id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
14,655 | import math
import numpy as np
import requests
import torch
import torch.nn as nn
from PIL import Image, ImageDraw
from utils.datasets import letterbox
from utils.general import non_max_suppression, make_divisible, scale_coords, xyxy2xywh
from utils.plots import color_list
def channel_shuffle(x, groups):
batchsize... | null |
14,656 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
from PIL import Image, ExifTags
from torch.utils.data import ... | null |
14,657 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
from PIL import Image, ExifTags
from torch.utils.data import ... | null |
14,658 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
from PIL import Image, ExifTags
from torch.utils.data import ... | null |
14,659 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
from PIL import Image, ExifTags
from torch.utils.data import ... | null |
14,660 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
from PIL import Image, ExifTags
from torch.utils.data import ... | null |
14,661 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
from PIL import Image, ExifTags
from torch.utils.data import ... | null |
14,662 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
from PIL import Image, ExifTags
from torch.utils.data import ... | null |
14,663 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
from PIL import Image, ExifTags
from torch.utils.data import ... | null |
14,664 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
from PIL import Image, ExifTags
from torch.utils.data import ... | null |
14,665 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
from PIL import Image, ExifTags
from torch.utils.data import ... | null |
14,666 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
from PIL import Image, ExifTags
from torch.utils.data import ... | Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files # Arguments path: Path to images directory weights: Train, val, test weights (list) |
14,667 | import json
import sys
from pathlib import Path
import torch
import yaml
from tqdm import tqdm
from utils.datasets import LoadImagesAndLabels
from utils.datasets import img2label_paths
from utils.general import colorstr, xywh2xyxy, check_dataset
def check_wandb_config_file(data_config_file):
wandb_config = '_wandb... | null |
14,668 | import json
import sys
from pathlib import Path
import torch
import yaml
from tqdm import tqdm
from utils.datasets import LoadImagesAndLabels
from utils.datasets import img2label_paths
from utils.general import colorstr, xywh2xyxy, check_dataset
try:
import wandb
from wandb import init, finish
except ImportErro... | null |
14,669 | import argparse
import yaml
from wandb_utils import WandbLogger
class WandbLogger():
def __init__(self, opt, name, run_id, data_dict, job_type='Training'):
# Pre-training routine --
self.job_type = job_type
self.wandb, self.wandb_run, self.data_dict = wandb, None if not wandb else wandb.run... | null |
14,670 | import logging
import math
import os
import subprocess
import time
from contextlib import contextmanager
from copy import deepcopy
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torchvision
logger = logging.getLogger(__name__)
def ... | null |
14,671 | import logging
import math
import os
import subprocess
import time
from contextlib import contextmanager
from copy import deepcopy
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torchvision
def initialize_weights(model):
for m... | null |
14,672 | import logging
import math
import os
import subprocess
import time
from contextlib import contextmanager
from copy import deepcopy
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torchvision
def find_modules(model, mclass=nn.Conv2d... | null |
14,673 | import logging
import math
import os
import subprocess
import time
from contextlib import contextmanager
from copy import deepcopy
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torchvision
def sparsity(model):
# Return global ... | null |
14,674 | import logging
import math
import os
import subprocess
import time
from contextlib import contextmanager
from copy import deepcopy
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torchvision
def fuse_conv_and_bn(conv, bn):
# Fu... | null |
14,675 | import logging
import math
import os
import subprocess
import time
from contextlib import contextmanager
from copy import deepcopy
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torchvision
try:
import thop # for FLOPS computa... | null |
14,676 | import logging
import math
import os
import subprocess
import time
from contextlib import contextmanager
from copy import deepcopy
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torchvision
def load_classifier(name='resnet101', n=... | null |
14,677 | import logging
import math
import os
import subprocess
import time
from contextlib import contextmanager
from copy import deepcopy
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torchvision
def scale_img(img, ratio=1.0, same_shape... | null |
14,678 | import logging
import math
import os
import subprocess
import time
from contextlib import contextmanager
from copy import deepcopy
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torchvision
def copy_attr(a, b, include=(), exclude=... | null |
14,679 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image, ExifTa... | null |
14,680 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image, ExifTa... | null |
14,681 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image, ExifTa... | null |
14,682 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image, ExifTa... | null |
14,683 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image, ExifTa... | null |
14,684 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image, ExifTa... | null |
14,685 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image, ExifTa... | null |
14,686 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image, ExifTa... | null |
14,687 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image, ExifTa... | null |
14,688 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image, ExifTa... | null |
14,689 | import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image, ExifTa... | Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files # Arguments path: Path to images directory weights: Train, val, test weights (list) |
14,690 | import os
import platform
import subprocess
import time
from pathlib import Path
import requests
import torch
def get_token(cookie="./cookie"):
with open(cookie) as f:
for line in f:
if "download" in line:
return line.split()[-1]
return ""
def gdrive_download(id='16TiPfZj7ht... | null |
14,691 | import glob
import math
import os
import random
from copy import copy
from pathlib import Path
import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import torch
import yaml
from PIL import Image, ImageDraw
from scipy.signal import butter, filtfilt
fro... | null |
14,692 | import glob
import math
import os
import random
from copy import copy
from pathlib import Path
import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import torch
import yaml
from PIL import Image, ImageDraw
from scipy.signal import butter, filtfilt
fro... | null |
14,693 | import glob
import math
import os
import random
from copy import copy
from pathlib import Path
import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import torch
import yaml
from PIL import Image, ImageDraw
from scipy.signal import butter, filtfilt
fro... | null |
14,694 | import glob
import math
import os
import random
from copy import copy
from pathlib import Path
import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import torch
import yaml
from PIL import Image, ImageDraw
from scipy.signal import butter, filtfilt
fro... | null |
14,695 | import glob
import math
import os
import random
from copy import copy
from pathlib import Path
import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import torch
import yaml
from PIL import Image, ImageDraw
from scipy.signal import butter, filtfilt
fro... | null |
14,696 | import glob
import math
import os
import random
from copy import copy
from pathlib import Path
import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import torch
import yaml
from PIL import Image, ImageDraw
from scipy.signal import butter, filtfilt
fro... | null |
14,697 | import glob
import math
import os
import random
from copy import copy
from pathlib import Path
import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import torch
import yaml
from PIL import Image, ImageDraw
from scipy.signal import butter, filtfilt
fro... | null |
14,698 | import glob
import math
import os
import random
from copy import copy
from pathlib import Path
import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import torch
import yaml
from PIL import Image, ImageDraw
from scipy.signal import butter, filtfilt
fro... | null |
14,699 | import glob
import math
import os
import random
from copy import copy
from pathlib import Path
import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import torch
import yaml
from PIL import Image, ImageDraw
from scipy.signal import butter, filtfilt
fro... | null |
14,700 | import glob
import math
import os
import random
from copy import copy
from pathlib import Path
import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import torch
import yaml
from PIL import Image, ImageDraw
from scipy.signal import butter, filtfilt
fro... | null |
14,701 | import torch
def decode_infer(output, stride):
# logging.info(torch.tensor(output.shape[0]))
# logging.info(output.shape)
# # bz is batch-size
# bz = tuple(torch.tensor(output.shape[0]))
# gridsize = tuple(torch.tensor(output.shape[-1]))
# logging.info(gridsize)
sh = torch.tensor(output.sha... | null |
14,702 | import glob
import logging
import math
import os
import random
import re
import subprocess
import time
from pathlib import Path
import cv2
import numpy as np
import torch
import torchvision
import yaml
from utils.google_utils import gsutil_getsize
from utils.metrics import fitness
from utils.torch_utils import init_tor... | null |
14,703 | import glob
import logging
import math
import os
import random
import re
import subprocess
import time
from pathlib import Path
import cv2
import numpy as np
import torch
import torchvision
import yaml
from utils.google_utils import gsutil_getsize
from utils.metrics import fitness
from utils.torch_utils import init_tor... | null |
14,704 | import glob
import logging
import math
import os
import random
import re
import subprocess
import time
from pathlib import Path
import cv2
import numpy as np
import torch
import torchvision
import yaml
from utils.google_utils import gsutil_getsize
from utils.metrics import fitness
from utils.torch_utils import init_tor... | null |
14,705 | import glob
import logging
import math
import os
import random
import re
import subprocess
import time
from pathlib import Path
import cv2
import numpy as np
import torch
import torchvision
import yaml
from utils.google_utils import gsutil_getsize
from utils.metrics import fitness
from utils.torch_utils import init_tor... | null |
14,706 | import glob
import logging
import math
import os
import random
import re
import subprocess
import time
from pathlib import Path
import cv2
import numpy as np
import torch
import torchvision
import yaml
from utils.google_utils import gsutil_getsize
from utils.metrics import fitness
from utils.torch_utils import init_tor... | null |
14,707 | import glob
import logging
import math
import os
import random
import re
import subprocess
import time
from pathlib import Path
import cv2
import numpy as np
import torch
import torchvision
import yaml
from utils.google_utils import gsutil_getsize
from utils.metrics import fitness
from utils.torch_utils import init_tor... | null |
14,708 | import glob
import logging
import math
import os
import random
import re
import subprocess
import time
from pathlib import Path
import cv2
import numpy as np
import torch
import torchvision
import yaml
from utils.google_utils import gsutil_getsize
from utils.metrics import fitness
from utils.torch_utils import init_tor... | null |
14,709 | import glob
import logging
import math
import os
import random
import re
import subprocess
import time
from pathlib import Path
import cv2
import numpy as np
import torch
import torchvision
import yaml
from utils.google_utils import gsutil_getsize
from utils.metrics import fitness
from utils.torch_utils import init_tor... | null |
14,710 | import glob
import logging
import math
import os
import random
import re
import subprocess
import time
from pathlib import Path
import cv2
import numpy as np
import torch
import torchvision
import yaml
from utils.google_utils import gsutil_getsize
from utils.metrics import fitness
from utils.torch_utils import init_tor... | null |
14,711 | import glob
import logging
import math
import os
import random
import re
import subprocess
import time
from pathlib import Path
import cv2
import numpy as np
import torch
import torchvision
import yaml
from utils.google_utils import gsutil_getsize
from utils.metrics import fitness
from utils.torch_utils import init_tor... | null |
14,712 | import glob
import logging
import math
import os
import random
import re
import subprocess
import time
from pathlib import Path
import cv2
import numpy as np
import torch
import torchvision
import yaml
from utils.google_utils import gsutil_getsize
from utils.metrics import fitness
from utils.torch_utils import init_tor... | Performs Non-Maximum Suppression (NMS) on inference results Returns: detections with shape: nx6 (x1, y1, x2, y2, conf, cls) |
14,713 | import glob
import logging
import math
import os
import random
import re
import subprocess
import time
from pathlib import Path
import cv2
import numpy as np
import torch
import torchvision
import yaml
from utils.google_utils import gsutil_getsize
from utils.metrics import fitness
from utils.torch_utils import init_tor... | null |
14,714 | import glob
import logging
import math
import os
import random
import re
import subprocess
import time
from pathlib import Path
import cv2
import numpy as np
import torch
import torchvision
import yaml
from utils.google_utils import gsutil_getsize
from utils.metrics import fitness
from utils.torch_utils import init_tor... | null |
14,715 | from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import torch
from . import general
def compute_ap(recall, precision):
""" Compute the average precision, given the recall and precision curves
# Arguments
recall: The recall curve (list)
precision: The precision curve... | Compute the average precision, given the recall and precision curves. Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. # Arguments tp: True positives (nparray, nx1 or nx10). conf: Objectness value from 0-1 (nparray). pred_cls: Predicted object classes (nparray). target_cls: True object classes (nparra... |
14,717 | from typing import List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from mmdet.structures.bbox import BaseBoxes
The provided code snippet includes necessary dependencies for implementing the `find_inside_bboxes` function. Write a Python function `def find_inside_bboxes(bb... | Find bboxes as long as a part of bboxes is inside the image. Args: bboxes (Tensor): Shape (N, 4). img_h (int): Image height. img_w (int): Image width. Returns: Tensor: Index of the remaining bboxes. |
14,718 | from typing import List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from mmdet.structures.bbox import BaseBoxes
def bbox_flip(bboxes: Tensor,
img_shape: Tuple[int],
direction: str = 'horizontal') -> Tensor:
"""Flip bboxes horizontally or ver... | Map bboxes from the original image scale to testing scale. |
14,719 | from typing import List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from mmdet.structures.bbox import BaseBoxes
def bbox_flip(bboxes: Tensor,
img_shape: Tuple[int],
direction: str = 'horizontal') -> Tensor:
"""Flip bboxes horizontally or ver... | Map bboxes from testing scale to original image scale. |
14,720 | from typing import List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from mmdet.structures.bbox import BaseBoxes
def get_box_tensor(boxes: Union[Tensor, BaseBoxes]) -> Tensor:
"""Get tensor data from box type boxes.
Args:
boxes (Tensor or BaseBoxes): boxes w... | Convert a list of bboxes to roi format. Args: bbox_list (List[Union[Tensor, :obj:`BaseBoxes`]): a list of bboxes corresponding to a batch of images. Returns: Tensor: shape (n, box_dim + 1), where ``box_dim`` depends on the different box types. For example, If the box type in ``bbox_list`` is HorizontalBoxes, the output... |
14,721 | from typing import List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from mmdet.structures.bbox import BaseBoxes
The provided code snippet includes necessary dependencies for implementing the `roi2bbox` function. Write a Python function `def roi2bbox(rois: Tensor) -> List[... | Convert rois to bounding box format. Args: rois (Tensor): RoIs with the shape (n, 5) where the first column indicates batch id of each RoI. Returns: List[Tensor]: Converted boxes of corresponding rois. |
14,722 | from typing import List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from mmdet.structures.bbox import BaseBoxes
The provided code snippet includes necessary dependencies for implementing the `bbox2result` function. Write a Python function `def bbox2result(bboxes: Union[Te... | Convert detection results to a list of numpy arrays. Args: bboxes (Tensor | np.ndarray): shape (n, 5) labels (Tensor | np.ndarray): shape (n, ) num_classes (int): class number, including background class Returns: List(np.ndarray]): bbox results of each class |
14,723 | from typing import List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from mmdet.structures.bbox import BaseBoxes
The provided code snippet includes necessary dependencies for implementing the `distance2bbox` function. Write a Python function `def distance2bbox( points:... | Decode distance prediction to bounding box. Args: points (Tensor): Shape (B, N, 2) or (N, 2). distance (Tensor): Distance from the given point to 4 boundaries (left, top, right, bottom). Shape (B, N, 4) or (N, 4) max_shape (Union[Sequence[int], Tensor, Sequence[Sequence[int]]], optional): Maximum bounds for boxes, spec... |
14,724 | from typing import List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from mmdet.structures.bbox import BaseBoxes
The provided code snippet includes necessary dependencies for implementing the `bbox2distance` function. Write a Python function `def bbox2distance(points: Tens... | Decode bounding box based on distances. Args: points (Tensor): Shape (n, 2) or (b, n, 2), [x, y]. bbox (Tensor): Shape (n, 4) or (b, n, 4), "xyxy" format max_dis (float, optional): Upper bound of the distance. eps (float): a small value to ensure target < max_dis, instead <= Returns: Tensor: Decoded distances. |
14,725 | from typing import List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from mmdet.structures.bbox import BaseBoxes
The provided code snippet includes necessary dependencies for implementing the `bbox_rescale` function. Write a Python function `def bbox_rescale(bboxes: Tensor... | Rescale bounding box w.r.t. scale_factor. Args: bboxes (Tensor): Shape (n, 4) for bboxes or (n, 5) for rois scale_factor (float): rescale factor Returns: Tensor: Rescaled bboxes. |
14,726 | from typing import List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from mmdet.structures.bbox import BaseBoxes
The provided code snippet includes necessary dependencies for implementing the `bbox_cxcywh_to_xyxy` function. Write a Python function `def bbox_cxcywh_to_xyxy(... | Convert bbox coordinates from (cx, cy, w, h) to (x1, y1, x2, y2). Args: bbox (Tensor): Shape (n, 4) for bboxes. Returns: Tensor: Converted bboxes. |
14,727 | from typing import List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from mmdet.structures.bbox import BaseBoxes
The provided code snippet includes necessary dependencies for implementing the `bbox_xyxy_to_cxcywh` function. Write a Python function `def bbox_xyxy_to_cxcywh(... | Convert bbox coordinates from (x1, y1, x2, y2) to (cx, cy, w, h). Args: bbox (Tensor): Shape (n, 4) for bboxes. Returns: Tensor: Converted bboxes. |
14,728 | from typing import List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from mmdet.structures.bbox import BaseBoxes
def bbox2corner(bboxes: torch.Tensor) -> torch.Tensor:
"""Convert bbox coordinates from (x1, y1, x2, y2) to corners ((x1, y1),
(x2, y1), (x1, y2), (x2, y... | Geometric transformation for bbox. Args: bboxes (Union[torch.Tensor, np.ndarray]): Shape (n, 4) for bboxes. homography_matrix (Union[torch.Tensor, np.ndarray]): Shape (3, 3) for geometric transformation. img_shape (Tuple[int, int], optional): Image shape. Defaults to None. Returns: Union[torch.Tensor, np.ndarray]: Conv... |
14,729 | from typing import List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from mmdet.structures.bbox import BaseBoxes
The provided code snippet includes necessary dependencies for implementing the `cat_boxes` function. Write a Python function `def cat_boxes(data_list: List[Unio... | Concatenate boxes with type of tensor or box type. Args: data_list (List[Union[Tensor, :obj:`BaseBoxes`]]): A list of tensors or box types need to be concatenated. dim (int): The dimension over which the box are concatenated. Defaults to 0. Returns: Union[Tensor, :obj`BaseBoxes`]: Concatenated results. |
14,730 | from typing import List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from mmdet.structures.bbox import BaseBoxes
The provided code snippet includes necessary dependencies for implementing the `stack_boxes` function. Write a Python function `def stack_boxes(data_list: List[... | Stack boxes with type of tensor or box type. Args: data_list (List[Union[Tensor, :obj:`BaseBoxes`]]): A list of tensors or box types need to be stacked. dim (int): The dimension over which the box are stacked. Defaults to 0. Returns: Union[Tensor, :obj`BaseBoxes`]: Stacked results. |
14,731 | from typing import List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from mmdet.structures.bbox import BaseBoxes
The provided code snippet includes necessary dependencies for implementing the `scale_boxes` function. Write a Python function `def scale_boxes(boxes: Union[Ten... | Scale boxes with type of tensor or box type. Args: boxes (Tensor or :obj:`BaseBoxes`): boxes need to be scaled. Its type can be a tensor or a box type. scale_factor (Tuple[float, float]): factors for scaling boxes. The length should be 2. Returns: Union[Tensor, :obj:`BaseBoxes`]: Scaled boxes. |
14,732 | from typing import List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from mmdet.structures.bbox import BaseBoxes
The provided code snippet includes necessary dependencies for implementing the `get_box_wh` function. Write a Python function `def get_box_wh(boxes: Union[Tenso... | Get the width and height of boxes with type of tensor or box type. Args: boxes (Tensor or :obj:`BaseBoxes`): boxes with type of tensor or box type. Returns: Tuple[Tensor, Tensor]: the width and height of boxes. |
14,733 | from typing import List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from mmdet.structures.bbox import BaseBoxes
The provided code snippet includes necessary dependencies for implementing the `empty_box_as` function. Write a Python function `def empty_box_as(boxes: Union[T... | Generate empty box according to input ``boxes` type and device. Args: boxes (Tensor or :obj:`BaseBoxes`): boxes with type of tensor or box type. Returns: Union[Tensor, BaseBoxes]: Generated empty box. |
14,734 | from typing import Callable, Optional, Tuple, Type, Union
import numpy as np
import torch
from torch import Tensor
from .base_boxes import BaseBoxes
def _register_box(name: str, box_type: Type, force: bool = False) -> None:
"""Register a box type.
Args:
name (str): The name of box type.
box_type... | Register a box type. A record will be added to ``bbox_types``, whose key is the box type name and value is the box type itself. Simultaneously, a reverse dictionary ``_box_type_to_name`` will be updated. It can be used as a decorator or a normal function. Args: name (str): The name of box type. bbox_type (type, Optiona... |
14,735 | from typing import Callable, Optional, Tuple, Type, Union
import numpy as np
import torch
from torch import Tensor
from .base_boxes import BaseBoxes
def _register_box_converter(src_type: Union[str, type],
dst_type: Union[str, type],
converter: Callable,
... | Register a box converter. A record will be added to ``box_converter``, whose key is '{src_type_name}2{dst_type_name}' and value is the convert function. It can be used as a decorator or a normal function. Args: src_type (str or type): source box type name or class. dst_type (str or type): destination box type name or c... |
14,736 | from typing import Callable, Optional, Tuple, Type, Union
import numpy as np
import torch
from torch import Tensor
from .base_boxes import BaseBoxes
BoxType = Union[np.ndarray, Tensor, BaseBoxes]
box_converters: dict = {}
def get_box_type(box_type: Union[str, type]) -> Tuple[str, type]:
"""get both box type name an... | Convert boxes from source type to destination type. If ``boxes`` is a instance of BaseBoxes, the ``src_type`` will be set as the type of ``boxes``. Args: boxes (np.ndarray or Tensor or :obj:`BaseBoxes`): boxes need to convert. src_type (str or type, Optional): source box type. Defaults to None. dst_type (str or type, O... |
14,737 | from typing import Callable, Optional, Tuple, Type, Union
import numpy as np
import torch
from torch import Tensor
from .base_boxes import BaseBoxes
def get_box_type(box_type: Union[str, type]) -> Tuple[str, type]:
"""get both box type name and class.
Args:
box_type (str or type): Single box type name o... | A decorator which automatically casts results['gt_bboxes'] to the destination box type. It commenly used in mmdet.datasets.transforms to make the transforms up- compatible with the np.ndarray type of results['gt_bboxes']. The speed of processing of np.ndarray and BaseBoxes data are the same: - np.ndarray: 0.0509 img/s ... |
14,738 | import torch
def fp16_clamp(x, min=None, max=None):
if not x.is_cuda and x.dtype == torch.float16:
# clamp for cpu float16, tensor fp16 has no clamp implementation
return x.float().clamp(min, max).half()
return x.clamp(min, max)
The provided code snippet includes necessary dependencies for impl... | Calculate overlap between two set of bboxes. FP16 Contributed by https://github.com/open-mmlab/mmdetection/pull/4889 Note: Assume bboxes1 is M x 4, bboxes2 is N x 4, when mode is 'iou', there are some new generated variable when calculating IOU using bbox_overlaps function: 1) is_aligned is False area1: M x 1 area2: N ... |
14,739 | import numpy as np
import pycocotools.mask as mask_util
import torch
from mmengine.utils import slice_list
The provided code snippet includes necessary dependencies for implementing the `split_combined_polys` function. Write a Python function `def split_combined_polys(polys, poly_lens, polys_per_mask)` to solve the fo... | Split the combined 1-D polys into masks. A mask is represented as a list of polys, and a poly is represented as a 1-D array. In dataset, all masks are concatenated into a single 1-D tensor. Here we need to split the tensor into original representations. Args: polys (list): a list (length = image num) of 1-D tensors pol... |
14,740 | import numpy as np
import pycocotools.mask as mask_util
import torch
from mmengine.utils import slice_list
The provided code snippet includes necessary dependencies for implementing the `encode_mask_results` function. Write a Python function `def encode_mask_results(mask_results)` to solve the following problem:
Encod... | Encode bitmap mask to RLE code. Args: mask_results (list): bitmap mask results. Returns: list | tuple: RLE encoded mask. |
14,741 | import numpy as np
import pycocotools.mask as mask_util
import torch
from mmengine.utils import slice_list
The provided code snippet includes necessary dependencies for implementing the `mask2bbox` function. Write a Python function `def mask2bbox(masks)` to solve the following problem:
Obtain tight bounding boxes of b... | Obtain tight bounding boxes of binary masks. Args: masks (Tensor): Binary mask of shape (n, h, w). Returns: Tensor: Bboxe with shape (n, 4) of \ positive region in binary mask. |
14,742 | import itertools
from abc import ABCMeta, abstractmethod
from typing import Sequence, Type, TypeVar
import cv2
import mmcv
import numpy as np
import pycocotools.mask as maskUtils
import torch
from mmcv.ops.roi_align import roi_align
The provided code snippet includes necessary dependencies for implementing the `polygo... | Convert masks from the form of polygons to bitmaps. Args: polygons (list[ndarray]): masks in polygon representation height (int): mask height width (int): mask width Return: ndarray: the converted masks in bitmap representation |
14,743 | import itertools
from abc import ABCMeta, abstractmethod
from typing import Sequence, Type, TypeVar
import cv2
import mmcv
import numpy as np
import pycocotools.mask as maskUtils
import torch
from mmcv.ops.roi_align import roi_align
The provided code snippet includes necessary dependencies for implementing the `bitmap... | Convert masks from the form of bitmaps to polygons. Args: bitmap (ndarray): masks in bitmap representation. Return: list[ndarray]: the converted mask in polygon representation. bool: whether the mask has holes. |
14,744 | import numpy as np
import torch
from torch.nn.modules.utils import _pair
def mask_target_single(pos_proposals, pos_assigned_gt_inds, gt_masks, cfg):
"""Compute mask target for each positive proposal in the image.
Args:
pos_proposals (Tensor): Positive proposals.
pos_assigned_gt_inds (Tensor): As... | Compute mask target for positive proposals in multiple images. Args: pos_proposals_list (list[Tensor]): Positive proposals in multiple images, each has shape (num_pos, 4). pos_assigned_gt_inds_list (list[Tensor]): Assigned GT indices for each positive proposals, each has shape (num_pos,). gt_masks_list (list[:obj:`Base... |
14,745 | import copy
import warnings
from pathlib import Path
from typing import Optional, Sequence, Union
import numpy as np
import torch
import torch.nn as nn
from mmcv.ops import RoIPool
from mmcv.transforms import Compose
from mmengine.config import Config
from mmengine.runner import load_checkpoint
from mmdet.registry impo... | Initialize a detector from config file. Args: config (str, :obj:`Path`, or :obj:`mmengine.Config`): Config file path, :obj:`Path`, or the config object. checkpoint (str, optional): Checkpoint path. If left as None, the model will not load any weights. palette (str): Color palette used for visualization. If palette is s... |
14,746 | import copy
import warnings
from pathlib import Path
from typing import Optional, Sequence, Union
import numpy as np
import torch
import torch.nn as nn
from mmcv.ops import RoIPool
from mmcv.transforms import Compose
from mmengine.config import Config
from mmengine.runner import load_checkpoint
from mmdet.registry impo... | Inference image(s) with the detector. Args: model (nn.Module): The loaded detector. imgs (str, ndarray, Sequence[str/ndarray]): Either image files or loaded images. test_pipeline (:obj:`Compose`): Test pipeline. Returns: :obj:`DetDataSample` or list[:obj:`DetDataSample`]: If imgs is a list or tuple, the same length lis... |
14,747 | import copy
import warnings
from pathlib import Path
from typing import Optional, Sequence, Union
import numpy as np
import torch
import torch.nn as nn
from mmcv.ops import RoIPool
from mmcv.transforms import Compose
from mmengine.config import Config
from mmengine.runner import load_checkpoint
from mmdet.registry impo... | Async inference image(s) with the detector. Args: model (nn.Module): The loaded detector. img (str | ndarray): Either image files or loaded images. Returns: Awaitable detection results. |
14,748 | from typing import List, Tuple, Union
import mmcv
import numpy as np
from mmengine.utils import is_str
The provided code snippet includes necessary dependencies for implementing the `palette_val` function. Write a Python function `def palette_val(palette: List[tuple]) -> List[tuple]` to solve the following problem:
Co... | Convert palette to matplotlib palette. Args: palette (List[tuple]): A list of color tuples. Returns: List[tuple[float]]: A list of RGB matplotlib color tuples. |
14,749 | from typing import List, Tuple, Union
import mmcv
import numpy as np
from mmengine.utils import is_str
The provided code snippet includes necessary dependencies for implementing the `get_palette` function. Write a Python function `def get_palette(palette: Union[List[tuple], str, tuple], num_classes: in... | Get palette from various inputs. Args: palette (list[tuple] | str | tuple): palette inputs. num_classes (int): the number of classes. Returns: list[tuple[int]]: A list of color tuples. |
14,750 | from typing import List, Tuple, Union
import mmcv
import numpy as np
from mmengine.utils import is_str
The provided code snippet includes necessary dependencies for implementing the `_get_adaptive_scales` function. Write a Python function `def _get_adaptive_scales(areas: np.ndarray, min_area: ... | Get adaptive scales according to areas. The scale range is [0.5, 1.0]. When the area is less than ``min_area``, the scale is 0.5 while the area is larger than ``max_area``, the scale is 1.0. Args: areas (ndarray): The areas of bboxes or masks with the shape of (n, ). min_area (int): Lower bound areas for adaptive scale... |
14,751 | from typing import List, Tuple, Union
import mmcv
import numpy as np
from mmengine.utils import is_str
The provided code snippet includes necessary dependencies for implementing the `jitter_color` function. Write a Python function `def jitter_color(color: tuple) -> tuple` to solve the following problem:
Randomly jitte... | Randomly jitter the given color in order to better distinguish instances with the same class. Args: color (tuple): The RGB color tuple. Each value is between [0, 255]. Returns: tuple: The jittered color tuple. |
14,752 | import datetime
import itertools
import os.path as osp
import tempfile
from typing import Dict, Optional, Sequence, Tuple, Union
import mmcv
import numpy as np
from mmengine.evaluator import BaseMetric
from mmengine.fileio import FileClient, dump, load
from mmengine.logging import MMLogger, print_log
from terminaltable... | Parse the Panoptic Quality results. Args: pq_results (dict): Panoptic Quality results. Returns: dict: Panoptic Quality results parsed. |
14,753 | import datetime
import itertools
import os.path as osp
import tempfile
from typing import Dict, Optional, Sequence, Tuple, Union
import mmcv
import numpy as np
from mmengine.evaluator import BaseMetric
from mmengine.fileio import FileClient, dump, load
from mmengine.logging import MMLogger, print_log
from terminaltable... | Print the panoptic evaluation results table. Args: pq_results(dict): The Panoptic Quality results. classwise_results(dict, optional): The classwise Panoptic Quality. results. The keys are class names and the values are metrics. Defaults to None. logger (:obj:`MMLogger` | str, optional): Logger used for printing related... |
14,754 | from mmengine.utils import is_str
The provided code snippet includes necessary dependencies for implementing the `wider_face_classes` function. Write a Python function `def wider_face_classes() -> list` to solve the following problem:
Class names of WIDERFace.
Here is the function:
def wider_face_classes() -> list:
... | Class names of WIDERFace. |
14,755 | from mmengine.utils import is_str
The provided code snippet includes necessary dependencies for implementing the `voc_classes` function. Write a Python function `def voc_classes() -> list` to solve the following problem:
Class names of PASCAL VOC.
Here is the function:
def voc_classes() -> list:
"""Class names o... | Class names of PASCAL VOC. |
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