id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
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1,112 | import json
import requests
import openai
import tiktoken
import os
import time
from functools import wraps
import threading
def timeout_decorator(timeout):
class TimeoutException(Exception):
pass
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
result = [Tim... | null |
1,113 | import json
import requests
import openai
import tiktoken
import os
import time
from functools import wraps
import threading
def send_chat_request(request):
endpoint = 'http://10.15.82.10:8006/v1/chat/completions'
model = 'gpt-3.5-turbo'
# gpt4 gpt4-32k和gpt-3.5-turbo
headers = {
'Content-Type':... | null |
1,114 | import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim
import torch.optim.lr_scheduler as lr_scheduler
import time
import os
import glob
import configs
import backbone
from data.datamgr import SimpleDataManager, SetDataManager
from methods.baselinetrain import Basel... | null |
1,115 | import torch
import numpy as np
def one_hot(y, num_class):
return torch.zeros((len(y), num_class)).scatter_(1, y.unsqueeze(1), 1) | null |
1,116 | import torch
import numpy as np
def DBindex(cl_data_file):
class_list = cl_data_file.keys()
cl_num= len(class_list)
cl_means = []
stds = []
DBs = []
for cl in class_list:
cl_means.append( np.mean(cl_data_file[cl], axis = 0) )
stds.append( np.sqrt(np.mean( np.sum(np.square( cl_da... | null |
1,117 | import torch
import numpy as np
def sparsity(cl_data_file):
class_list = cl_data_file.keys()
cl_sparsity = []
for cl in class_list:
cl_sparsity.append(np.mean([np.sum(x!=0) for x in cl_data_file[cl] ]) )
return np.mean(cl_sparsity) | null |
1,118 | import torch
from torch.autograd import Variable
import torch.nn as nn
import math
import numpy as np
import torch.nn.functional as F
from torch.nn.utils.weight_norm import WeightNorm
def init_layer(L):
# Initialization using fan-in
if isinstance(L, nn.Conv2d):
n = L.kernel_size[0]*L.kernel_size[1]*L.o... | null |
1,119 | import torch
from torch.autograd import Variable
import torch.nn as nn
import math
import numpy as np
import torch.nn.functional as F
from torch.nn.utils.weight_norm import WeightNorm
class ConvNet(nn.Module):
def __init__(self, depth, flatten = True):
def forward(self,x):
def Conv4():
return ConvNet(4) | null |
1,120 | import torch
from torch.autograd import Variable
import torch.nn as nn
import math
import numpy as np
import torch.nn.functional as F
from torch.nn.utils.weight_norm import WeightNorm
class ConvNet(nn.Module):
def __init__(self, depth, flatten = True):
def forward(self,x):
def Conv6():
return ConvNet(6) | null |
1,121 | import torch
from torch.autograd import Variable
import torch.nn as nn
import math
import numpy as np
import torch.nn.functional as F
from torch.nn.utils.weight_norm import WeightNorm
class ConvNetNopool(nn.Module): #Relation net use a 4 layer conv with pooling in only first two layers, else no pooling
def __init__... | null |
1,122 | import torch
from torch.autograd import Variable
import torch.nn as nn
import math
import numpy as np
import torch.nn.functional as F
from torch.nn.utils.weight_norm import WeightNorm
class ConvNetNopool(nn.Module): #Relation net use a 4 layer conv with pooling in only first two layers, else no pooling
def __init__... | null |
1,123 | import torch
from torch.autograd import Variable
import torch.nn as nn
import math
import numpy as np
import torch.nn.functional as F
from torch.nn.utils.weight_norm import WeightNorm
class ConvNetS(nn.Module):
def __init__(self, depth, flatten = True):
def forward(self,x):
def Conv4S():
return ConvNetS(... | null |
1,124 | import torch
from torch.autograd import Variable
import torch.nn as nn
import math
import numpy as np
import torch.nn.functional as F
from torch.nn.utils.weight_norm import WeightNorm
class ConvNetSNopool(nn.Module):
def __init__(self, depth):
def forward(self,x):
def Conv4SNP():
return ConvNetSNopool(4) | null |
1,125 | import torch
from torch.autograd import Variable
import torch.nn as nn
import math
import numpy as np
import torch.nn.functional as F
from torch.nn.utils.weight_norm import WeightNorm
class SimpleBlock(nn.Module):
maml = False #Default
def __init__(self, indim, outdim, half_res):
super(SimpleBlock, self... | null |
1,126 | import torch
from torch.autograd import Variable
import torch.nn as nn
import math
import numpy as np
import torch.nn.functional as F
from torch.nn.utils.weight_norm import WeightNorm
class SimpleBlock(nn.Module):
maml = False #Default
def __init__(self, indim, outdim, half_res):
super(SimpleBlock, self... | null |
1,127 | import torch
from torch.autograd import Variable
import torch.nn as nn
import math
import numpy as np
import torch.nn.functional as F
from torch.nn.utils.weight_norm import WeightNorm
class SimpleBlock(nn.Module):
maml = False #Default
def __init__(self, indim, outdim, half_res):
super(SimpleBlock, self... | null |
1,128 | import torch
from torch.autograd import Variable
import torch.nn as nn
import math
import numpy as np
import torch.nn.functional as F
from torch.nn.utils.weight_norm import WeightNorm
class BottleneckBlock(nn.Module):
maml = False #Default
def __init__(self, indim, outdim, half_res):
super(BottleneckBlo... | null |
1,129 | import torch
from torch.autograd import Variable
import torch.nn as nn
import math
import numpy as np
import torch.nn.functional as F
from torch.nn.utils.weight_norm import WeightNorm
class BottleneckBlock(nn.Module):
maml = False #Default
def __init__(self, indim, outdim, half_res):
super(BottleneckBlo... | null |
1,130 | import numpy as np
import os
import glob
import argparse
import backbone
def parse_args(script):
parser = argparse.ArgumentParser(description= 'few-shot script %s' %(script))
parser.add_argument('--dataset' , default='CUB', help='CUB/miniImagenet/cross/omniglot/cross_char')
parser.add_argument('... | null |
1,131 | import numpy as np
import os
import glob
import argparse
import backbone
def get_assigned_file(checkpoint_dir,num):
assign_file = os.path.join(checkpoint_dir, '{:d}.tar'.format(num))
return assign_file | null |
1,132 | import numpy as np
import os
import glob
import argparse
import backbone
def get_resume_file(checkpoint_dir):
filelist = glob.glob(os.path.join(checkpoint_dir, '*.tar'))
if len(filelist) == 0:
return None
filelist = [ x for x in filelist if os.path.basename(x) != 'best_model.tar' ]
epochs = np... | null |
1,133 | import numpy as np
import torch
from torch.autograd import Variable
import os
import glob
import h5py
import configs
import backbone
from data.datamgr import SimpleDataManager
from methods.baselinetrain import BaselineTrain
from methods.baselinefinetune import BaselineFinetune
from methods.protonet import ProtoNet
from... | null |
1,134 | import torch
import numpy as np
import h5py
class SimpleHDF5Dataset:
def __init__(self, file_handle = None):
if file_handle == None:
self.f = ''
self.all_feats_dset = []
self.all_labels = []
self.total = 0
else:
self.f = file_handle
... | null |
1,135 | import backbone
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import torch.nn.functional as F
from methods.meta_template import MetaTemplate
def euclidean_dist( x, y):
# x: N x D
# y: M x D
n = x.size(0)
m = y.size(0)
d = x.size(1)
assert d == y.size(... | null |
1,136 | import backbone
import utils
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import torch.nn.functional as F
def DBindex(cl_data_file):
#For the definition Davis Bouldin index (DBindex), see https://en.wikipedia.org/wiki/Davies%E2%80%93Bouldin_index
#DB index present t... | null |
1,137 | import logging
import os
import time
import numpy as np
import numpy.ma as ma
from tqdm import tqdm
import torch
import torch.nn as nn
from torch.nn import functional as F
from utils.utils import AverageMeter
from utils.utils import get_confusion_matrix
from utils.utils import adjust_learning_rate
import utils.distribu... | null |
1,138 | import logging
import os
import time
import numpy as np
import numpy.ma as ma
from tqdm import tqdm
import torch
import torch.nn as nn
from torch.nn import functional as F
from utils.utils import AverageMeter
from utils.utils import get_confusion_matrix
from utils.utils import adjust_learning_rate
import utils.distribu... | null |
1,139 | import logging
import os
import time
import numpy as np
import numpy.ma as ma
from tqdm import tqdm
import torch
import torch.nn as nn
from torch.nn import functional as F
from utils.utils import AverageMeter
from utils.utils import get_confusion_matrix
from utils.utils import adjust_learning_rate
import utils.distribu... | null |
1,140 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from yacs.config import CfgNode as CN
def update_config(cfg, args):
cfg.defrost()
cfg.merge_from_file(args.cfg)
cfg.merge_from_list(args.opts)
cfg.freeze() | null |
1,141 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import functools
import numpy as np
import torch
import torch.nn as nn
import torch._utils
import torch.nn.functional as F
from .bn_helper import BatchNorm2d, BatchNorm2d_class, relu_inp... | 3x3 convolution with padding |
1,142 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import functools
import numpy as np
import torch
import torch.nn as nn
import torch._utils
import torch.nn.functional as F
from .bn_helper import BatchNorm2d, BatchNorm2d_class, relu_inp... | null |
1,144 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import functools
import numpy as np
import torch
import torch.nn as nn
import torch._utils
import torch.nn.functional as F
from .bn_helper import BatchNorm2d, BatchNorm2d_class, relu_inp... | null |
1,145 | import os
import logging
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models.utils import load_state_dict_from_url
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,... | 3x3 convolution with padding |
1,146 | import os
import logging
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models.utils import load_state_dict_from_url
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... | 1x1 convolution |
1,147 | import os
import logging
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models.utils import load_state_dict_from_url
def _hrnet(arch, pretrained, progress, **kwargs):
try:
from ..config.hrnet_config import MODEL_CONFIGS
except ImportError:
from segmentation.config.hrnet_c... | r"""HRNet-18 model |
1,148 | import os
import logging
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models.utils import load_state_dict_from_url
def _hrnet(arch, pretrained, progress, **kwargs):
try:
from ..config.hrnet_config import MODEL_CONFIGS
except ImportError:
from segmentation.config.hrnet_c... | r"""HRNet-32 model |
1,149 | import os
import logging
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models.utils import load_state_dict_from_url
def _hrnet(arch, pretrained, progress, **kwargs):
try:
from ..config.hrnet_config import MODEL_CONFIGS
except ImportError:
from segmentation.config.hrnet_c... | r"""HRNet-48 model |
1,150 | from os import path
import torch.autograd as autograd
import torch.cuda.comm as comm
from torch.autograd.function import once_differentiable
from torch.utils.cpp_extension import load
def _check(fn, *args, **kwargs):
success = fn(*args, **kwargs)
if not success:
raise RuntimeError("CUDA Error encounter... | null |
1,151 | from os import path
import torch.autograd as autograd
import torch.cuda.comm as comm
from torch.autograd.function import once_differentiable
from torch.utils.cpp_extension import load
def _broadcast_shape(x):
out_size = []
for i, s in enumerate(x.size()):
if i != 1:
out_size.append(1)
... | null |
1,152 | from os import path
import torch.autograd as autograd
import torch.cuda.comm as comm
from torch.autograd.function import once_differentiable
from torch.utils.cpp_extension import load
def _reduce(x):
if len(x.size()) == 2:
return x.sum(dim=0)
else:
n, c = x.size()[0:2]
return x.contiguo... | null |
1,153 | from os import path
import torch.autograd as autograd
import torch.cuda.comm as comm
from torch.autograd.function import once_differentiable
from torch.utils.cpp_extension import load
def _count_samples(x):
count = 1
for i, s in enumerate(x.size()):
if i != 1:
count *= s
return count | null |
1,154 | from os import path
import torch.autograd as autograd
import torch.cuda.comm as comm
from torch.autograd.function import once_differentiable
from torch.utils.cpp_extension import load
_backend = load(name="inplace_abn",
extra_cflags=["-O3"],
sources=[path.join(_src_path, f) for f in [
... | null |
1,155 | from os import path
import torch.autograd as autograd
import torch.cuda.comm as comm
from torch.autograd.function import once_differentiable
from torch.utils.cpp_extension import load
_backend = load(name="inplace_abn",
extra_cflags=["-O3"],
sources=[path.join(_src_path, f) for f in [
... | null |
1,156 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import time
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
def create_logger(cfg, cfg_name, phase='train'):
root_output_dir = Path(cfg.OUTPUT_DIR)
... | null |
1,157 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
from collections import namedtuple
import torch
import torch.nn as nn
The provided code snippet includes necessary dependencies for implementing the `get_model_summary` function. Write ... | :param model: :param input_tensors: :param item_length: :return: |
1,158 | import torch
from lib.models.seg_hrnet import get_seg_model
state_dict_url = 'https://github.com/huawei-noah/ghostnet/raw/master/pytorch/models/state_dict_93.98.pth'
The provided code snippet includes necessary dependencies for implementing the `hrnet_w48_cityscapes` function. Write a Python function `def hrnet_w48_ci... | # This docstring shows up in hub.help() HRNetW48 model pretrained on Cityscapes pretrained (bool): kwargs, load pretrained weights into the model |
1,159 | import argparse
import os
import pprint
import shutil
import sys
import logging
import time
import timeit
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim
from tensorboardX import SummaryWriter
import _init_paths
import models
import ... | null |
1,160 | import argparse
import os
import pprint
import shutil
import sys
import logging
import time
import timeit
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim
from tensorboardX import SummaryWriter
import _init_paths
import models
import ... | null |
1,161 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path as osp
import sys
def add_path(path):
if path not in sys.path:
sys.path.insert(0, path) | null |
1,162 | import os
import platform
import shlex
from tempfile import NamedTemporaryFile
from typing import Any, Callable
import typer
from click import BadParameter, UsageError
from sgpt.__version__ import __version__
from sgpt.integration import bash_integration, zsh_integration
The provided code snippet includes necessary de... | Opens the user's default editor to let them input a prompt, and returns the edited text. :return: String prompt. |
1,163 | import os
import platform
import shlex
from tempfile import NamedTemporaryFile
from typing import Any, Callable
import typer
from click import BadParameter, UsageError
from sgpt.__version__ import __version__
from sgpt.integration import bash_integration, zsh_integration
The provided code snippet includes necessary de... | Runs a command in the user's shell. It is aware of the current user's $SHELL. :param command: A shell command to run. |
1,164 | import os
import platform
import shlex
from tempfile import NamedTemporaryFile
from typing import Any, Callable
import typer
from click import BadParameter, UsageError
from sgpt.__version__ import __version__
from sgpt.integration import bash_integration, zsh_integration
def option_callback(func: Callable) -> Callable... | null |
1,165 | import os
import platform
import shlex
from tempfile import NamedTemporaryFile
from typing import Any, Callable
import typer
from click import BadParameter, UsageError
from sgpt.__version__ import __version__
from sgpt.integration import bash_integration, zsh_integration
bash_integration = """
# Shell-GPT integration ... | Installs shell integration. Currently only supports ZSH and Bash. Allows user to get shell completions in terminal by using hotkey. Replaces current "buffer" of the shell with the completion. |
1,166 | import os
import platform
import shlex
from tempfile import NamedTemporaryFile
from typing import Any, Callable
import typer
from click import BadParameter, UsageError
from sgpt.__version__ import __version__
from sgpt.integration import bash_integration, zsh_integration
__version__ = "1.4.0"
The provided code snippe... | Displays the current installed version of ShellGPT |
1,167 | import os
import platform
import shutil
from pathlib import Path
from typing import Any
from ..config import cfg
from ..utils import option_callback
FUNCTIONS_FOLDER = Path(cfg.get("OPENAI_FUNCTIONS_PATH"))
def install_functions(*_args: Any) -> None:
current_folder = os.path.dirname(os.path.abspath(__file__))
... | null |
1,168 | import os
import readline
import sys
import typer
from click import BadArgumentUsage
from click.types import Choice
from sgpt.config import cfg
from sgpt.function import get_openai_schemas
from sgpt.handlers.chat_handler import ChatHandler
from sgpt.handlers.default_handler import DefaultHandler
from sgpt.handlers.rep... | null |
1,169 | import importlib.util
import sys
from abc import ABCMeta
from pathlib import Path
from typing import Any, Callable, Dict, List
from .config import cfg
functions = [Function(str(path)) for path in functions_folder.glob("*.py")]
def get_function(name: str) -> Callable[..., Any]:
for function in functions:
if... | null |
1,170 | import importlib.util
import sys
from abc import ABCMeta
from pathlib import Path
from typing import Any, Callable, Dict, List
from .config import cfg
functions = [Function(str(path)) for path in functions_folder.glob("*.py")]
def get_openai_schemas() -> List[Dict[str, Any]]:
return [function.openai_schema for fun... | null |
1,171 | from io import open
from setuptools import setup
with open('requirements.txt', encoding="utf-8-sig") as f:
requirements = f.readlines()
def readme():
with open('README.md', encoding="utf-8-sig") as f:
README = f.read()
return README | null |
1,172 | import os
import sys
import time
import random
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.init as init
import torch.optim as optim
import torch.utils.data
from torch.cuda.amp import autocast, GradScaler
import numpy as np
from utils import CTCLabelConverter, AttnLabelConvert... | dataset preparation |
1,173 | import torch
import pickle
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `applyLM` function. Write a Python function `def applyLM(parentBeam, childBeam, classes, lm)` to solve the following problem:
calculate LM score of child beam by taking score from parent beam an... | calculate LM score of child beam by taking score from parent beam and bigram probability of last two chars |
1,174 | import torch
import pickle
import numpy as np
class BeamEntry:
"information about one single beam at specific time-step"
def __init__(self):
self.prTotal = 0 # blank and non-blank
self.prNonBlank = 0 # non-blank
self.prBlank = 0 # blank
self.prText = 1 # LM score
self.lmA... | beam search as described by the paper of Hwang et al. and the paper of Graves et al. |
1,175 | import torch
import pickle
import numpy as np
def consecutive(data, mode ='first', stepsize=1):
group = np.split(data, np.where(np.diff(data) != stepsize)[0]+1)
group = [item for item in group if len(item)>0]
if mode == 'first': result = [l[0] for l in group]
elif mode == 'last': result = [l[-1] for l i... | null |
1,176 | import os
import sys
import re
import six
import math
import torch
import pandas as pd
from natsort import natsorted
from PIL import Image
import numpy as np
from torch.utils.data import Dataset, ConcatDataset, Subset
from torch._utils import _accumulate
import torchvision.transforms as transforms
def contrast_grey(im... | null |
1,177 | import os
import sys
import re
import six
import math
import torch
import pandas as pd
from natsort import natsorted
from PIL import Image
import numpy as np
from torch.utils.data import Dataset, ConcatDataset, Subset
from torch._utils import _accumulate
import torchvision.transforms as transforms
def tensor2im(image... | null |
1,178 | import os
import sys
import re
import six
import math
import torch
import pandas as pd
from natsort import natsorted
from PIL import Image
import numpy as np
from torch.utils.data import Dataset, ConcatDataset, Subset
from torch._utils import _accumulate
import torchvision.transforms as transforms
def save_image(imag... | null |
1,179 | import argparse
import os
import shutil
import time
import yaml
import multiprocessing as mp
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import wandb
from config.load_config import load_yaml, DotDict
from data.dataset import SynthTextDataSet
from loss.mseloss import Maploss_v2, Map... | null |
1,180 | import numpy as np
import cv2
from skimage import io
def loadImage(img_file):
img = io.imread(img_file) # RGB order
if img.shape[0] == 2:
img = img[0]
if len(img.shape) == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
if img.shape[2] == 4:
img = img[:, :, :3]
img = np.arra... | null |
1,181 | import numpy as np
import cv2
from skimage import io
def denormalizeMeanVariance(
in_img, mean=(0.485, 0.456, 0.406), variance=(0.229, 0.224, 0.225)
):
# should be RGB order
img = in_img.copy()
img *= variance
img += mean
img *= 255.0
img = np.clip(img, 0, 255).astype(np.uint8)
return i... | null |
1,182 | import cv2
import numpy as np
from skimage.segmentation import watershed
def segment_region_score(watershed_param, region_score, word_image, pseudo_vis_opt):
region_score = np.float32(region_score) / 255
fore = np.uint8(region_score > 0.75)
back = np.uint8(region_score < 0.05)
unknown = 1 - (fore + back... | null |
1,183 | import random
import cv2
import numpy as np
from PIL import Image
from torchvision.transforms.functional import resized_crop, crop
from torchvision.transforms import RandomResizedCrop, RandomCrop
from torchvision.transforms import InterpolationMode
def rescale(img, bboxes, target_size=2240):
h, w = img.shape[0:2]
... | null |
1,184 | import random
import cv2
import numpy as np
from PIL import Image
from torchvision.transforms.functional import resized_crop, crop
from torchvision.transforms import RandomResizedCrop, RandomCrop
from torchvision.transforms import InterpolationMode
def random_resize_crop_synth(augment_targets, size):
image, region... | null |
1,185 | import random
import cv2
import numpy as np
from PIL import Image
from torchvision.transforms.functional import resized_crop, crop
from torchvision.transforms import RandomResizedCrop, RandomCrop
from torchvision.transforms import InterpolationMode
def random_resize_crop(
augment_targets, scale, ratio, size, thres... | null |
1,186 | import random
import cv2
import numpy as np
from PIL import Image
from torchvision.transforms.functional import resized_crop, crop
from torchvision.transforms import RandomResizedCrop, RandomCrop
from torchvision.transforms import InterpolationMode
def random_crop(augment_targets, size):
image, region_score, affin... | null |
1,187 | import random
import cv2
import numpy as np
from PIL import Image
from torchvision.transforms.functional import resized_crop, crop
from torchvision.transforms import RandomResizedCrop, RandomCrop
from torchvision.transforms import InterpolationMode
def random_horizontal_flip(imgs):
if random.random() < 0.5:
... | null |
1,188 | import random
import cv2
import numpy as np
from PIL import Image
from torchvision.transforms.functional import resized_crop, crop
from torchvision.transforms import RandomResizedCrop, RandomCrop
from torchvision.transforms import InterpolationMode
def random_scale(images, word_level_char_bbox, scale_range):
scale... | null |
1,189 | import random
import cv2
import numpy as np
from PIL import Image
from torchvision.transforms.functional import resized_crop, crop
from torchvision.transforms import RandomResizedCrop, RandomCrop
from torchvision.transforms import InterpolationMode
def random_rotate(images, max_angle):
angle = random.random() * 2 ... | null |
1,190 | import math
import numpy as np
def getX(K, B, Ypoint):
return int((Ypoint-B)/K) | null |
1,191 | import math
import numpy as np
def lineBiasAndK(Apoint, Bpoint):
K = pointAngle(Apoint, Bpoint)
B = Apoint[1] - K*Apoint[0]
return K, B
def sidePoint(Apoint, Bpoint, h, w, placehold, enlarge_size):
K, B = lineBiasAndK(Apoint, Bpoint)
angle = abs(math.atan(pointAngle(Apoint, Bpoint)))
distance = ... | null |
1,192 | import os
import yaml
from functools import reduce
CONFIG_PATH = os.path.dirname(__file__)
import os
os.environ["LRU_CACHE_CAPACITY"] = "1"
def load_yaml(config_name):
with open(os.path.join(CONFIG_PATH, config_name)+ '.yaml') as file:
config = yaml.safe_load(file)
return config | null |
1,193 | import argparse
import os
import cv2
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from tqdm import tqdm
import wandb
from config.load_config import load_yaml, DotDict
from model.craft import CRAFT
from metrics.eval_det_iou import DetectionIoUEvaluator
from utils.inference_boxes import (
test... | null |
1,194 | import argparse
import os
import shutil
import time
import multiprocessing as mp
import yaml
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import wandb
from config.load_config import load_yaml, DotDict
from data.dataset import SynthTextDataSet, CustomDataset
from loss.mseloss import ... | null |
1,195 | import torch
import torch.nn as nn
import torch.nn.init as init
import torchvision
from torchvision import models
from packaging import version
def init_weights(modules):
for m in modules:
if isinstance(m, nn.Conv2d):
init.xavier_uniform_(m.weight.data)
if m.bias is not None:
... | null |
1,196 | from collections import OrderedDict
import os
import cv2
import numpy as np
from data import imgproc
from utils import craft_utils
def saveInput(
imagename, vis_dir, image, region_scores, affinity_scores, confidence_mask
):
image = np.uint8(image.copy())
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
... | null |
1,197 | from collections import OrderedDict
import os
import cv2
import numpy as np
from data import imgproc
from utils import craft_utils
def saveImage(
imagename,
vis_dir,
image,
bboxes,
affi_bboxes,
region_scores,
affinity_scores,
confidence_mask,
):
output_image = np.uint8(image.copy())... | null |
1,198 | from collections import OrderedDict
import os
import cv2
import numpy as np
from data import imgproc
from utils import craft_utils
The provided code snippet includes necessary dependencies for implementing the `save_parser` function. Write a Python function `def save_parser(args)` to solve the following problem:
final... | final options |
1,199 | import os
import torch
import cv2
import math
import numpy as np
from data import imgproc
def save_outputs(image, region_scores, affinity_scores, text_threshold, link_threshold,
low_text, outoput_path, confidence_mask = None):
"""save image, region_scores, and affinity_sco... | takes images, region_scores, and affinity_scores as tensors (cab be GPU). :param images: 4D tensor :param region_scores: 3D tensor with values between 0 ~ 1 :param affinity_scores: 3D tensor with values between 0 ~ 1 :param text_threshold: :param link_threshold: :param low_text: :param output_dir: direcotry to save the... |
1,200 | import os
import re
import itertools
import cv2
import time
import numpy as np
import torch
from torch.autograd import Variable
from utils.craft_utils import getDetBoxes, adjustResultCoordinates
from data import imgproc
from data.dataset import SynthTextDataSet
import math
import xml.etree.ElementTree as elemTree
def x... | null |
1,201 | import os
import re
import itertools
import cv2
import time
import numpy as np
import torch
from torch.autograd import Variable
from utils.craft_utils import getDetBoxes, adjustResultCoordinates
from data import imgproc
from data.dataset import SynthTextDataSet
import math
import xml.etree.ElementTree as elemTree
def ... | null |
1,202 | import argparse
import onnx
import torch
import easyocr
import numpy as np
def export_detector(detector_onnx_save_path,
in_shape=[1, 3, 608, 800],
lang_list=["en"],
model_storage_directory=None,
user_network_directory=None,
... | null |
1,203 | import argparse
import onnx
import torch
import easyocr
import numpy as np
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('-l', '--lang_list',
nargs='+', type=str,
default=["en"],
help='-l en ch_sim ... (language ... | null |
1,204 | import numpy as np
from skimage import io
import cv2
def denormalizeMeanVariance(in_img, mean=(0.485, 0.456, 0.406), variance=(0.229, 0.224, 0.225)):
# should be RGB order
img = in_img.copy()
img *= variance
img += mean
img *= 255.0
img = np.clip(img, 0, 255).astype(np.uint8)
return img | null |
1,205 | import numpy as np
from skimage import io
import cv2
def cvt2HeatmapImg(img):
img = (np.clip(img, 0, 1) * 255).astype(np.uint8)
img = cv2.applyColorMap(img, cv2.COLORMAP_JET)
return img | null |
1,206 | import argparse
import easyocr
def parse_args():
parser = argparse.ArgumentParser(description="Process EasyOCR.")
parser.add_argument(
"-l",
"--lang",
nargs='+',
required=True,
type=str,
help="for languages",
)
parser.add_argument(
"--gpu",
... | null |
1,207 | from __future__ import print_function
import torch
import pickle
import numpy as np
import math
import cv2
from PIL import Image, JpegImagePlugin
from scipy import ndimage
import hashlib
import sys, os
from zipfile import ZipFile
from .imgproc import loadImage
def consecutive(data, mode ='first', stepsize=1):
group... | null |
1,208 | from __future__ import print_function
import torch
import pickle
import numpy as np
import math
import cv2
from PIL import Image, JpegImagePlugin
from scipy import ndimage
import hashlib
import sys, os
from zipfile import ZipFile
from .imgproc import loadImage
The provided code snippet includes necessary dependencies ... | calculate LM score of child beam by taking score from parent beam and bigram probability of last two chars |
1,209 | from __future__ import print_function
import torch
import pickle
import numpy as np
import math
import cv2
from PIL import Image, JpegImagePlugin
from scipy import ndimage
import hashlib
import sys, os
from zipfile import ZipFile
from .imgproc import loadImage
class BeamEntry:
"information about one single beam at ... | null |
1,210 | from __future__ import print_function
import torch
import pickle
import numpy as np
import math
import cv2
from PIL import Image, JpegImagePlugin
from scipy import ndimage
import hashlib
import sys, os
from zipfile import ZipFile
from .imgproc import loadImage
def merge_to_free(merge_result, free_list):
merge_resu... | null |
1,211 | from __future__ import print_function
import torch
import pickle
import numpy as np
import math
import cv2
from PIL import Image, JpegImagePlugin
from scipy import ndimage
import hashlib
import sys, os
from zipfile import ZipFile
from .imgproc import loadImage
def group_text_box(polys, slope_ths = 0.1, ycenter_ths = 0... | null |
1,212 | from __future__ import print_function
import torch
import pickle
import numpy as np
import math
import cv2
from PIL import Image, JpegImagePlugin
from scipy import ndimage
import hashlib
import sys, os
from zipfile import ZipFile
from .imgproc import loadImage
def four_point_transform(image, rect):
(tl, tr, br, bl)... | null |
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