id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
3,266 | import argparse
import logging
import os
import re
import io
import textwrap
from os import path
def find_python_files(directory):
for root, dirs, files in os.walk(directory):
for filename in files:
if filename.endswith('.py'):
yield path.join(root, filename) | null |
3,267 | import argparse
import logging
import contextlib
import datetime
import os
import re
import subprocess
import unittest
from os import path
The provided code snippet includes necessary dependencies for implementing the `find_files` function. Write a Python function `def find_files(rootdir, regexp_files, ignore_dirs)` t... | Find the files we need to apply this to. |
3,268 | import argparse
import logging
import contextlib
import datetime
import os
import re
import subprocess
import unittest
from os import path
LICENSE = '__license__ = "GNU GPLv2"'
def find_existing_copyright(lines):
"""Find the line numbers for an existing copyright.
Returns:
Two integers, one for the copyri... | Process the copyright on a single file, return the modified contents. |
3,269 | import inspect
import os
import json
import threading
import traceback
import requests
import time
import asyncio
import aiohttp
from PyQt5.QtCore import pyqtSignal, QObject
from ..common.config import cfg, Language
from ..common.logger import logger
from ..common.signals import signalBus
from ..common.util import getP... | null |
3,270 | import time
import win32gui
import win32con
import win32api
import ctypes
import qasync
import asyncio
from PyQt5.QtCore import QObject
from PyQt5.QtWidgets import QApplication
from ..common.config import cfg, Language
from ..lol.connector import LolClientConnector, connector
def getTeammates(game, targetPuuid):
""... | null |
3,271 | import time
import win32gui
import win32con
import win32api
import ctypes
import qasync
import asyncio
from PyQt5.QtCore import QObject
from PyQt5.QtWidgets import QApplication
from ..common.config import cfg, Language
from ..lol.connector import LolClientConnector, connector
async def parseGameData(game):
timeStam... | null |
3,272 | import time
import win32gui
import win32con
import win32api
import ctypes
import qasync
import asyncio
from PyQt5.QtCore import QObject
from PyQt5.QtWidgets import QApplication
from ..common.config import cfg, Language
from ..lol.connector import LolClientConnector, connector
def translateTier(orig: str, short=False) -... | null |
3,273 | import time
import win32gui
import win32con
import win32api
import ctypes
import qasync
import asyncio
from PyQt5.QtCore import QObject
from PyQt5.QtWidgets import QApplication
from ..common.config import cfg, Language
from ..lol.connector import LolClientConnector, connector
class ToolsTranslator(QObject):
def __i... | null |
3,274 | import time
import win32gui
import win32con
import win32api
import ctypes
import qasync
import asyncio
from PyQt5.QtCore import QObject
from PyQt5.QtWidgets import QApplication
from ..common.config import cfg, Language
from ..lol.connector import LolClientConnector, connector
async def parseSummonerGameInfo(item, isRan... | null |
3,275 | import time
import win32gui
import win32con
import win32api
import ctypes
import qasync
import asyncio
from PyQt5.QtCore import QObject
from PyQt5.QtWidgets import QApplication
from ..common.config import cfg, Language
from ..lol.connector import LolClientConnector, connector
def parseSummonerOrder(team):
summoner... | null |
3,276 | import time
import win32gui
import win32con
import win32api
import ctypes
import qasync
import asyncio
from PyQt5.QtCore import QObject
from PyQt5.QtWidgets import QApplication
from ..common.config import cfg, Language
from ..lol.connector import LolClientConnector, connector
def sortedSummonersByGameRole(summoners: li... | null |
3,277 | import time
import win32gui
import win32con
import win32api
import ctypes
import qasync
import asyncio
from PyQt5.QtCore import QObject
from PyQt5.QtWidgets import QApplication
from ..common.config import cfg, Language
from ..lol.connector import LolClientConnector, connector
def sortedSummonersByGameRole(summoners: li... | null |
3,278 | import time
import win32gui
import win32con
import win32api
import ctypes
import qasync
import asyncio
from PyQt5.QtCore import QObject
from PyQt5.QtWidgets import QApplication
from ..common.config import cfg, Language
from ..lol.connector import LolClientConnector, connector
def separateTeams(data, currentSummonerId):... | 输入 session 以及当前召唤师 id，输出 summonerId -> 颜色的映射 |
3,279 | import time
import win32gui
import win32con
import win32api
import ctypes
import qasync
import asyncio
from PyQt5.QtCore import QObject
from PyQt5.QtWidgets import QApplication
from ..common.config import cfg, Language
from ..lol.connector import LolClientConnector, connector
async def parseGameData(game):
timeStam... | null |
3,280 | import time
import win32gui
import win32con
import win32api
import ctypes
import qasync
import asyncio
from PyQt5.QtCore import QObject
from PyQt5.QtWidgets import QApplication
from ..common.config import cfg, Language
from ..lol.connector import LolClientConnector, connector
cfg = Config()
connector = LolClientConne... | null |
3,281 | import time
import win32gui
import win32con
import win32api
import ctypes
import qasync
import asyncio
from PyQt5.QtCore import QObject
from PyQt5.QtWidgets import QApplication
from ..common.config import cfg, Language
from ..lol.connector import LolClientConnector, connector
connector = LolClientConnector()
The prov... | #### 需要管理员权限 调用 Win API 手动调整窗口大小 / 位置 详情请见 https://github.com/LeagueTavern/fix-lcu-window @return: 当且仅当需要修复且权限不足时返回 `False` |
3,282 | from enum import Enum
from typing import Tuple
import traceback
from PyQt5.QtGui import QColor, QClipboard
from PyQt5.QtCore import QObject
from app.common.qfluentwidgets import StyleSheetBase, Theme, qconfig, isDarkTheme
from app.common.config import cfg
from app.common.signals import signalBus
def __getStyleSheetColo... | null |
3,283 | from enum import Enum
from typing import Tuple
import traceback
from PyQt5.QtGui import QColor, QClipboard
from PyQt5.QtCore import QObject
from app.common.qfluentwidgets import StyleSheetBase, Theme, qconfig, isDarkTheme
from app.common.config import cfg
from app.common.signals import signalBus
def __getStyleSheetColo... | null |
3,284 | from enum import Enum
from typing import Tuple
import traceback
from PyQt5.QtGui import QColor, QClipboard
from PyQt5.QtCore import QObject
from app.common.qfluentwidgets import StyleSheetBase, Theme, qconfig, isDarkTheme
from app.common.config import cfg
from app.common.signals import signalBus
def __getStyleSheetColo... | null |
3,285 | from enum import Enum
from typing import Tuple
import traceback
from PyQt5.QtGui import QColor, QClipboard
from PyQt5.QtCore import QObject
from app.common.qfluentwidgets import StyleSheetBase, Theme, qconfig, isDarkTheme
from app.common.config import cfg
from app.common.signals import signalBus
def __getDefaultColor(... | null |
3,286 | from enum import Enum
from typing import Tuple
import traceback
from PyQt5.QtGui import QColor, QClipboard
from PyQt5.QtCore import QObject
from app.common.qfluentwidgets import StyleSheetBase, Theme, qconfig, isDarkTheme
from app.common.config import cfg
from app.common.signals import signalBus
def __getStyleSheetColo... | null |
3,287 | from enum import Enum
from typing import Tuple
import traceback
from PyQt5.QtGui import QColor, QClipboard
from PyQt5.QtCore import QObject
from app.common.qfluentwidgets import StyleSheetBase, Theme, qconfig, isDarkTheme
from app.common.config import cfg
from app.common.signals import signalBus
def __getStyleSheetColo... | null |
3,288 | import requests
import base64
import subprocess
import psutil
from app.common.config import cfg, VERSION
def getTasklistPath():
for path in ['tasklist',
'C:/Windows/System32/tasklist.exe']:
try:
cmd = f'{path} /FI "imagename eq LeagueClientUx.exe" /NH'
_ = subproces... | null |
3,289 | import requests
import base64
import subprocess
import psutil
from app.common.config import cfg, VERSION
def getLolClientPidSlowly():
for process in psutil.process_iter():
if process.name() in ['LeagueClientUx.exe', 'LeagueClientUx']:
return process.pid
return -1 | null |
3,290 | import requests
import base64
import subprocess
import psutil
from app.common.config import cfg, VERSION
def getLolClientPid(path):
processes = subprocess.check_output(
f'{path} /FI "imagename eq LeagueClientUx.exe" /NH', shell=True)
if b'LeagueClientUx.exe' in processes:
arr = processes.split... | null |
3,291 | import requests
import base64
import subprocess
import psutil
from app.common.config import cfg, VERSION
def getLolClientPids(path):
processes = subprocess.check_output(
f'{path} /FI "imagename eq LeagueClientUx.exe" /NH', shell=True)
if not b'LeagueClientUx.exe' in processes:
return 0
pi... | null |
3,292 | import requests
import base64
import subprocess
import psutil
from app.common.config import cfg, VERSION
def getLolClientPidsSlowly():
pids = []
for process in psutil.process_iter():
if process.name() in ['LeagueClientUx.exe', 'LeagueClientUx']:
pids.append(process.pid)
return pids | null |
3,293 | import requests
import base64
import subprocess
import psutil
from app.common.config import cfg, VERSION
def isLolGameProcessExist(path):
processes = subprocess.check_output(
f'{path} /FI "imagename eq League of Legends.exe" /NH', shell=True)
return b'League of Legends.exe' in processes | null |
3,294 | import requests
import base64
import subprocess
import psutil
from app.common.config import cfg, VERSION
The provided code snippet includes necessary dependencies for implementing the `getPortTokenServerByPid` function. Write a Python function `def getPortTokenServerByPid(pid)` to solve the following problem:
通过进程 id ... | 通过进程 id 获得启动命令行参数中的 port、token 以及登录服务器 |
3,295 | import subprocess
bat = '''@echo off
:start
tasklist | find /i "Seraphine.exe" > nul
if NOT errorlevel 1 (
echo Seraphine is running, waiting...
timeout /t 1 > nul
goto start
)
for /d %%i in (*) do (
rmdir "%%~fi" /s /q
)
for %%i in (*) do (
if NOT "%%i" equ "updater.bat" (
del "%%i" /s /q
... | null |
3,296 | from enum import Enum
import os
import sys
from PyQt5.QtCore import QLocale
from .qfluentwidgets import (qconfig, QConfig, ConfigItem, FolderValidator, BoolValidator,
OptionsConfigItem, OptionsValidator, ConfigSerializer,
RangeConfigItem, RangeValidator, EnumSer... | null |
3,297 | import os.path
from data.base_dataset import BaseDataset, get_transform, get_affine_mat, apply_img_affine, apply_lm_affine
from data.image_folder import make_dataset
from PIL import Image
import random
import util.util as util
import numpy as np
import json
import torch
from scipy.io import loadmat, savemat
import pick... | flist format: impath label\nimpath label\n ...(same to caffe's filelist) |
3,298 | import os.path
from data.base_dataset import BaseDataset, get_transform, get_affine_mat, apply_img_affine, apply_lm_affine
from data.image_folder import make_dataset
from PIL import Image
import random
import util.util as util
import numpy as np
import json
import torch
from scipy.io import loadmat, savemat
import pick... | null |
3,299 | import os.path
from data.base_dataset import BaseDataset, get_transform, get_affine_mat, apply_img_affine, apply_lm_affine
from data.image_folder import make_dataset
from PIL import Image
import random
import util.util as util
import numpy as np
import json
import torch
from scipy.io import loadmat, savemat
import pick... | null |
3,300 | import random
import numpy as np
import torch.utils.data as data
from PIL import Image
import torchvision.transforms as transforms
from abc import ABC, abstractmethod
def get_transform(grayscale=False):
transform_list = []
if grayscale:
transform_list.append(transforms.Grayscale(1))
transform_list ... | null |
3,301 | import random
import numpy as np
import torch.utils.data as data
from PIL import Image
import torchvision.transforms as transforms
from abc import ABC, abstractmethod
def get_affine_mat(opt, size):
shift_x, shift_y, scale, rot_angle, flip = 0., 0., 1., 0., False
w, h = size
if 'shift' in opt.preprocess:
... | null |
3,302 | import random
import numpy as np
import torch.utils.data as data
from PIL import Image
import torchvision.transforms as transforms
from abc import ABC, abstractmethod
def apply_img_affine(img, affine_inv, method=Image.BICUBIC):
return img.transform(img.size, Image.AFFINE, data=affine_inv.flatten()[:6], resample=Im... | null |
3,303 | import random
import numpy as np
import torch.utils.data as data
from PIL import Image
import torchvision.transforms as transforms
from abc import ABC, abstractmethod
def apply_lm_affine(landmark, affine, flip, size):
_, h = size
lm = landmark.copy()
lm[:, 1] = h - 1 - lm[:, 1]
lm = np.concatenate((lm,... | null |
3,304 | import numpy as np
import torch.utils.data as data
from PIL import Image
import os
import os.path
def is_image_file(filename):
return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
def make_dataset(dir, max_dataset_size=float("inf")):
images = []
assert os.path.isdir(dir) or os.path.isli... | null |
3,305 | import numpy as np
import torch.utils.data as data
from PIL import Image
import os
import os.path
def default_loader(path):
return Image.open(path).convert('RGB') | null |
3,306 | import numpy as np
import torch
import torch.nn as nn
from kornia.geometry import warp_affine
import torch.nn.functional as F
def resize_n_crop(image, M, dsize=112):
# image: (b, c, h, w)
# M : (b, 2, 3)
return warp_affine(image, M, dsize=(dsize, dsize), align_corners=True) | null |
3,307 | import numpy as np
import torch
import torch.nn as nn
from kornia.geometry import warp_affine
import torch.nn.functional as F
def perceptual_loss(id_featureA, id_featureB):
cosine_d = torch.sum(id_featureA * id_featureB, dim=-1)
# assert torch.sum((cosine_d > 1).float()) == 0
return torch.sum(1 - cosin... | null |
3,308 | import numpy as np
import torch
import torch.nn as nn
from kornia.geometry import warp_affine
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `photo_loss` function. Write a Python function `def photo_loss(imageA, imageB, mask, eps=1e-6)` to solve the follo... | l2 norm (with sqrt, to ensure backward stabililty, use eps, otherwise Nan may occur) Parameters: imageA --torch.tensor (B, 3, H, W), range (0, 1), RGB order imageB --same as imageA |
3,309 | import numpy as np
import torch
import torch.nn as nn
from kornia.geometry import warp_affine
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `landmark_loss` function. Write a Python function `def landmark_loss(predict_lm, gt_lm, weight=None)` to solve the... | weighted mse loss Parameters: predict_lm --torch.tensor (B, 68, 2) gt_lm --torch.tensor (B, 68, 2) weight --numpy.array (1, 68) |
3,310 | import numpy as np
import torch
import torch.nn as nn
from kornia.geometry import warp_affine
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `reg_loss` function. Write a Python function `def reg_loss(coeffs_dict, opt=None)` to solve the following problem:... | l2 norm without the sqrt, from yu's implementation (mse) tf.nn.l2_loss https://www.tensorflow.org/api_docs/python/tf/nn/l2_loss Parameters: coeffs_dict -- a dict of torch.tensors , keys: id, exp, tex, angle, gamma, trans |
3,311 | import numpy as np
import torch
import torch.nn as nn
from kornia.geometry import warp_affine
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `reflectance_loss` function. Write a Python function `def reflectance_loss(texture, mask)` to solve the following ... | minimize texture variance (mse), albedo regularization to ensure an uniform skin albedo Parameters: texture --torch.tensor, (B, N, 3) mask --torch.tensor, (N), 1 or 0 |
3,312 | import torch.nn as nn
from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Sequential, Module
import torch
class MobileFaceNet(Module):
def __init__(self, fp16=False, num_features=512):
super(MobileFaceNet, self).__init__()
scale = 2
self.fp16 = fp16
self.layers = nn... | null |
3,313 | import torch
from torch import nn
The provided code snippet includes necessary dependencies for implementing the `conv3x3` function. Write a Python function `def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1)` to solve the following problem:
3x3 convolution with padding
Here is the function:
def conv... | 3x3 convolution with padding |
3,314 | import torch
from torch import nn
The provided code snippet includes necessary dependencies for implementing the `conv1x1` function. Write a Python function `def conv1x1(in_planes, out_planes, stride=1)` to solve the following problem:
1x1 convolution
Here is the function:
def conv1x1(in_planes, out_planes, stride=1... | 1x1 convolution |
3,315 | import torch
from torch import nn
class IBasicBlock(nn.Module):
def __init__(self, inplanes, planes, stride=1, downsample=None,
groups=1, base_width=64, dilation=1):
def forward(self, x):
def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
def iresnet18(pretrained=False, p... | null |
3,316 | import torch
from torch import nn
class IBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None,
groups=1, base_width=64, dilation=1):
super(IBasicBlock, self).__init__()
if groups != 1 or base_width != 64:
raise ValueErro... | null |
3,317 | import torch
from torch import nn
class IBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None,
groups=1, base_width=64, dilation=1):
super(IBasicBlock, self).__init__()
if groups != 1 or base_width != 64:
raise ValueErro... | null |
3,318 | import torch
from torch import nn
class IBasicBlock(nn.Module):
def __init__(self, inplanes, planes, stride=1, downsample=None,
groups=1, base_width=64, dilation=1):
def forward(self, x):
def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
def iresnet100(pretrained=False, ... | null |
3,319 | import torch
from torch import nn
class IBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None,
groups=1, base_width=64, dilation=1):
super(IBasicBlock, self).__init__()
if groups != 1 or base_width != 64:
raise ValueErro... | null |
3,320 | import torch
from torch import nn
from torch.utils.checkpoint import checkpoint_sequential
The provided code snippet includes necessary dependencies for implementing the `conv3x3` function. Write a Python function `def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1)` to solve the following problem:
3x3 ... | 3x3 convolution with padding |
3,321 | import torch
from torch import nn
from torch.utils.checkpoint import checkpoint_sequential
The provided code snippet includes necessary dependencies for implementing the `conv1x1` function. Write a Python function `def conv1x1(in_planes, out_planes, stride=1)` to solve the following problem:
1x1 convolution
Here is t... | 1x1 convolution |
3,322 | import torch
from torch import nn
from torch.utils.checkpoint import checkpoint_sequential
class IBasicBlock(nn.Module):
def __init__(self, inplanes, planes, stride=1, downsample=None,
groups=1, base_width=64, dilation=1):
def forward(self, x):
def _iresnet(arch, block, layers, pretrained... | null |
3,323 | import argparse
import cv2
import numpy as np
import torch
from backbones import get_model
def get_model(name, **kwargs):
# resnet
if name == "r18":
return iresnet18(False, **kwargs)
elif name == "r34":
return iresnet34(False, **kwargs)
elif name == "r50":
return iresnet50(False... | null |
3,324 | import argparse
import os
import pickle
import timeit
import cv2
import mxnet as mx
import numpy as np
import pandas as pd
import prettytable
import skimage.transform
from sklearn.metrics import roc_curve
from sklearn.preprocessing import normalize
from onnx_helper import ArcFaceORT
class ArcFaceORT:
def __init__(... | null |
3,325 | import argparse
import os
import pickle
import timeit
import cv2
import mxnet as mx
import numpy as np
import pandas as pd
import prettytable
import skimage.transform
from sklearn.metrics import roc_curve
from sklearn.preprocessing import normalize
from onnx_helper import ArcFaceORT
def read_template_media_list(path):... | null |
3,326 | import argparse
import os
import pickle
import timeit
import cv2
import mxnet as mx
import numpy as np
import pandas as pd
import prettytable
import skimage.transform
from sklearn.metrics import roc_curve
from sklearn.preprocessing import normalize
from onnx_helper import ArcFaceORT
def read_template_pair_list(path):
... | null |
3,327 | import argparse
import os
import pickle
import timeit
import cv2
import mxnet as mx
import numpy as np
import pandas as pd
import prettytable
import skimage.transform
from sklearn.metrics import roc_curve
from sklearn.preprocessing import normalize
from onnx_helper import ArcFaceORT
def read_image_feature(path):
w... | null |
3,328 | import argparse
import os
import pickle
import timeit
import cv2
import mxnet as mx
import numpy as np
import pandas as pd
import prettytable
import skimage.transform
from sklearn.metrics import roc_curve
from sklearn.preprocessing import normalize
from onnx_helper import ArcFaceORT
def image2template_feature(img_feat... | null |
3,329 | import argparse
import os
import pickle
import timeit
import cv2
import mxnet as mx
import numpy as np
import pandas as pd
import prettytable
import skimage.transform
from sklearn.metrics import roc_curve
from sklearn.preprocessing import normalize
from onnx_helper import ArcFaceORT
def verification(template_norm_feat... | null |
3,330 | import argparse
import os
import pickle
import timeit
import cv2
import mxnet as mx
import numpy as np
import pandas as pd
import prettytable
import skimage.transform
from sklearn.metrics import roc_curve
from sklearn.preprocessing import normalize
from onnx_helper import ArcFaceORT
def verification2(template_norm_fea... | null |
3,331 | import torch
from torch import nn
class CosFace(nn.Module):
def __init__(self, s=64.0, m=0.40):
super(CosFace, self).__init__()
self.s = s
self.m = m
def forward(self, cosine, label):
index = torch.where(label != -1)[0]
m_hot = torch.zeros(index.size()[0], cosine.size()[1... | null |
3,332 | import os
import pickle
import matplotlib
import pandas as pd
import matplotlib.pyplot as plt
import timeit
import sklearn
import argparse
import cv2
import numpy as np
import torch
from skimage import transform as trans
from backbones import get_model
from sklearn.metrics import roc_curve, auc
from menpo.visualize.vie... | null |
3,333 | import os
import pickle
import matplotlib
import pandas as pd
import matplotlib.pyplot as plt
import timeit
import sklearn
import argparse
import cv2
import numpy as np
import torch
from skimage import transform as trans
from backbones import get_model
from sklearn.metrics import roc_curve, auc
from menpo.visualize.vie... | null |
3,334 | import os
import pickle
import matplotlib
import pandas as pd
import matplotlib.pyplot as plt
import timeit
import sklearn
import argparse
import cv2
import numpy as np
import torch
from skimage import transform as trans
from backbones import get_model
from sklearn.metrics import roc_curve, auc
from menpo.visualize.vie... | null |
3,335 | import os
import pickle
import matplotlib
import pandas as pd
import matplotlib.pyplot as plt
import timeit
import sklearn
import argparse
import cv2
import numpy as np
import torch
from skimage import transform as trans
from backbones import get_model
from sklearn.metrics import roc_curve, auc
from menpo.visualize.vie... | null |
3,336 | import os
import pickle
import matplotlib
import pandas as pd
import matplotlib.pyplot as plt
import timeit
import sklearn
import argparse
import cv2
import numpy as np
import torch
from skimage import transform as trans
from backbones import get_model
from sklearn.metrics import roc_curve, auc
from menpo.visualize.vie... | null |
3,337 | import os
import pickle
import matplotlib
import pandas as pd
import matplotlib.pyplot as plt
import timeit
import sklearn
import argparse
import cv2
import numpy as np
import torch
from skimage import transform as trans
from backbones import get_model
from sklearn.metrics import roc_curve, auc
from menpo.visualize.vie... | null |
3,338 | import os
import pickle
import matplotlib
import pandas as pd
import matplotlib.pyplot as plt
import timeit
import sklearn
import argparse
import cv2
import numpy as np
import torch
from skimage import transform as trans
from backbones import get_model
from sklearn.metrics import roc_curve, auc
from menpo.visualize.vie... | null |
3,339 | import os
import pickle
import matplotlib
import pandas as pd
import matplotlib.pyplot as plt
import timeit
import sklearn
import argparse
import cv2
import numpy as np
import torch
from skimage import transform as trans
from backbones import get_model
from sklearn.metrics import roc_curve, auc
from menpo.visualize.vie... | null |
3,340 | import os
import pickle
import matplotlib
import pandas as pd
import matplotlib.pyplot as plt
import timeit
import sklearn
import argparse
import cv2
import numpy as np
import torch
from skimage import transform as trans
from backbones import get_model
from sklearn.metrics import roc_curve, auc
from menpo.visualize.vie... | null |
3,341 | import numpy as np
import onnx
import torch
def convert_onnx(net, path_module, output, opset=11, simplify=False):
assert isinstance(net, torch.nn.Module)
img = np.random.randint(0, 255, size=(112, 112, 3), dtype=np.int32)
img = img.astype(np.float)
img = (img / 255. - 0.5) / 0.5 # torch style norm
... | null |
3,342 | import logging
import os
import sys
def init_logging(rank, models_root):
if rank == 0:
log_root = logging.getLogger()
log_root.setLevel(logging.INFO)
formatter = logging.Formatter("Training: %(asctime)s-%(message)s")
handler_file = logging.FileHandler(os.path.join(models_root, "trai... | null |
3,343 | import os
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from menpo.visualize.viewmatplotlib import sample_colours_from_colourmap
from prettytable import PrettyTable
from sklearn.metrics import roc_curve, auc
def read_template_pair_list(path):
pairs = pd.read_csv(pa... | null |
3,344 | import importlib
import os.path as osp
def get_config(config_file):
assert config_file.startswith('configs/'), 'config file setting must start with configs/'
temp_config_name = osp.basename(config_file)
temp_module_name = osp.splitext(temp_config_name)[0]
config = importlib.import_module("configs.base"... | null |
3,345 | import datetime
import os
import pickle
import mxnet as mx
import numpy as np
import sklearn
import torch
from mxnet import ndarray as nd
from scipy import interpolate
from sklearn.decomposition import PCA
from sklearn.model_selection import KFold
def calculate_roc(thresholds,
embeddings1,
... | null |
3,346 | import datetime
import os
import pickle
import mxnet as mx
import numpy as np
import sklearn
import torch
from mxnet import ndarray as nd
from scipy import interpolate
from sklearn.decomposition import PCA
from sklearn.model_selection import KFold
def load_bin(path, image_size):
try:
with open(path, 'rb') ... | null |
3,347 | import datetime
import os
import pickle
import mxnet as mx
import numpy as np
import sklearn
import torch
from mxnet import ndarray as nd
from scipy import interpolate
from sklearn.decomposition import PCA
from sklearn.model_selection import KFold
def dumpR(data_set,
backbone,
batch_size,
... | null |
3,348 | import numpy as np
import torch
import torch.nn.functional as F
from scipy.io import loadmat
from src.face3d.util.load_mats import transferBFM09
import os
def perspective_projection(focal, center):
# return p.T (N, 3) @ (3, 3)
return np.array([
focal, 0, center,
0, focal, center,
0, 0... | null |
3,349 | import os
import numpy as np
import torch.nn.functional as F
from torch.nn import init
import functools
from torch.optim import lr_scheduler
import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Optional
from .arcface_torch.backbones import get_model
from korni... | null |
3,350 | import os
import numpy as np
import torch.nn.functional as F
from torch.nn import init
import functools
from torch.optim import lr_scheduler
import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Optional
from .arcface_torch.backbones import get_model
from korni... | null |
3,351 | import os
import numpy as np
import torch.nn.functional as F
from torch.nn import init
import functools
from torch.optim import lr_scheduler
import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Optional
from .arcface_torch.backbones import get_model
from korni... | Return a learning rate scheduler Parameters: optimizer -- the optimizer of the network opt (option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions． opt.lr_policy is the name of learning rate policy: linear | step | plateau | cosine For other schedulers (step, plateau, and cosine), we us... |
3,352 | import os
import numpy as np
import torch.nn.functional as F
from torch.nn import init
import functools
from torch.optim import lr_scheduler
import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Optional
from .arcface_torch.backbones import get_model
from korni... | null |
3,353 | import os
import numpy as np
import torch.nn.functional as F
from torch.nn import init
import functools
from torch.optim import lr_scheduler
import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Optional
from .arcface_torch.backbones import get_model
from korni... | null |
3,354 | import os
import numpy as np
import torch.nn.functional as F
from torch.nn import init
import functools
from torch.optim import lr_scheduler
import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Optional
from .arcface_torch.backbones import get_model
from korni... | 3x3 convolution with padding |
3,355 | import os
import numpy as np
import torch.nn.functional as F
from torch.nn import init
import functools
from torch.optim import lr_scheduler
import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Optional
from .arcface_torch.backbones import get_model
from korni... | 1x1 convolution |
3,356 | import os
import numpy as np
import torch.nn.functional as F
from torch.nn import init
import functools
from torch.optim import lr_scheduler
import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Optional
from .arcface_torch.backbones import get_model
from korni... | r"""ResNet-18 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr |
3,357 | import os
import numpy as np
import torch.nn.functional as F
from torch.nn import init
import functools
from torch.optim import lr_scheduler
import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Optional
from .arcface_torch.backbones import get_model
from korni... | r"""ResNet-34 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr |
3,358 | import os
import numpy as np
import torch.nn.functional as F
from torch.nn import init
import functools
from torch.optim import lr_scheduler
import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Optional
from .arcface_torch.backbones import get_model
from korni... | r"""ResNet-50 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr |
3,359 | import os
import numpy as np
import torch.nn.functional as F
from torch.nn import init
import functools
from torch.optim import lr_scheduler
import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Optional
from .arcface_torch.backbones import get_model
from korni... | r"""ResNet-101 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr |
3,360 | import os
import numpy as np
import torch.nn.functional as F
from torch.nn import init
import functools
from torch.optim import lr_scheduler
import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Optional
from .arcface_torch.backbones import get_model
from korni... | r"""ResNet-152 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr |
3,361 | import os
import numpy as np
import torch.nn.functional as F
from torch.nn import init
import functools
from torch.optim import lr_scheduler
import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Optional
from .arcface_torch.backbones import get_model
from korni... | r"""ResNeXt-50 32x4d model from `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr |
3,362 | import os
import numpy as np
import torch.nn.functional as F
from torch.nn import init
import functools
from torch.optim import lr_scheduler
import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Optional
from .arcface_torch.backbones import get_model
from korni... | r"""ResNeXt-101 32x8d model from `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr |
3,363 | import os
import numpy as np
import torch.nn.functional as F
from torch.nn import init
import functools
from torch.optim import lr_scheduler
import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Optional
from .arcface_torch.backbones import get_model
from korni... | r"""Wide ResNet-50-2 model from `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_. The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2... |
3,364 | import os
import numpy as np
import torch.nn.functional as F
from torch.nn import init
import functools
from torch.optim import lr_scheduler
import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Optional
from .arcface_torch.backbones import get_model
from korni... | r"""Wide ResNet-101-2 model from `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_. The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-... |
3,365 | from __future__ import print_function
import numpy as np
import torch
from PIL import Image
import os
import importlib
import argparse
from argparse import Namespace
import torchvision
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
... | null |
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