id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
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166,012 | import os
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Dict
import itertools
import numpy as np
from timm.models.layers import DropPath, trunc_normal_, to_2tuple
EfficientFormer_width = {
'L': [40, 80, 192, 384], # 26m 83.3% 6attn
'S2': [32, 64, ... | null |
166,013 | import os
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Dict
import itertools
import numpy as np
from timm.models.layers import DropPath, trunc_normal_, to_2tuple
EfficientFormer_width = {
'L': [40, 80, 192, 384], # 26m 83.3% 6attn
'S2': [32, 64, ... | null |
166,014 | import os
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Dict
import itertools
import numpy as np
from timm.models.layers import DropPath, trunc_normal_, to_2tuple
EfficientFormer_width = {
'L': [40, 80, 192, 384], # 26m 83.3% 6attn
'S2': [32, 64, ... | null |
166,015 | import os
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Dict
import itertools
import numpy as np
from timm.models.layers import DropPath, trunc_normal_, to_2tuple
EfficientFormer_width = {
'L': [40, 80, 192, 384], # 26m 83.3% 6attn
'S2': [32, 64, ... | null |
166,016 | from functools import partial
import numpy as np
import torch
import torch.utils.checkpoint as checkpoint
from einops import rearrange
from timm.models.layers import DropPath, trunc_normal_
from torch import nn
def _make_divisible(v, divisor, min_value=None):
if min_value is None:
min_value = divisor
n... | null |
166,017 | from functools import partial
import numpy as np
import torch
import torch.utils.checkpoint as checkpoint
from einops import rearrange
from timm.models.layers import DropPath, trunc_normal_
from torch import nn
class NextViT(nn.Module):
def __init__(self, stem_chs, depths, path_dropout, attn_drop=0, drop=0, num_cla... | null |
166,018 | from functools import partial
import numpy as np
import torch
import torch.utils.checkpoint as checkpoint
from einops import rearrange
from timm.models.layers import DropPath, trunc_normal_
from torch import nn
class NextViT(nn.Module):
def __init__(self, stem_chs, depths, path_dropout, attn_drop=0, drop=0, num_cla... | null |
166,019 | from functools import partial
import numpy as np
import torch
import torch.utils.checkpoint as checkpoint
from einops import rearrange
from timm.models.layers import DropPath, trunc_normal_
from torch import nn
class NextViT(nn.Module):
def __init__(self, stem_chs, depths, path_dropout, attn_drop=0, drop=0, num_cla... | null |
166,020 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from timm.models.layers import trunc_normal_, DropPath
class ConvNeXtV2(nn.Module):
""" ConvNeXt V2
Args:
in_chans (int): Number of input image channels. Default: 3
num_classes (int): Number of classes for clas... | null |
166,021 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from timm.models.layers import trunc_normal_, DropPath
class ConvNeXtV2(nn.Module):
""" ConvNeXt V2
Args:
in_chans (int): Number of input image channels. Default: 3
num_classes (int): Number of classes for clas... | null |
166,022 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from timm.models.layers import trunc_normal_, DropPath
class ConvNeXtV2(nn.Module):
""" ConvNeXt V2
Args:
in_chans (int): Number of input image channels. Default: 3
num_classes (int): Number of classes for clas... | null |
166,023 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from timm.models.layers import trunc_normal_, DropPath
class ConvNeXtV2(nn.Module):
def __init__(self, in_chans=3, num_classes=1000,
depths=[3, 3, 9, 3], dims=[96, 192, 384, 768],
drop_... | null |
166,024 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from timm.models.layers import trunc_normal_, DropPath
class ConvNeXtV2(nn.Module):
def __init__(self, in_chans=3, num_classes=1000,
depths=[3, 3, 9, 3], dims=[96, 192, 384, 768],
drop_... | null |
166,025 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from timm.models.layers import trunc_normal_, DropPath
class ConvNeXtV2(nn.Module):
""" ConvNeXt V2
Args:
in_chans (int): Number of input image channels. Default: 3
num_classes (int): Number of classes for clas... | null |
166,026 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from timm.models.layers import trunc_normal_, DropPath
class ConvNeXtV2(nn.Module):
""" ConvNeXt V2
Args:
in_chans (int): Number of input image channels. Default: 3
num_classes (int): Number of classes for clas... | null |
166,027 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from timm.models.layers import trunc_normal_, DropPath
class ConvNeXtV2(nn.Module):
""" ConvNeXt V2
Args:
in_chans (int): Number of input image channels. Default: 3
num_classes (int): Number of classes for clas... | null |
166,028 |
def parse_model(d, ch): # model_dict, input_channels(3)
# Parse a YOLOv5 model.yaml dictionary
LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}")
anchors, nc, gd, gw, act = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation')
i... | null |
166,029 |
def _forward_once(self, x, profile=False, visualize=False):
y, dt = [], [] # outputs
for m in self.model:
if m.f != -1: # if not from previous layer
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
if profile:
self._... | null |
166,030 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import itertools
from timm.models.layers import SqueezeExcite
import numpy as np
import itertools
def replace_batchnorm(net):
class EfficientViT(torch.nn.Module):
def __init__(self, img_size=400,
... | null |
166,031 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import itertools
from timm.models.layers import SqueezeExcite
import numpy as np
import itertools
def replace_batchnorm(net):
for child_name, child in net.named_children():
if hasattr(child, 'fuse'... | null |
166,032 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import itertools
from timm.models.layers import SqueezeExcite
import numpy as np
import itertools
def replace_batchnorm(net):
for child_name, child in net.named_children():
if hasattr(child, 'fuse'... | null |
166,033 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import itertools
from timm.models.layers import SqueezeExcite
import numpy as np
import itertools
def replace_batchnorm(net):
class EfficientViT(torch.nn.Module):
def __init__(self, img_size=400,
... | null |
166,034 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import itertools
from timm.models.layers import SqueezeExcite
import numpy as np
import itertools
def replace_batchnorm(net):
for child_name, child in net.named_children():
if hasattr(child, 'fuse'... | null |
166,035 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import itertools
from timm.models.layers import SqueezeExcite
import numpy as np
import itertools
def replace_batchnorm(net):
for child_name, child in net.named_children():
if hasattr(child, 'fuse'... | null |
166,036 | import torch
import torch.nn as nn
from torch.nn.modules.utils import _pair as to_2tuple
from timm.layers import DropPath, to_2tuple
from functools import partial
import numpy as np
class LSKNet(nn.Module):
def __init__(self, img_size=224, in_chans=3, embed_dims=[64, 128, 256, 512],
mlp_ratios=[8, 8... | null |
166,037 | import torch
import torch.nn as nn
from torch.nn.modules.utils import _pair as to_2tuple
from timm.layers import DropPath, to_2tuple
from functools import partial
import numpy as np
class LSKNet(nn.Module):
def __init__(self, img_size=224, in_chans=3, embed_dims=[64, 128, 256, 512],
mlp_ratios=[8, 8... | null |
166,038 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
def update_weight(model_dict, weight_dict):
idx, temp_dict = 0, {}
for k, v in weight_dict.items():
if k in ... | null |
166,039 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
def update_weight(model_dict, weight_dict):
idx, temp_dict = 0, {}
for k, v in weight_dict.items():
if k in ... | null |
166,040 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
def update_weight(model_dict, weight_dict):
class FocalNet(nn.Module):
def __init__(self,
img_size... | null |
166,041 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
def update_weight(model_dict, weight_dict):
idx, temp_dict = 0, {}
for k, v in weight_dict.items():
if k in ... | null |
166,042 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
def update_weight(model_dict, weight_dict):
idx, temp_dict = 0, {}
for k, v in weight_dict.items():
if k in ... | null |
166,043 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
def update_weight(model_dict, weight_dict):
idx, temp_dict = 0, {}
for k, v in weight_dict.items():
if k in ... | null |
166,044 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
def update_weight(model_dict, weight_dict):
idx, temp_dict = 0, {}
for k, v in weight_dict.items():
if k in ... | null |
166,045 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
def update_weight(model_dict, weight_dict):
idx, temp_dict = 0, {}
for k, v in weight_dict.items():
if k in ... | null |
166,046 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
def update_weight(model_dict, weight_dict):
idx, temp_dict = 0, {}
for k, v in weight_dict.items():
if k in ... | null |
166,047 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
def update_weight(model_dict, weight_dict):
idx, temp_dict = 0, {}
for k, v in weight_dict.items():
if k in ... | null |
166,048 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
def update_weight(model_dict, weight_dict):
class FocalNet(nn.Module):
def __init__(self,
img_size... | null |
166,049 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
def update_weight(model_dict, weight_dict):
class FocalNet(nn.Module):
def __init__(self,
img_size... | null |
166,050 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
def update_weight(model_dict, weight_dict):
idx, temp_dict = 0, {}
for k, v in weight_dict.items():
if k in ... | null |
166,051 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
def update_weight(model_dict, weight_dict):
idx, temp_dict = 0, {}
for k, v in weight_dict.items():
if k in ... | null |
166,052 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
def update_weight(model_dict, weight_dict):
idx, temp_dict = 0, {}
for k, v in weight_dict.items():
if k in ... | null |
166,053 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import trunc_normal_, DropPath, to_2tuple
from functools import partial
import torch.utils.checkpoint as checkpoint
import numpy as np
def get_conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias,
... | null |
166,054 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import trunc_normal_, DropPath, to_2tuple
from functools import partial
import torch.utils.checkpoint as checkpoint
import numpy as np
def get_bn(dim, use_sync_bn=False):
if use_sync_bn:
return nn.SyncBatchNorm(dim)
els... | null |
166,055 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import trunc_normal_, DropPath, to_2tuple
from functools import partial
import torch.utils.checkpoint as checkpoint
import numpy as np
def fuse_bn(conv, bn):
conv_bias = 0 if conv.bias is None else conv.bias
std = (bn.running_v... | null |
166,056 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import trunc_normal_, DropPath, to_2tuple
from functools import partial
import torch.utils.checkpoint as checkpoint
import numpy as np
def convert_dilated_to_nondilated(kernel, dilate_rate):
identity_kernel = torch.ones((1, 1, 1, 1)... | null |
166,057 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import trunc_normal_, DropPath, to_2tuple
from functools import partial
import torch.utils.checkpoint as checkpoint
import numpy as np
UniRepLKNet_A_F_P_depths = (2, 2, 6, 2)
class UniRepLKNet(nn.Module):
r""" UniRepLKNet
A ... | null |
166,058 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import trunc_normal_, DropPath, to_2tuple
from functools import partial
import torch.utils.checkpoint as checkpoint
import numpy as np
UniRepLKNet_A_F_P_depths = (2, 2, 6, 2)
class UniRepLKNet(nn.Module):
r""" UniRepLKNet
A ... | null |
166,059 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import trunc_normal_, DropPath, to_2tuple
from functools import partial
import torch.utils.checkpoint as checkpoint
import numpy as np
UniRepLKNet_A_F_P_depths = (2, 2, 6, 2)
class UniRepLKNet(nn.Module):
r""" UniRepLKNet
A ... | null |
166,060 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import trunc_normal_, DropPath, to_2tuple
from functools import partial
import torch.utils.checkpoint as checkpoint
import numpy as np
UniRepLKNet_N_depths = (2, 2, 8, 2)
class UniRepLKNet(nn.Module):
r""" UniRepLKNet
A PyTo... | null |
166,061 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import trunc_normal_, DropPath, to_2tuple
from functools import partial
import torch.utils.checkpoint as checkpoint
import numpy as np
UniRepLKNet_T_depths = (3, 3, 18, 3)
class UniRepLKNet(nn.Module):
def __init__(self,
... | null |
166,062 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import trunc_normal_, DropPath, to_2tuple
from functools import partial
import torch.utils.checkpoint as checkpoint
import numpy as np
UniRepLKNet_S_B_L_XL_depths = (3, 3, 27, 3)
class UniRepLKNet(nn.Module):
r""" UniRepLKNet
... | null |
166,063 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import trunc_normal_, DropPath, to_2tuple
from functools import partial
import torch.utils.checkpoint as checkpoint
import numpy as np
UniRepLKNet_S_B_L_XL_depths = (3, 3, 27, 3)
class UniRepLKNet(nn.Module):
r""" UniRepLKNet
... | null |
166,064 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import trunc_normal_, DropPath, to_2tuple
from functools import partial
import torch.utils.checkpoint as checkpoint
import numpy as np
UniRepLKNet_S_B_L_XL_depths = (3, 3, 27, 3)
class UniRepLKNet(nn.Module):
def __init__(self,
... | null |
166,065 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import trunc_normal_, DropPath, to_2tuple
from functools import partial
import torch.utils.checkpoint as checkpoint
import numpy as np
UniRepLKNet_S_B_L_XL_depths = (3, 3, 27, 3)
class UniRepLKNet(nn.Module):
r""" UniRepLKNet
... | null |
166,066 | import warnings
import cv2, os, shutil
import numpy as np
from ultralytics import YOLO
def get_video_cfg(path):
video = cv2.VideoCapture(path)
size = (int(video.get(cv2.CAP_PROP_FRAME_WIDTH)), int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)))
fps = int(video.get(cv2.CAP_PROP_FPS))
return cv2.VideoWriter_fourc... | null |
166,067 | import warnings
import cv2, os, shutil
import numpy as np
from ultralytics import YOLO
def plot_and_counting(result):
image_plot = result.plot()
box_count = result.boxes.shape[0]
cv2.putText(image_plot, f'Object Counts:{box_count}', (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 0, 255), 4)
return image_... | null |
166,068 | import warnings
import torch, yaml, cv2, os, shutil
import numpy as np
import matplotlib.pyplot as plt
from tqdm import trange
from PIL import Image
from models.yolo import Model
from utils.datasets import letterbox
from utils.general import xywh2xyxy, non_max_suppression
from models.experimental import attempt_load
fr... | null |
166,069 | import warnings
import torch, yaml, cv2, os, shutil, sys
import numpy as np
np.random.seed(0)
import matplotlib.pyplot as plt
from tqdm import trange
from PIL import Image
from ultralytics.nn.tasks import attempt_load_weights
from ultralytics.utils.torch_utils import intersect_dicts
from ultralytics.utils.ops import xy... | null |
166,070 | import warnings
import torch, yaml, cv2, os, shutil, sys
import numpy as np
import matplotlib.pyplot as plt
from tqdm import trange
from PIL import Image
from ultralytics.nn.tasks import attempt_load_weights
from ultralytics.utils.torch_utils import intersect_dicts
from ultralytics.utils.ops import xywh2xyxy, non_max_s... | null |
166,071 | import warnings
import torch, yaml, cv2, os, shutil
import numpy as np
import matplotlib.pyplot as plt
from tqdm import trange
from PIL import Image
from models.yolo import Model
from utils.augmentations import letterbox
from utils.general import xywh2xyxy, non_max_suppression
from models.experimental import attempt_lo... | null |
166,072 | import warnings
import torch, yaml, cv2, os, shutil
import numpy as np
import matplotlib.pyplot as plt
from tqdm import trange
from PIL import Image
from models.yolo import Model
from utils.general import intersect_dicts
from utils.augmentations import letterbox
from utils.general import xywh2xyxy, non_max_suppression
... | null |
166,073 | import os
import glob
import json
import shutil
import numpy as np
import xml.etree.ElementTree as ET
def find_classes(path):
classes = []
for i in os.listdir(path):
try:
in_file = open(os.path.join(path, i), encoding='utf-8')
tree=ET.parse(in_file)
root = tree.getro... | null |
166,074 | import os
import glob
import json
import shutil
import numpy as np
import xml.etree.ElementTree as ET
START_BOUNDING_BOX_ID = 1
def get(root, name):
return root.findall(name)
def get_and_check(root, name, length):
vars = root.findall(name)
if len(vars) == 0:
raise NotImplementedError('Can not find %... | null |
166,075 | import numpy as np
import torch
from torch import nn
from torch.nn import init
The provided code snippet includes necessary dependencies for implementing the `autopad` function. Write a Python function `def autopad(k, p=None, d=1)` to solve the following problem:
Pad to 'same' shape outputs.
Here is the function:
de... | Pad to 'same' shape outputs. |
166,076 | import numpy as np
import torch
from torch import nn
from torch.nn import init
def spatial_shift1(x):
b, w, h, c = x.size()
x[:, 1:, :, :c // 4] = x[:, :w - 1, :, :c // 4]
x[:, :w - 1, :, c // 4:c // 2] = x[:, 1:, :, c // 4:c // 2]
x[:, :, 1:, c // 2:c * 3 // 4] = x[:, :, :h - 1, c // 2:c * 3 // 4]
... | null |
166,077 | import numpy as np
import torch
from torch import nn
from torch.nn import init
def spatial_shift2(x):
b, w, h, c = x.size()
x[:, :, 1:, :c // 4] = x[:, :, :h - 1, :c // 4]
x[:, :, :h - 1, c // 4:c // 2] = x[:, :, 1:, c // 4:c // 2]
x[:, 1:, :, c // 2:c * 3 // 4] = x[:, :w - 1, :, c // 2:c * 3 // 4]
... | null |
166,078 | from typing import Tuple, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from torch import Tensor, LongTensor
def _grid2seq(x:Tensor, region_size:Tuple[int], num_heads:int):
"""
Args:
x: BCHW tensor
region size: int
num_heads: num... | Args: query, key, value: (B, C, H, W) tensor scale: the scale/temperature for dot product attention region_graph: (B, nhead, h_q*w_q, topk) tensor, topk <= h_k*w_k region_size: region/window size for queries, (rh, rw) key_region_size: optional, if None, key_region_size=region_size auto_pad: required to be true if the i... |
166,079 | import torch, time, math, thop, tqdm, torchvision
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.conv import _ConvNd
from torch.nn.modules.utils import _pair
from torch.nn.parameter import Parameter
from prettytable import PrettyTable
from ops_dcnv3.modules import DCNv3
def time_synchroniz... | null |
166,080 | import torch, time, math, thop, tqdm, torchvision
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.conv import _ConvNd
from torch.nn.modules.utils import _pair
from torch.nn.parameter import Parameter
from prettytable import PrettyTable
from ops_dcnv3.modules import DCNv3
def autopad(k, p=No... | null |
166,081 | import warnings
import argparse
import logging
import math
import os
import random
import time
import sys
from copy import deepcopy
from pathlib import Path
from threading import Thread
import numpy as np
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
... | null |
166,082 | import warnings
import argparse
import logging
import math
import os
import random
import time
import sys
from copy import deepcopy
from pathlib import Path
from threading import Thread
import numpy as np
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
... | null |
166,083 | import os, cv2, tqdm, shutil
import numpy as np
def xywh2xyxy(box):
box[:, 0] = box[:, 0] - box[:, 2] / 2
box[:, 1] = box[:, 1] - box[:, 3] / 2
box[:, 2] = box[:, 0] + box[:, 2]
box[:, 3] = box[:, 1] + box[:, 3]
return box | null |
166,084 | import os, cv2, tqdm, shutil
import numpy as np
def iou(box1, box2):
x11, y11, x12, y12 = np.split(box1, 4, axis=1)
x21, y21, x22, y22 = np.split(box2, 4, axis=1)
xa = np.maximum(x11, np.transpose(x21))
xb = np.minimum(x12, np.transpose(x22))
ya = np.maximum(y11, np.transpose(y21))
yb = np.mi... | null |
166,085 | import os, cv2, tqdm, shutil
import numpy as np
def draw_box(img, box, color):
cv2.rectangle(img, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), color, thickness=2)
return img | null |
166,086 | import pkg_resources as pkg
def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=False, hard=False, verbose=False):
def set_seeds(seed=0, deterministic=False):
# Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html
random.seed(seed)
np.... | null |
166,087 | import cv2
import numpy as np
import matplotlib.pylab as plt
from segment_anything import SamPredictor, sam_model_registry
def show_mask(mask, ax, random_color=False):
if random_color:
color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
else:
color = np.array([30/255, 144/255... | null |
166,088 | import cv2
import numpy as np
import matplotlib.pylab as plt
from segment_anything import SamPredictor, sam_model_registry
def show_points(coords, labels, ax, marker_size=375):
pos_points = coords[labels==1]
neg_points = coords[labels==0]
ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker... | null |
166,089 | import cv2
import numpy as np
import matplotlib.pylab as plt
from segment_anything import SamPredictor, sam_model_registry
def show_box(box, ax):
x0, y0 = box[0], box[1]
w, h = box[2] - box[0], box[3] - box[1]
ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2)) | null |
166,090 | import os
import cv2
import json
from tqdm import tqdm
from sklearn.model_selection import train_test_split
import argparse
def yolo2coco(arg):
root_path = arg.root_dir
print("Loading data from ",root_path)
assert os.path.exists(root_path)
originLabelsDir = os.path.join(root_path, 'labels/test') ... | null |
166,091 |
The provided code snippet includes necessary dependencies for implementing the `feature_visualization` function. Write a Python function `def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp'))` to solve the following problem:
x: Features to be visualized module_type: Module type stag... | x: Features to be visualized module_type: Module type stage: Module stage within model n: Maximum number of feature maps to plot save_dir: Directory to save results |
166,092 | import datetime
import os
from typing import List
import tensorflow_model_analysis as tfma
from tfx.components import CsvExampleGen
from tfx.components import Evaluator
from tfx.components import ExampleValidator
from tfx.components import Pusher
from tfx.components import SchemaGen
from tfx.components import Statistic... | Implements the chicago taxi pipeline with TFX. |
166,093 | import argparse
import base64
import csv
import json
import os
import subprocess
import tempfile
from typing import List
from absl import app
from absl.flags import argparse_flags
import requests
import tensorflow_data_validation as tfdv
from tensorflow_transform import coders as tft_coders
from tensorflow_transform.tf... | Reads a schema from the provided location. Args: path: The location of the file holding a serialized Schema proto. Returns: An instance of Schema or None if the input argument is None |
166,094 | import argparse
import base64
import csv
import json
import os
import subprocess
import tempfile
from typing import List
from absl import app
from absl.flags import argparse_flags
import requests
import tensorflow_data_validation as tfdv
from tensorflow_transform import coders as tft_coders
from tensorflow_transform.tf... | Sends requests to the model and prints the results. Args: model_handle: handle to the model. This can be either "aiplatform:model:version" or "host:port" examples_file: path to csv file containing examples, with the first line assumed to have the column headers num_examples: number of requests to send to the server sch... |
166,095 | import argparse
import base64
import csv
import json
import os
import subprocess
import tempfile
from typing import List
from absl import app
from absl.flags import argparse_flags
import requests
import tensorflow_data_validation as tfdv
from tensorflow_transform import coders as tft_coders
from tensorflow_transform.tf... | Command lines flag parsing. |
166,096 | from typing import List
from absl import logging
import tensorflow as tf
import tensorflow_transform as tft
from tfx.components.trainer.fn_args_utils import DataAccessor
from tfx.components.trainer.fn_args_utils import FnArgs
from tfx_bsl.tfxio import dataset_options
_CATEGORICAL_FEATURE_KEYS = [
'trip_start_hour',... | tf.transform's callback function for preprocessing inputs. Args: inputs: map from feature keys to raw not-yet-transformed features. Returns: Map from string feature key to transformed feature operations. |
166,097 | from typing import List
from absl import logging
import tensorflow as tf
import tensorflow_transform as tft
from tfx.components.trainer.fn_args_utils import DataAccessor
from tfx.components.trainer.fn_args_utils import FnArgs
from tfx_bsl.tfxio import dataset_options
def _get_tf_examples_serving_signature(model, tf_tra... | Train the model based on given args. Args: fn_args: Holds args used to train the model as name/value pairs. |
166,098 | from typing import List
import tensorflow as tf
from tensorflow import estimator as tf_estimator
import tensorflow_model_analysis as tfma
import tensorflow_transform as tft
from tensorflow_transform.tf_metadata import schema_utils
from tfx.components.trainer.fn_args_utils import DataAccessor
from tfx_bsl.tfxio import d... | tf.transform's callback function for preprocessing inputs. Args: inputs: map from feature keys to raw not-yet-transformed features. Returns: Map from string feature key to transformed feature operations. |
166,099 | from typing import List
import tensorflow as tf
from tensorflow import estimator as tf_estimator
import tensorflow_model_analysis as tfma
import tensorflow_transform as tft
from tensorflow_transform.tf_metadata import schema_utils
from tfx.components.trainer.fn_args_utils import DataAccessor
from tfx_bsl.tfxio import d... | Build the estimator using the high level API. Args: trainer_fn_args: Holds args used to train the model as name/value pairs. schema: Holds the schema of the training examples. Returns: A dict of the following: - estimator: The estimator that will be used for training and eval. - train_spec: Spec for training. - eval_sp... |
166,100 | import os
from typing import List
import absl
import tensorflow_model_analysis as tfma
from tfx.components import CsvExampleGen
from tfx.components import Evaluator
from tfx.components import ExampleValidator
from tfx.components import Pusher
from tfx.components import SchemaGen
from tfx.components import StatisticsGen... | Implements the chicago taxi pipeline with TFX. |
166,101 | import os
from typing import List
import absl
import tensorflow_model_analysis as tfma
from tfx.components import CsvExampleGen
from tfx.components import Evaluator
from tfx.components import ExampleValidator
from tfx.components import Pusher
from tfx.components import SchemaGen
from tfx.components import StatisticsGen... | Implements the chicago taxi pipeline with TFX. |
166,102 | import os
from typing import List
import absl
import tensorflow as tf
from tensorflow import keras
import tensorflow_transform as tft
from tfx.components.trainer.rewriting import converters
from tfx.components.trainer.rewriting import rewriter
from tfx.components.trainer.rewriting import rewriter_factory
from tfx.dsl.i... | Callback function for preprocessing inputs. Args: inputs: map from feature keys to raw not-yet-transformed features. Returns: Map from string feature key to transformed feature operations. |
166,103 | import os
from typing import List
import absl
import tensorflow as tf
from tensorflow import keras
import tensorflow_transform as tft
from tfx.components.trainer.rewriting import converters
from tfx.components.trainer.rewriting import rewriter
from tfx.components.trainer.rewriting import rewriter_factory
from tfx.dsl.i... | Train the model based on given args. Args: fn_args: Holds args used to train the model as name/value pairs. |
166,104 | import os
from typing import List
import absl
import tensorflow_model_analysis as tfma
from tfx.v1 import dsl
from tfx.v1 import orchestration
from tfx.v1 import proto
from tfx.v1 import types
from tfx.v1.components import Evaluator
from tfx.v1.components import ExampleValidator
from tfx.v1.components import ImportExam... | Implements the page prediction pipline with TFX. |
166,105 | from typing import Any, Dict, List, Union
import apache_beam as beam
from apache_beam.io.gcp.internal.clients import bigquery
import tensorflow as tf
def _sanitize_page_path(page_path: str):
"""Remove everything after the query."""
return page_path.split('?')[0]
def create_tensorflow_example(
features: Dict[str... | Converts a Google Analytics Session to Tensorflow Examples. |
166,106 | from typing import Any, Dict, List, Union
import apache_beam as beam
from apache_beam.io.gcp.internal.clients import bigquery
import tensorflow as tf
def is_duplicate_event(first_event: Dict[str, Any],
second_event: Dict[str, Any]):
return (first_event['time'] == second_event['time'] or
... | null |
166,107 | from typing import Any, Dict, List, Union
import apache_beam as beam
from apache_beam.io.gcp.internal.clients import bigquery
import tensorflow as tf
class ExampleGeneratingDoFn(beam.DoFn):
"""Creates Tensorflow Examples from the provided Google Analytics session."""
def process(self, entry: Dict[str, Any]):
se... | Run the apache beam pipeline with the specified flags. |
166,108 | import datetime
import os
from typing import List
from absl import logging
import keras_tuner
import tensorflow as tf
from tensorflow import keras
import tensorflow_transform as tft
import tfx.v1 as tfx
from tfx_bsl.public import tfxio
from tensorflow_cloud.core import machine_config
from tensorflow_cloud.tuner import ... | tf.transform's callback function for preprocessing inputs. Args: inputs: map from feature keys to raw not-yet-transformed features. Returns: Map from string feature key to transformed feature operations. |
166,109 | import datetime
import os
from typing import List
from absl import logging
import keras_tuner
import tensorflow as tf
from tensorflow import keras
import tensorflow_transform as tft
import tfx.v1 as tfx
from tfx_bsl.public import tfxio
from tensorflow_cloud.core import machine_config
from tensorflow_cloud.tuner import ... | Build the tuner using the CloudTuner API. Args: fn_args: Holds args as name/value pairs. See https://www.tensorflow.org/tfx/api_docs/python/tfx/components/trainer/fn_args_utils/FnArgs. - transform_graph_path: optional transform graph produced by TFT. - custom_config: An optional dictionary passed to the component. In t... |
166,110 | import datetime
import os
from typing import List
from absl import logging
import keras_tuner
import tensorflow as tf
from tensorflow import keras
import tensorflow_transform as tft
import tfx.v1 as tfx
from tfx_bsl.public import tfxio
from tensorflow_cloud.core import machine_config
from tensorflow_cloud.tuner import ... | Train the model based on given args. Args: fn_args: Holds args as name/value pairs. See https://www.tensorflow.org/tfx/api_docs/python/tfx/components/trainer/fn_args_utils/FnArgs. - train_files: List of file paths containing training tf.Example data. - eval_files: List of file paths containing eval tf.Example data. - d... |
166,111 | from typing import List
from absl import logging
import tensorflow as tf
import tensorflow_transform as tft
from tfx import v1 as tfx
from tfx_bsl.public import tfxio
FEATURE_KEYS = [
'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'body_mass_g'
]
_LABEL_KEY = 'species'
def transformed_name(key):
retu... | tf.transform's callback function for preprocessing inputs. Args: inputs: map from feature keys to raw not-yet-transformed features. Returns: Map from string feature key to transformed feature operations. |
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