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e8b0040 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | import os
import torch
from PIL import Image
from collections import OrderedDict
from toolkit.dhelper import traverse_recursively
import random
from torch import nn
import numpy as np
import timm
import einops
import torch.nn.functional as F
class SRMConv2d_simple(nn.Module):
def __init__(self, inc=3):
super(SRMConv2d_simple, self).__init__()
self.truc = nn.Hardtanh(-3, 3)
self.kernel = torch.from_numpy(self._build_kernel(inc)).float()
def forward(self, x):
out = F.conv2d(x, self.kernel, stride=1, padding=2)
out = self.truc(out)
return out
def _build_kernel(self, inc):
# filter1: KB
filter1 = [[0, 0, 0, 0, 0],
[0, -1, 2, -1, 0],
[0, 2, -4, 2, 0],
[0, -1, 2, -1, 0],
[0, 0, 0, 0, 0]]
# filter2：KV
filter2 = [[-1, 2, -2, 2, -1],
[2, -6, 8, -6, 2],
[-2, 8, -12, 8, -2],
[2, -6, 8, -6, 2],
[-1, 2, -2, 2, -1]]
# filter3：hor 2rd
filter3 = [[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 1, -2, 1, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]
filter1 = np.asarray(filter1, dtype=float) / 4.
filter2 = np.asarray(filter2, dtype=float) / 12.
filter3 = np.asarray(filter3, dtype=float) / 2.
# statck the filters
filters = [[filter1], # , filter1, filter1],
[filter2], # , filter2, filter2],
[filter3]] # , filter3, filter3]]
filters = np.array(filters)
filters = np.repeat(filters, inc, axis=1)
return filters
class MultiClassificationProcessor_mfolder(torch.utils.data.Dataset):
def __init__(self, transform=None):
self.transformer_ = transform
self.extension_ = '.jpg .jpeg .png .bmp .webp .tif .eps'
# load category info
self.ctg_names_ = [] # ctg_idx to ctg_name
self.ctg_name2idx_ = OrderedDict() # ctg_name to ctg_idx
# load image infos
self.img_names_ = [] # img_idx to img_name
self.img_paths_ = [] # img_idx to img_path
self.img_labels_ = [] # img_idx to img_label
self.srm = SRMConv2d_simple()
def load_data_from_dir_test(self, folders):
# Load image from folder.
# Args:
# dataset_list: dictionary where key is a label and value is a list of folder paths.
print(folders)
img_paths = []
traverse_recursively(folders, img_paths, self.extension_)
for img_path in img_paths:
img_name = os.path.basename(img_path)
self.img_names_.append(img_name)
self.img_paths_.append(img_path)
length = len(img_paths)
print('log: {} image num is {}'.format(folders, length))
def load_data_from_dir(self, dataset_list):
# Load image from folder.
# Args:
# dataset_list: dictionary where key is a label and value is a list of folder paths.
for ctg_name, folders in dataset_list.items():
if ctg_name not in self.ctg_name2idx_:
self.ctg_name2idx_[ctg_name] = len(self.ctg_names_)
self.ctg_names_.append(ctg_name)
for img_root in folders:
img_paths = []
traverse_recursively(img_root, img_paths, self.extension_)
print(img_root)
length = len(img_paths)
for i in range(length):
img_path = img_paths[i]
img_name = os.path.basename(img_path)
self.img_names_.append(img_name)
self.img_paths_.append(img_path)
self.img_labels_.append(self.ctg_name2idx_[ctg_name])
print('log: category is %d(%s), image num is %d' % (self.ctg_name2idx_[ctg_name], ctg_name, length))
def load_data_from_txt(self, img_list_txt, ctg_list_txt):
"""Load image from txt.
Args:
img_list_txt: image txt, format is [file_path, ctg_idx].
ctg_list_txt: category txt, format is [ctg_name, ctg_idx].
"""
# check
assert os.path.exists(img_list_txt), 'log: does not exist: {}'.format(img_list_txt)
assert os.path.exists(ctg_list_txt), 'log: does not exist: {}'.format(ctg_list_txt)
# load category
# : open category info file
with open(ctg_list_txt) as f:
ctg_infos = [line.strip() for line in f.readlines()]
# :load category name & category index
for ctg_info in ctg_infos:
tmp = ctg_info.split(' ')
ctg_name = tmp[0]
ctg_idx = int(tmp[1])
self.ctg_name2idx_[ctg_name] = ctg_idx
self.ctg_names_.append(ctg_name)
# load sample
# : open image info file
with open(img_list_txt) as f:
img_infos = [line.strip() for line in f.readlines()]
random.shuffle(img_infos)
# : load image path & category index
for img_info in img_infos:
img_path, ctg_name = img_info.rsplit(' ', 1)
img_name = img_path.split('/')[-1]
ctg_idx = int(ctg_name)
self.img_names_.append(img_name)
self.img_paths_.append(img_path)
self.img_labels_.append(ctg_idx)
for ctg_name in self.ctg_names_:
print('log: category is %d(%s), image num is %d' % (self.ctg_name2idx_[ctg_name], ctg_name, self.img_labels_.count(self.ctg_name2idx_[ctg_name])))
def _add_new_channels_worker(self, image):
new_channels = []
image = einops.rearrange(image, "h w c -> c h w")
image = (image- torch.as_tensor(timm.data.constants.IMAGENET_DEFAULT_MEAN).view(-1, 1, 1)) / torch.as_tensor(timm.data.constants.IMAGENET_DEFAULT_STD).view(-1, 1, 1)
srm = self.srm(image.unsqueeze(0)).squeeze(0)
new_channels.append(einops.rearrange(srm, "c h w -> h w c").numpy())
new_channels = np.concatenate(new_channels, axis=2)
return torch.from_numpy(new_channels).float()
def add_new_channels(self, images):
images_copied = einops.rearrange(images, "c h w -> h w c")
new_channels = self._add_new_channels_worker(images_copied)
images_copied = torch.concatenate([images_copied, new_channels], dim=-1)
images_copied = einops.rearrange(images_copied, "h w c -> c h w")
return images_copied
def __getitem__(self, index):
img_path = self.img_paths_[index]
img_data = Image.open(img_path).convert('RGB')
img_size = img_data.size[::-1] # [h, w]
all_data = []
for transform in self.transformer_:
current_data = transform(img_data)
current_data = self.add_new_channels(current_data)
all_data.append(current_data)
img_label = self.img_labels_[index]
return torch.stack(all_data, dim=0), img_label, img_path, img_size
def __len__(self):
return len(self.img_names_)
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