id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
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3,869 | from setuptools import find_packages, setup
import os
import subprocess
import sys
import time
import torch
from torch.utils.cpp_extension import (BuildExtension, CppExtension,
CUDAExtension)
version_file = 'basicsr/version.py'
def get_hash():
def write_version_py():
content ... | null |
3,870 | from setuptools import find_packages, setup
import os
import subprocess
import sys
import time
import torch
from torch.utils.cpp_extension import (BuildExtension, CppExtension,
CUDAExtension)
version_file = 'basicsr/version.py'
def get_version():
with open(version_file, 'r') ... | null |
3,871 | from setuptools import find_packages, setup
import os
import subprocess
import sys
import time
import torch
from torch.utils.cpp_extension import (BuildExtension, CppExtension,
CUDAExtension)
def make_cuda_ext(name, module, sources, sources_cuda=None):
if sources_cuda is None... | null |
3,872 | from setuptools import find_packages, setup
import os
import subprocess
import sys
import time
import torch
from torch.utils.cpp_extension import (BuildExtension, CppExtension,
CUDAExtension)
def get_requirements(filename='requirements.txt'):
return []
here = os.path.dirn... | null |
3,873 | import argparse
import datetime
import logging
import math
import random
import time
import torch
from os import path as osp
from basicsr.data import create_dataloader, create_dataset
from basicsr.data.data_sampler import EnlargedSampler
from basicsr.data.prefetch_dataloader import CPUPrefetcher, CUDAPrefetcher
from ba... | null |
3,874 | import argparse
import datetime
import logging
import math
import random
import time
import torch
from os import path as osp
from basicsr.data import create_dataloader, create_dataset
from basicsr.data.data_sampler import EnlargedSampler
from basicsr.data.prefetch_dataloader import CPUPrefetcher, CUDAPrefetcher
from ba... | null |
3,875 | import argparse
import datetime
import logging
import math
import random
import time
import torch
from os import path as osp
from basicsr.data import create_dataloader, create_dataset
from basicsr.data.data_sampler import EnlargedSampler
from basicsr.data.prefetch_dataloader import CPUPrefetcher, CUDAPrefetcher
from ba... | null |
3,876 | import cv2
import math
import numpy as np
from scipy.ndimage.filters import convolve
from scipy.special import gamma
from basicsr.metrics.metric_util import reorder_image, to_y_channel
def niqe(img,
mu_pris_param,
cov_pris_param,
gaussian_window,
block_size_h=96,
block_size_... | Calculate NIQE (Natural Image Quality Evaluator) metric. Ref: Making a "Completely Blind" Image Quality Analyzer. This implementation could produce almost the same results as the official MATLAB codes: http://live.ece.utexas.edu/research/quality/niqe_release.zip We use the official params estimated from the pristine da... |
3,877 | import numpy as np
import torch
import torch.nn as nn
from scipy import linalg
from tqdm import tqdm
from basicsr.models.archs.inception import InceptionV3
def load_patched_inception_v3(device='cuda',
resize_input=True,
normalize_input=False):
# we may no... | null |
3,878 | import numpy as np
import torch
import torch.nn as nn
from scipy import linalg
from tqdm import tqdm
from basicsr.models.archs.inception import InceptionV3
The provided code snippet includes necessary dependencies for implementing the `extract_inception_features` function. Write a Python function `def extract_inceptio... | Extract inception features. Args: data_generator (generator): A data generator. inception (nn.Module): Inception model. len_generator (int): Length of the data_generator to show the progressbar. Default: None. device (str): Device. Default: cuda. Returns: Tensor: Extracted features. |
3,879 | import numpy as np
import torch
import torch.nn as nn
from scipy import linalg
from tqdm import tqdm
from basicsr.models.archs.inception import InceptionV3
The provided code snippet includes necessary dependencies for implementing the `calculate_fid` function. Write a Python function `def calculate_fid(mu1, sigma1, mu... | Numpy implementation of the Frechet Distance. The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2) is d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)). Stable version by Dougal J. Sutherland. Args: mu1 (np.array): The sample mean over activations. sigma1 (np.array):... |
3,880 | import cv2
import numpy as np
from basicsr.metrics.metric_util import reorder_image, to_y_channel
from skimage.metrics import structural_similarity
import torch
def calculate_psnr(img1,
img2,
crop_border,
input_order='HWC',
test_y_channel=False... | null |
3,881 | import cv2
import numpy as np
from basicsr.metrics.metric_util import reorder_image, to_y_channel
from skimage.metrics import structural_similarity
import torch
def prepare_for_ssim(img, k):
import torch
with torch.no_grad():
img = torch.from_numpy(img).unsqueeze(0).unsqueeze(0).float()
conv = ... | null |
3,882 | import cv2
import numpy as np
from basicsr.metrics.metric_util import reorder_image, to_y_channel
from skimage.metrics import structural_similarity
import torch
def prepare_for_ssim_rgb(img, k):
import torch
with torch.no_grad():
img = torch.from_numpy(img).float() #HxWx3
conv = torch.nn.Conv2... | null |
3,883 | import cv2
import numpy as np
from basicsr.metrics.metric_util import reorder_image, to_y_channel
from skimage.metrics import structural_similarity
import torch
def calculate_ssim(img1,
img2,
crop_border,
input_order='HWC',
test_y_channel=False... | null |
3,884 | import cv2
import numpy as np
from basicsr.metrics.metric_util import reorder_image, to_y_channel
from skimage.metrics import structural_similarity
import torch
def calculate_skimage_ssim(img1, img2):
return structural_similarity(img1, img2, multichannel=True)
def calculate_skimage_ssim_left(img1, img2):
img1 ... | null |
3,885 | import cv2
import random
from cv2 import rotate
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `paired_random_crop` function. Write a Python function `def paired_random_crop(img_gts, img_lqs, gt_patch_size, scale, gt_path)` to solve the following problem:
Paired rando... | Paired random crop. It crops lists of lq and gt images with corresponding locations. Args: img_gts (list[ndarray] | ndarray): GT images. Note that all images should have the same shape. If the input is an ndarray, it will be transformed to a list containing itself. img_lqs (list[ndarray] | ndarray): LQ images. Note tha... |
3,886 | import cv2
import random
from cv2 import rotate
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `paired_random_crop_hw` function. Write a Python function `def paired_random_crop_hw(img_gts, img_lqs, gt_patch_size_h, gt_patch_size_w, scale, gt_path)` to solve the follow... | Paired random crop. It crops lists of lq and gt images with corresponding locations. Args: img_gts (list[ndarray] | ndarray): GT images. Note that all images should have the same shape. If the input is an ndarray, it will be transformed to a list containing itself. img_lqs (list[ndarray] | ndarray): LQ images. Note tha... |
3,887 | import cv2
import random
from cv2 import rotate
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `augment` function. Write a Python function `def augment(imgs, hflip=True, rotation=True, flows=None, return_status=False, vflip=False)` to solve the following problem:
Augm... | Augment: horizontal flips OR rotate (0, 90, 180, 270 degrees). We use vertical flip and transpose for rotation implementation. All the images in the list use the same augmentation. Args: imgs (list[ndarray] | ndarray): Images to be augmented. If the input is an ndarray, it will be transformed to a list. hflip (bool): H... |
3,888 | import cv2
import random
from cv2 import rotate
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `img_rotate` function. Write a Python function `def img_rotate(img, angle, center=None, scale=1.0)` to solve the following problem:
Rotate image. Args: img (ndarray): Image ... | Rotate image. Args: img (ndarray): Image to be rotated. angle (float): Rotation angle in degrees. Positive values mean counter-clockwise rotation. center (tuple[int]): Rotation center. If the center is None, initialize it as the center of the image. Default: None. scale (float): Isotropic scale factor. Default: 1.0. |
3,889 | import cv2
import numpy as np
import torch
from os import path as osp
from torch.nn import functional as F
from basicsr.data.transforms import mod_crop
from basicsr.utils import img2tensor, scandir
def mod_crop(img, scale):
"""Mod crop images, used during testing.
Args:
img (ndarray): Input image.
... | Read a sequence of images from a given folder path. Args: path (list[str] | str): List of image paths or image folder path. require_mod_crop (bool): Require mod crop for each image. Default: False. scale (int): Scale factor for mod_crop. Default: 1. Returns: Tensor: size (t, c, h, w), RGB, [0, 1]. |
3,891 | import cv2
import numpy as np
import torch
from os import path as osp
from torch.nn import functional as F
from basicsr.data.transforms import mod_crop
from basicsr.utils import img2tensor, scandir
The provided code snippet includes necessary dependencies for implementing the `paired_paths_from_lmdb` function. Write a... | Generate paired paths from lmdb files. Contents of lmdb. Taking the `lq.lmdb` for example, the file structure is: lq.lmdb ├── data.mdb ├── lock.mdb ├── meta_info.txt The data.mdb and lock.mdb are standard lmdb files and you can refer to https://lmdb.readthedocs.io/en/release/ for more details. The meta_info.txt is a sp... |
3,892 | import cv2
import numpy as np
import torch
from os import path as osp
from torch.nn import functional as F
from basicsr.data.transforms import mod_crop
from basicsr.utils import img2tensor, scandir
The provided code snippet includes necessary dependencies for implementing the `paired_paths_from_meta_info_file` functio... | Generate paired paths from an meta information file. Each line in the meta information file contains the image names and image shape (usually for gt), separated by a white space. Example of an meta information file: ``` 0001_s001.png (480,480,3) 0001_s002.png (480,480,3) ``` Args: folders (list[str]): A list of folder ... |
3,893 | import cv2
import numpy as np
import torch
from os import path as osp
from torch.nn import functional as F
from basicsr.data.transforms import mod_crop
from basicsr.utils import img2tensor, scandir
The provided code snippet includes necessary dependencies for implementing the `paired_paths_from_folder` function. Write... | Generate paired paths from folders. Args: folders (list[str]): A list of folder path. The order of list should be [input_folder, gt_folder]. keys (list[str]): A list of keys identifying folders. The order should be in consistent with folders, e.g., ['lq', 'gt']. filename_tmpl (str): Template for each filename. Note tha... |
3,896 | import cv2
import numpy as np
import torch
from os import path as osp
from torch.nn import functional as F
from basicsr.data.transforms import mod_crop
from basicsr.utils import img2tensor, scandir
def generate_gaussian_kernel(kernel_size=13, sigma=1.6):
"""Generate Gaussian kernel used in `duf_downsample`.
Arg... | Downsamping with Gaussian kernel used in the DUF official code. Args: x (Tensor): Frames to be downsampled, with shape (b, t, c, h, w). kernel_size (int): Kernel size. Default: 13. scale (int): Downsampling factor. Supported scale: (2, 3, 4). Default: 4. Returns: Tensor: DUF downsampled frames. |
3,897 | import functools
from torch.nn import functional as F
def weight_reduce_loss(loss, weight=None, reduction='mean'):
"""Apply element-wise weight and reduce loss.
Args:
loss (Tensor): Element-wise loss.
weight (Tensor): Element-wise weights. Default: None.
reduction (str): Same as built-in... | Create a weighted version of a given loss function. To use this decorator, the loss function must have the signature like `loss_func(pred, target, **kwargs)`. The function only needs to compute element-wise loss without any reduction. This decorator will add weight and reduction arguments to the function. The decorated... |
3,898 | import torch
from torch import nn as nn
from torch.nn import functional as F
import numpy as np
from basicsr.models.losses.loss_util import weighted_loss
def l1_loss(pred, target):
return F.l1_loss(pred, target, reduction='none') | null |
3,899 | import torch
from torch import nn as nn
from torch.nn import functional as F
import numpy as np
from basicsr.models.losses.loss_util import weighted_loss
def mse_loss(pred, target):
return F.mse_loss(pred, target, reduction='none') | null |
3,900 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class AvgPool2d(nn.Module):
def __init__(self, kernel_size=None, base_size=None, auto_pad=True, fast_imp=False, train_size=None):
super().__init__()
self.kernel_size = kernel_size
self.base_size = base_size... | null |
3,901 | import math
import torch
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torch.nn.modules.batchnorm import _BatchNorm
from basicsr.utils import get_root_logger
import time
The provided code snippet includes necessary dependencies for implementing the `default_init... | Initialize network weights. Args: module_list (list[nn.Module] | nn.Module): Modules to be initialized. scale (float): Scale initialized weights, especially for residual blocks. Default: 1. bias_fill (float): The value to fill bias. Default: 0 kwargs (dict): Other arguments for initialization function. |
3,902 | import math
import torch
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torch.nn.modules.batchnorm import _BatchNorm
from basicsr.utils import get_root_logger
import time
The provided code snippet includes necessary dependencies for implementing the `make_layer` ... | Make layers by stacking the same blocks. Args: basic_block (nn.module): nn.module class for basic block. num_basic_block (int): number of blocks. Returns: nn.Sequential: Stacked blocks in nn.Sequential. |
3,903 | import math
import torch
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torch.nn.modules.batchnorm import _BatchNorm
from basicsr.utils import get_root_logger
import time
The provided code snippet includes necessary dependencies for implementing the `flow_warp` f... | Warp an image or feature map with optical flow. Args: x (Tensor): Tensor with size (n, c, h, w). flow (Tensor): Tensor with size (n, h, w, 2), normal value. interp_mode (str): 'nearest' or 'bilinear'. Default: 'bilinear'. padding_mode (str): 'zeros' or 'border' or 'reflection'. Default: 'zeros'. align_corners (bool): B... |
3,904 | import math
import torch
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torch.nn.modules.batchnorm import _BatchNorm
from basicsr.utils import get_root_logger
import time
The provided code snippet includes necessary dependencies for implementing the `resize_flow`... | Resize a flow according to ratio or shape. Args: flow (Tensor): Precomputed flow. shape [N, 2, H, W]. size_type (str): 'ratio' or 'shape'. sizes (list[int | float]): the ratio for resizing or the final output shape. 1) The order of ratio should be [ratio_h, ratio_w]. For downsampling, the ratio should be smaller than 1... |
3,905 | import math
import torch
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torch.nn.modules.batchnorm import _BatchNorm
from basicsr.utils import get_root_logger
import time
The provided code snippet includes necessary dependencies for implementing the `pixel_unshuf... | Pixel unshuffle. Args: x (Tensor): Input feature with shape (b, c, hh, hw). scale (int): Downsample ratio. Returns: Tensor: the pixel unshuffled feature. |
3,906 | import math
import torch
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torch.nn.modules.batchnorm import _BatchNorm
from basicsr.utils import get_root_logger
import time
def measure_inference_speed(model, data, max_iter=200, log_interval=50):
model.eval()
... | null |
3,909 | import math
import requests
from tqdm import tqdm
from .misc import sizeof_fmt
def get_confirm_token(response):
for key, value in response.cookies.items():
if key.startswith('download_warning'):
return value
return None
def save_response_content(response,
destinatio... | Download files from google drive. Ref: https://stackoverflow.com/questions/25010369/wget-curl-large-file-from-google-drive # noqa E501 Args: file_id (str): File id. save_path (str): Save path. |
3,910 | import math
import numpy as np
import torch
def calculate_weights_indices(in_length, out_length, scale, kernel,
kernel_width, antialiasing):
"""Calculate weights and indices, used for imresize function.
Args:
in_length (int): Input length.
out_length (int): Output l... | imresize function same as MATLAB. It now only supports bicubic. The same scale applies for both height and width. Args: img (Tensor | Numpy array): Tensor: Input image with shape (c, h, w), [0, 1] range. Numpy: Input image with shape (h, w, c), [0, 1] range. scale (float): Scale factor. The same scale applies for both ... |
3,911 | import math
import numpy as np
import torch
def _convert_input_type_range(img):
"""Convert the type and range of the input image.
It converts the input image to np.float32 type and range of [0, 1].
It is mainly used for pre-processing the input image in colorspace
convertion functions such as rgb2ycbcr ... | Convert a RGB image to YCbCr image. This function produces the same results as Matlab's `rgb2ycbcr` function. It implements the ITU-R BT.601 conversion for standard-definition television. See more details in https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. It differs from a similar function in cv2.cvtColor:... |
3,912 | import math
import numpy as np
import torch
def _convert_input_type_range(img):
"""Convert the type and range of the input image.
It converts the input image to np.float32 type and range of [0, 1].
It is mainly used for pre-processing the input image in colorspace
convertion functions such as rgb2ycbcr ... | Convert a YCbCr image to RGB image. This function produces the same results as Matlab's ycbcr2rgb function. It implements the ITU-R BT.601 conversion for standard-definition television. See more details in https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. It differs from a similar function in cv2.cvtColor: `... |
3,913 | import math
import numpy as np
import torch
def _convert_input_type_range(img):
"""Convert the type and range of the input image.
It converts the input image to np.float32 type and range of [0, 1].
It is mainly used for pre-processing the input image in colorspace
convertion functions such as rgb2ycbcr ... | Convert a YCbCr image to BGR image. The bgr version of ycbcr2rgb. It implements the ITU-R BT.601 conversion for standard-definition television. See more details in https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. It differs from a similar function in cv2.cvtColor: `YCrCb <-> BGR`. In OpenCV, it implements a... |
3,914 | import argparse
from os import path as osp
from basicsr.utils import scandir
from basicsr.utils.lmdb_util import make_lmdb_from_imgs
def prepare_keys(folder_path, suffix='png'):
def make_lmdb_from_imgs(data_path,
lmdb_path,
img_path_list,
keys,
... | null |
3,915 | import argparse
from os import path as osp
from basicsr.utils import scandir
from basicsr.utils.lmdb_util import make_lmdb_from_imgs
def prepare_keys(folder_path, suffix='png'):
"""Prepare image path list and keys for DIV2K dataset.
Args:
folder_path (str): Folder path.
Returns:
list[str]: I... | null |
3,916 | import argparse
from os import path as osp
from basicsr.utils import scandir
from basicsr.utils.lmdb_util import make_lmdb_from_imgs
def prepare_keys(folder_path, suffix='png'):
"""Prepare image path list and keys for DIV2K dataset.
Args:
folder_path (str): Folder path.
Returns:
list[str]: I... | null |
3,917 | import argparse
from os import path as osp
from basicsr.utils import scandir
from basicsr.utils.lmdb_util import make_lmdb_from_imgs
def prepare_keys(folder_path, suffix='png'):
"""Prepare image path list and keys for DIV2K dataset.
Args:
folder_path (str): Folder path.
Returns:
list[str]: I... | folder_path = './datasets/SIDD/val/input_crops' lmdb_path = './datasets/SIDD/val/input_crops.lmdb' mat_path = './datasets/SIDD/ValidationNoisyBlocksSrgb.mat' if not osp.exists(folder_path): os.makedirs(folder_path) assert osp.exists(mat_path) data = scio.loadmat(mat_path)['ValidationNoisyBlocksSrgb'] N, B, H ,W, C = da... |
3,918 | import cv2
import math
import numpy as np
import os
import torch
from torchvision.utils import make_grid
The provided code snippet includes necessary dependencies for implementing the `img2tensor` function. Write a Python function `def img2tensor(imgs, bgr2rgb=True, float32=True)` to solve the following problem:
Numpy... | Numpy array to tensor. Args: imgs (list[ndarray] | ndarray): Input images. bgr2rgb (bool): Whether to change bgr to rgb. float32 (bool): Whether to change to float32. Returns: list[tensor] | tensor: Tensor images. If returned results only have one element, just return tensor. |
3,919 | import cv2
import math
import numpy as np
import os
import torch
from torchvision.utils import make_grid
The provided code snippet includes necessary dependencies for implementing the `tensor2img` function. Write a Python function `def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1))` to solve the f... | Convert torch Tensors into image numpy arrays. After clamping to [min, max], values will be normalized to [0, 1]. Args: tensor (Tensor or list[Tensor]): Accept shapes: 1) 4D mini-batch Tensor of shape (B x 3/1 x H x W); 2) 3D Tensor of shape (3/1 x H x W); 3) 2D Tensor of shape (H x W). Tensor channel should be in RGB ... |
3,920 | import cv2
import math
import numpy as np
import os
import torch
from torchvision.utils import make_grid
The provided code snippet includes necessary dependencies for implementing the `imfrombytes` function. Write a Python function `def imfrombytes(content, flag='color', float32=False)` to solve the following problem:... | Read an image from bytes. Args: content (bytes): Image bytes got from files or other streams. flag (str): Flags specifying the color type of a loaded image, candidates are `color`, `grayscale` and `unchanged`. float32 (bool): Whether to change to float32., If True, will also norm to [0, 1]. Default: False. Returns: nda... |
3,921 | import cv2
import math
import numpy as np
import os
import torch
from torchvision.utils import make_grid
def padding(img_lq, img_gt, gt_size):
h, w, _ = img_lq.shape
h_pad = max(0, gt_size - h)
w_pad = max(0, gt_size - w)
if h_pad == 0 and w_pad == 0:
return img_lq, img_gt
img_lq = c... | null |
3,922 | import cv2
import math
import numpy as np
import os
import torch
from torchvision.utils import make_grid
The provided code snippet includes necessary dependencies for implementing the `imwrite` function. Write a Python function `def imwrite(img, file_path, params=None, auto_mkdir=True)` to solve the following problem:... | Write image to file. Args: img (ndarray): Image array to be written. file_path (str): Image file path. params (None or list): Same as opencv's :func:`imwrite` interface. auto_mkdir (bool): If the parent folder of `file_path` does not exist, whether to create it automatically. Returns: bool: Successful or not. |
3,923 | import cv2
import math
import numpy as np
import os
import torch
from torchvision.utils import make_grid
The provided code snippet includes necessary dependencies for implementing the `crop_border` function. Write a Python function `def crop_border(imgs, crop_border)` to solve the following problem:
Crop borders of im... | Crop borders of images. Args: imgs (list[ndarray] | ndarray): Images with shape (h, w, c). crop_border (int): Crop border for each end of height and weight. Returns: list[ndarray]: Cropped images. |
3,924 | import numpy as np
import os
import random
import time
import torch
from os import path as osp
from .dist_util import master_only
from .logger import get_root_logger
The provided code snippet includes necessary dependencies for implementing the `set_random_seed` function. Write a Python function `def set_random_seed(s... | Set random seeds. |
3,925 | import numpy as np
import os
import random
import time
import torch
from os import path as osp
from .dist_util import master_only
from .logger import get_root_logger
def mkdir_and_rename(path):
"""mkdirs. If path exists, rename it with timestamp and create a new one.
Args:
path (str): Folder path.
"... | Make dirs for experiments. |
3,926 | import numpy as np
import os
import random
import time
import torch
from os import path as osp
from .dist_util import master_only
from .logger import get_root_logger
def scandir(dir_path, suffix=None, recursive=False, full_path=False):
"""Scan a directory to find the interested files.
Args:
dir_path (st... | Scan a directory to find the interested files. Args: dir_path (str): Path of the directory. keywords (str | tuple(str), optional): File keywords that we are interested in. Default: None. recursive (bool, optional): If set to True, recursively scan the directory. Default: False. full_path (bool, optional): If set to Tru... |
3,927 | import numpy as np
import os
import random
import time
import torch
from os import path as osp
from .dist_util import master_only
from .logger import get_root_logger
def get_root_logger(logger_name='basicsr',
log_level=logging.INFO,
log_file=None):
"""Get the root logger.
... | Check resume states and pretrain_network paths. Args: opt (dict): Options. resume_iter (int): Resume iteration. |
3,928 | import cv2
import numpy as np
import os
def dequantize_flow(dx, dy, max_val=0.02, denorm=True):
"""Recover from quantized flow.
Args:
dx (ndarray): Quantized dx.
dy (ndarray): Quantized dy.
max_val (float): Maximum value used when quantizing.
denorm (bool): Whether to multiply fl... | Read an optical flow map. Args: flow_path (ndarray or str): Flow path. quantize (bool): whether to read quantized pair, if set to True, remaining args will be passed to :func:`dequantize_flow`. concat_axis (int): The axis that dx and dy are concatenated, can be either 0 or 1. Ignored if quantize is False. Returns: ndar... |
3,929 | import cv2
import numpy as np
import os
def quantize_flow(flow, max_val=0.02, norm=True):
"""Quantize flow to [0, 255].
After this step, the size of flow will be much smaller, and can be
dumped as jpeg images.
Args:
flow (ndarray): (h, w, 2) array of optical flow.
max_val (float): Maximu... | Write optical flow to file. If the flow is not quantized, it will be saved as a .flo file losslessly, otherwise a jpeg image which is lossy but of much smaller size. (dx and dy will be concatenated horizontally into a single image if quantize is True.) Args: flow (ndarray): (h, w, 2) array of optical flow. filename (st... |
3,931 | import datetime
import logging
import time
from .dist_util import get_dist_info, master_only
The provided code snippet includes necessary dependencies for implementing the `init_wandb_logger` function. Write a Python function `def init_wandb_logger(opt)` to solve the following problem:
We now only use wandb to sync te... | We now only use wandb to sync tensorboard log. |
3,932 | import datetime
import logging
import time
from .dist_util import get_dist_info, master_only
__version__ = '1.2.0+386ca20'
The provided code snippet includes necessary dependencies for implementing the `get_env_info` function. Write a Python function `def get_env_info()` to solve the following problem:
Get environmen... | Get environment information. Currently, only log the software version. |
3,933 | import torch
from basicsr.models import create_model
from basicsr.utils import FileClient, imfrombytes, img2tensor, padding, tensor2img, imwrite, set_random_seed
import argparse
from basicsr.utils.options import dict2str, parse
from basicsr.utils.dist_util import get_dist_info, init_dist
import random
def parse(opt_pa... | null |
3,934 | import torch
from basicsr.models import create_model
from basicsr.utils import FileClient, imfrombytes, img2tensor, padding, tensor2img, imwrite, set_random_seed
import argparse
from basicsr.utils.options import dict2str, parse
from basicsr.utils.dist_util import get_dist_info, init_dist
import random
def imread(img_p... | null |
3,935 | import torch
import numpy as np
import cv2
import tempfile
import matplotlib.pyplot as plt
from cog import BasePredictor, Path, Input, BaseModel
from basicsr.models import create_model
from basicsr.utils import img2tensor as _img2tensor, tensor2img, imwrite
from basicsr.utils.options import parse
def imread(img_path):... | null |
3,936 | import torch
import numpy as np
import cv2
import tempfile
import matplotlib.pyplot as plt
from cog import BasePredictor, Path, Input, BaseModel
from basicsr.models import create_model
from basicsr.utils import img2tensor as _img2tensor, tensor2img, imwrite
from basicsr.utils.options import parse
def img2tensor(img, b... | null |
3,937 | import torch
import numpy as np
import cv2
import tempfile
import matplotlib.pyplot as plt
from cog import BasePredictor, Path, Input, BaseModel
from basicsr.models import create_model
from basicsr.utils import img2tensor as _img2tensor, tensor2img, imwrite
from basicsr.utils.options import parse
def single_image_infe... | null |
3,938 | import torch
import numpy as np
import cv2
import tempfile
import matplotlib.pyplot as plt
from cog import BasePredictor, Path, Input, BaseModel
from basicsr.models import create_model
from basicsr.utils import img2tensor as _img2tensor, tensor2img, imwrite
from basicsr.utils.options import parse
def stereo_image_infe... | null |
3,939 | import cv2
import numpy as np
import os
import sys
from multiprocessing import Pool
from os import path as osp
from tqdm import tqdm
from basicsr.utils import scandir_SIDD
from basicsr.utils.create_lmdb import create_lmdb_for_SIDD
def worker(path, opt):
"""Worker for each process.
Args:
path (str): Imag... | Crop images to subimages. Args: opt (dict): Configuration dict. It contains: input_folder (str): Path to the input folder. save_folder (str): Path to save folder. n_thread (int): Thread number. |
3,940 | import cv2
import numpy as np
import os
import sys
from multiprocessing import Pool
from os import path as osp
from tqdm import tqdm
from basicsr.utils import scandir
from basicsr.utils.create_lmdb import create_lmdb_for_gopro
def worker(path, opt):
"""Worker for each process.
Args:
path (str): Image pa... | Crop images to subimages. Args: opt (dict): Configuration dict. It contains: input_folder (str): Path to the input folder. save_folder (str): Path to save folder. n_thread (int): Thread number. |
3,941 | import os
import time
from basicsr.utils.create_lmdb import create_lmdb_for_reds
def make_val_300(folder, dst):
if not os.path.exists(dst):
os.mkdir(dst)
templates = '*9.*'
cp_command = 'cp {} {}'.format(os.path.join(folder, templates), dst)
os.system(cp_command) | null |
3,942 | import os
import time
from basicsr.utils.create_lmdb import create_lmdb_for_reds
def flatten_folders(folder):
for vid in range(300):
vidfolder_path = '{:03}'.format(vid)
if not os.path.exists(os.path.join(folder, vidfolder_path)):
continue
print('working on .. {} .. {}'.format... | null |
3,943 | from datetime import datetime
import time
import requests
import sys
import json
from azure.identity import AzureCliCredential
import logging
from azure.ai.ml import MLClient
from sseclient import SSEClient
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
logger.addHandler(handler)
def apply_delta(ba... | null |
3,944 | import os
import re
from io import open
from typing import Any, List, Match, cast
from setuptools import find_namespace_packages, setup
with open(os.path.join(PACKAGE_FOLDER_PATH, "version.txt"), "r") as fd:
version_content = fd.read()
print(version_content)
version = cast(Match[Any], re.search(r'^VERSION\s... | null |
3,945 | from openai import OpenAIError
from promptflow.exceptions import ErrorTarget, SystemErrorException, UserErrorException
openai_error_code_ref_message = "Error reference: https://platform.openai.com/docs/guides/error-codes/api-errors"
def to_openai_error_message(e: Exception) -> str:
ex_type = type(e).__name__
e... | null |
3,946 | from enum import Enum
from typing import Union
from promptflow.tools.common import handle_openai_error, init_openai_client, init_azure_openai_client
from promptflow.tools.exception import InvalidConnectionType
from promptflow._internal import tool
from promptflow.connections import AzureOpenAIConnection, OpenAIConnecti... | null |
3,947 | from enum import Enum
from typing import Dict, List, Union
import json
import requests
from promptflow import tool, ToolProvider
from promptflow.connections import AzureContentSafetyConnection
from promptflow.tools.exception import AzureContentSafetyInputValueError, AzureContentSafetySystemError
class TextCategorySensi... | null |
3,948 | from enum import Enum
from typing import Dict, List, Union
import json
import requests
from promptflow import tool, ToolProvider
from promptflow.connections import AzureContentSafetyConnection
from promptflow.tools.exception import AzureContentSafetyInputValueError, AzureContentSafetySystemError
class TextCategorySensi... | null |
3,949 | from enum import Enum
from typing import Dict, List, Union
import json
import requests
from promptflow import tool, ToolProvider
from promptflow.connections import AzureContentSafetyConnection
from promptflow.tools.exception import AzureContentSafetyInputValueError, AzureContentSafetySystemError
class Decision(object):... | null |
3,950 | from enum import Enum
from promptflow.tools.common import render_jinja_template, handle_openai_error, \
parse_chat, to_bool, validate_functions, process_function_call, \
post_process_chat_api_response, init_openai_client
from promptflow._internal import ToolProvider, tool, register_apis
from promptflow.connecti... | null |
3,951 | from enum import Enum
from promptflow.tools.common import render_jinja_template, handle_openai_error, \
parse_chat, to_bool, validate_functions, process_function_call, \
post_process_chat_api_response, init_openai_client
from promptflow._internal import ToolProvider, tool, register_apis
from promptflow.connecti... | null |
3,952 | import json
from promptflow.tools.common import render_jinja_template, handle_openai_error, parse_chat, to_bool, \
validate_functions, process_function_call, post_process_chat_api_response, init_azure_openai_client
from promptflow._internal import enable_cache, ToolProvider, tool, register_apis
from promptflow.conn... | null |
3,953 | import json
from promptflow.tools.common import render_jinja_template, handle_openai_error, parse_chat, to_bool, \
validate_functions, process_function_call, post_process_chat_api_response, init_azure_openai_client
from promptflow._internal import enable_cache, ToolProvider, tool, register_apis
from promptflow.conn... | null |
3,954 | import functools
import json
import os
import re
import requests
import sys
import time
import tempfile
from abc import abstractmethod
from datetime import datetime, timedelta
from enum import Enum
from typing import Any, Dict, List, Tuple, Optional, Union
from promptflow._core.tool import ToolProvider, tool
from promp... | null |
3,955 | import functools
import json
import os
import re
import requests
import sys
import time
import tempfile
from abc import abstractmethod
from datetime import datetime, timedelta
from enum import Enum
from typing import Any, Dict, List, Tuple, Optional, Union
from promptflow._core.tool import ToolProvider, tool
from promp... | null |
3,956 | import functools
import json
import os
import re
import requests
import sys
import time
import tempfile
from abc import abstractmethod
from datetime import datetime, timedelta
from enum import Enum
from typing import Any, Dict, List, Tuple, Optional, Union
from promptflow._core.tool import ToolProvider, tool
from promp... | null |
3,957 | import functools
import json
import os
import re
import requests
import sys
import time
import tempfile
from abc import abstractmethod
from datetime import datetime, timedelta
from enum import Enum
from typing import Any, Dict, List, Tuple, Optional, Union
from promptflow._core.tool import ToolProvider, tool
from promp... | null |
3,958 | import functools
import json
import os
import re
import requests
import sys
import time
import tempfile
from abc import abstractmethod
from datetime import datetime, timedelta
from enum import Enum
from typing import Any, Dict, List, Tuple, Optional, Union
from promptflow._core.tool import ToolProvider, tool
from promp... | null |
3,959 | import functools
import json
import os
import re
import requests
import sys
import time
import tempfile
from abc import abstractmethod
from datetime import datetime, timedelta
from enum import Enum
from typing import Any, Dict, List, Tuple, Optional, Union
from promptflow._core.tool import ToolProvider, tool
from promp... | null |
3,960 | import functools
import json
import os
import re
import requests
import sys
import time
import tempfile
from abc import abstractmethod
from datetime import datetime, timedelta
from enum import Enum
from typing import Any, Dict, List, Tuple, Optional, Union
from promptflow._core.tool import ToolProvider, tool
from promp... | null |
3,961 | from typing import List, Dict
from promptflow.tools.common import render_jinja_template, handle_openai_error, parse_chat, \
preprocess_template_string, find_referenced_image_set, convert_to_chat_list, init_azure_openai_client, \
post_process_chat_api_response, list_deployment_connections, build_deployment_dict,... | null |
3,962 | import functools
import json
import os
import re
import sys
import time
from typing import List, Mapping
from jinja2 import Template
from openai import APIConnectionError, APIStatusError, OpenAIError, RateLimitError, APITimeoutError, BadRequestError
from promptflow.tools.exception import ChatAPIInvalidRole, WrappedOpen... | null |
3,963 | import functools
import json
import os
import re
import sys
import time
from typing import List, Mapping
from jinja2 import Template
from openai import APIConnectionError, APIStatusError, OpenAIError, RateLimitError, APITimeoutError, BadRequestError
from promptflow.tools.exception import ChatAPIInvalidRole, WrappedOpen... | null |
3,964 | import functools
import json
import os
import re
import sys
import time
from typing import List, Mapping
from jinja2 import Template
from openai import APIConnectionError, APIStatusError, OpenAIError, RateLimitError, APITimeoutError, BadRequestError
from promptflow.tools.exception import ChatAPIInvalidRole, WrappedOpen... | A decorator function that used to handle OpenAI error. OpenAI Error falls into retriable vs non-retriable ones. For retriable error, the decorator use below parameters to control its retry activity with exponential backoff: `tries` : max times for the function invocation, type is int 'delay': base delay seconds for exp... |
3,965 | import functools
import json
import os
import re
import sys
import time
from typing import List, Mapping
from jinja2 import Template
from openai import APIConnectionError, APIStatusError, OpenAIError, RateLimitError, APITimeoutError, BadRequestError
from promptflow.tools.exception import ChatAPIInvalidRole, WrappedOpen... | null |
3,966 | import functools
import json
import os
import re
import sys
import time
from typing import List, Mapping
from jinja2 import Template
from openai import APIConnectionError, APIStatusError, OpenAIError, RateLimitError, APITimeoutError, BadRequestError
from promptflow.tools.exception import ChatAPIInvalidRole, WrappedOpen... | null |
3,967 | import functools
import json
import os
import re
import sys
import time
from typing import List, Mapping
from jinja2 import Template
from openai import APIConnectionError, APIStatusError, OpenAIError, RateLimitError, APITimeoutError, BadRequestError
from promptflow.tools.exception import ChatAPIInvalidRole, WrappedOpen... | null |
3,968 | import functools
import json
import os
import re
import sys
import time
from typing import List, Mapping
from jinja2 import Template
from openai import APIConnectionError, APIStatusError, OpenAIError, RateLimitError, APITimeoutError, BadRequestError
from promptflow.tools.exception import ChatAPIInvalidRole, WrappedOpen... | Remove the image input decorator from the template string and place the image input in a new line. |
3,969 | import functools
import json
import os
import re
import sys
import time
from typing import List, Mapping
from jinja2 import Template
from openai import APIConnectionError, APIStatusError, OpenAIError, RateLimitError, APITimeoutError, BadRequestError
from promptflow.tools.exception import ChatAPIInvalidRole, WrappedOpen... | null |
3,970 | import functools
import json
import os
import re
import sys
import time
from typing import List, Mapping
from jinja2 import Template
from openai import APIConnectionError, APIStatusError, OpenAIError, RateLimitError, APITimeoutError, BadRequestError
from promptflow.tools.exception import ChatAPIInvalidRole, WrappedOpen... | null |
3,971 | import json
import sys
from enum import Enum
import requests
from promptflow._internal import ToolProvider, tool
from promptflow.connections import SerpConnection
from promptflow.exceptions import PromptflowException
from promptflow.tools.exception import SerpAPIUserError, SerpAPISystemError
class SafeMode(str, Enum):
... | null |
3,972 | from pathlib import Path
from ruamel.yaml import YAML
def collect_tools_from_directory(base_dir) -> dict:
tools = {}
yaml = YAML()
for f in Path(base_dir).glob("**/*.yaml"):
with open(f, "r") as f:
tools_in_file = yaml.load(f)
for identifier, tool in tools_in_file.items():
... | List package tools |
3,973 | from promptflow._internal import tool
from promptflow.tools.common import render_jinja_template
def render_jinja_template(prompt, trim_blocks=True, keep_trailing_newline=True, **kwargs):
try:
return Template(prompt, trim_blocks=trim_blocks, keep_trailing_newline=keep_trailing_newline).render(**kwargs)
... | null |
3,974 | from dataclasses import dataclass
from datetime import datetime
from itertools import chain
from typing import Any, List, Mapping
from promptflow._utils.exception_utils import ExceptionPresenter, RootErrorCode
from promptflow._utils.openai_metrics_calculator import OpenAIMetricsCalculator
from promptflow.contracts.run_... | null |
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