter(accuracy=True)
train_old_accu = ClassErrorMeter(accuracy=True)
utils.display_weight_norm(self._ex.logger, self._parallel_network, self._increments, "Initial trainset")
utils.display_feature_norm(self._ex.logger, self._parallel_network, train_loader, self._n_classes,
self._increments, "Initial trainset")
self._optimizer.zero_grad()
self._optimizer.step()
for epoch in range(self._n_epochs):
_loss = 0.0
accu.reset()
train_new_accu.reset()
train_old_accu.reset()
if self._warmup:
self._warmup_scheduler.step()
if epoch == self._cfg['warmup_epochs']:
self._network.classifier.reset_parameters()
for i, (inputs, targets) in enumerate(train_loader, start=1):
self.train()
self._optimizer.zero_grad()
old_classes = targets < (self._n_classes - self._task_size)
new_classes = targets >= (self._n_classes - self._task_size)
loss = self._forward_loss(inputs, targets, old_classes, new_classes, accu=accu)
if not utils.check_loss(loss):
import pdb
pdb.set_trace()
loss.backward()
self._optimizer.step()
if self._cfg["postprocessor"]["enable"]:
if self._cfg["postprocessor"]["type"].lower() == "wa":
for p in self._network.classifier.parameters():
p.data.clamp_(0.0)
_loss += loss
_loss = _loss.item()
if not self._warmup:
self._scheduler.step()
self._ex.logger.info(
"Task {}/{}, Epoch {}/{} => Clf loss: {}, Train Accu: {}, Train@5 Acc: {}, old acc:{}".format(
self._task + 1,
self._n_tasks,
epoch + 1,
self._n_epochs,
round(_loss / i, 3),
round(accu.value()[0], 3),
round(accu.value()[1], 3),
round(train_old_accu.value()[0], 3),
))
if self._val_per_n_epoch > 0 and epoch % self._val_per_n_epoch == 0:
self.validate(val_loader)
self._inc_dataset.shared_data_inc = train_loader.dataset.share_memory
utils.display_weight_norm(self._ex.logger, self._parallel_network, self._increments, "After training")
utils.display_feature_norm(self._ex.logger, self._parallel_network, train_loader, self._n_classes,
self._increments, "Trainset")
self._run.info[f"trial{self._trial_i}"][f"task{self._task}_train_accu"] = round(accu.value()[0], 3)
def _forward_loss(self, inputs, targets, old_classes, new_classes, accu=None):
inputs, targets = inputs.to(self._device, non_blocking=True), targets.to(self._device, non_blocking=True)
logits = self._parallel_network(inputs)['logit']
if accu is not None:
accu.add(logits, targets)
return self._compute_loss(inputs, targets, logits)
def _compute_loss(self, inputs, targets, logits):
loss = F.cross_entropy(logits, targets)
if self._old_model is not None:
log_probs_new = (logits[:, :-self._task_size] / self._temperature).log_softmax(dim=1)
logits_old = self._old_model(inputs)['logit'].detach()
if self._task > 1:
logits_old = self._old_model.module.postprocessor.post_process(logits_old, self._task_size)
probs_old = (logits_old / self._temperature).softmax(dim=1)
loss_kl = F.kl_div(log_probs_new, probs_old, reduction="batchmean")
lamb = (self._n_classes - self._task_size) / self._n_classes
loss = (1 - lamb) * loss + lamb * loss_kl
return loss
def _after_task(self, taski, inc_dataset):
network = deepcopy(self._parallel_network)
network.eval()
self._ex.logger.info("save model")
if self._cfg["save_ckpt"] and taski >= self._cfg["start_task"]:
save_path = os.path.join(os.getcwd(), "ckpts")
torch.save(network.cpu().state_dict(), "{}/step{}.ckpt".format(save_path, self._task))
utils.display_weight_norm(self._ex.logger, network, self._increments, "After training")
if self._memory_size.memsize != 0:
self._ex.logger.info("build memory")
self.build_exemplars(inc_dataset, self._coreset_strategy)
self._parallel_network.eval()
if self._cfg["postprocessor"]["enable"] and self._task > 0:
self._update_postprocessor(inc_dataset)
self._old_model = deepcopy(self._parallel_network)
self._old_model.module.freeze()
del self._inc_dataset.shared_data_inc
self._inc_dataset.shared_data_inc = None
def _eval_task(self, data_loader):
if self._infer_head == "softmax":
ypred, ytrue = self._compute_accuracy_by_netout(data_loader)
else:
raise ValueError()
return ypred, ytrue
# -----------
# Private API
# -----------
def _compute_accuracy_by_netout(self, data_loader):
preds, targets = [], []
self._parallel_network.eval()
with torch.no_grad():
for i, (inputs, lbls) in enumerate(data_loader):
inputs = inputs.to(self._device, non_blocking=True)
_preds = self._network(inputs)['logit']
if self._cfg["postprocessor"]["enable"] and self._task > 0:
_preds = self._network.postprocessor.post_process(_preds, self._task_size)
preds.append(_preds.detach().cpu().numpy())
targets.append(lbls.long().cpu().numpy())
preds = np.concatenate(preds, axis=0)
targets = np.concatenate(targets, axis=0)
return preds, targets
def update_prototype(self):
self._class_means = update_classes_mean(self._parallel_network,
self._inc_dataset,
self._n_classes,
self._task_size,
share_memory=self._inc_dataset.shared_data_inc,
metric="none")
def _update_postprocessor(self, inc_dataset):
if self._cfg["postprocessor"]["type"].lower() == "bic":
bic_loader = inc_dataset._get_loader(inc_dataset.data_inc, inc_dataset.targets_inc, mode="balanced_train")
bic_loss = None
self._network.postprocessor.reset(n_classes=self._n_classes)
self._network.postprocessor.update(self._ex.logger,
self._task_size,
self._parallel_network,
bic_loader,
loss_criterion=bic_loss)
elif self._cfg["postprocessor"]["type"].lower() == "wa":
self._ex.logger.info("Post processor wa update !")
self._network.postprocessor.update(self._network.classifier, self._task_size)
def build_exemplars(self, inc_dataset, coreset_strategy):
save_path = os.path.join(os.getcwd(), f"ckpts/mem/mem_step{self._task}.ckpt")
if self._cfg["load_mem"] and os.path.exists(save_path):
memory_states = torch.load(save_path)
self._inc_dataset.data_memory = memory_states['x']
self._inc_dataset.targets_memory = memory_states['y']
self._herding_matrix = memory_states['herding']
self._ex.logger.info(f"Load saved step{self._task} memory!")
return
if coreset_strategy == "random":
from inclearn.tools.memory import random_selection
self._inc_dataset.data_memory, self._inc_dataset.targets_memory = random_selection(
self._n_classes,
self._task_size,
self._parallel_network,
self._ex.logger,
inc_dataset,
self._memory_per_class,
)
elif coreset_strategy == "iCaRL":
from inclearn.tools.memory import herding
data_inc = self._inc_dataset.shared_data_inc if self._inc_dataset.shared_data_inc is not None else self._inc_dataset.data_inc
self._inc_dataset.data_memory, self._inc_dataset.targets_memory, self._herding_matrix = herding(
self._n_classes,
self._task_size,
self._parallel_network,
self._herding_matrix,
inc_dataset,
data_inc,
self._memory_per_class,
self._ex.logger,
)
else:
raise ValueError()
def validate(self, data_loader):
if self._infer_head == 'NCM':
self.update_prototype()
ypred, ytrue = self._eval_task(data_loader)
test_acc_stats = utils.compute_accuracy(ypred, ytrue, increments=self._increments, n_classes=self._n_classes)
self._ex.logger.info(f"test top1acc:{test_acc_stats['top1']}")
| 14,420 | 42.436747 | 137 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/models/incmodel.py | import numpy as np
import random
import time
import math
import os
from copy import deepcopy
from scipy.spatial.distance import cdist
import torch
from torch.nn import DataParallel
from torch.nn import functional as F
from inclearn.convnet import network
from inclearn.models.base import IncrementalLearner
from inclearn.tools import factory, utils
from inclearn.tools.metrics import ClassErrorMeter
from inclearn.tools.memory import MemorySize
from inclearn.tools.scheduler import GradualWarmupScheduler
from inclearn.convnet.utils import extract_features, update_classes_mean, finetune_last_layer
# Constants
EPSILON = 1e-8
class IncModel(IncrementalLearner):
def __init__(self, cfg, trial_i, _run, ex, tensorboard, inc_dataset):
super().__init__()
self._cfg = cfg
self._device = cfg['device']
self._ex = ex
self._run = _run # the sacred _run object.
# Data
self._inc_dataset = inc_dataset
self._n_classes = 0
self._trial_i = trial_i # which class order is used
# Optimizer paras
self._opt_name = cfg["optimizer"]
self._warmup = cfg['warmup']
self._lr = cfg["lr"]
self._weight_decay = cfg["weight_decay"]
self._n_epochs = cfg["epochs"]
self._scheduling = cfg["scheduling"]
self._lr_decay = cfg["lr_decay"]
# Classifier Learning Stage
self._decouple = cfg["decouple"]
# Logging
self._tensorboard = tensorboard
if f"trial{self._trial_i}" not in self._run.info:
self._run.info[f"trial{self._trial_i}"] = {}
self._val_per_n_epoch = cfg["val_per_n_epoch"]
# Model
self._der = cfg['der'] # Whether to expand the representation
self._network = network.BasicNet(
cfg["convnet"],
cfg=cfg,
nf=cfg["channel"],
device=self._device,
use_bias=cfg["use_bias"],
dataset=cfg["dataset"],
)
self._parallel_network = DataParallel(self._network)
self._train_head = cfg["train_head"]
self._infer_head = cfg["infer_head"]
self._old_model = None
# Learning
self._temperature = cfg["temperature"]
self._distillation = cfg["distillation"]
# Memory
self._memory_size = MemorySize(cfg["mem_size_mode"], inc_dataset, cfg["memory_size"],
cfg["fixed_memory_per_cls"])
self._herding_matrix = []
self._coreset_strategy = cfg["coreset_strategy"]
if self._cfg["save_ckpt"]:
save_path = os.path.join(os.getcwd(), "ckpts")
if not os.path.exists(save_path):
os.mkdir(save_path)
if self._cfg["save_mem"]:
save_path = os.path.join(os.getcwd(), "ckpts/mem")
if not os.path.exists(save_path):
os.mkdir(save_path)
def eval(self):
self._parallel_network.eval()
def train(self):
if self._der:
self._parallel_network.train()
self._parallel_network.module.convnets[-1].train()
if self._task >= 1:
for i in range(self._task):
self._parallel_network.module.convnets[i].eval()
else:
self._parallel_network.train()
def _before_task(self, taski, inc_dataset):
self._ex.logger.info(f"Begin step {taski}")
# Update Task info
self._task = taski
self._n_classes += self._task_size
# Memory
self._memory_size.update_n_classes(self._n_classes)
self._memory_size.update_memory_per_cls(self._network, self._n_classes, self._task_size)
self._ex.logger.info("Now {} examplars per class.".format(self._memory_per_class))
self._network.add_classes(self._task_size)
self._network.task_size = self._task_size
self.set_optimizer()
def set_optimizer(self, lr=None):
if lr is None:
lr = self._lr
if self._cfg["dynamic_weight_decay"]:
# used in BiC official implementation
weight_decay = self._weight_decay * self._cfg["task_max"] / (self._task + 1)
else:
weight_decay = self._weight_decay
self._ex.logger.info("Step {} weight decay {:.5f}".format(self._task, weight_decay))
if self._der and self._task > 0:
for i in range(self._task):
for p in self._parallel_network.module.convnets[i].parameters():
p.requires_grad = False
self._optimizer = factory.get_optimizer(filter(lambda p: p.requires_grad, self._network.parameters()),
self._opt_name, lr, weight_decay)
if "cos" in self._cfg["scheduler"]:
self._scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self._optimizer, self._n_epochs)
else:
self._scheduler = torch.optim.lr_scheduler.MultiStepLR(self._optimizer,
self._scheduling,
gamma=self._lr_decay)
if self._warmup:
print("warmup")
self._warmup_scheduler = GradualWarmupScheduler(self._optimizer,
multiplier=1,
total_epoch=self._cfg['warmup_epochs'],
after_scheduler=self._scheduler)
def _train_task(self, train_loader, val_loader):
self._ex.logger.info(f"nb {len(train_loader.dataset)}")
topk = 5 if self._n_classes > 5 else self._task_size
accu = ClassErrorMeter(accuracy=True, topk=[1, topk])
train_new_accu = ClassErrorMeter(accuracy=True)
train_old_accu = ClassErrorMeter(accuracy=True)
utils.display_weight_norm(self._ex.logger, self._parallel_network, self._increments, "Initial trainset")
utils.display_feature_norm(self._ex.logger, self._parallel_network, train_loader, self._n_classes,
self._increments, "Initial trainset")
self._optimizer.zero_grad()
self._optimizer.step()
for epoch in range(self._n_epochs):
_loss, _loss_aux = 0.0, 0.0
accu.reset()
train_new_accu.reset()
train_old_accu.reset()
if self._warmup:
self._warmup_scheduler.step()
if epoch == self._cfg['warmup_epochs']:
self._network.classifier.reset_parameters()
if self._cfg['use_aux_cls']:
self._network.aux_classifier.reset_parameters()
for i, (inputs, targets) in enumerate(train_loader, start=1):
self.train()
self._optimizer.zero_grad()
old_classes = targets < (self._n_classes - self._task_size)
new_classes = targets >= (self._n_classes - self._task_size)
loss_ce, loss_aux = self._forward_loss(
inputs,
targets,
old_classes,
new_classes,
accu=accu,
new_accu=train_new_accu,
old_accu=train_old_accu,
)
if self._cfg["use_aux_cls"] and self._task > 0:
loss = loss_ce + loss_aux
else:
loss = loss_ce
if not utils.check_loss(loss):
import pdb
pdb.set_trace()
loss.backward()
self._optimizer.step()
if self._cfg["postprocessor"]["enable"]:
if self._cfg["postprocessor"]["type"].lower() == "wa":
for p in self._network.classifier.parameters():
p.data.clamp_(0.0)
_loss += loss_ce
_loss_aux += loss_aux
_loss = _loss.item()
_loss_aux = _loss_aux.item()
if not self._warmup:
self._scheduler.step()
self._ex.logger.info(
"Task {}/{}, Epoch {}/{} => Clf loss: {} Aux loss: {}, Train Accu: {}, Train@5 Acc: {}, old acc:{}".
format(
self._task + 1,
self._n_tasks,
epoch + 1,
self._n_epochs,
round(_loss / i, 3),
round(_loss_aux / i, 3),
round(accu.value()[0], 3),
round(accu.value()[1], 3),
round(train_old_accu.value()[0], 3),
))
if self._val_per_n_epoch > 0 and epoch % self._val_per_n_epoch == 0:
self.validate(val_loader)
# For the large-scale dataset, we manage the data in the shared memory.
self._inc_dataset.shared_data_inc = train_loader.dataset.share_memory
utils.display_weight_norm(self._ex.logger, self._parallel_network, self._increments, "After training")
utils.display_feature_norm(self._ex.logger, self._parallel_network, train_loader, self._n_classes,
self._increments, "Trainset")
self._run.info[f"trial{self._trial_i}"][f"task{self._task}_train_accu"] = round(accu.value()[0], 3)
def _forward_loss(self, inputs, targets, old_classes, new_classes, accu=None, new_accu=None, old_accu=None):
inputs, targets = inputs.to(self._device, non_blocking=True), targets.to(self._device, non_blocking=True)
outputs = self._parallel_network(inputs)
if accu is not None:
accu.add(outputs['logit'], targets)
# accu.add(logits.detach(), targets.cpu().numpy())
# if new_accu is not None:
# new_accu.add(logits[new_classes].detach(), targets[new_classes].cpu().numpy())
# if old_accu is not None:
# old_accu.add(logits[old_classes].detach(), targets[old_classes].cpu().numpy())
return self._compute_loss(inputs, targets, outputs, old_classes, new_classes)
def _compute_loss(self, inputs, targets, outputs, old_classes, new_classes):
loss = F.cross_entropy(outputs['logit'], targets)
if outputs['aux_logit'] is not None:
aux_targets = targets.clone()
if self._cfg["aux_n+1"]:
aux_targets[old_classes] = 0
aux_targets[new_classes] -= sum(self._inc_dataset.increments[:self._task]) - 1
aux_loss = F.cross_entropy(outputs['aux_logit'], aux_targets)
else:
aux_loss = torch.zeros([1]).cuda()
return loss, aux_loss
def _after_task(self, taski, inc_dataset):
network = deepcopy(self._parallel_network)
network.eval()
self._ex.logger.info("save model")
if self._cfg["save_ckpt"] and taski >= self._cfg["start_task"]:
save_path = os.path.join(os.getcwd(), "ckpts")
torch.save(network.cpu().state_dict(), "{}/step{}.ckpt".format(save_path, self._task))
if (self._cfg["decouple"]['enable'] and taski > 0):
if self._cfg["decouple"]["fullset"]:
train_loader = inc_dataset._get_loader(inc_dataset.data_inc, inc_dataset.targets_inc, mode="train")
else:
train_loader = inc_dataset._get_loader(inc_dataset.data_inc,
inc_dataset.targets_inc,
mode="balanced_train")
# finetuning
self._parallel_network.module.classifier.reset_parameters()
finetune_last_layer(self._ex.logger,
self._parallel_network,
train_loader,
self._n_classes,
nepoch=self._decouple["epochs"],
lr=self._decouple["lr"],
scheduling=self._decouple["scheduling"],
lr_decay=self._decouple["lr_decay"],
weight_decay=self._decouple["weight_decay"],
loss_type="ce",
temperature=self._decouple["temperature"])
network = deepcopy(self._parallel_network)
if self._cfg["save_ckpt"]:
save_path = os.path.join(os.getcwd(), "ckpts")
torch.save(network.cpu().state_dict(), "{}/decouple_step{}.ckpt".format(save_path, self._task))
if self._cfg["postprocessor"]["enable"]:
self._update_postprocessor(inc_dataset)
if self._cfg["infer_head"] == 'NCM':
self._ex.logger.info("compute prototype")
self.update_prototype()
if self._memory_size.memsize != 0:
self._ex.logger.info("build memory")
self.build_exemplars(inc_dataset, self._coreset_strategy)
if self._cfg["save_mem"]:
save_path = os.path.join(os.getcwd(), "ckpts/mem")
memory = {
'x': inc_dataset.data_memory,
'y': inc_dataset.targets_memory,
'herding': self._herding_matrix
}
if not os.path.exists(save_path):
os.makedirs(save_path)
if not (os.path.exists(f"{save_path}/mem_step{self._task}.ckpt") and self._cfg['load_mem']):
torch.save(memory, "{}/mem_step{}.ckpt".format(save_path, self._task))
self._ex.logger.info(f"Save step{self._task} memory!")
self._parallel_network.eval()
self._old_model = deepcopy(self._parallel_network)
self._old_model.module.freeze()
del self._inc_dataset.shared_data_inc
self._inc_dataset.shared_data_inc = None
def _eval_task(self, data_loader):
if self._infer_head == "softmax":
ypred, ytrue = self._compute_accuracy_by_netout(data_loader)
elif self._infer_head == "NCM":
ypred, ytrue = self._compute_accuracy_by_ncm(data_loader)
else:
raise ValueError()
return ypred, ytrue
def _compute_accuracy_by_netout(self, data_loader):
preds, targets = [], []
self._parallel_network.eval()
with torch.no_grad():
for i, (inputs, lbls) in enumerate(data_loader):
inputs = inputs.to(self._device, non_blocking=True)
_preds = self._parallel_network(inputs)['logit']
if self._cfg["postprocessor"]["enable"] and self._task > 0:
_preds = self._network.postprocessor.post_process(_preds, self._task_size)
preds.append(_preds.detach().cpu().numpy())
targets.append(lbls.long().cpu().numpy())
preds = np.concatenate(preds, axis=0)
targets = np.concatenate(targets, axis=0)
return preds, targets
def _compute_accuracy_by_ncm(self, loader):
features, targets_ = extract_features(self._parallel_network, loader)
targets = np.zeros((targets_.shape[0], self._n_classes), np.float32)
targets[range(len(targets_)), targets_.astype("int32")] = 1.0
class_means = (self._class_means.T / (np.linalg.norm(self._class_means.T, axis=0) + EPSILON)).T
features = (features.T / (np.linalg.norm(features.T, axis=0) + EPSILON)).T
# Compute score for iCaRL
sqd = cdist(class_means, features, "sqeuclidean")
score_icarl = (-sqd).T
return score_icarl[:, :self._n_classes], targets_
def _update_postprocessor(self, inc_dataset):
if self._cfg["postprocessor"]["type"].lower() == "bic":
if self._cfg["postprocessor"]["disalign_resample"] is True:
bic_loader = inc_dataset._get_loader(inc_dataset.data_inc,
inc_dataset.targets_inc,
mode="train",
resample='disalign_resample')
else:
xdata, ydata = inc_dataset._select(inc_dataset.data_train,
inc_dataset.targets_train,
low_range=0,
high_range=self._n_classes)
bic_loader = inc_dataset._get_loader(xdata, ydata, shuffle=True, mode='train')
bic_loss = None
self._network.postprocessor.reset(n_classes=self._n_classes)
self._network.postprocessor.update(self._ex.logger,
self._task_size,
self._parallel_network,
bic_loader,
loss_criterion=bic_loss)
elif self._cfg["postprocessor"]["type"].lower() == "wa":
self._ex.logger.info("Post processor wa update !")
self._network.postprocessor.update(self._network.classifier, self._task_size)
def update_prototype(self):
if hasattr(self._inc_dataset, 'shared_data_inc'):
shared_data_inc = self._inc_dataset.shared_data_inc
else:
shared_data_inc = None
self._class_means = update_classes_mean(self._parallel_network,
self._inc_dataset,
self._n_classes,
self._task_size,
share_memory=self._inc_dataset.shared_data_inc,
metric='None')
def build_exemplars(self, inc_dataset, coreset_strategy):
save_path = os.path.join(os.getcwd(), f"ckpts/mem/mem_step{self._task}.ckpt")
if self._cfg["load_mem"] and os.path.exists(save_path):
memory_states = torch.load(save_path)
self._inc_dataset.data_memory = memory_states['x']
self._inc_dataset.targets_memory = memory_states['y']
self._herding_matrix = memory_states['herding']
self._ex.logger.info(f"Load saved step{self._task} memory!")
return
if coreset_strategy == "random":
from inclearn.tools.memory import random_selection
self._inc_dataset.data_memory, self._inc_dataset.targets_memory = random_selection(
self._n_classes,
self._task_size,
self._parallel_network,
self._ex.logger,
inc_dataset,
self._memory_per_class,
)
elif coreset_strategy == "iCaRL":
from inclearn.tools.memory import herding
data_inc = self._inc_dataset.shared_data_inc if self._inc_dataset.shared_data_inc is not None else self._inc_dataset.data_inc
self._inc_dataset.data_memory, self._inc_dataset.targets_memory, self._herding_matrix = herding(
self._n_classes,
self._task_size,
self._parallel_network,
self._herding_matrix,
inc_dataset,
data_inc,
self._memory_per_class,
self._ex.logger,
)
else:
raise ValueError()
def validate(self, data_loader):
if self._infer_head == 'NCM':
self.update_prototype()
ypred, ytrue = self._eval_task(data_loader)
test_acc_stats = utils.compute_accuracy(ypred, ytrue, increments=self._increments, n_classes=self._n_classes)
self._ex.logger.info(f"test top1acc:{test_acc_stats['top1']}")
| 19,955 | 43.445434 | 137 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/datasets/dataset.py | import os.path as osp
import numpy as np
import glob
import albumentations as A
from albumentations.pytorch import ToTensorV2
from torchvision import datasets, transforms
import torch
def get_datasets(dataset_names):
return [get_dataset(dataset_name) for dataset_name in dataset_names.split("-")]
def get_dataset(dataset_name):
if dataset_name == "cifar10":
return iCIFAR10
elif dataset_name == "cifar100":
return iCIFAR100
elif "imagenet100" in dataset_name:
return iImageNet100
elif dataset_name == "imagenet":
return iImageNet
else:
raise NotImplementedError("Unknown dataset {}.".format(dataset_name))
class DataHandler:
base_dataset = None
train_transforms = []
common_transforms = [ToTensorV2()]
class_order = None
class iCIFAR10(DataHandler):
base_dataset_cls = datasets.cifar.CIFAR10
transform_type = 'torchvision'
train_transforms = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
# transforms.ColorJitter(brightness=63 / 255),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
test_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
def __init__(self, data_folder, train, is_fine_label=False):
self.base_dataset = self.base_dataset_cls(data_folder, train=train, download=True)
self.data = self.base_dataset.data
self.targets = self.base_dataset.targets
self.n_cls = 10
@property
def is_proc_inc_data(self):
return False
@classmethod
def class_order(cls, trial_i):
return [4, 0, 2, 5, 8, 3, 1, 6, 9, 7]
class iCIFAR100(iCIFAR10):
label_list = [
'apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle', 'bicycle', 'bottle', 'bowl', 'boy',
'bridge', 'bus', 'butterfly', 'camel', 'can', 'castle', 'caterpillar', 'cattle', 'chair', 'chimpanzee', 'clock',
'cloud', 'cockroach', 'couch', 'crab', 'crocodile', 'cup', 'dinosaur', 'dolphin', 'elephant', 'flatfish',
'forest', 'fox', 'girl', 'hamster', 'house', 'kangaroo', 'keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion',
'lizard', 'lobster', 'man', 'maple_tree', 'motorcycle', 'mountain', 'mouse', 'mushroom', 'oak_tree', 'orange',
'orchid', 'otter', 'palm_tree', 'pear', 'pickup_truck', 'pine_tree', 'plain', 'plate', 'poppy', 'porcupine',
'possum', 'rabbit', 'raccoon', 'ray', 'road', 'rocket', 'rose', 'sea', 'seal', 'shark', 'shrew', 'skunk',
'skyscraper', 'snail', 'snake', 'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper', 'table', 'tank',
'telephone', 'television', 'tiger', 'tractor', 'train', 'trout', 'tulip', 'turtle', 'wardrobe', 'whale',
'willow_tree', 'wolf', 'woman', 'worm'
]
base_dataset_cls = datasets.cifar.CIFAR100
transform_type = 'torchvision'
train_transforms = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=63 / 255),
transforms.ToTensor(),
transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761)),
])
test_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761)),
])
def __init__(self, data_folder, train, is_fine_label=False):
self.base_dataset = self.base_dataset_cls(data_folder, train=train, download=True)
self.data = self.base_dataset.data
self.targets = self.base_dataset.targets
self.n_cls = 100
self.transform_type = 'torchvision'
@property
def is_proc_inc_data(self):
return False
@classmethod
def class_order(cls, trial_i):
if trial_i == 0:
return [
62, 54, 84, 20, 94, 22, 40, 29, 78, 27, 26, 79, 17, 76, 68, 88, 3, 19, 31, 21, 33, 60, 24, 14, 6, 10,
16, 82, 70, 92, 25, 5, 28, 9, 61, 36, 50, 90, 8, 48, 47, 56, 11, 98, 35, 93, 44, 64, 75, 66, 15, 38, 97,
42, 43, 12, 37, 55, 72, 95, 18, 7, 23, 71, 49, 53, 57, 86, 39, 87, 34, 63, 81, 89, 69, 46, 2, 1, 73, 32,
67, 91, 0, 51, 83, 13, 58, 80, 74, 65, 4, 30, 45, 77, 99, 85, 41, 96, 59, 52
]
elif trial_i == 1:
return [
68, 56, 78, 8, 23, 84, 90, 65, 74, 76, 40, 89, 3, 92, 55, 9, 26, 80, 43, 38, 58, 70, 77, 1, 85, 19, 17,
50, 28, 53, 13, 81, 45, 82, 6, 59, 83, 16, 15, 44, 91, 41, 72, 60, 79, 52, 20, 10, 31, 54, 37, 95, 14,
71, 96, 98, 97, 2, 64, 66, 42, 22, 35, 86, 24, 34, 87, 21, 99, 0, 88, 27, 18, 94, 11, 12, 47, 25, 30,
46, 62, 69, 36, 61, 7, 63, 75, 5, 32, 4, 51, 48, 73, 93, 39, 67, 29, 49, 57, 33
]
elif trial_i == 2: #PODNet
return [
87, 0, 52, 58, 44, 91, 68, 97, 51, 15, 94, 92, 10, 72, 49, 78, 61, 14, 8, 86, 84, 96, 18, 24, 32, 45,
88, 11, 4, 67, 69, 66, 77, 47, 79, 93, 29, 50, 57, 83, 17, 81, 41, 12, 37, 59, 25, 20, 80, 73, 1, 28, 6,
46, 62, 82, 53, 9, 31, 75, 38, 63, 33, 74, 27, 22, 36, 3, 16, 21, 60, 19, 70, 90, 89, 43, 5, 42, 65, 76,
40, 30, 23, 85, 2, 95, 56, 48, 71, 64, 98, 13, 99, 7, 34, 55, 54, 26, 35, 39
]
elif trial_i == 3: #PODNet
return [
58, 30, 93, 69, 21, 77, 3, 78, 12, 71, 65, 40, 16, 49, 89, 46, 24, 66, 19, 41, 5, 29, 15, 73, 11, 70,
90, 63, 67, 25, 59, 72, 80, 94, 54, 33, 18, 96, 2, 10, 43, 9, 57, 81, 76, 50, 32, 6, 37, 7, 68, 91, 88,
95, 85, 4, 60, 36, 22, 27, 39, 42, 34, 51, 55, 28, 53, 48, 38, 17, 83, 86, 56, 35, 45, 79, 99, 84, 97,
82, 98, 26, 47, 44, 62, 13, 31, 0, 75, 14, 52, 74, 8, 20, 1, 92, 87, 23, 64, 61
]
elif trial_i == 4: #PODNet
return [
71, 54, 45, 32, 4, 8, 48, 66, 1, 91, 28, 82, 29, 22, 80, 27, 86, 23, 37, 47, 55, 9, 14, 68, 25, 96, 36,
90, 58, 21, 57, 81, 12, 26, 16, 89, 79, 49, 31, 38, 46, 20, 92, 88, 40, 39, 98, 94, 19, 95, 72, 24, 64,
18, 60, 50, 63, 61, 83, 76, 69, 35, 0, 52, 7, 65, 42, 73, 74, 30, 41, 3, 6, 53, 13, 56, 70, 77, 34, 97,
75, 2, 17, 93, 33, 84, 99, 51, 62, 87, 5, 15, 10, 78, 67, 44, 59, 85, 43, 11
]
class DataHandler:
base_dataset = None
train_transforms = []
common_transforms = [ToTensorV2()]
class_order = None
class iImageNet(DataHandler):
base_dataset_cls = datasets.ImageFolder
transform_type = 'albumentations'
if transform_type == 'albumentations':
train_transforms = A.Compose([
A.RandomResizedCrop(224, 224),
A.HorizontalFlip(),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
),
ToTensorV2()
])
test_transforms = A.Compose([
A.Resize(256, 256),
A.CenterCrop(224, 224),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
),
ToTensorV2()
])
else:
train_transforms = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
# transforms.ColorJitter(brightness=63 / 255),
])
test_transforms = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
def __init__(self, data_folder, train, is_fine_label=False):
if train is True:
self.base_dataset = self.base_dataset_cls(osp.join(data_folder, "train"))
else:
self.base_dataset = self.base_dataset_cls(osp.join(data_folder, "val"))
self.data, self.targets = zip(*self.base_dataset.samples)
self.data = np.array(self.data)
self.targets = np.array(self.targets)
self.n_cls = 1000
@property
def is_proc_inc_data(self):
return False
@classmethod
def class_order(cls, trial_i):
return [
54, 7, 894, 512, 126, 337, 988, 11, 284, 493, 133, 783, 192, 979, 622, 215, 240, 548, 238, 419, 274, 108,
928, 856, 494, 836, 473, 650, 85, 262, 508, 590, 390, 174, 637, 288, 658, 219, 912, 142, 852, 160, 704, 289,
123, 323, 600, 542, 999, 634, 391, 761, 490, 842, 127, 850, 665, 990, 597, 722, 748, 14, 77, 437, 394, 859,
279, 539, 75, 466, 886, 312, 303, 62, 966, 413, 959, 782, 509, 400, 471, 632, 275, 730, 105, 523, 224, 186,
478, 507, 470, 906, 699, 989, 324, 812, 260, 911, 446, 44, 765, 759, 67, 36, 5, 30, 184, 797, 159, 741, 954,
465, 533, 585, 150, 101, 897, 363, 818, 620, 824, 154, 956, 176, 588, 986, 172, 223, 461, 94, 141, 621, 659,
360, 136, 578, 163, 427, 70, 226, 925, 596, 336, 412, 731, 755, 381, 810, 69, 898, 310, 120, 752, 93, 39,
326, 537, 905, 448, 347, 51, 615, 601, 229, 947, 348, 220, 949, 972, 73, 913, 522, 193, 753, 921, 257, 957,
691, 155, 820, 584, 948, 92, 582, 89, 379, 392, 64, 904, 169, 216, 694, 103, 410, 374, 515, 484, 624, 409,
156, 455, 846, 344, 371, 468, 844, 276, 740, 562, 503, 831, 516, 663, 630, 763, 456, 179, 996, 936, 248,
333, 941, 63, 738, 802, 372, 828, 74, 540, 299, 750, 335, 177, 822, 643, 593, 800, 459, 580, 933, 306, 378,
76, 227, 426, 403, 322, 321, 808, 393, 27, 200, 764, 651, 244, 479, 3, 415, 23, 964, 671, 195, 569, 917,
611, 644, 707, 355, 855, 8, 534, 657, 571, 811, 681, 543, 313, 129, 978, 592, 573, 128, 243, 520, 887, 892,
696, 26, 551, 168, 71, 398, 778, 529, 526, 792, 868, 266, 443, 24, 57, 15, 871, 678, 745, 845, 208, 188,
674, 175, 406, 421, 833, 106, 994, 815, 581, 676, 49, 619, 217, 631, 934, 932, 568, 353, 863, 827, 425, 420,
99, 823, 113, 974, 438, 874, 343, 118, 340, 472, 552, 937, 0, 10, 675, 316, 879, 561, 387, 726, 255, 407,
56, 927, 655, 809, 839, 640, 297, 34, 497, 210, 606, 971, 589, 138, 263, 587, 993, 973, 382, 572, 735, 535,
139, 524, 314, 463, 895, 376, 939, 157, 858, 457, 935, 183, 114, 903, 767, 666, 22, 525, 902, 233, 250, 825,
79, 843, 221, 214, 205, 166, 431, 860, 292, 976, 739, 899, 475, 242, 961, 531, 110, 769, 55, 701, 532, 586,
729, 253, 486, 787, 774, 165, 627, 32, 291, 962, 922, 222, 705, 454, 356, 445, 746, 776, 404, 950, 241, 452,
245, 487, 706, 2, 137, 6, 98, 647, 50, 91, 202, 556, 38, 68, 649, 258, 345, 361, 464, 514, 958, 504, 826,
668, 880, 28, 920, 918, 339, 315, 320, 768, 201, 733, 575, 781, 864, 617, 171, 795, 132, 145, 368, 147, 327,
713, 688, 848, 690, 975, 354, 853, 148, 648, 300, 436, 780, 693, 682, 246, 449, 492, 162, 97, 59, 357, 198,
519, 90, 236, 375, 359, 230, 476, 784, 117, 940, 396, 849, 102, 122, 282, 181, 130, 467, 88, 271, 793, 151,
847, 914, 42, 834, 521, 121, 29, 806, 607, 510, 837, 301, 669, 78, 256, 474, 840, 52, 505, 547, 641, 987,
801, 629, 491, 605, 112, 429, 401, 742, 528, 87, 442, 910, 638, 785, 264, 711, 369, 428, 805, 744, 380, 725,
480, 318, 997, 153, 384, 252, 985, 538, 654, 388, 100, 432, 832, 565, 908, 367, 591, 294, 272, 231, 213,
196, 743, 817, 433, 328, 970, 969, 4, 613, 182, 685, 724, 915, 311, 931, 865, 86, 119, 203, 268, 718, 317,
926, 269, 161, 209, 807, 645, 513, 261, 518, 305, 758, 872, 58, 65, 146, 395, 481, 747, 41, 283, 204, 564,
185, 777, 33, 500, 609, 286, 567, 80, 228, 683, 757, 942, 134, 673, 616, 960, 450, 350, 544, 830, 736, 170,
679, 838, 819, 485, 430, 190, 566, 511, 482, 232, 527, 411, 560, 281, 342, 614, 662, 47, 771, 861, 692, 686,
277, 373, 16, 946, 265, 35, 9, 884, 909, 610, 358, 18, 737, 977, 677, 803, 595, 135, 458, 12, 46, 418, 599,
187, 107, 992, 770, 298, 104, 351, 893, 698, 929, 502, 273, 20, 96, 791, 636, 708, 267, 867, 772, 604, 618,
346, 330, 554, 816, 664, 716, 189, 31, 721, 712, 397, 43, 943, 804, 296, 109, 576, 869, 955, 17, 506, 963,
786, 720, 628, 779, 982, 633, 891, 734, 980, 386, 365, 794, 325, 841, 878, 370, 695, 293, 951, 66, 594, 717,
116, 488, 796, 983, 646, 499, 53, 1, 603, 45, 424, 875, 254, 237, 199, 414, 307, 362, 557, 866, 341, 19,
965, 143, 555, 687, 235, 790, 125, 173, 364, 882, 727, 728, 563, 495, 21, 558, 709, 719, 877, 352, 83, 998,
991, 469, 967, 760, 498, 814, 612, 715, 290, 72, 131, 259, 441, 924, 773, 48, 625, 501, 440, 82, 684, 862,
574, 309, 408, 680, 623, 439, 180, 652, 968, 889, 334, 61, 766, 399, 598, 798, 653, 930, 149, 249, 890, 308,
881, 40, 835, 577, 422, 703, 813, 857, 995, 602, 583, 167, 670, 212, 751, 496, 608, 84, 639, 579, 178, 489,
37, 197, 789, 530, 111, 876, 570, 700, 444, 287, 366, 883, 385, 536, 460, 851, 81, 144, 60, 251, 13, 953,
270, 944, 319, 885, 710, 952, 517, 278, 656, 919, 377, 550, 207, 660, 984, 447, 553, 338, 234, 383, 749,
916, 626, 462, 788, 434, 714, 799, 821, 477, 549, 661, 206, 667, 541, 642, 689, 194, 152, 981, 938, 854,
483, 332, 280, 546, 389, 405, 545, 239, 896, 672, 923, 402, 423, 907, 888, 140, 870, 559, 756, 25, 211, 158,
723, 635, 302, 702, 453, 218, 164, 829, 247, 775, 191, 732, 115, 331, 901, 416, 873, 754, 900, 435, 762,
124, 304, 329, 349, 295, 95, 451, 285, 225, 945, 697, 417
]
class iImageNet100(DataHandler):
base_dataset_cls = datasets.ImageFolder
transform_type = 'albumentations'
if transform_type == 'albumentations':
train_transforms = A.Compose([
A.RandomResizedCrop(224, 224),
A.HorizontalFlip(),
# A.ColorJitter(brightness=63 / 255),
A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
ToTensorV2()
])
test_transforms = A.Compose([
A.Resize(256, 256),
A.CenterCrop(224, 224),
A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
ToTensorV2()
])
else:
train_transforms = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
# transforms.ColorJitter(brightness=63 / 255),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
test_transforms = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
def __init__(self, data_folder, train, is_fine_label=False):
if train is True:
self.base_dataset = self.base_dataset_cls(osp.join(data_folder, "train"))
else:
self.base_dataset = self.base_dataset_cls(osp.join(data_folder, "val"))
self.data, self.targets = zip(*self.base_dataset.samples)
self.data = np.array(self.data)
self.targets = np.array(self.targets)
self.n_cls = 100
@property
def is_proc_inc_data(self):
return False
@classmethod
def class_order(cls, trial_i):
return [
68, 56, 78, 8, 23, 84, 90, 65, 74, 76, 40, 89, 3, 92, 55, 9, 26, 80, 43, 38, 58, 70, 77, 1, 85, 19, 17, 50,
28, 53, 13, 81, 45, 82, 6, 59, 83, 16, 15, 44, 91, 41, 72, 60, 79, 52, 20, 10, 31, 54, 37, 95, 14, 71, 96,
98, 97, 2, 64, 66, 42, 22, 35, 86, 24, 34, 87, 21, 99, 0, 88, 27, 18, 94, 11, 12, 47, 25, 30, 46, 62, 69,
36, 61, 7, 63, 75, 5, 32, 4, 51, 48, 73, 93, 39, 67, 29, 49, 57, 33
]
| 16,245 | 51.918567 | 120 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/datasets/data.py | import random
import cv2
import numpy as np
import os.path as osp
from copy import deepcopy
from PIL import Image
import multiprocessing as mp
from multiprocessing import Pool
import albumentations as A
from albumentations.pytorch import ToTensorV2
import warnings
warnings.filterwarnings("ignore", "Corrupt EXIF data", UserWarning)
import torch
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler, WeightedRandomSampler
from torchvision import datasets, transforms
from torchvision.datasets.folder import pil_loader
from .dataset import get_dataset
from inclearn.tools.data_utils import construct_balanced_subset
def get_data_folder(data_folder, dataset_name):
return osp.join(data_folder, dataset_name)
class IncrementalDataset:
def __init__(
self,
trial_i,
dataset_name,
random_order=False,
shuffle=True,
workers=10,
batch_size=128,
seed=1,
increment=10,
validation_split=0.0,
resampling=False,
data_folder="./data",
start_class=0,
):
# The info about incremental split
self.trial_i = trial_i
self.start_class = start_class
#the number of classes for each step in incremental stage
self.task_size = increment
self.increments = []
self.random_order = random_order
self.validation_split = validation_split
#-------------------------------------
#Dataset Info
#-------------------------------------
self.data_folder = get_data_folder(data_folder, dataset_name)
self.dataset_name = dataset_name
self.train_dataset = None
self.test_dataset = None
self.n_tot_cls = -1
datasets = get_dataset(dataset_name)
self._setup_data(datasets)
self._workers = workers
self._shuffle = shuffle
self._batch_size = batch_size
self._resampling = resampling
#Currently, don't support multiple datasets
self.train_transforms = datasets.train_transforms
self.test_transforms = datasets.test_transforms
#torchvision or albumentations
self.transform_type = datasets.transform_type
# memory Mt
self.data_memory = None
self.targets_memory = None
# Incoming data D_t
self.data_cur = None
self.targets_cur = None
# Available data \tilde{D}_t = D_t \cup M_t
self.data_inc = None # Cur task data + memory
self.targets_inc = None
# Available data stored in cpu memory.
self.shared_data_inc = None
self.shared_test_data = None
#Current states for Incremental Learning Stage.
self._current_task = 0
@property
def n_tasks(self):
return len(self.increments)
def new_task(self):
if self._current_task >= len(self.increments):
raise Exception("No more tasks.")
min_class, max_class, x_train, y_train, x_test, y_test = self._get_cur_step_data_for_raw_data()
self.data_cur, self.targets_cur = x_train, y_train
if self.data_memory is not None:
print("Set memory of size: {}.".format(len(self.data_memory)))
if len(self.data_memory) != 0:
x_train = np.concatenate((x_train, self.data_memory))
y_train = np.concatenate((y_train, self.targets_memory))
self.data_inc, self.targets_inc = x_train, y_train
self.data_test_inc, self.targets_test_inc = x_test, y_test
train_loader = self._get_loader(x_train, y_train, mode="train")
val_loader = self._get_loader(x_test, y_test, shuffle=False, mode="test")
test_loader = self._get_loader(x_test, y_test, shuffle=False, mode="test")
task_info = {
"min_class": min_class,
"max_class": max_class,
"increment": self.increments[self._current_task],
"task": self._current_task,
"max_task": len(self.increments),
"n_train_data": len(x_train),
"n_test_data": len(y_train),
}
self._current_task += 1
return task_info, train_loader, val_loader, test_loader
def _get_cur_step_data_for_raw_data(self, ):
min_class = sum(self.increments[:self._current_task])
max_class = sum(self.increments[:self._current_task + 1])
x_train, y_train = self._select(self.data_train, self.targets_train, low_range=min_class, high_range=max_class)
x_test, y_test = self._select(self.data_test, self.targets_test, low_range=0, high_range=max_class)
return min_class, max_class, x_train, y_train, x_test, y_test
#--------------------------------
# Data Setup
#--------------------------------
def _setup_data(self, dataset):
# FIXME: handles online loading of images
self.data_train, self.targets_train = [], []
self.data_test, self.targets_test = [], []
self.data_val, self.targets_val = [], []
self.increments = []
self.class_order = []
current_class_idx = 0 # When using multiple datasets
train_dataset = dataset(self.data_folder, train=True)
test_dataset = dataset(self.data_folder, train=False)
self.train_dataset = train_dataset
self.test_datasets = test_dataset
self.n_tot_cls = self.train_dataset.n_cls #number of classes in whole dataset
self._setup_data_for_raw_data(dataset, train_dataset, test_dataset, current_class_idx)
# !list
self.data_train = np.concatenate(self.data_train)
self.targets_train = np.concatenate(self.targets_train)
self.data_val = np.concatenate(self.data_val)
self.targets_val = np.concatenate(self.targets_val)
self.data_test = np.concatenate(self.data_test)
self.targets_test = np.concatenate(self.targets_test)
def _setup_data_for_raw_data(self, dataset, train_dataset, test_dataset, current_class_idx=0):
increment = self.task_size
x_train, y_train = train_dataset.data, np.array(train_dataset.targets)
x_val, y_val, x_train, y_train = self._split_per_class(x_train, y_train, self.validation_split)
x_test, y_test = test_dataset.data, np.array(test_dataset.targets)
# Get Class Order
order = [i for i in range(len(np.unique(y_train)))]
if self.random_order:
random.seed(self._seed) # Ensure that following order is determined by seed:
random.shuffle(order)
elif dataset.class_order(self.trial_i) is not None:
order = dataset.class_order(self.trial_i)
self.class_order.append(order)
y_train = self._map_new_class_index(y_train, order)
y_val = self._map_new_class_index(y_val, order)
y_test = self._map_new_class_index(y_test, order)
y_train += current_class_idx
y_val += current_class_idx
y_test += current_class_idx
current_class_idx += len(order)
if self.start_class == 0:
self.increments = [increment for _ in range(len(order) // increment)]
else:
self.increments.append(self.start_class)
for _ in range((len(order) - self.start_class) // increment):
self.increments.append(increment)
self.data_train.append(x_train)
self.targets_train.append(y_train)
self.data_val.append(x_val)
self.targets_val.append(y_val)
self.data_test.append(x_test)
self.targets_test.append(y_test)
@staticmethod
def _split_per_class(x, y, validation_split=0.0):
"""Splits train data for a subset of validation data.
Split is done so that each class has a much data.
"""
shuffled_indexes = np.random.permutation(x.shape[0])
x = x[shuffled_indexes]
y = y[shuffled_indexes]
x_val, y_val = [], []
x_train, y_train = [], []
for class_id in np.unique(y):
class_indexes = np.where(y == class_id)[0]
nb_val_elts = int(class_indexes.shape[0] * validation_split)
val_indexes = class_indexes[:nb_val_elts]
train_indexes = class_indexes[nb_val_elts:]
x_val.append(x[val_indexes])
y_val.append(y[val_indexes])
x_train.append(x[train_indexes])
y_train.append(y[train_indexes])
# !list
x_val, y_val = np.concatenate(x_val), np.concatenate(y_val)
x_train, y_train = np.concatenate(x_train), np.concatenate(y_train)
return x_val, y_val, x_train, y_train
@staticmethod
def _map_new_class_index(y, order):
"""Transforms targets for new class order."""
return np.array(list(map(lambda x: order.index(x), y)))
def _select(self, x, y, low_range=0, high_range=0):
idxes = sorted(np.where(np.logical_and(y >= low_range, y < high_range))[0])
if isinstance(x, list):
selected_x = [x[idx] for idx in idxes]
else:
selected_x = x[idxes]
return selected_x, y[idxes]
#--------------------------------
# Get Loader
#--------------------------------
def get_datainc_loader(self, mode='train'):
print(self.data_inc.shape)
train_loader = self._get_loader(self.data_inc, self.targets_inc, mode=mode)
return train_loader
def get_custom_loader_from_memory(self, class_indexes, mode="test"):
if not isinstance(class_indexes, list):
class_indexes = [class_indexes]
data, targets = [], []
for class_index in class_indexes:
class_data, class_targets = self._select(self.data_memory,
self.targets_memory,
low_range=class_index,
high_range=class_index + 1)
data.append(class_data)
targets.append(class_targets)
data = np.concatenate(data)
targets = np.concatenate(targets)
return data, targets, self._get_loader(data, targets, shuffle=False, mode=mode)
def _get_loader(self, x, y, share_memory=None, shuffle=True, mode="train", batch_size=None, resample=None):
if "balanced" in mode:
x, y = construct_balanced_subset(x, y)
batch_size = batch_size if batch_size is not None else self._batch_size
if "train" in mode:
trsf = self.train_transforms
resample_ = self._resampling if resample is None else True
if resample_ is False:
sampler = None
else:
sampler = get_weighted_random_sampler(y)
shuffle = False if resample_ is True else True
elif "test" in mode:
trsf = self.test_transforms
sampler = None
elif mode == "flip":
if "imagenet" in self.dataset_name:
trsf = A.Compose([A.HorizontalFlip(p=1.0), *self.test_transforms.transforms])
else:
trsf = transforms.Compose([transforms.RandomHorizontalFlip(p=1.0), *self.test_transforms.transforms])
sampler = None
else:
raise NotImplementedError("Unknown mode {}.".format(mode))
return DataLoader(DummyDataset(x,
y,
trsf,
trsf_type=self.transform_type,
share_memory_=share_memory,
dataset_name=self.dataset_name),
batch_size=batch_size,
shuffle=shuffle,
num_workers=self._workers,
sampler=sampler,
pin_memory=True)
def get_custom_loader(self, class_indexes, mode="test", data_source="train", imgs=None, tgts=None):
"""Returns a custom loader.
:param class_indexes: A list of class indexes that we want.
:param mode: Various mode for the transformations applied on it.
:param data_source: Whether to fetch from the train, val, or test set.
:return: The raw data and a loader.
"""
if not isinstance(class_indexes, list): # TODO: deprecated, should always give a list
class_indexes = [class_indexes]
if data_source == "train":
x, y = self.data_inc, self.targets_inc
elif data_source == "val":
x, y = self.data_val, self.targets_val
elif data_source == "test":
x, y = self.data_test, self.targets_test
elif data_source == 'specified' and imgs is not None and tgts is not None:
x, y = imgs, tgts
else:
raise ValueError("Unknown data source <{}>.".format(data_source))
data, targets = [], []
for class_index in class_indexes:
class_data, class_targets, = self._select(x, y, low_range=class_index, high_range=class_index + 1)
data.append(class_data)
targets.append(class_targets)
data = np.concatenate(data)
targets = np.concatenate(targets)
return data, targets, self._get_loader(data, targets, shuffle=False, mode=mode)
class DummyDataset(torch.utils.data.Dataset):
def __init__(self, x, y, trsf, trsf_type, share_memory_=None, dataset_name=None):
self.dataset_name = dataset_name
self.x, self.y = x, y
self.trsf = trsf
self.trsf_type = trsf_type
self.manager = mp.Manager()
self.buffer_size = 4000000
if share_memory_ is None:
if self.x.shape[0] > self.buffer_size:
self.share_memory = self.manager.list([None for i in range(self.buffer_size)])
else:
self.share_memory = self.manager.list([None for i in range(len(x))])
else:
self.share_memory = share_memory_
def __len__(self):
if isinstance(self.x, list):
return len(self.x)
else:
return self.x.shape[0]
def __getitem__(self, idx):
x, y, = self.x[idx], self.y[idx]
if isinstance(x, np.ndarray):
# assume cifar
x = Image.fromarray(x)
else:
# Assume the dataset is ImageNet
if idx < len(self.share_memory):
if self.share_memory[idx] is not None:
x = self.share_memory[idx]
else:
x = cv2.imread(x)
x = x[:, :, ::-1]
self.share_memory[idx] = x
else:
x = cv2.imread(x)
x = x[:, :, ::-1]
if 'torch' in self.trsf_type:
x = self.trsf(x)
else:
x = self.trsf(image=x)['image']
return x, y
| 14,954 | 37.44473 | 119 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/codes/base/main.py | '''
@Author : Yan Shipeng, Xie Jiangwei
@Contact: yanshp@shanghaitech.edu.cn, xiejw@shanghaitech.edu.cn
'''
import sys
import os
import os.path as osp
import copy
import time
import shutil
import cProfile
import logging
from pathlib import Path
import numpy as np
import random
from easydict import EasyDict as edict
from tensorboardX import SummaryWriter
repo_name = 'DER-ClassIL.pytorch'
base_dir = osp.realpath(".")[:osp.realpath(".").index(repo_name) + len(repo_name)]
sys.path.insert(0, base_dir)
from sacred import Experiment
ex = Experiment(base_dir=base_dir)
# Save which files
# ex.add_source_file(osp.join(base_dir, "inclearn/models/icarl.py"))
# ex.add_source_file(osp.join(base_dir, "inclearn/lib/data.py"))
# ex.add_source_file(osp.join(base_dir, "inclearn/lib/network.py"))
# ex.add_source_file(osp.join(base_dir, "inclearn/convnet/resnet.py"))
# ex.add_source_file(osp.join(os.getcwd(), "icarl.py"))
# ex.add_source_file(osp.join(os.getcwd(), "network.py"))
# ex.add_source_file(osp.join(os.getcwd(), "resnet.py"))
# MongoDB Observer
# ex.observers.append(MongoObserver.create(url='xx.xx.xx.xx:port', db_name='classil'))
import torch
from inclearn.tools import factory, results_utils, utils
from inclearn.learn.pretrain import pretrain
from inclearn.tools.metrics import IncConfusionMeter
def initialization(config, seed, mode, exp_id):
# Add it if your input size is fixed
# ref: https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936
torch.backends.cudnn.benchmark = True # This will result in non-deterministic results.
# ex.captured_out_filter = lambda text: 'Output capturing turned off.'
cfg = edict(config)
utils.set_seed(cfg['seed'])
if exp_id is None:
exp_id = -1
cfg.exp.savedir = "./logs"
logger = utils.make_logger(f"exp{exp_id}_{cfg.exp.name}_{mode}", savedir=cfg.exp.savedir)
# Tensorboard
exp_name = f'{exp_id}_{cfg["exp"]["name"]}' if exp_id is not None else f'../inbox/{cfg["exp"]["name"]}'
tensorboard_dir = cfg["exp"]["tensorboard_dir"] + f"/{exp_name}"
# If not only save latest tensorboard log.
# if Path(tensorboard_dir).exists():
# shutil.move(tensorboard_dir, cfg["exp"]["tensorboard_dir"] + f"/../inbox/{time.time()}_{exp_name}")
tensorboard = SummaryWriter(tensorboard_dir)
return cfg, logger, tensorboard
@ex.command
def train(_run, _rnd, _seed):
cfg, ex.logger, tensorboard = initialization(_run.config, _seed, "train", _run._id)
ex.logger.info(cfg)
cfg.data_folder = osp.join(base_dir, "data")
start_time = time.time()
_train(cfg, _run, ex, tensorboard)
ex.logger.info("Training finished in {}s.".format(int(time.time() - start_time)))
def _train(cfg, _run, ex, tensorboard):
device = factory.set_device(cfg)
trial_i = cfg['trial']
inc_dataset = factory.get_data(cfg, trial_i)
ex.logger.info("classes_order")
ex.logger.info(inc_dataset.class_order)
model = factory.get_model(cfg, trial_i, _run, ex, tensorboard, inc_dataset)
if _run.meta_info["options"]["--file_storage"] is not None:
_save_dir = osp.join(_run.meta_info["options"]["--file_storage"], str(_run._id))
else:
_save_dir = cfg["exp"]["ckptdir"]
results = results_utils.get_template_results(cfg)
for task_i in range(inc_dataset.n_tasks):
task_info, train_loader, val_loader, test_loader = inc_dataset.new_task()
model.set_task_info(
task=task_info["task"],
total_n_classes=task_info["max_class"],
increment=task_info["increment"],
n_train_data=task_info["n_train_data"],
n_test_data=task_info["n_test_data"],
n_tasks=inc_dataset.n_tasks,
)
model.before_task(task_i, inc_dataset)
# TODO: Move to incmodel.py
if 'min_class' in task_info:
ex.logger.info("Train on {}->{}.".format(task_info["min_class"], task_info["max_class"]))
# Pretraining at step0 if needed
if task_i == 0 and cfg["start_class"] > 0:
do_pretrain(cfg, ex, model, device, train_loader, test_loader)
inc_dataset.shared_data_inc = train_loader.dataset.share_memory
elif task_i < cfg['start_task']:
state_dict = torch.load(f'./ckpts/step{task_i}.ckpt')
model._parallel_network.load_state_dict(state_dict)
inc_dataset.shared_data_inc = train_loader.dataset.share_memory
else:
model.train_task(train_loader, val_loader)
model.after_task(task_i, inc_dataset)
ex.logger.info("Eval on {}->{}.".format(0, task_info["max_class"]))
ypred, ytrue = model.eval_task(test_loader)
acc_stats = utils.compute_accuracy(ypred, ytrue, increments=model._increments, n_classes=model._n_classes)
#Logging
model._tensorboard.add_scalar(f"taskaccu/trial{trial_i}", acc_stats["top1"]["total"], task_i)
_run.log_scalar(f"trial{trial_i}_taskaccu", acc_stats["top1"]["total"], task_i)
_run.log_scalar(f"trial{trial_i}_task_top5_accu", acc_stats["top5"]["total"], task_i)
ex.logger.info(f"top1:{acc_stats['top1']}")
ex.logger.info(f"top5:{acc_stats['top5']}")
results["results"].append(acc_stats)
top1_avg_acc, top5_avg_acc = results_utils.compute_avg_inc_acc(results["results"])
_run.info[f"trial{trial_i}"][f"avg_incremental_accu_top1"] = top1_avg_acc
_run.info[f"trial{trial_i}"][f"avg_incremental_accu_top5"] = top5_avg_acc
ex.logger.info("Average Incremental Accuracy Top 1: {} Top 5: {}.".format(
_run.info[f"trial{trial_i}"][f"avg_incremental_accu_top1"],
_run.info[f"trial{trial_i}"][f"avg_incremental_accu_top5"],
))
if cfg["exp"]["name"]:
results_utils.save_results(results, cfg["exp"]["name"])
def do_pretrain(cfg, ex, model, device, train_loader, test_loader):
if not os.path.exists(osp.join(ex.base_dir, 'pretrain/')):
os.makedirs(osp.join(ex.base_dir, 'pretrain/'))
model_path = osp.join(
ex.base_dir,
"pretrain/{}_{}_cosine_{}_multi_{}_aux{}_nplus1_{}_{}_trial_{}_{}_seed_{}_start_{}_epoch_{}.pth".format(
cfg["model"],
cfg["convnet"],
cfg["weight_normalization"],
cfg["der"],
cfg["use_aux_cls"],
cfg["aux_n+1"],
cfg["dataset"],
cfg["trial"],
cfg["train_head"],
cfg['seed'],
cfg["start_class"],
cfg["pretrain"]["epochs"],
),
)
if osp.exists(model_path):
print("Load pretrain model")
if hasattr(model._network, "module"):
model._network.module.load_state_dict(torch.load(model_path))
else:
model._network.load_state_dict(torch.load(model_path))
else:
pretrain(cfg, ex, model, device, train_loader, test_loader, model_path)
@ex.command
def test(_run, _rnd, _seed):
cfg, ex.logger, tensorboard = initialization(_run.config, _seed, "test", _run._id)
ex.logger.info(cfg)
trial_i = cfg['trial']
cfg.data_folder = osp.join(base_dir, "data")
inc_dataset = factory.get_data(cfg, trial_i)
# inc_dataset._current_task = taski
# train_loader = inc_dataset._get_loader(inc_dataset.data_cur, inc_dataset.targets_cur)
model = factory.get_model(cfg, trial_i, _run, ex, tensorboard, inc_dataset)
model._network.task_size = cfg.increment
test_results = results_utils.get_template_results(cfg)
for taski in range(inc_dataset.n_tasks):
task_info, train_loader, _, test_loader = inc_dataset.new_task()
model.set_task_info(
task=task_info["task"],
total_n_classes=task_info["max_class"],
increment=task_info["increment"],
n_train_data=task_info["n_train_data"],
n_test_data=task_info["n_test_data"],
n_tasks=task_info["max_task"]
)
model.before_task(taski, inc_dataset)
state_dict = torch.load(f'./ckpts/step{taski}.ckpt')
model._parallel_network.load_state_dict(state_dict)
model.eval()
#Build exemplars
model.after_task(taski, inc_dataset)
ypred, ytrue = model.eval_task(test_loader)
test_acc_stats = utils.compute_accuracy(ypred, ytrue, increments=model._increments, n_classes=model._n_classes)
test_results['results'].append(test_acc_stats)
ex.logger.info(f"task{taski} test top1acc:{test_acc_stats['top1']}")
avg_test_acc = results_utils.compute_avg_inc_acc(test_results['results'])
ex.logger.info(f"Test Average Incremental Accuracy: {avg_test_acc}")
if __name__ == "__main__":
# ex.add_config('./codes/base/configs/default.yaml')
ex.add_config("./configs/default.yaml")
ex.run_commandline()
| 8,825 | 37.710526 | 119 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/setup.py | from setuptools import setup, find_packages
setup(
name='pareto',
version='0.1',
packages=find_packages(),
zip_safe=False,
install_requires=[
'numpy',
'scipy',
'torch',
'torchvision',
'tqdm',
],
)
| 262 | 15.4375 | 43 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/metrics.py | from typing import Iterable
from torch import Tensor
__all__ = ['topk_accuracies', 'topk_accuracy']
def topk_accuracies(
output: Tensor,
label: Tensor,
ks: Iterable[int] = (1,),
):
assert output.dim() == 2
assert label.dim() == 1
assert output.size(0) == label.size(0)
maxk = max(ks)
_, pred = output.topk(maxk, dim=1, largest=True, sorted=True)
label = label.unsqueeze(1).expand_as(pred)
correct = pred.eq(label).float()
accu_list = []
for k in ks:
accu = correct[:, :k].sum(1).mean()
accu_list.append(accu.item())
return accu_list
def topk_accuracy(
output: Tensor,
label: Tensor,
k: int = 1,
):
return topk_accuracies(output, label, (k,))[0]
| 771 | 19.864865 | 65 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/networks/multi_lenet.py | from typing import Tuple, List
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
class MultiLeNet(nn.Module):
def __init__(self) -> None:
super(MultiLeNet, self).__init__()
self.conv1 = nn.Conv2d(1, 10, (5, 5))
self.conv2 = nn.Conv2d(10, 20, (5, 5))
self.fc1 = nn.Linear(20 * 4 * 4, 50)
self.fc3_1 = nn.Linear(50, 10)
self.fc3_2 = nn.Linear(50, 10)
def shared_parameters(self) -> List[Tensor]:
return [p for n, p in self.named_parameters() if not n.startswith('fc3')]
def forward(
self,
x: Tensor,
) -> Tuple[Tensor, Tensor]:
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, 2)
x = torch.flatten(x, 1)
x = F.relu(self.fc1(x))
x = (self.fc3_1(x), self.fc3_2(x))
return x
| 927 | 27.121212 | 81 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/optim/hvp_solver.py | from functools import partial
from typing import Tuple, List, Iterable, Callable
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn.utils import parameters_to_vector
__all__ = ['HVPSolver', 'AutogradHVPSolver', 'VisionHVPSolver']
class HVPSolver(object):
"""
Hessian-Vector product calculation
network: PyTorch network to compute hessian for
parameters: parameters which are computed hessian w.r.t.
dataloader: PyTorch dataloader that we get examples from to compute grads
device: gpu/cpu device
"""
def __init__(
self,
network: nn.Module,
parameters: Iterable[Tensor],
device: torch.device,
dataloader: torch.utils.data.DataLoader,
) -> None:
self.parameters = list(parameters)
self.size = int(sum(p.numel() for p in self.parameters))
self.network = network
self.device = device
self.dataloader = dataloader
# Make a copy since we will go over it a bunch
self.dataiter = iter(dataloader) if dataloader else None
self.apply = self.apply_batch
self.grad = self.grad_batch
def close(self) -> None:
try:
while True:
_ = next(self.dataiter)
except StopIteration:
pass
self.dataiter = None
self.dataloader = None
def set_hess(
self,
*,
batch: bool = True,
num_batches: int = None,
) -> None:
self.apply = self.apply_batch if batch else partial(self.apply_full, num_batches=num_batches)
def set_grad(
self,
*,
batch: bool = True,
num_batches: int = None,
) -> None:
self.grad = self.grad_batch if batch else partial(self.grad_full, num_batches=num_batches)
@torch.enable_grad()
def apply_batch(
self,
vec: Tensor,
weights: Tensor = None,
*,
grads: Tensor = None,
retain_graph: bool = True,
) -> Tuple[Tensor, Tensor]:
"""
Returns H * vec where H is the hessian of the loss w.r.t.
the vectorized model parameters
"""
raise NotImplementedError
@torch.enable_grad()
def apply_full(
self,
vec: Tensor,
weights: Tensor = None,
*,
grads: Tensor = None,
num_batches: int = None,
retain_graph: bool = False,
) -> Tensor:
apply_batch = self.apply_batch
num_batches = len(self.dataloader) if num_batches is None else num_batches
weighted_hvp = None
for _ in range(num_batches):
weighted_hvp_batch, _ = apply_batch(
vec, weights, grads=grads, retain_graph=retain_graph)
if weighted_hvp is None:
weighted_hvp = weighted_hvp_batch
else:
weighted_hvp.add_(weighted_hvp_batch)
weighted_hvp.div_(num_batches)
return weighted_hvp
def zero_grad(self) -> None:
"""
Zeros out the gradient info for each parameter in the model
"""
for p in self.parameters:
if p.grad is not None:
p.grad.data.zero_()
def set_data(
self,
dataloader: torch.utils.data.DataLoader,
) -> None:
self.dataloader = dataloader
self.dataiter = iter(dataloader)
@torch.enable_grad()
def get_losses(self) -> List[Tensor]:
raise NotImplementedError
@torch.enable_grad()
def grad_batch(
self,
*,
create_graph: bool = True,
) -> Tuple[Tensor, List[Tensor]]:
parameters = self.parameters
losses = self.get_losses()
param_grads = [list(torch.autograd.grad(
loss, parameters,
allow_unused=True, retain_graph=True, create_graph=create_graph)) for loss in losses]
for param_grad in param_grads:
for i, (param_grad_module, param) in enumerate(zip(param_grad, parameters)):
if param_grad_module is None:
param_grad[i] = torch.zeros_like(param)
grads = torch.stack([parameters_to_vector(param_grad) for param_grad in param_grads], dim=0)
return grads, losses
@torch.enable_grad()
def grad_full(
self,
*,
create_graph: bool = False,
num_batches: int = None,
) -> Tensor:
grad_batch = self.grad_batch
num_batches = len(self.dataloader) if num_batches is None else num_batches
grads = None
for _ in range(num_batches):
grads_batch, _ = grad_batch(create_graph=create_graph)
if grads is None:
grads = grads_batch
else:
grads.add_(grads_batch)
grads.div_(num_batches)
grads = grads.clone().detach()
return grads
class AutogradHVPSolver(HVPSolver):
"""
Use PyTorch autograd for Hessian-Vector product calculation
"""
def get_losses(self) -> List[Tensor]:
raise NotImplementedError
@torch.enable_grad()
def apply_batch(
self,
vec: Tensor,
weights: Tensor = None,
*,
grads: Tensor = None,
retain_graph: bool = True,
) -> Tuple[Tensor, Tensor]:
"""
Returns H * vec where H is the hessian of the loss w.r.t.
the vectorized model parameters
"""
if grads is None:
# compute original gradient, tracking computation graph
self.zero_grad()
grads, _ = self.grad_batch(create_graph=True)
self.zero_grad()
if weights is None:
weighted_grad = grads.sum(dim=0)
else:
weighted_grad = torch.matmul(weights, grads)
dot = vec.dot(weighted_grad)
param_weighted_hvp = torch.autograd.grad(dot, self.parameters, retain_graph=retain_graph)
# concatenate the results over the different components of the network
weighted_hvp = parameters_to_vector([p.contiguous() for p in param_weighted_hvp])
return weighted_hvp, grads
class VisionHVPSolver(AutogradHVPSolver):
def __init__(
self,
network: nn.Module,
device: torch.device,
dataloader: torch.utils.data.DataLoader,
closures: List[Callable],
*,
shared: bool = False,
) -> None:
parameters = network.shared_parameters() if shared else network.parameters()
super(VisionHVPSolver, self).__init__(network, parameters, device, dataloader)
self.closures = closures
@torch.enable_grad()
def get_losses(self) -> List[Tensor]:
try:
inputs, targets = next(self.dataiter)
except StopIteration:
self.dataiter = iter(self.dataloader)
inputs, targets = next(self.dataiter)
inputs = inputs.to(self.device)
if isinstance(targets, list):
targets = [target.to(self.device) for target in targets]
else:
targets = targets.to(self.device)
logits = self.network(inputs)
return [c(self.network, logits, targets) for c in self.closures]
| 7,426 | 26.712687 | 101 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/optim/linalg_solver.py | from contextlib import contextmanager
from functools import partial
from typing import Tuple
import numpy as np
from scipy.sparse.linalg import LinearOperator, minres
import torch
import torch.nn as nn
from torch import Tensor
from .hvp_solver import HVPSolver
__all__ = ['PDError', 'HVPLinearOperator', 'KrylovSolver', 'MINRESSolver', 'CGSolver']
class PDError(RuntimeError):
pass
class HVPLinearOperator(LinearOperator):
def __init__(
self,
network: nn.Module,
hvp_solver: HVPSolver,
device: torch.device,
damping: float,
) -> None:
shape = (hvp_solver.size, hvp_solver.size)
dtype = list(network.parameters())[0].detach().cpu().numpy().dtype
super(HVPLinearOperator, self).__init__(dtype, shape)
self.network = network
self.hvp_solver = hvp_solver
self.device = device
self.damping = damping
self.jacobians = None
self.alphas = None
self.reset_parameters()
self.hvp_counter = 0
self.matvec_counter = 0
self.reset_counters()
def set_parameters(
self,
jacobians: Tensor,
alphas: Tensor,
) -> None:
self.jacobians = jacobians
self.alphas = alphas
def reset_parameters(self) -> None:
self.jacobians = None
self.alphas = None
def reset_counters(self) -> None:
self.hvp_counter = 0
self.matvec_counter = 0
def get_counters(self) -> Tuple[int, int]:
return self.hvp_counter, self.matvec_counter
def _matvec_tensor(
self,
tensor: Tensor,
) -> Tensor:
alphas_hvps, _ = self.hvp_solver.apply(
tensor, self.alphas, grads=self.jacobians, retain_graph=self.jacobians is not None) # (N,)
if self.damping > 0.0:
alphas_hvps.add_(tensor, alpha=self.damping)
self.hvp_counter += 1
self.matvec_counter += 1
return alphas_hvps
def _matvec(
self,
x: np.ndarray,
) -> np.ndarray:
"""HVP matrix-vector multiplication handler.
If self is a linear operator of shape (N, N), then this method will
be called on a shape (N,) or (N, 1) ndarray, and should return a
shape (N,) or (N, 1) ndarray.
In our case, it computes alpha_hession @ x.
"""
tensor = torch.as_tensor(x.astype(self.dtype), device=self.device)
ret = self._matvec_tensor(tensor)
return ret.detach().cpu().numpy()
class KrylovSolver(object):
def solve(
self,
lazy_jacobians: Tensor,
jacobians: Tensor,
alphas: Tensor,
rhs: Tensor,
*,
verbose: bool = False,
) -> Tuple[Tensor, Tuple[int, int]]:
raise NotImplementedError
class MINRESSolver(KrylovSolver):
def __init__(
self,
network: nn.Module,
hvp_solver: HVPSolver,
device: torch.device,
shift: float,
tol: float,
damping: float,
maxiter: int,
) -> None:
self.device = device
self.linear_operator = HVPLinearOperator(network, hvp_solver, device, damping)
self.minres = partial(minres, shift=shift, tol=tol, maxiter=maxiter)
self.shape = self.linear_operator.shape
self.dtype = self.linear_operator.dtype
@contextmanager
def solve(
self,
lazy_jacobians: Tensor,
jacobians: Tensor,
alphas: Tensor,
rhs: Tensor,
*,
verbose: bool = False,
) -> Tuple[Tensor, Tuple[int, int]]:
"""Control counters automatically.
Parameters
----------
lazy_jacobians : torch.Tensor or None
If not None, it is for gradient reusing. A matrix with shape (M,N).
jacobians : torch.Tensor
A matrix with shape (M,N). It should be identical to `rhs` and
`lazy_jacobians` in this case (if `lazy_jacobians` is not None).
alphas: torch.Tensor
An array with shape (M,).
rhs: torch.Tensor
A matrix with shape (N,).
"""
try:
self.linear_operator.set_parameters(lazy_jacobians, alphas)
x0 = jacobians.mean(0).neg().clone().detach().cpu().numpy()
rhs = rhs.cpu().numpy()
results = self.minres(self.linear_operator, rhs, show=verbose, x0=x0)
d = torch.as_tensor(results[0].astype(self.dtype), device=self.device)
yield d, self.linear_operator.get_counters()
finally:
self.linear_operator.reset_parameters()
self.linear_operator.reset_counters()
class CGSolver(KrylovSolver):
def __init__(
self,
hvp_solver: HVPSolver,
device: torch.device,
tol: float,
damping: float,
maxiter: int,
pd_strict: bool = False,
) -> None:
self.hvp_solver = hvp_solver
self.device = device
self.tol = tol
self.damping = damping
self.maxiter = maxiter
self.pd_strict = pd_strict
self.hvp_counter = 0
self.matvec_counter = 0
self.reset_counters()
def reset_counters(self) -> None:
self.hvp_counter = 0
self.matvec_counter = 0
def cg(
self,
lazy_jacobians: Tensor,
alphas: Tensor,
rhs: Tensor,
x0: Tensor = None,
*,
verbose: bool = False,
) -> Tensor:
hvp_solver_apply = self.hvp_solver.apply
tol = self.tol
damping = self.damping
maxiter = self.maxiter
pd_strict = self.pd_strict
if x0 is None:
x0 = torch.ones_like(rhs)
x_next = x0.clone()
r = hvp_solver_apply(x0, alphas, lazy_jacobians)
r.add_(x0, alpha=damping).sub_(rhs)
p = r.neg()
r_k_norm = r.dot(r).item()
if maxiter is None:
n = len(rhs)
maxiter = 2 * n
for i in range(maxiter):
Ap = hvp_solver_apply(p, alphas, lazy_jacobians).add(p, alpha=damping)
pAp = p.dot(Ap).item()
if pAp <= 0:
if verbose:
print(i, round(pAp, 5), round(r_kplus1_norm, 5))
if pd_strict:
if x0.dot(hvp_solver_apply(x0, alphas, lazy_jacobians).add(x0, alpha=damping)) <= 0:
raise PDError
x_next.copy_(x0)
break
x0.copy_(x_next)
alpha = r_k_norm / pAp
x_next.add_(p, alpha=alpha)
r.add_(Ap, alpha=alpha)
r_kplus1_norm = r.dot(r).item()
beta = r_kplus1_norm / r_k_norm
r_k_norm = r_kplus1_norm
if verbose:
print(i, round(pAp, 5), round(r_kplus1_norm, 5))
if r_kplus1_norm < tol:
break
p = p.mul(beta).sub(r)
return x_next
def get_counters(self) -> Tuple[int, int]:
return self.hvp_counter, self.matvec_counter
@contextmanager
def solve(
self,
lazy_jacobians: Tensor,
jacobians: Tensor,
alphas: Tensor,
rhs: Tensor,
*,
verbose: bool = False,
) -> Tuple[Tensor, Tuple[int, int]]:
"""Control counters automatically.
Parameters
----------
lazy_jacobians : torch.Tensor or None
If not None, it is for gradient reusing. A matrix with shape (M,N).
jacobians : torch.Tensor
A matrix with shape (M,N). It should be identical to `rhs` and
`lazy_jacobians` in this case (if `lazy_jacobians` is not None).
alphas: torch.Tensor
An array with shape (M,).
rhs: torch.Tensor
A matrix with shape (N,).
"""
try:
x0 = jacobians.mean(0).neg().clone().detach()
d = self.cg(lazy_jacobians, alphas, rhs, x0, verbose=verbose)
yield d, self.get_counters()
finally:
self.reset_counters()
| 8,325 | 26.66113 | 104 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/optim/min_norm_solver.py | # This code is from
# Multi-Task Learning as Multi-Objective Optimization
# Ozan Sener, Vladlen Koltun
# Neural Information Processing Systems (NeurIPS) 2018
# https://github.com/intel-isl/MultiObjectiveOptimization
from itertools import combinations
import numpy as np
import torch
__all__ = ['find_min_norm_element']
def _min_norm_element_from2(v1v1, v1v2, v2v2):
r"""
Analytical solution for min_{c} |cx_1 + (1-c)x_2|_2^2
d is the distance (objective) optimized
v1v1 = <x1,x1>
v1v2 = <x1,x2>
v2v2 = <x2,x2>
"""
if v1v2 >= v1v1:
# Case: Fig 1, third column
gamma = 0.999
cost = v1v1
return gamma, cost
if v1v2 >= v2v2:
# Case: Fig 1, first column
gamma = 0.001
cost = v2v2
return gamma, cost
# Case: Fig 1, second column
gamma = (v2v2 - v1v2) / (v1v1 + v2v2 - 2 * v1v2)
# v2v2 - gamm * gamma * (v1 - v2)^2
# cost = v2v2 - gamma * gamma * (v1v1 + v2v2 - 2 * v1v2)
# = v2v2 - gamma * (v2v2 - v1v2)
cost = v2v2 + gamma * (v1v2 - v2v2)
return gamma, cost
def _min_norm_2d(vecs):
r"""
Find the minimum norm solution as combination of two points
This is correct only in 2D
ie. min_c |\sum c_i x_i|_2^2 st. \sum c_i = 1 , 1 >= c_1 >= 0 for all i, c_i + c_j = 1.0 for some i, j
"""
dmin = None
dps = vecs.matmul(vecs.t()).cpu().numpy()
for i, j in combinations(range(len(vecs)), 2):
c, d = _min_norm_element_from2(dps[i, i], dps[i, j], dps[j, j])
if dmin is None:
dmin = d
if d <= dmin:
dmin = d
sol = [(i, j), c, d]
return sol, dps
def _projection2simplex(y):
r"""
Given y, it solves argmin_z |y-z|_2 st \sum z = 1 , 1 >= z_i >= 0 for all i
"""
m = len(y)
sorted_y = np.flip(np.sort(y), axis=0)
tmpsum = 0.0
tmax_f = (np.sum(y) - 1.0) / m
for i in range(m - 1):
tmpsum += sorted_y[i]
tmax = (tmpsum - 1) / (i + 1.0)
if tmax > sorted_y[i + 1]:
tmax_f = tmax
break
return np.maximum(y - tmax_f, np.zeros(y.shape))
def _next_point(cur_val, grad, n):
proj_grad = grad - (np.sum(grad) / n)
tm1 = -cur_val[proj_grad < 0] / proj_grad[proj_grad < 0]
tm2 = (1.0 - cur_val[proj_grad > 0]) / (proj_grad[proj_grad > 0])
t = 1
if len(tm1[tm1 > 1e-7]) > 0:
t = np.min(tm1[tm1 > 1e-7])
if len(tm2[tm2 > 1e-7]) > 0:
t = min(t, np.min(tm2[tm2 > 1e-7]))
next_point = proj_grad * t + cur_val
next_point = _projection2simplex(next_point)
return next_point
@torch.no_grad()
def find_min_norm_element(vecs, max_iter=250, stop_crit=1e-5):
r"""
Given a list of vectors (vecs), this method finds the minimum norm element in the convex hull
as min |u|_2 st. u = \sum c_i vecs[i] and \sum c_i = 1.
It is quite geometric, and the main idea is the fact that if d_{ij} = min |u|_2 st u = c x_i + (1-c) x_j;
the solution lies in (0, d_{i,j})
Hence, we find the best 2-task solution, and then run the projected gradient descent until convergence
"""
# Solution lying at the combination of two points
init_sol, dps = _min_norm_2d(vecs.detach())
n = len(vecs)
sol_vec = np.zeros(n)
sol_vec[init_sol[0][0]] = init_sol[1]
sol_vec[init_sol[0][1]] = 1 - init_sol[1]
if n < 3:
# This is optimal for n=2, so return the solution
return sol_vec, init_sol[2]
iter_count = 0
while iter_count < max_iter:
grad_dir = -1.0 * np.dot(dps, sol_vec)
new_point = _next_point(sol_vec, grad_dir, n)
# Re-compute the inner products for line search
v1v1 = 0.0
v1v2 = 0.0
v2v2 = 0.0
for i in range(n):
for j in range(n):
v1v1 += sol_vec[i] * sol_vec[j] * dps[i, j]
v1v2 += sol_vec[i] * new_point[j] * dps[i, j]
v2v2 += new_point[i] * new_point[j] * dps[i, j]
nc, nd = _min_norm_element_from2(v1v1, v1v2, v2v2)
new_sol_vec = nc * sol_vec + (1 - nc) * new_point
change = new_sol_vec - sol_vec
if np.sum(np.abs(change)) < stop_crit:
break
sol_vec = new_sol_vec
return sol_vec, nd
@torch.no_grad()
def gradient_normalizers(grads, losses, normalization_type):
gn = {}
if normalization_type == 'l2':
for t in grads:
gn[t] = np.sqrt(np.sum([gr.pow(2).sum().item()
for gr in grads[t]]))
elif normalization_type == 'loss':
for t in grads:
gn[t] = losses[t]
elif normalization_type == 'loss+':
for t in grads:
gn[t] = losses[t] * \
np.sqrt(np.sum([gr.pow(2).sum().item() for gr in grads[t]]))
elif normalization_type == 'none':
for t in grads:
gn[t] = 1.0
else:
print('ERROR: Invalid Normalization Type')
return gn
| 4,953 | 29.9625 | 109 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/optim/kkt_solver.py | from typing import Tuple, Mapping
import numpy as np
import torch
import torch.nn as nn
from torch import Tensor
from .hvp_solver import HVPSolver
from .min_norm_solver import find_min_norm_element
from .linalg_solver import KrylovSolver, MINRESSolver, CGSolver
__all__ = ['KKTSolver', 'KrylovKKTSolver', 'CGKKTSolver', 'MINRESKKTSolver']
class KKTSolver(object):
def __init__(
self,
network: nn.Module,
hvp_solver: HVPSolver,
device: torch.device,
*,
kkt_momentum: float = 0.0,
create_graph: bool = False,
grad_correction: bool = False,
) -> None:
self.network = network
self.hvp_solver = hvp_solver
self.device = device
self.kkt_momentum = kkt_momentum
self.jacobians_momentum_buffer = None
self.alphas_momentum_buffer = None
self.create_graph = create_graph
self.grad_correction = grad_correction
def zero_grad(self) -> None:
self.hvp_solver.zero_grad()
def _jacobians_alphas_rhs(
self,
weights: Tensor,
*,
verbose: bool = True,
) -> Tuple[Tensor, Tensor, Tensor]:
grad_correction = self.grad_correction
kkt_momentum = self.kkt_momentum
hvp_solver = self.hvp_solver
jacobians = hvp_solver.grad(create_graph=self.create_graph)
alphas, _ = find_min_norm_element(jacobians.detach())
alphas = jacobians.new_tensor(alphas).detach()
if verbose:
print(jacobians.norm(dim=1).detach().cpu().numpy())
if jacobians.size(0) == 2:
cosine = jacobians[0].dot(jacobians[1]).div(jacobians[0].norm(2) * jacobians[1].norm(2)).item()
angle = np.rad2deg(np.arccos(cosine))
print(f'alphas={alphas},angle={angle}')
else:
print(f'alphas={alphas}')
if grad_correction:
alphas_jacobians = alphas.view(1, -1).matmul(jacobians).view(1, -1).detach()
jacobians.sub_(alphas_jacobians)
if kkt_momentum > 0.0:
if self.alphas_momentum_buffer is None:
self.alphas_momentum_buffer = torch.clone(alphas).detach()
alphas_buf = self.alphas_momentum_buffer
alphas_buf.mul_(kkt_momentum).add_(alphas, alpha=1 - kkt_momentum)
alphas = alphas_buf
jacobians_buf = self.jacobians_momentum_buffer
jacobians_buf.mul_(kkt_momentum).add_(jacobians.detach(), alpha=1 - kkt_momentum)
jacobians = jacobians_buf
rhs = weights.view(1, -1).matmul(jacobians).view(-1)
return jacobians, alphas, rhs.clone().detach()
@torch.no_grad()
def _print_alpha_beta_cosine(
self,
jacobians: Tensor,
alphas: Tensor,
direction: Tensor
) -> None:
direction = self.hvp_solver.apply(direction, alphas)
jacobians = jacobians.neg().detach()
v1v1 = jacobians[0].dot(jacobians[0]).item()
v1v2 = jacobians[0].dot(jacobians[1]).item()
v2v2 = jacobians[1].dot(jacobians[1]).item()
xv1 = direction.dot(jacobians[0]).item()
xv2 = direction.dot(jacobians[1]).item()
# (alpha * v1 + beta * v2 - x) * v1 = 0.
# (alpha * v1 + beta * v2 - x) * v2 = 0.
# alpha * v1v1 + beta * v1v2 = xv1
# alpha * v1v2 + beta * v2v2 = xv2
# J = v1v1 * v2v2 - 2 * v1v2
# [v2v2, -v1v2] [xv1]
# [-v1v2, v1v1] [xv2]
# alpha = (v2v2 * xv1 - v1v2 * xv2) / J
# beta = (xv2 * v1v1 - xv1 * v1v2) / J
# J does not matter since we care about the cosine angle only, not the absolute difference.
alpha = xv1 * v2v2 - xv2 * v1v2
beta = xv2 * v1v1 - xv1 * v1v2
total = abs(alpha) + abs(beta)
alpha /= total
beta /= total
span = alpha * jacobians[0] + beta * jacobians[1]
cosine = np.rad2deg(np.arccos(span.div(span.norm(2)).dot(direction.div(direction.norm(2))).item()))
print(alpha, beta, cosine)
def backward(
self,
weights: Tensor,
*,
verbose: bool = False,
) -> None:
jacobians, alphas, rhs = self._jacobians_alphas_rhs(weights, verbose=verbose)
direction = self._explore(jacobians, alphas, rhs, weights, verbose=verbose)
self.apply_grad(direction, normalize=True)
def _explore(
self,
jacobians: Tensor,
alphas: Tensor,
rhs: Tensor,
weights: Tensor,
*,
verbose: bool,
) -> Tensor:
raise NotImplementedError
@torch.no_grad()
def cosine(self) -> float:
jacobians, _ = self.hvp_solver.grad_batch(create_graph=False)
cosine = jacobians[0].dot(jacobians[1]).div(jacobians[0].norm(2) * jacobians[1].norm(2)).item()
return cosine
@torch.no_grad()
def apply_grad(
self,
direction: Tensor,
*,
normalize: bool = True,
) -> None:
if normalize:
direction.div_(direction.norm())
offset = 0
for p in self.hvp_solver.parameters:
numel = p.numel()
p.grad = direction[offset:offset + numel].view_as(p.data).clone()
offset += numel
assert offset == direction.size(0)
class KrylovKKTSolver(KKTSolver):
def __init__(
self,
network: nn.Module,
hvp_solver: HVPSolver,
device: torch.device,
krylov_solver: KrylovSolver,
*,
stochastic: bool = True,
kkt_momentum: float = 0.0,
create_graph: bool = False,
grad_correction: bool = False,
) -> None:
super(KrylovKKTSolver, self).__init__(
network, hvp_solver, device,
kkt_momentum=kkt_momentum,
create_graph=create_graph,
grad_correction=grad_correction,
)
self.stochastic = stochastic
self.krylov_solver = krylov_solver
def _explore(
self,
jacobians: Tensor,
alphas: Tensor,
rhs: Tensor,
weights: Tensor,
*,
verbose: bool,
) -> Tensor:
lazy_jacobians = None if self.stochastic else self.hvp_solver.grad_batch(create_graph=True)[0]
with self.krylov_solver.solve(lazy_jacobians, jacobians, alphas, rhs, verbose=verbose) as results:
direction, _ = results
return direction
class CGKKTSolver(KrylovKKTSolver):
def __init__(
self,
network: nn.Module,
hvp_solver: HVPSolver,
device: torch.device,
*,
stochastic: bool = True,
kkt_momentum: float = 0.0,
create_graph: bool = False,
grad_correction: bool = False,
tol: float = 1e-5,
damping: float = 0.0,
maxiter: int = 5,
pd_strict: bool = True,
) -> None:
krylov_solver = CGSolver(hvp_solver, device, tol, damping, maxiter, pd_strict)
super(CGKKTSolver, self).__init__(
network, hvp_solver, device, krylov_solver,
stochastic=stochastic,
kkt_momentum=kkt_momentum,
create_graph=create_graph,
grad_correction=grad_correction,
)
class MINRESKKTSolver(KrylovKKTSolver):
def __init__(
self,
network: nn.Module,
hvp_solver: HVPSolver,
device: torch.device,
*,
stochastic: bool = True,
kkt_momentum: float = 0.0,
create_graph: bool = False,
grad_correction: bool = False,
shift: float = 0.0,
tol: float = 1e-5,
damping: float = 0.0,
maxiter: int = 50,
) -> None:
krylov_solver = MINRESSolver(network, hvp_solver, device, shift, tol, damping, maxiter)
super(MINRESKKTSolver, self).__init__(
network, hvp_solver, device, krylov_solver,
stochastic=stochastic,
kkt_momentum=kkt_momentum,
create_graph=create_graph,
grad_correction=grad_correction,
)
| 8,382 | 29.483636 | 111 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/datasets/multi_mnist.py | from pathlib import Path
import codecs
import gzip
import urllib
import random
import numpy as np
from scipy import ndimage
from PIL import Image
import torch
class MultiMNIST(torch.utils.data.Dataset):
urls = [
'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz',
'http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz',
'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz',
]
raw_folder = 'raw'
processed_folder = 'processed'
training_file = 'training.pth'
test_file = 'test.pth'
def __init__(self, root, train=True, transform=None, target_transform=None, download=False):
self.root = Path(root)
self.transform = transform
self.target_transform = target_transform
self.train = train # training set or test set
if download:
self.download()
if not self._check_exists():
raise RuntimeError('Dataset not found.' +
' You can use download=True to download it')
if train:
self.data, self.labels_l, self.labels_r = torch.load(
self.root / self.processed_folder /self.training_file)
else:
self.data, self.labels_l, self.labels_r = torch.load(
self.root / self.processed_folder / self.test_file)
if transform is not None:
self.data = [self.transform(Image.fromarray(
img.numpy().astype(np.uint8), mode='L')) for img in self.data]
def __getitem__(self, index):
img, target_l, target_r = self.data[index], self.labels_l[index], self.labels_r[index]
return img, torch.stack([target_l, target_r])
def __len__(self):
return len(self.data)
def _check_exists(self):
return (self.root / self.processed_folder / self.training_file).is_file() and \
(self.root / self.processed_folder / self.test_file).is_file()
def download(self):
if self._check_exists():
return
# download files
(self.root / self.raw_folder).mkdir(parents=True, exist_ok=True)
(self.root / self.processed_folder).mkdir(parents=True, exist_ok=True)
for url in self.urls:
print('Downloading ' + url)
data = urllib.request.urlopen(url)
filename = url.rpartition('/')[2]
file_path = self.root / self.raw_folder / filename
with open(file_path, 'wb') as f:
f.write(data.read())
with open(self.root / self.raw_folder / '.'.join(filename.split('.')[:-1]), 'wb') as out_f, \
gzip.GzipFile(file_path) as zip_f:
out_f.write(zip_f.read())
file_path.unlink()
# process and save as torch files
print('Processing...')
multi_mnist_ims, extension = self.read_image_file(
self.root / self.raw_folder / 'train-images-idx3-ubyte', shift_pix=4, rand_shift=True)
multi_mnist_labels_l, multi_mnist_labels_r = self.read_label_file(
self.root / self.raw_folder / 'train-labels-idx1-ubyte', extension)
tmulti_mnist_ims, textension = self.read_image_file(
self.root / self.raw_folder / 't10k-images-idx3-ubyte', shift_pix=4, rand_shift=True)
tmulti_mnist_labels_l, tmulti_mnist_labels_r = self.read_label_file(
self.root / self.raw_folder / 't10k-labels-idx1-ubyte', textension)
multi_mnist_training_set = (multi_mnist_ims, multi_mnist_labels_l, multi_mnist_labels_r)
multi_mnist_test_set = (tmulti_mnist_ims, tmulti_mnist_labels_l, tmulti_mnist_labels_r)
with open(self.root / self.processed_folder / self.training_file, 'wb') as f:
torch.save(multi_mnist_training_set, f)
with open(self.root / self.processed_folder / self.test_file, 'wb') as f:
torch.save(multi_mnist_test_set, f)
print('Done!')
def __repr__(self):
fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
fmt_str += ' Number of datapoints: {}\n'.format(self.__len__())
tmp = 'train' if self.train is True else 'test'
fmt_str += ' Split: {}\n'.format(tmp)
fmt_str += ' Root Location: {}\n'.format(self.root)
tmp = ' Transforms (if any): '
fmt_str += '{0}{1}\n'.format(
tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
tmp = ' Target Transforms (if any): '
fmt_str += '{0}{1}'.format(
tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
return fmt_str
@staticmethod
def get_int(b):
return int(codecs.encode(b, 'hex'), 16)
@staticmethod
def read_label_file(path, extension):
with open(path, 'rb') as f:
data_1 = f.read()
assert MultiMNIST.get_int(data_1[:4]) == 2049
with open(path, 'rb') as f:
data_2 = f.read()
assert MultiMNIST.get_int(data_2[:4]) == 2049
length = MultiMNIST.get_int(data_1[4:8])
parsed_1 = np.frombuffer(data_1, dtype=np.uint8, offset=8)
parsed_2 = np.frombuffer(data_2, dtype=np.uint8, offset=8)
multi_labels_l = np.zeros(length, dtype=np.long)
multi_labels_r = np.zeros(length, dtype=np.long)
for im_id in range(length):
multi_labels_l[im_id] = parsed_1[im_id]
multi_labels_r[im_id] = parsed_2[extension[im_id]]
return (torch.from_numpy(multi_labels_l).view(-1).long(),
torch.from_numpy(multi_labels_r).view(-1).long())
@staticmethod
def read_image_file(path, shift_pix=4, rand_shift=True, rot_range=(0, 0), corot=True):
with open(path, 'rb') as f:
data_1 = f.read()
assert MultiMNIST.get_int(data_1[:4]) == 2051
with open(path, 'rb') as f:
data_2 = f.read()
assert MultiMNIST.get_int(data_2[:4]) == 2051
length = MultiMNIST.get_int(data_1[4:8])
num_rows = MultiMNIST.get_int(data_1[8:12])
num_cols = MultiMNIST.get_int(data_1[12:16])
parsed_1 = np.frombuffer(data_1, dtype=np.uint8, offset=16)
pv_1 = parsed_1.reshape(length, num_rows, num_cols)
parsed_2 = np.frombuffer(data_2, dtype=np.uint8, offset=16)
pv_2 = parsed_2.reshape(length, num_rows, num_cols)
multi_data = np.zeros((length, num_rows, num_cols))
extension = np.zeros(length, dtype=np.int32)
rights = np.random.permutation(length)
for left in range(length):
extension[left] = rights[left]
lim = pv_1[left, :, :]
rim = pv_2[rights[left], :, :]
if not rot_range[0] == rot_range[1] == 0:
if corot:
rot_deg = random.randint(rot_range[0], rot_range[1])
lim = ndimage.rotate(lim, rot_deg, reshape=False)
rim = ndimage.rotate(rim, rot_deg, reshape=False)
else:
rot_deg = random.randint(rot_range[0], rot_range[1])
lim = ndimage.rotate(lim, rot_deg, reshape=False)
rot_deg = random.randint(rot_range[0], rot_range[1])
rim = ndimage.rotate(rim, rot_deg, reshape=False)
# in case of 100% overlapping
shift_pix1 = shift_pix2 = 0
if rand_shift:
if random.choice([True, False]):
shift_pix1 = random.randint(0, shift_pix - 1)
shift_pix2 = random.randint(0, shift_pix)
else:
shift_pix1 = random.randint(0, shift_pix)
shift_pix2 = random.randint(1, shift_pix)
new_im = np.zeros((36, 36))
new_im[shift_pix1:shift_pix1 + 28, shift_pix1:shift_pix1 + 28] += lim
new_im[shift_pix2 + 4:shift_pix2 + 4 + 28, shift_pix2 + 4:shift_pix2 + 4 + 28] += rim
new_im = np.clip(new_im, 0, 255)
multi_data_im = np.array(Image.fromarray(new_im).resize((28, 28), resample=Image.NEAREST))
multi_data[left, :, :] = multi_data_im
return torch.from_numpy(multi_data).view(length, num_rows, num_cols), extension
| 8,297 | 42.904762 | 105 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/submission/min_norm_solver.py | import sys
from itertools import combinations
import numpy as np
import torch
def _min_norm_element_from2(v1v1, v1v2, v2v2):
"""
Analytical solution for min_{c} |cx_1 + (1-c)x_2|_2^2
d is the distance (objective) optimzed
v1v1 = <x1,x1>
v1v2 = <x1,x2>
v2v2 = <x2,x2>
"""
if v1v2 >= v1v1:
# Case: Fig 1, third column
gamma = 0.999
cost = v1v1
return gamma, cost
if v1v2 >= v2v2:
# Case: Fig 1, first column
gamma = 0.001
cost = v2v2
return gamma, cost
# Case: Fig 1, second column
gamma = (v2v2 - v1v2) / (v1v1 + v2v2 - 2 * v1v2)
# v2v2 - gamm * gamma * (v1 - v2)^2
# cost = v2v2 - gamma * gamma * (v1v1 + v2v2 - 2 * v1v2)
# = v2v2 - gamma * (v2v2 - v1v2)
cost = v2v2 + gamma * (v1v2 - v2v2)
return gamma, cost
def _min_norm_2d(vecs):
"""
Find the minimum norm solution as combination of two points
This is correct only in 2D
ie. min_c |\sum c_i x_i|_2^2 st. \sum c_i = 1 , 1 >= c_1 >= 0 for all i, c_i + c_j = 1.0 for some i, j
"""
dmin = None
dps = vecs.matmul(vecs.t()).cpu().numpy()
for i, j in combinations(range(len(vecs)), 2):
c, d = _min_norm_element_from2(dps[i, i], dps[i, j], dps[j, j])
if dmin is None:
dmin = d
if d <= dmin:
dmin = d
sol = [(i, j), c, d]
return sol, dps
def _projection2simplex(y):
"""
Given y, it solves argmin_z |y-z|_2 st \sum z = 1 , 1 >= z_i >= 0 for all i
"""
m = len(y)
sorted_y = np.flip(np.sort(y), axis=0)
tmpsum = 0.0
tmax_f = (np.sum(y) - 1.0) / m
for i in range(m - 1):
tmpsum += sorted_y[i]
tmax = (tmpsum - 1) / (i + 1.0)
if tmax > sorted_y[i + 1]:
tmax_f = tmax
break
return np.maximum(y - tmax_f, np.zeros(y.shape))
def _next_point(cur_val, grad, n):
proj_grad = grad - (np.sum(grad) / n)
tm1 = -cur_val[proj_grad < 0] / proj_grad[proj_grad < 0]
tm2 = (1.0 - cur_val[proj_grad > 0]) / (proj_grad[proj_grad > 0])
t = 1
if len(tm1[tm1 > 1e-7]) > 0:
t = np.min(tm1[tm1 > 1e-7])
if len(tm2[tm2 > 1e-7]) > 0:
t = min(t, np.min(tm2[tm2 > 1e-7]))
next_point = proj_grad * t + cur_val
next_point = _projection2simplex(next_point)
return next_point
def find_min_norm_element(vecs, max_iter=250, stop_crit=1e-5):
"""
Given a list of vectors (vecs), this method finds the minimum norm element in the convex hull
as min |u|_2 st. u = \sum c_i vecs[i] and \sum c_i = 1.
It is quite geometric, and the main idea is the fact that if d_{ij} = min |u|_2 st u = c x_i + (1-c) x_j;
the solution lies in (0, d_{i,j})
Hence, we find the best 2-task solution, and then run the projected gradient descent until convergence
"""
# Solution lying at the combination of two points
init_sol, dps = _min_norm_2d(vecs.detach())
n = len(vecs)
sol_vec = np.zeros(n)
sol_vec[init_sol[0][0]] = init_sol[1]
sol_vec[init_sol[0][1]] = 1 - init_sol[1]
if n < 3:
# This is optimal for n=2, so return the solution
return sol_vec, init_sol[2]
iter_count = 0
while iter_count < max_iter:
grad_dir = -1.0 * np.dot(dps, sol_vec)
new_point = _next_point(sol_vec, grad_dir, n)
# Re-compute the inner products for line search
v1v1 = 0.0
v1v2 = 0.0
v2v2 = 0.0
for i in range(n):
for j in range(n):
v1v1 += sol_vec[i] * sol_vec[j] * dps[i, j]
v1v2 += sol_vec[i] * new_point[j] * dps[i, j]
v2v2 += new_point[i] * new_point[j] * dps[i, j]
nc, nd = _min_norm_element_from2(v1v1, v1v2, v2v2)
new_sol_vec = nc * sol_vec + (1 - nc) * new_point
change = new_sol_vec - sol_vec
if np.sum(np.abs(change)) < stop_crit:
break
sol_vec = new_sol_vec
return sol_vec, nd
if __name__ == '__main__':
import numpy as np
import cvxpy as cp
n = 10
v1 = np.random.normal(size=n)
v2 = np.random.normal(size=n)
v1v1 = v1.dot(v1)
v1v2 = v1.dot(v2)
v2v2 = v2.dot(v2)
# min \|c * x1 + (1 - c) * x2\|^2.
# Ground truth.
alpha = cp.Variable(2)
V = np.array([v1, v2]) # V: 2 * n.
objective = cp.Minimize(cp.sum_squares(V.T @ alpha))
constraints = [alpha >= 0, cp.sum(alpha) == 1]
prob = cp.Problem(objective, constraints)
loss = prob.solve()
gamma, cost = _min_norm_element_from2(v1v1, v1v2, v2v2)
print('loss:', loss, 'alpha:', alpha.value)
print('loss:', cost, 'alpha:', [gamma, 1 - gamma])
| 4,675 | 29.966887 | 109 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/multi_mnist/weighted_sum.py | import random
from pathlib import Path
from termcolor import colored
import numpy as np
import torch
import torch.nn.functional as F
from torch.optim import SGD
from torch.optim.lr_scheduler import CosineAnnealingLR
from torchvision import transforms
from pareto.metrics import topk_accuracy
from pareto.datasets import MultiMNIST
from pareto.networks import MultiLeNet
from pareto.utils import evenly_dist_weights
@torch.no_grad()
def evaluate(network, dataloader, device, closures, header=''):
num_samples = 0
losses = np.zeros(2)
top1s = np.zeros(2)
network.train(False)
for images, labels in dataloader:
batch_size = len(images)
num_samples += batch_size
images = images.to(device)
labels = labels.to(device)
logits = network(images)
losses_batch = [c(network, logits, labels).item() for c in closures]
losses += batch_size * np.array(losses_batch)
top1s[0] += batch_size * topk_accuracy(logits[0], labels[:, 0], k=1)
top1s[1] += batch_size * topk_accuracy(logits[1], labels[:, 1], k=1)
losses /= num_samples
top1s /= num_samples
loss_msg = '[{}]'.format('/'.join([f'{loss:.6f}' for loss in losses]))
top1_msg = '[{}]'.format('/'.join([f'{top1 * 100.0:.2f}%' for top1 in top1s]))
msgs = [
f'{header}:' if header else '',
'loss', colored(loss_msg, 'yellow'),
'top@1', colored(top1_msg, 'yellow')
]
print(' '.join(msgs))
return losses, top1s
def train(pref, ckpt_name):
# prepare hyper-parameters
seed = 42
cuda_enabled = True
cuda_deterministic = False
batch_size = 256
num_workers = 2
lr = 0.01
momentum = 0.9
weight_decay = 0.0
num_epochs = 30
# prepare path
root_path = Path(__file__).resolve().parent
dataset_path = root_path / 'MultiMNIST'
ckpt_path = root_path / 'weighted_sum'
root_path.mkdir(parents=True, exist_ok=True)
dataset_path.mkdir(parents=True, exist_ok=True)
ckpt_path.mkdir(parents=True, exist_ok=True)
# fix random seed
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if cuda_enabled and torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
# prepare device
if cuda_enabled and torch.cuda.is_available():
import torch.backends.cudnn as cudnn
device = torch.device('cuda')
if cuda_deterministic:
cudnn.benchmark = False
cudnn.deterministic = True
else:
cudnn.benchmark = True
else:
device = torch.device('cpu')
# prepare dataset
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
trainset = MultiMNIST(dataset_path, train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
testset = MultiMNIST(dataset_path, train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
# prepare network
network = MultiLeNet()
network.to(device)
# prepare losses
criterion = F.cross_entropy
closures = [lambda n, l, t: criterion(l[0], t[:, 0]), lambda n, l, t: criterion(l[1], t[:, 1])]
# prepare optimizer
optimizer = SGD(network.parameters(), lr=lr, momentum=momentum, weight_decay=weight_decay)
lr_scheduler = CosineAnnealingLR(optimizer, num_epochs * len(trainloader))
# save initial state
if not (ckpt_path / 'random.pth').is_file():
random_ckpt = {
'state_dict': network.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict()
}
torch.save(random_ckpt, ckpt_path / 'random.pth')
random_ckpt = torch.load(ckpt_path / 'random.pth', map_location='cpu')
network.load_state_dict(random_ckpt['state_dict'])
optimizer.load_state_dict(random_ckpt['optimizer'])
lr_scheduler.load_state_dict(random_ckpt['lr_scheduler'])
# first evaluation
evaluate(network, testloader, device, closures, f'{ckpt_name}')
# training
num_steps = len(trainloader)
for epoch in range(1, num_epochs + 1):
network.train(True)
trainiter = iter(trainloader)
for _ in range(1, num_steps + 1):
images, labels = next(trainiter)
images = images.to(device)
labels = labels.to(device)
logits = network(images)
losses = [c(network, logits, labels) for c in closures]
loss = sum(w * l for w, l in zip(pref, losses))
optimizer.zero_grad()
loss.backward()
optimizer.step()
lr_scheduler.step()
losses, tops = evaluate(network, testloader, device, closures, f'{ckpt_name}: {epoch}/{num_epochs}')
# saving
ckpt = {
'state_dict': network.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'preference': pref,
}
record = {'losses': losses, 'tops': tops}
ckpt['record'] = record
torch.save(ckpt, ckpt_path / f'{ckpt_name}.pth')
def weighted_sum(num_prefs=5):
prefs = evenly_dist_weights(num_prefs + 2, 2)
for i, pref in enumerate(prefs):
train(pref, str(i))
if __name__ == '__main__':
weighted_sum(5)
| 5,501 | 26.928934 | 117 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/multi_mnist/cpmtl.py | import random
from pathlib import Path
from termcolor import colored
import numpy as np
import torch
import torch.nn.functional as F
from torch.optim import SGD
from torchvision import transforms
from pareto.metrics import topk_accuracy
from pareto.optim import VisionHVPSolver, MINRESKKTSolver
from pareto.datasets import MultiMNIST
from pareto.networks import MultiLeNet
from pareto.utils import TopTrace
@torch.no_grad()
def evaluate(network, dataloader, device, closures, header=''):
num_samples = 0
losses = np.zeros(2)
top1s = np.zeros(2)
network.train(False)
for images, labels in dataloader:
batch_size = len(images)
num_samples += batch_size
images = images.to(device)
labels = labels.to(device)
logits = network(images)
losses_batch = [c(network, logits, labels).item() for c in closures]
losses += batch_size * np.array(losses_batch)
top1s[0] += batch_size * topk_accuracy(logits[0], labels[:, 0], k=1)
top1s[1] += batch_size * topk_accuracy(logits[1], labels[:, 1], k=1)
losses /= num_samples
top1s /= num_samples
loss_msg = '[{}]'.format('/'.join([f'{loss:.6f}' for loss in losses]))
top1_msg = '[{}]'.format('/'.join([f'{top1 * 100.0:.2f}%' for top1 in top1s]))
msgs = [
f'{header}:' if header else '',
'loss', colored(loss_msg, 'yellow'),
'top@1', colored(top1_msg, 'yellow')
]
print(' '.join(msgs))
return losses, top1s
def train(start_path, beta):
# prepare hyper-parameters
seed = 42
cuda_enabled = True
cuda_deterministic = False
batch_size = 2048
num_workers = 2
shared = False
stochastic = False
kkt_momentum = 0.0
create_graph = False
grad_correction = False
shift = 0.0
tol = 1e-5
damping = 0.1
maxiter = 50
lr = 0.1
momentum = 0.0
weight_decay = 0.0
num_steps = 10
verbose = False
# prepare path
ckpt_name = start_path.name.split('.')[0]
root_path = Path(__file__).resolve().parent
dataset_path = root_path / 'MultiMNIST'
ckpt_path = root_path / 'cpmtl' / ckpt_name
if not start_path.is_file():
raise RuntimeError('Pareto solutions not found.')
root_path.mkdir(parents=True, exist_ok=True)
dataset_path.mkdir(parents=True, exist_ok=True)
ckpt_path.mkdir(parents=True, exist_ok=True)
# fix random seed
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if cuda_enabled and torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
# prepare device
if cuda_enabled and torch.cuda.is_available():
import torch.backends.cudnn as cudnn
device = torch.device('cuda')
if cuda_deterministic:
cudnn.benchmark = False
cudnn.deterministic = True
else:
cudnn.benchmark = True
else:
device = torch.device('cpu')
# prepare dataset
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
trainset = MultiMNIST(dataset_path, train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
testset = MultiMNIST(dataset_path, train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
# prepare network
network = MultiLeNet()
network.to(device)
# initialize network
start_ckpt = torch.load(start_path, map_location='cpu')
network.load_state_dict(start_ckpt['state_dict'])
# prepare losses
criterion = F.cross_entropy
closures = [lambda n, l, t: criterion(l[0], t[:, 0]), lambda n, l, t: criterion(l[1], t[:, 1])]
# prepare HVP solver
hvp_solver = VisionHVPSolver(network, device, trainloader, closures, shared=shared)
hvp_solver.set_grad(batch=False)
hvp_solver.set_hess(batch=True)
# prepare KKT solver
kkt_solver = MINRESKKTSolver(
network, hvp_solver, device,
stochastic=stochastic, kkt_momentum=kkt_momentum, create_graph=create_graph,
grad_correction=grad_correction, shift=shift, tol=tol, damping=damping, maxiter=maxiter)
# prepare optimizer
optimizer = SGD(network.parameters(), lr=lr, momentum=momentum, weight_decay=weight_decay)
# first evaluation
losses, tops = evaluate(network, testloader, device, closures, f'{ckpt_name}')
# prepare utilities
top_trace = TopTrace(len(closures))
top_trace.print(tops, show=False)
beta = beta.to(device)
# training
for step in range(1, num_steps + 1):
network.train(True)
optimizer.zero_grad()
kkt_solver.backward(beta, verbose=verbose)
optimizer.step()
losses, tops = evaluate(network, testloader, device, closures, f'{ckpt_name}: {step}/{num_steps}')
top_trace.print(tops)
ckpt = {
'state_dict': network.state_dict(),
'optimizer': optimizer.state_dict(),
'beta': beta,
}
record = {'losses': losses, 'tops': tops}
ckpt['record'] = record
torch.save(ckpt, ckpt_path / f'{step:d}.pth')
hvp_solver.close()
def cpmtl():
root_path = Path(__file__).resolve().parent
start_root = root_path / 'weighted_sum'
beta = torch.Tensor([1, 0])
for start_path in sorted(start_root.glob('[0-9]*.pth'), key=lambda x: int(x.name.split('.')[0])):
train(start_path, beta)
if __name__ == "__main__":
cpmtl()
| 5,661 | 25.092166 | 117 | py |
DeepAA | DeepAA-master/resnet_imagenet.py | import os
import tensorflow as tf
# ref: https://github.com/gahaalt/resnets-in-tensorflow2/blob/master/Models/Resnets.py
_bn_momentum = 0.9
def regularized_padded_conv(*args, **kwargs):
return tf.keras.layers.Conv2D(*args, **kwargs, padding='same', kernel_regularizer=_regularizer, bias_regularizer=_regularizer,
kernel_initializer='he_normal', use_bias=False)
def bn_relu(x, gamma_initializer='ones'):
x = tf.keras.layers.experimental.SyncBatchNormalization(momentum=_bn_momentum, gamma_initializer=gamma_initializer)(x)
return tf.keras.layers.ReLU()(x)
def shortcut(x, filters, stride, mode):
if x.shape[-1] == filters: # maybe and stride==1
return x
elif mode == 'B':
return regularized_padded_conv(filters, 1, strides=stride)(x)
elif mode == 'B_original':
x = regularized_padded_conv(filters, 1, strides=stride)(x)
return tf.keras.layers.experimental.SyncBatchNormalization(momentum=_bn_momentum)(x)
elif mode == 'A':
return tf.pad(tf.keras.layers.MaxPool2D(1, stride)(x) if stride > 1 else x,
paddings=[(0, 0), (0, 0), (0, 0), (0, filters - x.shape[-1])])
else:
raise KeyError("Parameter shortcut_type not recognized!")
def original_block(x, filters, stride=1, **kwargs):
c1 = regularized_padded_conv(filters, 3, strides=stride)(x)
c2 = regularized_padded_conv(filters, 3)(bn_relu(c1))
c2 = tf.keras.layers.experimental.SyncBatchNormalization(momentum=_bn_momentum)(c2)
mode = 'B_original' if _shortcut_type == 'B' else _shortcut_type
x = shortcut(x, filters, stride, mode=mode)
return tf.keras.layers.ReLU()(x + c2)
def bootleneck_block(x, filters, stride=1, preact_block=False): # preact_block==False
# flow = bn_relu(x)
# if preact_block:
# x = flow
residual = x
c1 = regularized_padded_conv(filters // _bootleneck_width, 1)(bn_relu(x))
c2 = regularized_padded_conv(filters // _bootleneck_width, 3, strides=stride)(bn_relu(c1))
c3 = regularized_padded_conv(filters, 1)(bn_relu(c2))
if x.shape[-1] != filters or stride != 1:
residual = shortcut(x, filters, stride, mode=_shortcut_type)
return tf.keras.layers.ReLU()(residual + tf.keras.layers.experimental.SyncBatchNormalization(momentum=_bn_momentum, gamma_initializer='zeros')(c3))
def group_of_blocks(x, block_type, num_blocks, filters, stride, block_idx=0):
global _preact_shortcuts
preact_block = False
x = block_type(x, filters, stride, preact_block=preact_block)
for i in range(num_blocks - 1):
x = block_type(x, filters)
return x
def Resnet(input_shape, n_classes, l2_reg=1e-4, group_sizes=(2, 2, 2), features=(16, 32, 64), strides=(1, 2, 2),
shortcut_type='B', block_type='preactivated', first_conv={"filters": 16, "kernel_size": 3, "strides": 1},
dropout=0, cardinality=1, bootleneck_width=4, preact_shortcuts=True):
global _regularizer, _shortcut_type, _preact_projection, _dropout, _cardinality, _bootleneck_width, _preact_shortcuts
_bootleneck_width = bootleneck_width # used in ResNeXts and bootleneck blocks
_regularizer = tf.keras.regularizers.l2(l2_reg)
_shortcut_type = shortcut_type # used in blocks
_cardinality = cardinality # used in ResNeXts
_dropout = dropout # used in Wide ResNets
_preact_shortcuts = preact_shortcuts
block_types = {
# 'preactivated': preactivation_block,
'bootleneck': bootleneck_block,
'original': original_block
}
selected_block = block_types[block_type]
inputs = tf.keras.layers.Input(shape=input_shape)
flow = regularized_padded_conv(**first_conv)(inputs)
# if block_type == 'original':
flow = bn_relu(flow)
flow = tf.keras.layers.MaxPool2D(pool_size=(3,3), strides=2, padding='same')(flow)
for block_idx, (group_size, feature, stride) in enumerate(zip(group_sizes, features, strides)):
flow = group_of_blocks(flow,
block_type=selected_block,
num_blocks=group_size,
block_idx=block_idx,
filters=feature,
stride=stride)
# if block_type != 'original':
# flow = bn_relu(flow)
flow = tf.keras.layers.GlobalAveragePooling2D()(flow)
outputs = tf.keras.layers.Dense(n_classes, kernel_regularizer=_regularizer, bias_regularizer=_regularizer, use_bias=True)(flow)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
return model
def imagenet_resnet50(block_type='bootleneck', shortcut_type='B_original', l2_reg=0.5e-4, load_weights=False, input_shape=(224,224,3), n_classes=1000):
bootleneck_width = 4
model = Resnet(input_shape=input_shape, n_classes=n_classes, l2_reg=l2_reg, group_sizes=(3,4,6,3),
features=(64*bootleneck_width, 128*bootleneck_width, 256*bootleneck_width, 512*bootleneck_width),
strides=(1, 2, 2, 2), first_conv={"filters": 64, "kernel_size": 7, "strides": 2},
shortcut_type=shortcut_type,
block_type=block_type, preact_shortcuts=False,
bootleneck_width=bootleneck_width)
return model
def imagenet_resnet50_pretrained(input_shape, n_classes, l2_reg):
_regularizer = tf.keras.regularizers.l2(l2_reg)
inputs = tf.keras.layers.Input(shape=input_shape)
base_model = tf.keras.applications.resnet50.ResNet50(include_top=False, input_shape=input_shape,
pooling='avg', weights='imagenet')
base_model.trainable = False
x = base_model(inputs, training=False) # do not update batch augmentation
outputs = tf.keras.layers.Dense(n_classes, kernel_regularizer=_regularizer, bias_regularizer=_regularizer, use_bias=True)(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
return model
def imagenet_resnet18(block_type='original', shortcut_type='B_original', l2_reg=0.5e-4, load_weights=False, input_shape=(224,224,3), n_classes=1000):
model = Resnet(input_shape=input_shape, n_classes=n_classes, l2_reg=l2_reg, group_sizes=(2,2,2,2),
features=(64, 128, 256, 512),
strides=(1, 2, 2, 2), first_conv={"filters": 64, "kernel_size": 7, "strides": 2},
shortcut_type=shortcut_type,
block_type=block_type, preact_shortcuts=False,
bootleneck_width=None)
return model
def load_weights_func(model, model_name):
try:
model.load_weights(os.path.join('saved_models', model_name + '.tf'))
except tf.errors.NotFoundError:
print("No weights found for this model!")
return model
if __name__ == '__main__':
model = imagenet_resnet50() | 6,826 | 46.082759 | 151 | py |
DeepAA | DeepAA-master/lr_scheduler.py | import tensorflow as tf
from tensorflow.keras.optimizers.schedules import LearningRateSchedule
from tensorflow.python.framework import ops
from tensorflow.python.ops import math_ops, control_flow_ops
class GradualWarmup_Cosine_Scheduler(LearningRateSchedule):
def __init__(self, starting_lr, initial_lr, ending_lr, warmup_steps, total_steps, name=None):
super(GradualWarmup_Cosine_Scheduler, self).__init__()
self.starting_lr = starting_lr
self.initial_lr = initial_lr
self.ending_lr = ending_lr
self.warmup_steps = warmup_steps
self.total_steps = total_steps
self.name = name
def __call__(self, step):
with ops.name_scope_v2(self.name or 'GradualWarmup_Cosine') as name:
initial_lr = ops.convert_to_tensor_v2(self.initial_lr, name='initial_learning_rate')
dtype = initial_lr.dtype
starting_lr = math_ops.cast(self.starting_lr, dtype)
ending_lr = math_ops.cast(self.ending_lr, dtype)
warmup_steps = math_ops.cast(self.warmup_steps, dtype)
total_steps = math_ops.cast(self.total_steps, dtype)
one = math_ops.cast(1.0, dtype)
point5 = math_ops.cast(0.5, dtype)
pi = math_ops.cast(3.1415926536, dtype)
step = math_ops.cast(step, dtype)
lr = tf.cond(step < warmup_steps,
true_fn=lambda: self._warmup_schedule(starting_lr, initial_lr, step, warmup_steps),
false_fn=lambda: self._cosine_annealing_schedule(initial_lr, ending_lr, step, warmup_steps, total_steps, pi,
point5, one))
return lr
def _warmup_schedule(self, starting_lr, initial_lr, step, warmup_steps):
ratio = math_ops.divide(step, warmup_steps)
lr = math_ops.add(starting_lr,
math_ops.multiply(initial_lr - starting_lr, ratio))
return lr
def _cosine_annealing_schedule(self, initial_lr, ending_lr, step, warmup_steps, total_steps, pi, point5, one):
ratio = math_ops.divide(step - warmup_steps, total_steps - warmup_steps)
cosine_ratio_pi = math_ops.cos(math_ops.multiply(ratio, pi))
second_part = math_ops.multiply(point5,
math_ops.multiply(initial_lr - ending_lr,
one + cosine_ratio_pi))
lr = math_ops.add(ending_lr, second_part)
return lr
def get_config(self):
return {
'starting_lr': self.starting_lr,
'initial_lr': self.initial_lr,
'ending_lr': self.ending_lr,
'warmup_steps': self.warmup_steps,
'total_steps': self.total_steps,
'name': self.name
} | 2,824 | 46.083333 | 133 | py |
DeepAA | DeepAA-master/DeepAA_utils.py | import os
import logging
import numpy as np
import copy
import random
import datetime
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import tensorflow as tf
tf.get_logger().setLevel(logging.ERROR)
from data_generator import DataGenerator, DataAugmentation
from utils import CTLHistory
from lr_scheduler import GradualWarmup_Cosine_Scheduler
import resnet
from resnet_imagenet import imagenet_resnet50
from data_generator import get_cifar10_data, get_cifar100_data
from augmentation import AutoContrast, Invert, Equalize, Solarize, Posterize, Contrast, Brightness, Sharpness, \
Identity, Color, ShearX, ShearY, TranslateX, TranslateY, Rotate
from augmentation import RandCrop, RandCutout, RandFlip, RandCutout60
from augmentation import RandResizeCrop_imagenet, centerCrop_imagenet
from policy import DA_Policy_logits
from augmentation import IMAGENET_SIZE
import torch
import threading
import queue
from imagenet_data_utils import get_imagenet_split
def aug_op_cifar_list(): # oeprators and their ranges
l = [
(Identity, 0., 1.0), # 0
(ShearX, -0.3, 0.3), # 1
(ShearY, -0.3, 0.3), # 2
(TranslateX, -0.45, 0.45), # 3
(TranslateY, -0.45, 0.45), # 4
(Rotate, -30., 30.), # 5
(AutoContrast, 0., 1.), # 6
(Invert, 0., 1.), # 7
(Equalize, 0., 1.), # 8
(Solarize, 0., 256.), # 9
(Posterize, 4., 8.), # 10,
(Contrast, 0.1, 1.9), # 11
(Color, 0.1, 1.9), # 12
(Brightness, 0.1, 1.9), # 13
(Sharpness, 0.1, 1.9), # 14
(RandFlip, 0., 1.0), # 15
(RandCutout, 0., 1.0), # 16
(RandCrop, 0., 1.0), # 17
]
names = []
for op in l:
info = op.__str__().split(' ')
name = '{}:({},{}'.format(info[1], info[-2], info[-1])
names.append(name)
return l, names
def aug_op_imagenet_list(): # 16 oeprations and their ranges
l = [
(Identity, 0., 1.0), # 0
(ShearX, -0.3, 0.3), # 1
(ShearY, -0.3, 0.3), # 2
(TranslateX, -0.45, 0.45), # 3
(TranslateY, -0.45, 0.45), # 4
(Rotate, -30., 30.), # 5
(AutoContrast, 0., 1.), # 6
(Invert, 0., 1.), # 7
(Equalize, 0., 1.), # 8
(Solarize, 0., 256.), # 9
(Posterize, 4., 8.), # 10
(Contrast, 0.1, 1.9), # 11
(Color, 0.1, 1.9), # 12
(Brightness, 0.1, 1.9), # 13
(Sharpness, 0.1, 1.9), # 14
(RandFlip, 0., 1.0), # 15
(RandCutout60, 0., 1.0), # 16
(RandResizeCrop_imagenet, 0., 1.),
]
names = []
for op in l:
info = op.__str__().split(' ')
name = '{}:({},{}'.format(info[1], info[-2], info[-1])
names.append(name)
return l, names
# Get the model
def get_model(args, model, n_classes):
if model == 'WRN_28_10':
model = resnet.cifar_WRN_28_10(dropout=0, l2_reg=0.00025,
preact_shortcuts=False, n_classes=n_classes, input_shape=args.img_size)
elif model == 'WRN_40_2':
model = resnet.cifar_WRN_40_2(dropout=0, l2_reg=0.00025,
preact_shortcuts=False, n_classes=n_classes, input_shape=args.img_size)
elif model == 'resnet50':
model = imagenet_resnet50()
else:
raise Exception('Unrecognized model')
return model
# metric to keep track of
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()
train_loss = tf.keras.metrics.Mean()
test_loss = tf.keras.metrics.Mean()
def get_img_size(args):
if 'cifar' in args.dataset:
return (32, 32, 3)
elif 'imagenet' in args.dataset:
return (*IMAGENET_SIZE, 3)
else:
raise Exception
# get the data
def get_dataset(args):
print('Loading train and retrain dataset.')
if args.dataset in ['cifar10', 'cifar100']:
if args.dataset == 'cifar10':
assert args.n_classes == 10
x_train_, y_train_, x_val, y_val, x_test, y_test = get_cifar10_data(val_size=10000)
x_train, y_train = x_train_[:args.pretrain_size], y_train_[:args.pretrain_size]
x_search, y_search = x_train_[args.pretrain_size:], y_train_[args.pretrain_size:]
elif args.dataset == 'cifar100':
assert args.n_classes == 100
x_train_, y_train_, x_val, y_val, x_test, y_test = get_cifar100_data(val_size=10000)
x_train, y_train = x_train_[:args.pretrain_size], y_train_[:args.pretrain_size]
x_search, y_search = x_train_[args.pretrain_size:], y_train_[args.pretrain_size:]
train_ds = DataGenerator(x_train, y_train, batch_size=args.batch_size, drop_last=True)
search_ds = DataGenerator(x_search, y_search, batch_size=args.batch_size, drop_last=True)
val_ds = DataGenerator(x_val, y_val, batch_size=args.val_batch_size, drop_last=True)
test_ds = DataGenerator(x_test, y_test, batch_size=args.test_batch_size, drop_last=False, shuffle=False) # setting shuffle=False for parallel evaluation
elif args.dataset == 'imagenet':
assert args.n_classes == 1000
def collate_fn_imagenet_list(l): # return a list
images, labels = zip(*l)
assert images[0].dtype == np.uint8
return list(images), np.array(labels, dtype=np.int32)
if args.dataset == 'imagenet':
train_ds_total, val_ds, search_ds, train_ds, test_ds = get_imagenet_split(n_GPU=1, seed=300)
assert len(train_ds) == 1 and isinstance(train_ds, list), 'Train_ds should be a length=1 list'
train_ds = train_ds[0]
test_ds = torch.utils.data.DataLoader(
test_ds, batch_size=256, shuffle=False, num_workers=64,
pin_memory=False,
drop_last=False, sampler=None,
collate_fn=collate_fn_imagenet_list,
)
else:
raise Exception('Unrecognized dataset')
return train_ds, val_ds, test_ds, search_ds
def get_augmentation(args):
if 'cifar' in args.dataset:
augmentation_default = DataAugmentation(num_classes=args.n_classes, dataset=args.dataset, image_shape=args.img_size,
ops_list=(None, None),
default_pre_aug=None,
default_post_aug=[RandCrop,
RandFlip,
RandCutout])
augmentation_search = DataAugmentation(num_classes=args.n_classes, dataset=args.dataset, image_shape=args.img_size,
ops_list=aug_op_cifar_list(),
default_pre_aug=None,
default_post_aug=None)
augmentation_test = DataAugmentation(num_classes=args.n_classes, dataset=args.dataset, image_shape=args.img_size,
ops_list=(None, None),
default_pre_aug=None,
default_post_aug=None)
elif 'imagenet' in args.dataset:
augmentation_default = DataAugmentation(num_classes=args.n_classes, dataset=args.dataset,
image_shape=args.img_size,
ops_list=(None, None),
default_pre_aug=None,
default_post_aug=[RandResizeCrop_imagenet, #
RandFlip])
augmentation_search = DataAugmentation(num_classes=args.n_classes, dataset=args.dataset, image_shape=args.img_size,
ops_list=aug_op_imagenet_list(),
default_pre_aug=None,
default_post_aug=None)
augmentation_test = DataAugmentation(num_classes=args.n_classes, dataset=args.dataset,
image_shape=args.img_size,
ops_list=(None, None),
default_pre_aug=None,
default_post_aug=[
centerCrop_imagenet,
])
return augmentation_default, augmentation_search, augmentation_test
def get_optim_net(args, nb_train_steps):
scheduler_lr = GradualWarmup_Cosine_Scheduler(starting_lr=0., initial_lr=args.pretrain_lr,
ending_lr=1e-7,
warmup_steps= 0,
total_steps=nb_train_steps * args.nb_epochs)
optim_net = tf.optimizers.SGD(learning_rate=scheduler_lr, momentum=0.9, nesterov=True)
return optim_net
def get_policy(args, op_names, ops_mid_magnitude, available_policies):
policy = DA_Policy_logits(args.l_ops, args.l_mags, args.l_uniq,
op_names=op_names,
ops_mid_magnitude=ops_mid_magnitude, N_repeat_random=args.N_repeat_random,
available_policies=available_policies)
return policy
def get_optim_policy(policy_lr):
optim_policy = tf.optimizers.Adam(learning_rate=policy_lr, beta_1=0.9, beta_2=0.999)
return optim_policy
# get the loss
def get_loss_fun():
train_loss_fun = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True,
reduction=tf.keras.losses.Reduction.NONE)
test_loss_fun = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True,
reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE)
val_loss_fun = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True,
reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE)
return train_loss_fun, test_loss_fun, val_loss_fun
def get_lops_luniq(args, ops_mid_magnitude):
if 'cifar' in args.dataset:
_, op_names = aug_op_cifar_list()
elif 'imagenet' in args.dataset:
_, op_names = aug_op_imagenet_list()
else:
raise Exception('Unknown dataset ={}'.format(args.dataset))
names_modified = [op_name.split(':')[0] for op_name in op_names]
l_ops = len(op_names)
l_uniq = 0
for k_name, name in enumerate(names_modified):
mid_mag = ops_mid_magnitude[name]
if mid_mag == 'random':
l_uniq += 1 # The op is a random op, however we only sample one op
elif mid_mag is not None and mid_mag >=0 and mid_mag <= args.l_mags-1:
l_uniq += args.l_mags-1
elif mid_mag is not None and mid_mag == -1: # magnitude==-1 means all l_mags are independnt policies; or mid_mag > args.l_mags-1)
l_uniq += args.l_mags
elif mid_mag is None:
l_uniq += 1
else:
raise Exception('mid_mag = {} is invalid'.format(mid_mag))
return l_ops, l_uniq
def get_all_policy(policy_train):
l_ops, l_mags = policy_train.l_ops, policy_train.l_mags
ops, mags = np.meshgrid(np.arange(l_ops), np.arange(l_mags), indexing='ij')
ops = np.reshape(ops, [l_ops*l_mags,1])
mags = np.reshape(mags, [l_ops*l_mags,1])
return ops.astype(np.int32), mags.astype(np.int32)
class PrefetchGenerator(threading.Thread):
def __init__(self, search_ds, val_ds, n_classes, search_bs=8, val_bs=64):
threading.Thread.__init__(self)
self.queue = queue.Queue(1)
self.search_ds = search_ds
self.val_ds = val_ds
self.n_classes = n_classes
self.search_bs = search_bs
self.val_bs = val_bs
self.daemon = True
self.start()
@staticmethod
def sample_label_and_batch(dataset, bs, n_classes, MAX_iterations=100):
for k in range(MAX_iterations):
try:
lab = random.randint(0, n_classes-1)
imgs, labs = dataset.sample_labeled_data_batch(lab, bs)
except:
print('Insufficient data in a single class, try {}/{}'.format(k, MAX_iterations))
continue
return lab, imgs, labs
raise Exception('Maximum number of iteration {} reached'.format(MAX_iterations))
def run(self):
while True:
images_val, labels_val, images_train, labels_train = [], [], [], []
for _ in range(self.search_bs):
lab, imgs_val, labs_val = PrefetchGenerator.sample_label_and_batch(self.val_ds, self.val_bs, self.n_classes)
imgs_train, labs_train = self.search_ds.sample_labeled_data_batch(lab, 1)
images_val.append(imgs_val)
labels_val.append(labs_val)
images_train.append(imgs_train)
labels_train.append(labs_train)
self.queue.put( (images_val, labels_val, images_train, labels_train) )
def next(self):
next_item = self.queue.get()
return next_item
def save_policy(args, all_using_policies, augmentation_search):
ops, mags = all_using_policies[0].unique_policy
op_names = augmentation_search.op_names
policy_probs = []
for k_policy, policy in enumerate(all_using_policies):
policy_probs.append(tf.nn.softmax(policy.logits).numpy())
policy_probs = np.stack(policy_probs, axis=0)
np.savez('./policy_port/policy_DeepAA_{}.npz'.format(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S-%f")),
policy_probs=policy_probs, l_ops=args.l_ops, l_mags=args.l_mags,
ops=ops, mags=mags, op_names=op_names) | 13,983 | 42.7 | 161 | py |
DeepAA | DeepAA-master/imagenet_data_utils.py | import numpy as np
import tensorflow as tf
from torchvision.datasets.imagenet import *
from torch import randperm, default_generator
from torch._utils import _accumulate
from torch.utils.data.dataset import Subset
_DATA_TYPE = tf.float32
CMYK_IMAGES = [
'n01739381_1309.JPEG',
'n02077923_14822.JPEG',
'n02447366_23489.JPEG',
'n02492035_15739.JPEG',
'n02747177_10752.JPEG',
'n03018349_4028.JPEG',
'n03062245_4620.JPEG',
'n03347037_9675.JPEG',
'n03467068_12171.JPEG',
'n03529860_11437.JPEG',
'n03544143_17228.JPEG',
'n03633091_5218.JPEG',
'n03710637_5125.JPEG',
'n03961711_5286.JPEG',
'n04033995_2932.JPEG',
'n04258138_17003.JPEG',
'n04264628_27969.JPEG',
'n04336792_7448.JPEG',
'n04371774_5854.JPEG',
'n04596742_4225.JPEG',
'n07583066_647.JPEG',
'n13037406_4650.JPEG',
]
PNG_IMAGES = ['n02105855_2933.JPEG']
class ImageNet(ImageFolder):
"""`ImageNet <http://image-net.org/>`_ 2012 Classification Dataset.
Copied from torchvision, besides warning below.
Args:
root (string): Root directory of the ImageNet Dataset.
split (string, optional): The dataset split, supports ``train``, or ``val``.
transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
loader (callable, optional): A function to load an image given its path.
Attributes:
classes (list): List of the class name tuples.
class_to_idx (dict): Dict with items (class_name, class_index).
wnids (list): List of the WordNet IDs.
wnid_to_idx (dict): Dict with items (wordnet_id, class_index).
imgs (list): List of (image path, class_index) tuples
targets (list): The class_index value for each image in the dataset
WARN::
This is the same ImageNet class as in torchvision.datasets.imagenet, but it has the `ignore_archive` argument.
This allows us to only copy the unzipped files before training.
"""
def __init__(self, root, split='train', download=None, ignore_archive=False, **kwargs):
if download is True:
msg = ("The dataset is no longer publicly accessible. You need to "
"download the archives externally and place them in the root "
"directory.")
raise RuntimeError(msg)
elif download is False:
msg = ("The use of the download flag is deprecated, since the dataset "
"is no longer publicly accessible.")
warnings.warn(msg, RuntimeWarning)
root = self.root = os.path.expanduser(root)
self.split = verify_str_arg(split, "split", ("train", "val"))
if not ignore_archive:
self.parse_archives()
wnid_to_classes = load_meta_file(self.root)[0]
super(ImageNet, self).__init__(self.split_folder, **kwargs)
self.root = root
self.wnids = self.classes
self.wnid_to_idx = self.class_to_idx
self.classes = [wnid_to_classes[wnid] for wnid in self.wnids]
self.class_to_idx = {cls: idx
for idx, clss in enumerate(self.classes)
for cls in clss}
def parse_archives(self):
if not check_integrity(os.path.join(self.root, META_FILE)):
parse_devkit_archive(self.root)
if not os.path.isdir(self.split_folder):
if self.split == 'train':
parse_train_archive(self.root)
elif self.split == 'val':
parse_val_archive(self.root)
@property
def split_folder(self):
return os.path.join(self.root, self.split)
def extra_repr(self):
return "Split: {split}".format(**self.__dict__)
class ImageNet_DeepAA(ImageNet):
def __init__(self, root, split='train', download=None, **kwargs):
super(ImageNet_DeepAA, self).__init__(root, split=split, download=download, ignore_archive=True, **kwargs)
_, self.labels_ = zip(*self.samples)
def on_epoch_end(self):
print('Dummy one_epoch_end for ImageNet dataset using torchvision')
pass
def sample_labeled_data_batch(self, label, val_bs): # generate val and train batch at the same time
matched_indices = [id for id, lab in enumerate(self.labels_) if lab==label]
matched_indices = np.array(matched_indices)
assert len(matched_indices) > val_bs, 'Make sure the have enough data'
np.random.shuffle(matched_indices)
val_indices = matched_indices[:val_bs]
val_samples, val_labels = zip(*[self[id] for id in val_indices])
val_samples = list(val_samples)
val_labels = np.array(val_labels, dtype=np.int32)
return val_samples, val_labels
class Subset_ImageNet(Subset):
def __init__(self, dataset, indices):
super(Subset_ImageNet, self).__init__(dataset, indices)
self.subset_labels_ = [self.dataset.labels_[k] for k in indices]
def on_epoch_end(self):
pass
def sample_labeled_data_batch(self, label, val_bs):
matched_indices = [self.indices[id] for id, lab in enumerate(self.subset_labels_) if lab == label]
matched_indices = np.array(matched_indices)
assert len(matched_indices) > val_bs, 'Make sure the have enough data'
np.random.shuffle(matched_indices)
val_indices = matched_indices[:val_bs]
val_samples, val_labels = zip(*[self.dataset[id] for id in val_indices]) # applies transforms
val_samples = list(val_samples)
val_labels = np.array(val_labels, dtype=np.int32)
return val_samples, val_labels
def random_split_ImageNet(dataset, lengths, generator=default_generator):
if sum(lengths) != len(dataset):
raise ValueError('Sum of input lengths does not equal the length of the input dataset')
indices = randperm(sum(lengths), generator=generator).tolist()
return [Subset_ImageNet(dataset, indices[offset - length : offset]) for offset, length in zip(_accumulate(lengths), lengths)]
def get_imagenet_split(val_size=400000, train_sep_size=100000, dataroot='./data', n_GPU=None, seed=300):
transform = lambda img: np.array(img, dtype=np.uint8)
total_trainset = ImageNet_DeepAA(root=os.path.join(dataroot, 'imagenet-pytorch'), transform=transform)
testset = ImageNet_DeepAA(root=os.path.join(dataroot, 'imagenet-pytorch'), split='val', transform=transform)
N_per_shard = (len(total_trainset) - val_size - train_sep_size)//n_GPU
remaining_data = len(total_trainset) - val_size - train_sep_size - n_GPU * N_per_shard
if remaining_data > 0:
splits = [val_size, train_sep_size, *[N_per_shard]*n_GPU, remaining_data]
else:
splits = [val_size, train_sep_size, *[N_per_shard]*n_GPU]
all_ds = random_split_ImageNet(total_trainset,
lengths=splits,
generator=torch.Generator().manual_seed(seed))
val_ds = all_ds[0]
train_ds_sep = all_ds[1]
pretrain_ds_splits = all_ds[2:2+n_GPU]
return total_trainset, val_ds, train_ds_sep, pretrain_ds_splits, testset | 7,325 | 40.625 | 129 | py |
DeepAA | DeepAA-master/augmentation.py | # code in this file is adpated from rpmcruz/autoaugment
# https://github.com/rpmcruz/autoaugment/blob/master/transformations.py
# https://github.com/ildoonet/pytorch-randaugment/blob/master/RandAugment/augmentations.py
import random
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
from PIL import Image
import math
IMAGENET_SIZE = (224, 224) # (width, height) may set to (244, 224)
_IMAGENET_PCA = {
'eigval': [0.2175, 0.0188, 0.0045],
'eigvec': [
[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203],
]
}
_CIFAR_MEAN, _CIFAR_STD = (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)
def ShearX(img, v): # [-0.3, 0.3]
assert -0.3 <= v <= 0.3
return img.transform(img.size, PIL.Image.AFFINE, (1, v, 0, 0, 1, 0))
def ShearY(img, v): # [-0.3, 0.3]
assert -0.3 <= v <= 0.3
return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, v, 1, 0))
def TranslateX(img, v): # [-150, 150] => percentage: [-0.45, 0.45]
assert -0.45 <= v <= 0.45
v = v * img.size[0]
return img.transform(img.size, PIL.Image.AFFINE, (1, 0, v, 0, 1, 0))
def TranslateY(img, v): # [-150, 150] => percentage: [-0.45, 0.45]
assert -0.45 <= v <= 0.45
v = v * img.size[1]
return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, 0, 1, v))
def Rotate(img, v): # [-30, 30]
assert -30 <= v <= 30
return img.rotate(v)
def AutoContrast(img, _):
return PIL.ImageOps.autocontrast(img)
def Invert(img, _):
return PIL.ImageOps.invert(img)
def Equalize(img, _):
return PIL.ImageOps.equalize(img)
def Flip(img, _): # not from the paper
return PIL.ImageOps.mirror(img)
def Solarize(img, v): # [0, 256]
assert 0 <= v <= 256
return PIL.ImageOps.solarize(img, v)
def SolarizeAdd(img, addition=0, threshold=128):
img_np = np.array(img).astype(np.int)
img_np = img_np + addition
img_np = np.clip(img_np, 0, 255)
img_np = img_np.astype(np.uint8)
img = Image.fromarray(img_np)
return PIL.ImageOps.solarize(img, threshold)
def Posterize(img, v): # [4, 8]
assert 4 <= v <= 8 # FastAA
v = int(v)
return PIL.ImageOps.posterize(img, v)
def Contrast(img, v): # [0.1,1.9]
assert 0.1 <= v <= 1.9
return PIL.ImageEnhance.Contrast(img).enhance(v)
def Color(img, v): # [0.1,1.9]
assert 0.1 <= v <= 1.9
return PIL.ImageEnhance.Color(img).enhance(v)
def Brightness(img, v): # [0.1,1.9]
assert 0.1 <= v <= 1.9
return PIL.ImageEnhance.Brightness(img).enhance(v)
def Sharpness(img, v): # [0.1,1.9]
assert 0.1 <= v <= 1.9
return PIL.ImageEnhance.Sharpness(img).enhance(v)
def RandCrop(img, _):
v = 4
return mean_pad_randcrop(img, v)
def RandCutout(img, _):
v = 16
w, h = img.size
x = random.uniform(0, w)
y = random.uniform(0, h)
x0 = int(min(w, max(0, x - v // 2))) # clip to the range (0, w)
x1 = int(min(w, max(0, x + v // 2)))
y0 = int(min(h, max(0, y - v // 2)))
y1 = int(min(h, max(0, y + v // 2)))
xy = (x0, y0, x1, y1)
color = (125, 123, 114)
# color = (0, 0, 0)
img = img.copy()
PIL.ImageDraw.Draw(img).rectangle(xy, color)
return img
def RandCutout60(img, _):
v = 60
w, h = img.size
x_left = max(0, w // 2 - 256 // 2)
x_right = min(w, w // 2 + 256 // 2)
y_bottom = max(0, h // 2 - 256 // 2)
y_top = min(h, h // 2 + 256 // 2)
x = random.uniform(x_left, x_right)
y = random.uniform(y_bottom, y_top)
x0 = int(min(w, max(0, x - v // 2)))
x1 = int(min(w, max(0, x + v // 2)))
y0 = int(min(h, max(0, y - v // 2)))
y1 = int(min(h, max(0, y + v // 2)))
xy = (x0, y0, x1, y1)
color = (125, 123, 114)
# color = (0, 0, 0)
img = img.copy()
PIL.ImageDraw.Draw(img).rectangle(xy, color)
return img
def RandFlip(img, _):
if random.random() > 0.5:
img = Flip(img, None)
return img
def mean_pad_randcrop(img, v):
# v: Pad with mean value=[125, 123, 114] by v pixels on each side and then take random crop
assert v <= 10, 'The maximum shift should be less then 10'
padded_size = (img.size[0] + 2*v, img.size[1] + 2*v)
new_img = PIL.Image.new('RGB', padded_size, color=(125, 123, 114))
new_img.paste(img, (v, v))
top = random.randint(0, v*2)
left = random.randint(0, v*2)
new_img = new_img.crop((left, top, left + img.size[0], top + img.size[1]))
return new_img
def Identity(img, v):
return img
def RandResizeCrop_imagenet(img, _):
# ported from torchvision
# for ImageNet use only
scale = (0.08, 1.0)
ratio = (3. / 4., 4. / 3.)
size = IMAGENET_SIZE # (224, 224)
def get_params(img, scale, ratio):
width, height = img.size
area = float(width * height)
log_ratio = [math.log(r) for r in ratio]
for _ in range(10):
target_area = area * random.uniform(scale[0], scale[1])
aspect_ratio = math.exp(random.uniform(log_ratio[0], log_ratio[1]))
w = round(math.sqrt(target_area * aspect_ratio))
h = round(math.sqrt(target_area / aspect_ratio))
if 0 < w <= width and 0 < h <= height:
top = random.randint(0, height - h)
left = random.randint(0, width - w)
return left, top, w, h
# fallback to central crop
in_ratio = float(width) / float(height)
if in_ratio < min(ratio):
w = width
h = round(w / min(ratio))
elif in_ratio > max(ratio):
h = height
w = round(h * max(ratio))
else:
w = width
h = height
top = (height - h) // 2
left = (width - w) // 2
return left, top, w, h
left, top, w_box, h_box = get_params(img, scale, ratio)
box = (left, top, left + w_box, top + h_box)
img = img.resize(size=size, resample=PIL.Image.CUBIC, box=box)
return img
def Resize_imagenet(img, size):
w, h = img.size
if isinstance(size, int):
short, long = (w, h) if w <= h else (h, w)
if short == size:
return img
new_short, new_long = size, int(size * long / short)
new_w, new_h = (new_short, new_long) if w <= h else (new_long, new_short)
return img.resize((new_w, new_h), PIL.Image.BICUBIC)
elif isinstance(size, tuple) or isinstance(size, list):
assert len(size) == 2, 'Check the size {}'.format(size)
return img.resize(size, PIL.Image.BICUBIC)
else:
raise Exception
def centerCrop_imagenet(img, _):
# for ImageNet only
# https://github.com/pytorch/vision/blob/master/torchvision/transforms/functional.py
crop_width, crop_height = IMAGENET_SIZE # (224,224)
image_width, image_height = img.size
if crop_width > image_width or crop_height > image_height:
padding_ltrb = [
(crop_width - image_width) // 2 if crop_width > image_width else 0,
(crop_height - image_height) // 2 if crop_height > image_height else 0,
(crop_width - image_width + 1) // 2 if crop_width > image_width else 0,
(crop_height - image_height + 1) // 2 if crop_height > image_height else 0,
]
img = pad(img, padding_ltrb, fill=0)
image_width, image_height = img.size
if crop_width == image_width and crop_height == image_height:
return img
crop_top = int(round((image_height - crop_height) / 2.))
crop_left = int(round((image_width - crop_width) / 2.))
return img.crop((crop_left, crop_top, crop_left + crop_width, crop_top + crop_height))
# def centerCrop_imagenet_default(img):
# return centerCrop_imagenet(img, None)
def _parse_fill(fill, img, name="fillcolor"):
# Process fill color for affine transforms
num_bands = len(img.getbands())
if fill is None:
fill = 0
if isinstance(fill, (int, float)) and num_bands > 1:
fill = tuple([fill] * num_bands)
if isinstance(fill, (list, tuple)):
if len(fill) != num_bands:
msg = ("The number of elements in 'fill' does not match the number of "
"bands of the image ({} != {})")
raise ValueError(msg.format(len(fill), num_bands))
fill = tuple(fill)
return {name: fill}
def pad(img, padding_ltrb, fill=0, padding_mode='constant'):
if isinstance(padding_ltrb, list):
padding_ltrb = tuple(padding_ltrb)
if padding_mode == 'constant':
opts = _parse_fill(fill, img, name='fill')
if img.mode == 'P':
palette = img.getpalette()
image = PIL.ImageOps.expand(img, border=padding_ltrb, **opts)
image.putpalette(palette)
return image
return PIL.ImageOps.expand(img, border=padding_ltrb, **opts)
elif len(padding_ltrb) == 4:
image_width, image_height = img.size
cropping = -np.minimum(padding_ltrb, 0)
if cropping.any():
crop_left, crop_top, crop_right, crop_bottom = cropping
img = img.crop((crop_left, crop_top, image_width - crop_right, image_height - crop_bottom))
pad_left, pad_top, pad_right, pad_bottom = np.maximum(padding_ltrb, 0)
if img.mode == 'P':
palette = img.getpalette()
img = np.asarray(img)
img = np.pad(img, ((pad_top, pad_bottom), (pad_left, pad_right)), padding_mode)
img = Image.fromarray(img)
img.putpalette(palette)
return img
img = np.asarray(img)
# RGB image
if len(img.shape) == 3:
img = np.pad(img, ((pad_top, pad_bottom), (pad_left, pad_right), (0, 0)), padding_mode)
# Grayscale image
if len(img.shape) == 2:
img = np.pad(img, ((pad_top, pad_bottom), (pad_left, pad_right)), padding_mode)
return Image.fromarray(img)
else:
raise Exception
def get_mid_magnitude(l_mags):
ops_mid_magnitude = {'Identity': None,
'ShearX': (l_mags - 1) // 2,
'ShearY': (l_mags - 1) // 2,
'TranslateX': (l_mags - 1) // 2,
'TranslateY': (l_mags - 1) // 2,
'Rotate': (l_mags - 1) // 2,
'AutoContrast': None,
'Invert': None,
'Equalize': None,
'Solarize': l_mags - 1,
'Posterize': l_mags - 1,
'Contrast': (l_mags - 1) // 2,
'Color': (l_mags - 1) // 2,
'Brightness': (l_mags - 1) // 2,
'Sharpness': (l_mags - 1) // 2,
'RandFlip': 'random',
'RandCutout': 'random',
'RandCutout60': 'random',
'RandCrop': 'random',
'RandResizeCrop_imagenet': 'random',
}
return ops_mid_magnitude | 11,099 | 31.840237 | 103 | py |
DeepAA | DeepAA-master/data_generator.py | import os
import copy
import logging
import numpy as np
import math
from PIL import Image
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import tensorflow as tf
tf.get_logger().setLevel(logging.ERROR)
from tensorflow.keras.utils import Sequence
from augmentation import IMAGENET_SIZE, centerCrop_imagenet
CIFAR_MEANS = np.array([0.49139968, 0.48215841, 0.44653091], dtype=np.float32)
CIFAR_STDS = np.array([0.2023, 0.1994, 0.2010], dtype=np.float32)
IMAGENET_MEANS = np.array([0.485, 0.456, 0.406], dtype=np.float32)
IMAGENET_STDS = np.array([0.229, 0.224, 0.225], dtype=np.float32)
def split_train_validation(x, y, val_size):
indices = np.arange(len(x))
np.random.shuffle(indices)
x_train, x_val, y_train, y_val = x[:-val_size], x[-val_size:], y[:-val_size], y[-val_size:]
return x_train, y_train, x_val, y_val
def get_cifar100_data(num_classes=100, val_size=10000):
(x_train_val, y_train_val), (x_test, y_test) = tf.keras.datasets.cifar100.load_data()
y_train_val = y_train_val.squeeze()
y_test = y_test.squeeze()
if val_size > 0:
x_train, y_train, x_val, y_val = split_train_validation(x_train_val, y_train_val, val_size=val_size)
else:
x_train, y_train = x_train_val, y_train_val
x_val, y_val = None, None
return x_train, y_train, x_val, y_val, x_test, y_test
def get_cifar10_data(num_classes=10, val_size=10000):
(x_train_val, y_train_val), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
y_train_val = y_train_val.squeeze()
y_test = y_test.squeeze()
if val_size > 0:
x_train, y_train, x_val, y_val = split_train_validation(x_train_val, y_train_val, val_size=val_size)
else:
x_train, y_train = x_train_val, y_train_val
x_val, y_val = None, None
return x_train, y_train, x_val, y_val, x_test, y_test
class DataGenerator(Sequence):
def __init__(self,
data,
labels,
img_dim=None,
batch_size=32,
num_classes=10,
shuffle=True,
drop_last=True,
):
self._data = data
self.data = self._data # initially without calling augment, the output data is not augmented
self.labels = labels
self.img_dim = img_dim
self.batch_size = batch_size
self.num_classes = num_classes
self.shuffle = shuffle
self.drop_last = drop_last
self.on_epoch_end()
def reset_augment(self):
self.data = self._data
def on_epoch_end(self):
self.indices = np.arange(len(self._data))
if self.shuffle:
np.random.shuffle(self.indices)
def sample_labeled_data_batch(self, label, bs):
# suffle indices every time
indices = np.arange(len(self._data))
np.random.shuffle(indices)
if isinstance(self.labels, list):
labels = [self.labels[k] for k in indices]
else:
labels = self.labels[indices]
matched_labels = np.array(labels) == int(label)
matched_indices = [id for id, isMatched in enumerate(matched_labels) if isMatched]
if len(matched_indices) - bs >=0:
start_idx = np.random.randint(0, len(matched_indices)-bs)
batch_indices = matched_indices[start_idx:start_idx + bs]
else:
print('Not enough matched data, required {}, but got {} instead'.format(bs, len(matched_indices)))
batch_indices = matched_indices
data_indices = indices[batch_indices]
return [self.data[k] for k in data_indices], np.array([self.labels[k] for k in data_indices], dtype=self.labels[0].dtype)
def __len__(self):
if self.drop_last:
return int(np.floor(len(self.data) / self.batch_size)) # drop the last batch
else:
return int(np.ceil(len(self.data) / self.batch_size)) # drop the last batch
def __getitem__(self, idx):
curr_batch = self.indices[idx*self.batch_size:(idx+1)*self.batch_size]
batch_len = len(curr_batch)
if isinstance(self.data, list) and isinstance(self.labels, list):
return [self.data[k] for k in curr_batch], np.array([self.labels[k] for k in curr_batch], np.int32)
else:
return self.data[curr_batch], self.labels[curr_batch]
class DataAugmentation(object):
def __init__(self, num_classes, dataset, image_shape, ops_list=None, default_pre_aug=None, default_post_aug=None):
self.ops, self.op_names = ops_list
self.default_pre_aug = default_pre_aug
self.default_post_aug = default_post_aug
self.num_classes = num_classes
self.dataset = dataset
self.image_shape = image_shape
if 'imagenet' in self.dataset:
assert self.image_shape == (*IMAGENET_SIZE, 3)
elif 'cifar' in self.dataset:
assert self.image_shape == (32, 32, 3)
else:
raise Exception('Unrecognized dataset')
def sequantially_augment(self, args):
idx, img_, op_idxs, mags, aug_finish = args
assert img_.dtype == np.uint8, 'Input images should be unporocessed, should stay in np.uint8'
img = copy.deepcopy(img_)
pil_img = Image.fromarray(img) # Convert to PIL.Image
if self.default_pre_aug is not None:
for op in self.default_pre_aug:
pil_img = op(pil_img)
if self.ops is not None:
for op_idx, mag in zip(op_idxs, mags):
op, minval, maxval = self.ops[op_idx]
assert mag > -1e-5 and mag < 1. + 1e-5, 'magnitudes should be in the range of (0., 1.)'
mag = mag * (maxval - minval) + minval
pil_img = op(pil_img, mag)
if self.default_post_aug is not None and self.use_post_aug:
for op in self.default_post_aug:
pil_img = op(pil_img, None)
if 'cifar' in self.dataset:
img = np.asarray(pil_img, dtype=np.uint8)
return idx, img
elif 'imagenet' in self.dataset:
if aug_finish:
pil_img = self.crop_IMAGENET(pil_img)
img = np.asarray(pil_img, dtype=np.uint8)
return idx, img
else:
raise Exception
def postprocessing_standardization(self, pil_img):
x = np.asarray(pil_img, dtype=np.float32) / 255.
if 'cifar' in self.dataset:
x = (x - CIFAR_MEANS) / CIFAR_STDS
elif 'imagenet' in self.dataset:
x = (x - IMAGENET_MEANS) / IMAGENET_STDS
else:
raise Exception('Unrecoginized dataset')
return x
def crop_IMAGENET(self, img):
# cropping imagenet dataset to the same size
if isinstance(img, np.ndarray):
assert img.shape == (IMAGENET_SIZE[1], IMAGENET_SIZE[0], 3) and img.dtype==np.uint8, 'numpy array should be {}, but got {}. crop_IMAGENET does not apply to numpy array, but got {}'.format(IMAGENET_SIZE, img.size, img.dtype)
return img
w, h = img.size
if w == IMAGENET_SIZE[0] and h == IMAGENET_SIZE[1]:
return img
return centerCrop_imagenet(img, None)
def check_data_type(self, images, labels):
assert images[0].dtype == np.uint8
if 'imagenet' in self.dataset:
assert type(labels[0]) == np.int32
elif 'cifar' in self.dataset:
assert type(labels[0]) == np.uint8
else:
raise Exception('Unrecognized dataset')
def __call__(self, images, labels, samples_op, samples_mag, use_post_aug, pool=None, chunksize=None, aug_finish=True):
self.check_data_type(images, labels)
self.use_post_aug = use_post_aug
self.batch_len = len(labels)
if aug_finish:
aug_imgs = np.empty([self.batch_len, *self.image_shape], dtype=np.float32)
else:
aug_imgs = [None]*self.batch_len
aug_results = pool.imap_unordered(self.sequantially_augment,
zip(range(self.batch_len), images, samples_op, samples_mag, [aug_finish]*self.batch_len),
chunksize=math.ceil(float(self.batch_len) / float(pool._processes)) if chunksize is None else chunksize)
for idx, img in aug_results:
aug_imgs[idx] = img
if aug_finish:
aug_imgs = self.postprocessing_standardization(aug_imgs)
return aug_imgs, labels | 8,476 | 41.174129 | 235 | py |
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