id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
14,856 | from functools import partial
from typing import List, Sequence, Tuple, Union
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmengine.utils import digit_version
from six.moves import map, zip
from torch import Tensor
from torch.autograd import Function
from torch.nn import functional ... | Unpack ``gt_instances``, ``gt_instances_ignore`` and ``img_metas`` based on ``batch_data_samples`` Args: batch_data_samples (List[:obj:`DetDataSample`]): The Data Samples. It usually includes information such as `gt_instance`, `gt_panoptic_seg` and `gt_sem_seg`. Returns: tuple: - batch_gt_instances (list[:obj:`Instance... |
14,857 | from functools import partial
from typing import List, Sequence, Tuple, Union
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmengine.utils import digit_version
from six.moves import map, zip
from torch import Tensor
from torch.autograd import Function
from torch.nn import functional ... | Handle predicted instances when RoI is empty. Note: If ``instance_results`` is not None, it will be modified in place internally, and then return ``instance_results`` Args: batch_img_metas (list[dict]): List of image information. device (torch.device): Device of tensor. task_type (str): Expected returned task type. it ... |
14,858 | from functools import partial
from typing import List, Sequence, Tuple, Union
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmengine.utils import digit_version
from six.moves import map, zip
from torch import Tensor
from torch.autograd import Function
from torch.nn import functional ... | Apply function to a list of arguments. Note: This function applies the ``func`` to multiple inputs and map the multiple outputs of the ``func`` into different list. Each list contains the same type of outputs corresponding to different inputs. Args: func (Function): A function that will be applied to a list of argument... |
14,859 | from functools import partial
from typing import List, Sequence, Tuple, Union
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmengine.utils import digit_version
from six.moves import map, zip
from torch import Tensor
from torch.autograd import Function
from torch.nn import functional ... | Unmap a subset of item (data) back to the original set of items (of size count) |
14,860 | from functools import partial
from typing import List, Sequence, Tuple, Union
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmengine.utils import digit_version
from six.moves import map, zip
from torch import Tensor
from torch.autograd import Function
from torch.nn import functional ... | Convert Mask to ndarray.. Args: mask (:obj:`BitmapMasks` or :obj:`PolygonMasks` or torch.Tensor or np.ndarray): The mask to be converted. Returns: np.ndarray: Ndarray mask of shape (n, h, w) that has been converted |
14,861 | from functools import partial
from typing import List, Sequence, Tuple, Union
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmengine.utils import digit_version
from six.moves import map, zip
from torch import Tensor
from torch.autograd import Function
from torch.nn import functional ... | flip tensor base on flip_direction. Args: src_tensor (Tensor): input feature map, shape (B, C, H, W). flip_direction (str): The flipping direction. Options are 'horizontal', 'vertical', 'diagonal'. Returns: out_tensor (Tensor): Flipped tensor. |
14,862 | from functools import partial
from typing import List, Sequence, Tuple, Union
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmengine.utils import digit_version
from six.moves import map, zip
from torch import Tensor
from torch.autograd import Function
from torch.nn import functional ... | Extract a multi-scale single image tensor from a multi-scale batch tensor based on batch index. Note: The default value of detach is True, because the proposal gradient needs to be detached during the training of the two-stage model. E.g Cascade Mask R-CNN. Args: mlvl_tensors (list[Tensor]): Batch tensor for all scale ... |
14,863 | from functools import partial
from typing import List, Sequence, Tuple, Union
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmengine.utils import digit_version
from six.moves import map, zip
from torch import Tensor
from torch.autograd import Function
from torch.nn import functional ... | Filter results using score threshold and topk candidates. Args: scores (Tensor): The scores, shape (num_bboxes, K). score_thr (float): The score filter threshold. topk (int): The number of topk candidates. results (dict or list or Tensor, Optional): The results to which the filtering rule is to be applied. The shape of... |
14,864 | from functools import partial
from typing import List, Sequence, Tuple, Union
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmengine.utils import digit_version
from six.moves import map, zip
from torch import Tensor
from torch.autograd import Function
from torch.nn import functional ... | Calculate the centroid coordinates of the mask. Args: mask (Tensor): The mask to be calculated, shape (h, w). esp (float): Avoid dividing by zero. Default: 1e-6. Returns: tuple[Tensor]: the coordinates of the center point of the mask. - center_h (Tensor): the center point of the height. - center_w (Tensor): the center ... |
14,865 | from functools import partial
from typing import List, Sequence, Tuple, Union
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmengine.utils import digit_version
from six.moves import map, zip
from torch import Tensor
from torch.autograd import Function
from torch.nn import functional ... | Generate the coordinate. Args: featmap_sizes (tuple): The feature to be calculated, of shape (N, C, W, H). device (str): The device where the feature will be put on. Returns: coord_feat (Tensor): The coordinate feature, of shape (N, 2, W, H). |
14,866 | from functools import partial
from typing import List, Sequence, Tuple, Union
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmengine.utils import digit_version
from six.moves import map, zip
from torch import Tensor
from torch.autograd import Function
from torch.nn import functional ... | Concat multi-level feature maps by image. [feature_level0, feature_level1...] -> [feature_image0, feature_image1...] Convert the shape of each element in mlvl_tensor from (N, C, H, W) to (N, H*W , C), then split the element to N elements with shape (H*W, C), and concat elements in same image of all level along first di... |
14,867 | from functools import partial
from typing import List, Sequence, Tuple, Union
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmengine.utils import digit_version
from six.moves import map, zip
from torch import Tensor
from torch.autograd import Function
from torch.nn import functional ... | Convert targets by image to targets by feature level. [target_img0, target_img1] -> [target_level0, target_level1, ...] |
14,868 | from functools import partial
from typing import List, Sequence, Tuple, Union
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmengine.utils import digit_version
from six.moves import map, zip
from torch import Tensor
from torch.autograd import Function
from torch.nn import functional ... | null |
14,869 | from functools import partial
from typing import List, Sequence, Tuple, Union
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmengine.utils import digit_version
from six.moves import map, zip
from torch import Tensor
from torch.autograd import Function
from torch.nn import functional ... | null |
14,870 | from functools import partial
from typing import List, Sequence, Tuple, Union
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmengine.utils import digit_version
from six.moves import map, zip
from torch import Tensor
from torch.autograd import Function
from torch.nn import functional ... | Filter ground truth (GT) instances by score and/or size. Args: batch_data_samples (SampleList): The Data Samples. It usually includes information such as `gt_instance`, `gt_panoptic_seg` and `gt_sem_seg`. score_thr (float): The score filter threshold. wh_thr (tuple): Minimum width and height of bbox. Returns: SampleLis... |
14,871 | from functools import partial
from typing import List, Sequence, Tuple, Union
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmengine.utils import digit_version
from six.moves import map, zip
from torch import Tensor
from torch.autograd import Function
from torch.nn import functional ... | Rename the key names in loss dict by adding a prefix. Args: prefix (str): The prefix for loss components. losses (dict): A dictionary of loss components. Returns: dict: A dictionary of loss components with prefix. |
14,872 | from functools import partial
from typing import List, Sequence, Tuple, Union
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmengine.utils import digit_version
from six.moves import map, zip
from torch import Tensor
from torch.autograd import Function
from torch.nn import functional ... | Reweight losses in the dict by weight. Args: losses (dict): A dictionary of loss components. weight (float): Weight for loss components. Returns: dict: A dictionary of weighted loss components. |
14,873 | from functools import partial
from typing import List, Sequence, Tuple, Union
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmengine.utils import digit_version
from six.moves import map, zip
from torch import Tensor
from torch.autograd import Function
from torch.nn import functional ... | Generate the relative coordinate maps with feat_stride. Args: locations (Tensor): The prior location of mask feature map. It has shape (num_priors, 2). centers (Tensor): The prior points of a object in all feature pyramid. It has shape (num_pos, 2) strides (Tensor): The prior strides of a object in all feature pyramid.... |
14,874 | from functools import partial
from typing import List, Sequence, Tuple, Union
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmengine.utils import digit_version
from six.moves import map, zip
from torch import Tensor
from torch.autograd import Function
from torch.nn import functional ... | aligned bilinear, used in original implement in CondInst: https://github.com/aim-uofa/AdelaiDet/blob/\ c0b2092ce72442b0f40972f7c6dda8bb52c46d16/adet/utils/comm.py#L23 |
14,875 | from functools import partial
from typing import List, Sequence, Tuple, Union
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmengine.utils import digit_version
from six.moves import map, zip
from torch import Tensor
from torch.autograd import Function
from torch.nn import functional ... | unfold_wo_center, used in original implement in BoxInst: https://github.com/aim-uofa/AdelaiDet/blob/\ 4a3a1f7372c35b48ebf5f6adc59f135a0fa28d60/\ adet/modeling/condinst/condinst.py#L53 |
14,876 | from math import sqrt
import torch
import torch.nn.functional as F
def gaussian2D(radius, sigma=1, dtype=torch.float32, device='cpu'):
"""Generate 2D gaussian kernel.
Args:
radius (int): Radius of gaussian kernel.
sigma (int): Sigma of gaussian function. Default: 1.
dtype (torch.dtype): ... | Generate 2D gaussian heatmap. Args: heatmap (Tensor): Input heatmap, the gaussian kernel will cover on it and maintain the max value. center (list[int]): Coord of gaussian kernel's center. radius (int): Radius of gaussian kernel. k (int): Coefficient of gaussian kernel. Default: 1. Returns: out_heatmap (Tensor): Update... |
14,877 | from math import sqrt
import torch
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `gaussian_radius` function. Write a Python function `def gaussian_radius(det_size, min_overlap)` to solve the following problem:
r"""Generate 2D gaussian radius. This functi... | r"""Generate 2D gaussian radius. This function is modified from the `official github repo <https://github.com/princeton-vl/CornerNet-Lite/blob/master/core/sample/ utils.py#L65>`_. Given ``min_overlap``, radius could computed by a quadratic equation according to Vieta's formulas. There are 3 cases for computing gaussian... |
14,878 | from math import sqrt
import torch
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `get_local_maximum` function. Write a Python function `def get_local_maximum(heat, kernel=3)` to solve the following problem:
Extract local maximum pixel with given kernel. ... | Extract local maximum pixel with given kernel. Args: heat (Tensor): Target heatmap. kernel (int): Kernel size of max pooling. Default: 3. Returns: heat (Tensor): A heatmap where local maximum pixels maintain its own value and other positions are 0. |
14,879 | from math import sqrt
import torch
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `get_topk_from_heatmap` function. Write a Python function `def get_topk_from_heatmap(scores, k=20)` to solve the following problem:
Get top k positions from heatmap. Args: s... | Get top k positions from heatmap. Args: scores (Tensor): Target heatmap with shape [batch, num_classes, height, width]. k (int): Target number. Default: 20. Returns: tuple[torch.Tensor]: Scores, indexes, categories and coords of topk keypoint. Containing following Tensors: - topk_scores (Tensor): Max scores of each top... |
14,880 | from math import sqrt
import torch
import torch.nn.functional as F
def gather_feat(feat, ind, mask=None):
"""Gather feature according to index.
Args:
feat (Tensor): Target feature map.
ind (Tensor): Target coord index.
mask (Tensor | None): Mask of feature map. Default: None.
Returns... | Transpose and gather feature according to index. Args: feat (Tensor): Target feature map. ind (Tensor): Target coord index. Returns: feat (Tensor): Transposed and gathered feature. |
14,881 | import torch
from mmcv.ops import point_sample
from torch import Tensor
def get_uncertainty(mask_preds: Tensor, labels: Tensor) -> Tensor:
"""Estimate uncertainty based on pred logits.
We estimate uncertainty as L1 distance between 0.0 and the logits
prediction in 'mask_preds' for the foreground class in `c... | Get ``num_points`` most uncertain points with random points during train. Sample points in [0, 1] x [0, 1] coordinate space based on their uncertainty. The uncertainties are calculated for each point using 'get_uncertainty()' function that takes point's logit prediction as input. Args: mask_preds (Tensor): A tensor of ... |
14,882 | from typing import Tuple
import torch
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `preprocess_panoptic_gt` function. Write a Python function `def preprocess_panoptic_gt(gt_labels: Tensor, gt_masks: Tensor, gt_semantic_seg: Tensor, n... | Preprocess the ground truth for a image. Args: gt_labels (Tensor): Ground truth labels of each bbox, with shape (num_gts, ). gt_masks (BitmapMasks): Ground truth masks of each instances of a image, shape (num_gts, h, w). gt_semantic_seg (Tensor | None): Ground truth of semantic segmentation with the shape (1, h, w). [0... |
14,883 | import re
from mmengine.config import Config
The provided code snippet includes necessary dependencies for implementing the `replace_cfg_vals` function. Write a Python function `def replace_cfg_vals(ori_cfg)` to solve the following problem:
Replace the string "${key}" with the corresponding value. Replace the "${key}"... | Replace the string "${key}" with the corresponding value. Replace the "${key}" with the value of ori_cfg.key in the config. And support replacing the chained ${key}. Such as, replace "${key0.key1}" with the value of cfg.key0.key1. Code is modified from `vars.py < https://github.com/microsoft/SoftTeacher/blob/main/ssod/... |
14,884 | import torch
The provided code snippet includes necessary dependencies for implementing the `split_batch` function. Write a Python function `def split_batch(img, img_metas, kwargs)` to solve the following problem:
Split data_batch by tags. Code is modified from <https://github.com/microsoft/SoftTeacher/blob/main/ssod/... | Split data_batch by tags. Code is modified from <https://github.com/microsoft/SoftTeacher/blob/main/ssod/utils/structure_utils.py> # noqa: E501 Args: img (Tensor): of shape (N, C, H, W) encoding input images. Typically these should be mean centered and std scaled. img_metas (list[dict]): List of image info dict where e... |
14,885 | import datetime
import os
import platform
import warnings
import cv2
import torch.multiprocessing as mp
from mmengine import DefaultScope
The provided code snippet includes necessary dependencies for implementing the `setup_multi_processes` function. Write a Python function `def setup_multi_processes(cfg)` to solve th... | Setup multi-processing environment variables. |
14,886 | import datetime
import os
import platform
import warnings
import cv2
import torch.multiprocessing as mp
from mmengine import DefaultScope
The provided code snippet includes necessary dependencies for implementing the `register_all_modules` function. Write a Python function `def register_all_modules(init_default_scope:... | Register all modules in mmdet into the registries. Args: init_default_scope (bool): Whether initialize the mmdet default scope. When `init_default_scope=True`, the global default scope will be set to `mmdet`, and all registries will build modules from mmdet's registry node. To understand more about the registry, please... |
14,887 | from mmengine.utils import get_git_hash
from mmengine.utils.dl_utils import collect_env as collect_base_env
import mmdet
The provided code snippet includes necessary dependencies for implementing the `collect_env` function. Write a Python function `def collect_env()` to solve the following problem:
Collect the informa... | Collect the information of the running environments. |
14,888 | import functools
import pickle
import warnings
from collections import OrderedDict
import numpy as np
import torch
import torch.distributed as dist
from mmengine.dist import get_dist_info
from torch._utils import (_flatten_dense_tensors, _take_tensors,
_unflatten_dense_tensors)
def _allreduce_... | Allreduce gradients. Args: params (list[torch.Parameters]): List of parameters of a model coalesce (bool, optional): Whether allreduce parameters as a whole. Defaults to True. bucket_size_mb (int, optional): Size of bucket, the unit is MB. Defaults to -1. |
14,889 | import functools
import pickle
import warnings
from collections import OrderedDict
import numpy as np
import torch
import torch.distributed as dist
from mmengine.dist import get_dist_info
from torch._utils import (_flatten_dense_tensors, _take_tensors,
_unflatten_dense_tensors)
The provided c... | Obtain the mean of tensor on different GPUs. |
14,890 | import functools
import pickle
import warnings
from collections import OrderedDict
import numpy as np
import torch
import torch.distributed as dist
from mmengine.dist import get_dist_info
from torch._utils import (_flatten_dense_tensors, _take_tensors,
_unflatten_dense_tensors)
The provided c... | Return a process group based on gloo backend, containing all the ranks The result is cached. |
14,891 | import functools
import pickle
import warnings
from collections import OrderedDict
import numpy as np
import torch
import torch.distributed as dist
from mmengine.dist import get_dist_info
from torch._utils import (_flatten_dense_tensors, _take_tensors,
_unflatten_dense_tensors)
def obj2tensor(... | Apply all reduce function for python dict object. The code is modified from https://github.com/Megvii- BaseDetection/YOLOX/blob/main/yolox/utils/allreduce_norm.py. NOTE: make sure that py_dict in different ranks has the same keys and the values should be in the same shape. Currently only supports nccl backend. Args: py... |
14,892 | import functools
import pickle
import warnings
from collections import OrderedDict
import numpy as np
import torch
import torch.distributed as dist
from mmengine.dist import get_dist_info
from torch._utils import (_flatten_dense_tensors, _take_tensors,
_unflatten_dense_tensors)
The provided c... | Make sure different ranks share the same seed. All workers must call this function, otherwise it will deadlock. This method is generally used in `DistributedSampler`, because the seed should be identical across all processes in the distributed group. In distributed sampling, different ranks should sample non-overlapped... |
14,893 | import asyncio
import contextlib
import logging
import os
import time
from typing import List
import torch
logger = logging.getLogger(__name__)
DEBUG_COMPLETED_TIME = bool(os.environ.get('DEBUG_COMPLETED_TIME', False))
The provided code snippet includes necessary dependencies for implementing the `completed` function.... | Async context manager that waits for work to complete on given CUDA streams. |
14,894 | import asyncio
import contextlib
import logging
import os
import time
from typing import List
import torch
logger = logging.getLogger(__name__)
The provided code snippet includes necessary dependencies for implementing the `concurrent` function. Write a Python function `async def concurrent(streamqueue: asyncio.Queue,... | Run code concurrently in different streams. :param streamqueue: asyncio.Queue instance. Queue tasks define the pool of streams used for concurrent execution. |
14,895 | import glob
import os
import os.path as osp
import warnings
from typing import Union
from mmengine.config import Config, ConfigDict
from mmengine.logging import print_log
The provided code snippet includes necessary dependencies for implementing the `find_latest_checkpoint` function. Write a Python function `def find_... | Find the latest checkpoint from the working directory. Args: path(str): The path to find checkpoints. suffix(str): File extension. Defaults to pth. Returns: latest_path(str | None): File path of the latest checkpoint. References: .. [1] https://github.com/microsoft/SoftTeacher /blob/main/ssod/utils/patch.py |
14,896 | import glob
import os
import os.path as osp
import warnings
from typing import Union
from mmengine.config import Config, ConfigDict
from mmengine.logging import print_log
The provided code snippet includes necessary dependencies for implementing the `update_data_root` function. Write a Python function `def update_data... | Update data root according to env MMDET_DATASETS. If set env MMDET_DATASETS, update cfg.data_root according to MMDET_DATASETS. Otherwise, using cfg.data_root as default. Args: cfg (:obj:`Config`): The model config need to modify logger (logging.Logger | str | None): the way to print msg |
14,897 | import glob
import os
import os.path as osp
import warnings
from typing import Union
from mmengine.config import Config, ConfigDict
from mmengine.logging import print_log
The provided code snippet includes necessary dependencies for implementing the `get_test_pipeline_cfg` function. Write a Python function `def get_te... | Get the test dataset pipeline from entire config. Args: cfg (str or :obj:`ConfigDict`): the entire config. Can be a config file or a ``ConfigDict``. Returns: :obj:`ConfigDict`: the config of test dataset. |
14,898 | import contextlib
import sys
import time
import torch
The provided code snippet includes necessary dependencies for implementing the `profile_time` function. Write a Python function `def profile_time(trace_name, name, enabled=True, stream=None, ... | Print time spent by CPU and GPU. Useful as a temporary context manager to find sweet spots of code suitable for async implementation. |
14,899 | import copy
import time
from functools import partial
from typing import List, Optional, Union
import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import fuse_conv_bn
from mmengine import MMLogger
from mmengine.config import Config
from mmengine.device import get_max_cuda_memory
from mmengine.dist impor... | Custom round function. |
14,900 | import copy
import time
from functools import partial
from typing import List, Optional, Union
import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import fuse_conv_bn
from mmengine import MMLogger
from mmengine.config import Config
from mmengine.device import get_max_cuda_memory
from mmengine.dist impor... | print process memory info. |
14,901 | import numpy as np
The provided code snippet includes necessary dependencies for implementing the `ensure_rng` function. Write a Python function `def ensure_rng(rng=None)` to solve the following problem:
Coerces input into a random number generator. If the input is None, then a global random state is returned. If the ... | Coerces input into a random number generator. If the input is None, then a global random state is returned. If the input is a numeric value, then that is used as a seed to construct a random state. Otherwise the input is returned as-is. Adapted from [1]_. Args: rng (int | numpy.random.RandomState | None): if None, then... |
14,902 | import warnings
from collections import abc
from contextlib import contextmanager
from functools import wraps
import torch
from mmengine.logging import MMLogger
The provided code snippet includes necessary dependencies for implementing the `cast_tensor_type` function. Write a Python function `def cast_tensor_type(inpu... | Recursively convert Tensor in inputs from ``src_type`` to ``dst_type``. Args: inputs: Inputs that to be casted. src_type (torch.dtype | torch.device): Source type. src_type (torch.dtype | torch.device): Destination type. Returns: The same type with inputs, but all contained Tensors have been cast. |
14,903 | import warnings
from collections import abc
from contextlib import contextmanager
from functools import wraps
import torch
from mmengine.logging import MMLogger
The provided code snippet includes necessary dependencies for implementing the `_ignore_torch_cuda_oom` function. Write a Python function `def _ignore_torch_c... | A context which ignores CUDA OOM exception from pytorch. Code is modified from <https://github.com/facebookresearch/detectron2/blob/main/detectron2/utils/memory.py> # noqa: E501 |
14,904 | import copy
import warnings
from mmengine.config import ConfigDict
def compat_runner_args(cfg):
if 'runner' not in cfg:
cfg.runner = ConfigDict({
'type': 'EpochBasedRunner',
'max_epochs': cfg.total_epochs
})
warnings.warn(
'config is now expected to have a... | This function would modify some filed to keep the compatibility of config. For example, it will move some args which will be deprecated to the correct fields. |
14,905 | import inspect
from mmengine.logging import print_log
def get_caller_name():
"""Get name of caller method."""
# this_func_frame = inspect.stack()[0][0] # i.e., get_caller_name
# callee_frame = inspect.stack()[1][0] # e.g., log_img_scale
caller_frame = inspect.stack()[2][0] # e.g., caller of log_img_s... | Log image size. Args: img_scale (tuple): Image size to be logged. shape_order (str, optional): The order of image shape. 'hw' for (height, width) and 'wh' for (width, height). Defaults to 'hw'. skip_square (bool, optional): Whether to skip logging for square img_scale. Defaults to False. Returns: bool: Whether to have ... |
14,906 | import argparse
import logging
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.logging import print_log
from mmengine.registry import RUNNERS
from mmengine.runner import Runner
from mmdet.utils import register_all_modules
def parse_args():
parser = argparse.ArgumentPars... | null |
14,907 | import json
import multiprocessing
import os
import sys
from itertools import product
from math import ceil
import cv2
import numpy as np
class PatchGenerator(object):
def __init__(self, info, type='normal', data_dir='/home/liwenxi/panda/raw/PANDA/image_train',
save_img_path='/home/liwenxi/panda/ra... | null |
14,908 | import json
import multiprocessing
import os
import sys
from itertools import product
from math import ceil
import cv2
import numpy as np
class PatchGenerator(object):
def __init__(self, info, type='normal', data_dir='/home/liwenxi/panda/raw/PANDA/image_train',
save_img_path='/home/liwenxi/panda/ra... | null |
14,909 | import argparse
import tempfile
from functools import partial
from pathlib import Path
import torch
from mmengine.config import Config, DictAction
from mmengine.logging import MMLogger
from mmengine.model import revert_sync_batchnorm
from mmengine.registry import init_default_scope
from mmengine.runner import Runner
fr... | null |
14,910 | import argparse
import tempfile
from functools import partial
from pathlib import Path
import torch
from mmengine.config import Config, DictAction
from mmengine.logging import MMLogger
from mmengine.model import revert_sync_batchnorm
from mmengine.registry import init_default_scope
from mmengine.runner import Runner
fr... | null |
14,911 | import argparse
import os.path as osp
import numpy as np
import torch
from mmengine.config import Config
from mmengine.fileio import dump
from mmengine.logging import MMLogger
from mmengine.utils import ProgressBar
from scipy.optimize import differential_evolution
from mmdet.registry import DATASETS
from mmdet.structur... | null |
14,912 | import argparse
import os
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import MultipleLocator
from mmcv.ops import nms
from mmengine import Config, DictAction
from mmengine.fileio import load
from mmengine.utils import ProgressBar
from mmdet.evaluation import bbox_overlaps
from mmdet.regist... | null |
14,913 | import argparse
import os
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import MultipleLocator
from mmcv.ops import nms
from mmengine import Config, DictAction
from mmengine.fileio import load
from mmengine.utils import ProgressBar
from mmdet.evaluation import bbox_overlaps
from mmdet.regist... | Calculate the confusion matrix. Args: dataset (Dataset): Test or val dataset. results (list[ndarray]): A list of detection results in each image. score_thr (float|optional): Score threshold to filter bboxes. Default: 0. nms_iou_thr (float|optional): nms IoU threshold, the detection results have done nms in the detector... |
14,914 | import argparse
import os
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import MultipleLocator
from mmcv.ops import nms
from mmengine import Config, DictAction
from mmengine.fileio import load
from mmengine.utils import ProgressBar
from mmdet.evaluation import bbox_overlaps
from mmdet.regist... | Draw confusion matrix with matplotlib. Args: confusion_matrix (ndarray): The confusion matrix. labels (list[str]): List of class names. save_dir (str|optional): If set, save the confusion matrix plot to the given path. Default: None. show (bool): Whether to show the plot. Default: True. title (str): Title of the plot. ... |
14,915 | import os.path as osp
from argparse import ArgumentParser
import numpy as np
from mmengine.fileio import load
def get_coco_style_results(filename,
task='bbox',
metric=None,
prints='mPC',
aggregate='benchmark'):
... | null |
14,916 | import os.path as osp
from argparse import ArgumentParser
import numpy as np
from mmengine.fileio import load
def get_distortions_from_results(eval_output):
def get_distortions_from_file(filename):
eval_output = load(filename)
return get_distortions_from_results(eval_output) | null |
14,917 | import argparse
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.utils import ProgressBar
from mmdet.models.utils import mask2ndarray
from mmdet.registry import DATASETS, VISUALIZERS
from mmdet.structures.bbox import BaseBoxes
from mmdet.utils import register_all_modules
def parse_arg... | null |
14,918 | import argparse
import mmengine
from mmengine import Config, DictAction
from mmengine.evaluator import Evaluator
from mmdet.registry import DATASETS
from mmdet.utils import register_all_modules
def parse_args():
parser = argparse.ArgumentParser(description='Evaluate metric of the '
... | null |
14,919 | import copy
import os
from argparse import ArgumentParser
from multiprocessing import Pool
import matplotlib.pyplot as plt
import numpy as np
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
def makeplot(rs, ps, outDir, class_name, iou_type):
cs = np.vstack([
np.ones((2, 3)),
... | null |
14,920 | import argparse
import json
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
def cal_train_time(log_dicts, args):
for i, log_dict in enumerate(log_dicts):
print(f'{"-" * 5}Analyze train time of {args.json_logs[i]}{"-" * 5}')
all_times = []... | null |
14,921 | import argparse
import json
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
def plot_curve(log_dicts, args):
if args.backend is not None:
plt.switch_backend(args.backend)
sns.set_style(args.style)
# if legend is None, use {filename}_{key}... | null |
14,922 | import argparse
import json
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
def add_plot_parser(subparsers):
parser_plt = subparsers.add_parser(
'plot_curve', help='parser for plotting curves')
parser_plt.add_argument(
'json_logs',
... | null |
14,923 | import argparse
import json
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
def load_json_logs(json_logs):
# load and convert json_logs to log_dict, key is epoch, value is a sub dict
# keys of sub dict is different metrics, e.g. memory, bbox_mAP
... | null |
14,924 | import argparse
import os
from mmengine import MMLogger
from mmengine.config import Config, DictAction
from mmengine.dist import init_dist
from mmengine.utils import mkdir_or_exist
from mmdet.utils import register_all_modules
from mmdet.utils.benchmark import (DataLoaderBenchmark, DatasetBenchmark,
... | null |
14,925 | import argparse
import os
from mmengine import MMLogger
from mmengine.config import Config, DictAction
from mmengine.dist import init_dist
from mmengine.utils import mkdir_or_exist
from mmdet.utils import register_all_modules
from mmdet.utils.benchmark import (DataLoaderBenchmark, DatasetBenchmark,
... | null |
14,926 | import argparse
import os
from mmengine import MMLogger
from mmengine.config import Config, DictAction
from mmengine.dist import init_dist
from mmengine.utils import mkdir_or_exist
from mmdet.utils import register_all_modules
from mmdet.utils.benchmark import (DataLoaderBenchmark, DatasetBenchmark,
... | null |
14,927 | import argparse
import os
from mmengine import MMLogger
from mmengine.config import Config, DictAction
from mmengine.dist import init_dist
from mmengine.utils import mkdir_or_exist
from mmdet.utils import register_all_modules
from mmdet.utils.benchmark import (DataLoaderBenchmark, DatasetBenchmark,
... | null |
14,928 | import argparse
import os.path as osp
from multiprocessing import Pool
import mmcv
import numpy as np
from mmengine.config import Config, DictAction
from mmengine.fileio import load
from mmengine.runner import Runner
from mmengine.structures import InstanceData, PixelData
from mmengine.utils import ProgressBar, check_f... | Evaluate mAP of single image det result. Args: det_result (list[list]): [[cls1_det, cls2_det, ...], ...]. The outer list indicates images, and the inner list indicates per-class detected bboxes. annotation (dict): Ground truth annotations where keys of annotations are: - bboxes: numpy array of shape (n, 4) - labels: nu... |
14,929 | import argparse
import os.path as osp
from multiprocessing import Pool
import mmcv
import numpy as np
from mmengine.config import Config, DictAction
from mmengine.fileio import load
from mmengine.runner import Runner
from mmengine.structures import InstanceData, PixelData
from mmengine.utils import ProgressBar, check_f... | null |
14,930 | from itertools import product
from math import ceil
from pathlib import Path
import warnings
import glob
import os
import pickle
import tqdm
import mmcv
import numpy as np
import torch
from mmcv.ops import RoIPool
from mmcv.transforms import Compose
from torch.utils.data import Dataset
from mmcv.ops import nms
from arg... | Get multiscale patch sizes and steps. Args: sizes (list): A list of patch sizes. steps (list): A list of steps to slide patches. ratios (list): Multiscale ratios. devidie to each size and step and generate patches in new scales. Returns: new_sizes (list): A list of multiscale patch sizes. new_steps (list): A list of st... |
14,931 | from itertools import product
from math import ceil
from pathlib import Path
import warnings
import glob
import os
import pickle
import tqdm
import mmcv
import numpy as np
import torch
from mmcv.ops import RoIPool
from mmcv.transforms import Compose
from torch.utils.data import Dataset
from mmcv.ops import nms
from arg... | inference patches with the detector. Split huge image(s) into patches and inference them with the detector. Finally, merge patch results on one huge image by nms. Args: model (nn.Module): The loaded detector. img (str | ndarray or): Either an image file or loaded image. sizes (list): The sizes of patches. steps (list):... |
14,932 | from itertools import product
from math import ceil
from pathlib import Path
import warnings
import glob
import os
import pickle
import tqdm
import mmcv
import numpy as np
import torch
from mmcv.ops import RoIPool
from mmcv.transforms import Compose
from torch.utils.data import Dataset
from mmcv.ops import nms
from arg... | null |
14,933 | import argparse
from collections import OrderedDict
import torch
The provided code snippet includes necessary dependencies for implementing the `moco_convert` function. Write a Python function `def moco_convert(src, dst)` to solve the following problem:
Convert keys in pycls pretrained moco models to mmdet style.
Her... | Convert keys in pycls pretrained moco models to mmdet style. |
14,934 | import argparse
import tempfile
from collections import OrderedDict
import torch
from mmengine import Config
def parse_config(config_strings):
temp_file = tempfile.NamedTemporaryFile()
config_path = f'{temp_file.name}.py'
with open(config_path, 'w') as f:
f.write(config_strings)
config = Config.... | null |
14,935 | import argparse
from collections import OrderedDict
import torch
from mmengine.fileio import load
arch_settings = {50: (3, 4, 6, 3), 101: (3, 4, 23, 3)}
def convert_bn(blobs, state_dict, caffe_name, torch_name, converted_names):
# detectron replace bn with affine channel layer
state_dict[torch_name + '.bias'] =... | Convert keys in detectron pretrained ResNet models to pytorch style. |
14,936 | import argparse
from collections import OrderedDict
import torch
from mmengine.fileio import load
from mmengine.runner import save_checkpoint
The provided code snippet includes necessary dependencies for implementing the `convert` function. Write a Python function `def convert(src: str, dst: str, prefix: str = 'd2_mod... | Convert Detectron2 checkpoint to MMDetection style. Args: src (str): The Detectron2 checkpoint path, should endswith `pkl`. dst (str): The MMDetection checkpoint path. prefix (str): The prefix of MMDetection model, defaults to 'd2_model'. |
14,937 | import argparse
from collections import OrderedDict
import torch
def convert_stem(model_key, model_weight, state_dict, converted_names):
new_key = model_key.replace('stem.conv', 'conv1')
new_key = new_key.replace('stem.bn', 'bn1')
state_dict[new_key] = model_weight
converted_names.add(model_key)
pri... | Convert keys in pycls pretrained RegNet models to mmdet style. |
14,938 | import argparse
import re
import tempfile
from collections import OrderedDict
import torch
from mmengine import Config
def is_head(key):
valid_head_list = [
'bbox_head', 'mask_head', 'semantic_head', 'grid_head', 'mask_iou_head'
]
return any(key.startswith(h) for h in valid_head_list)
def parse_conf... | Convert keys in checkpoints. There can be some breaking changes during the development of mmdetection, and this tool is used for upgrading checkpoints trained with old versions to the latest one. |
14,939 | import argparse
import subprocess
import torch
from mmengine.logging import print_log
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
parser.add_argument('out_file', help='output... | null |
14,940 | import argparse
import subprocess
import torch
from mmengine.logging import print_log
def process_checkpoint(in_file, out_file, save_keys=['meta', 'state_dict']):
checkpoint = torch.load(in_file, map_location='cpu')
# only keep `meta` and `state_dict` for smaller file size
ckpt_keys = list(checkpoint.keys... | null |
14,941 | import argparse
import glob
import os.path as osp
import cityscapesscripts.helpers.labels as CSLabels
import mmcv
import numpy as np
import pycocotools.mask as maskUtils
from mmengine.fileio import dump
from mmengine.utils import (Timer, mkdir_or_exist, track_parallel_progress,
track_progres... | null |
14,942 | import argparse
import glob
import os.path as osp
import cityscapesscripts.helpers.labels as CSLabels
import mmcv
import numpy as np
import pycocotools.mask as maskUtils
from mmengine.fileio import dump
from mmengine.utils import (Timer, mkdir_or_exist, track_parallel_progress,
track_progres... | null |
14,943 | import argparse
import glob
import os.path as osp
import cityscapesscripts.helpers.labels as CSLabels
import mmcv
import numpy as np
import pycocotools.mask as maskUtils
from mmengine.fileio import dump
from mmengine.utils import (Timer, mkdir_or_exist, track_parallel_progress,
track_progres... | null |
14,944 | import argparse
import glob
import os.path as osp
import cityscapesscripts.helpers.labels as CSLabels
import mmcv
import numpy as np
import pycocotools.mask as maskUtils
from mmengine.fileio import dump
from mmengine.utils import (Timer, mkdir_or_exist, track_parallel_progress,
track_progres... | null |
14,945 | import argparse
import os.path as osp
import xml.etree.ElementTree as ET
import numpy as np
from mmengine.fileio import dump, list_from_file
from mmengine.utils import mkdir_or_exist, track_progress
from mmdet.evaluation import voc_classes
def parse_xml(args):
xml_path, img_path = args
tree = ET.parse(xml_path)... | null |
14,946 | import argparse
import os.path as osp
import xml.etree.ElementTree as ET
import numpy as np
from mmengine.fileio import dump, list_from_file
from mmengine.utils import mkdir_or_exist, track_progress
from mmdet.evaluation import voc_classes
def parse_args():
parser = argparse.ArgumentParser(
description='Co... | null |
14,947 | import argparse
import os
from mmengine.fileio import dump, list_from_file
from mmengine.utils import mkdir_or_exist, scandir, track_iter_progress
from PIL import Image
def parse_args():
parser = argparse.ArgumentParser(
description='Convert images to coco format without annotations')
parser.add_argume... | null |
14,948 | import argparse
import os
from mmengine.fileio import dump, list_from_file
from mmengine.utils import mkdir_or_exist, scandir, track_iter_progress
from PIL import Image
def collect_image_infos(path, exclude_extensions=None):
img_infos = []
images_generator = scandir(path, recursive=True)
for image_path in... | null |
14,949 | import argparse
import os
from mmengine.fileio import dump, list_from_file
from mmengine.utils import mkdir_or_exist, scandir, track_iter_progress
from PIL import Image
def cvt_to_coco_json(img_infos, classes):
image_id = 0
coco = dict()
coco['images'] = []
coco['type'] = 'instance'
coco['categorie... | null |
14,950 | import argparse
import json
import logging
import os.path as osp
from multiprocessing import Pool
import mmcv
from mmengine.config import Config
from mmengine.fileio import FileClient, dump
from mmengine.logging import print_log
def parse_args():
parser = argparse.ArgumentParser(description='Collect image metas')
... | null |
14,951 | import argparse
import json
import logging
import os.path as osp
from multiprocessing import Pool
import mmcv
from mmengine.config import Config
from mmengine.fileio import FileClient, dump
from mmengine.logging import print_log
def get_image_metas(anno_str, img_prefix):
id_hw = {}
file_client = FileClient(bac... | null |
14,952 | import argparse
import tarfile
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from tarfile import TarFile
from zipfile import ZipFile
import torch
from mmengine.utils.path import mkdir_or_exist
def parse_args():
parser = argparse.ArgumentParser(
descriptio... | null |
14,953 | import argparse
import tarfile
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from tarfile import TarFile
from zipfile import ZipFile
import torch
from mmengine.utils.path import mkdir_or_exist
def download(url, dir, unzip=True, delete=False, threads=1):
def down... | null |
14,954 | import argparse
import tarfile
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from tarfile import TarFile
from zipfile import ZipFile
import torch
from mmengine.utils.path import mkdir_or_exist
def download_objects365v2(url, dir, unzip=True, delete=False, threads=1):
... | null |
14,955 | import argparse
import csv
import os.path as osp
from multiprocessing import Pool
import mmcv
from mmengine.config import Config
from mmengine.fileio import FileClient, dump
def parse_args():
parser = argparse.ArgumentParser(description='Collect image metas')
parser.add_argument('config', help='Config file pat... | null |
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