id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
14,756 | from mmengine.utils import is_str
The provided code snippet includes necessary dependencies for implementing the `imagenet_det_classes` function. Write a Python function `def imagenet_det_classes() -> list` to solve the following problem:
Class names of ImageNet Det.
Here is the function:
def imagenet_det_classes() ... | Class names of ImageNet Det. |
14,757 | from mmengine.utils import is_str
The provided code snippet includes necessary dependencies for implementing the `imagenet_vid_classes` function. Write a Python function `def imagenet_vid_classes() -> list` to solve the following problem:
Class names of ImageNet VID.
Here is the function:
def imagenet_vid_classes() ... | Class names of ImageNet VID. |
14,758 | from mmengine.utils import is_str
The provided code snippet includes necessary dependencies for implementing the `coco_classes` function. Write a Python function `def coco_classes() -> list` to solve the following problem:
Class names of COCO.
Here is the function:
def coco_classes() -> list:
"""Class names of C... | Class names of COCO. |
14,759 | from mmengine.utils import is_str
The provided code snippet includes necessary dependencies for implementing the `cityscapes_classes` function. Write a Python function `def cityscapes_classes() -> list` to solve the following problem:
Class names of Cityscapes.
Here is the function:
def cityscapes_classes() -> list:... | Class names of Cityscapes. |
14,760 | from mmengine.utils import is_str
The provided code snippet includes necessary dependencies for implementing the `oid_challenge_classes` function. Write a Python function `def oid_challenge_classes() -> list` to solve the following problem:
Class names of Open Images Challenge.
Here is the function:
def oid_challeng... | Class names of Open Images Challenge. |
14,761 | from mmengine.utils import is_str
The provided code snippet includes necessary dependencies for implementing the `oid_v6_classes` function. Write a Python function `def oid_v6_classes() -> list` to solve the following problem:
Class names of Open Images V6.
Here is the function:
def oid_v6_classes() -> list:
"""... | Class names of Open Images V6. |
14,762 | from mmengine.utils import is_str
The provided code snippet includes necessary dependencies for implementing the `objects365v1_classes` function. Write a Python function `def objects365v1_classes() -> list` to solve the following problem:
Class names of Objects365 V1.
Here is the function:
def objects365v1_classes()... | Class names of Objects365 V1. |
14,763 | from mmengine.utils import is_str
The provided code snippet includes necessary dependencies for implementing the `objects365v2_classes` function. Write a Python function `def objects365v2_classes() -> list` to solve the following problem:
Class names of Objects365 V2.
Here is the function:
def objects365v2_classes()... | Class names of Objects365 V2. |
14,764 | from multiprocessing import Pool
import numpy as np
from mmengine.logging import print_log
from mmengine.utils import is_str
from terminaltables import AsciiTable
from .bbox_overlaps import bbox_overlaps
from .class_names import get_classes
def average_precision(recalls, precisions, mode='area'):
"""Calculate avera... | Evaluate mAP of a dataset. Args: det_results (list[list]): [[cls1_det, cls2_det, ...], ...]. The outer list indicates images, and the inner list indicates per-class detected bboxes. annotations (list[dict]): Ground truth annotations where each item of the list indicates an image. Keys of annotations are: - `bboxes`: nu... |
14,765 | from collections.abc import Sequence
import numpy as np
from mmengine.logging import print_log
from terminaltables import AsciiTable
from .bbox_overlaps import bbox_overlaps
def _recalls(all_ious, proposal_nums, thrs):
img_num = all_ious.shape[0]
total_gt_num = sum([ious.shape[0] for ious in all_ious])
_iou... | Calculate recalls. Args: gts (list[ndarray]): a list of arrays of shape (n, 4) proposals (list[ndarray]): a list of arrays of shape (k, 4) or (k, 5) proposal_nums (int | Sequence[int]): Top N proposals to be evaluated. iou_thrs (float | Sequence[float]): IoU thresholds. Default: 0.5. logger (logging.Logger | str | None... |
14,766 | from collections.abc import Sequence
import numpy as np
from mmengine.logging import print_log
from terminaltables import AsciiTable
from .bbox_overlaps import bbox_overlaps
def _recalls(all_ious, proposal_nums, thrs):
img_num = all_ious.shape[0]
total_gt_num = sum([ious.shape[0] for ious in all_ious])
_iou... | Plot Proposal_num-Recalls curve. Args: recalls(ndarray or list): shape (k,) proposal_nums(ndarray or list): same shape as `recalls` |
14,767 | from collections.abc import Sequence
import numpy as np
from mmengine.logging import print_log
from terminaltables import AsciiTable
from .bbox_overlaps import bbox_overlaps
def _recalls(all_ious, proposal_nums, thrs):
img_num = all_ious.shape[0]
total_gt_num = sum([ious.shape[0] for ious in all_ious])
_iou... | Plot IoU-Recalls curve. Args: recalls(ndarray or list): shape (k,) iou_thrs(ndarray or list): same shape as `recalls` |
14,768 | import multiprocessing
import os
import mmcv
import numpy as np
from mmengine.fileio import FileClient
def pq_compute_single_core(proc_id,
annotation_set,
gt_folder,
pred_folder,
categories,
... | Evaluate the metrics of Panoptic Segmentation with multithreading. Same as the function with the same name in `panopticapi`. Args: matched_annotations_list (list): The matched annotation list. Each element is a tuple of annotations of the same image with the format (gt_anns, pred_anns). gt_folder (str): The path of the... |
14,769 | import json
from typing import List
import torch.nn as nn
from mmengine.dist import get_dist_info
from mmengine.logging import MMLogger
from mmengine.optim import DefaultOptimWrapperConstructor
from mmdet.registry import OPTIM_WRAPPER_CONSTRUCTORS
The provided code snippet includes necessary dependencies for implement... | Get the layer id to set the different learning rates in ``layer_wise`` decay_type. Args: var_name (str): The key of the model. max_layer_id (int): Maximum layer id. Returns: int: The id number corresponding to different learning rate in ``LearningRateDecayOptimizerConstructor``. |
14,770 | import json
from typing import List
import torch.nn as nn
from mmengine.dist import get_dist_info
from mmengine.logging import MMLogger
from mmengine.optim import DefaultOptimWrapperConstructor
from mmdet.registry import OPTIM_WRAPPER_CONSTRUCTORS
The provided code snippet includes necessary dependencies for implement... | Get the stage id to set the different learning rates in ``stage_wise`` decay_type. Args: var_name (str): The key of the model. max_stage_id (int): Maximum stage id. Returns: int: The id number corresponding to different learning rate in ``LearningRateDecayOptimizerConstructor``. |
14,771 |
def trigger_visualization_hook(cfg, args):
default_hooks = cfg.default_hooks
if 'visualization' in default_hooks:
visualization_hook = default_hooks['visualization']
# Turn on visualization
visualization_hook['draw'] = True
if args.show:
visualization_hook['show'] =... | null |
14,772 | from collections import OrderedDict
from mmengine.dist import get_dist_info
from mmengine.hooks import Hook
from torch import nn
from mmdet.registry import HOOKS
from mmdet.utils import all_reduce_dict
The provided code snippet includes necessary dependencies for implementing the `get_norm_states` function. Write a Py... | Get the state_dict of batch norms in the module. |
14,773 | from mmcv.transforms import LoadImageFromFile
from mmdet.datasets.transforms import LoadAnnotations, LoadPanopticAnnotations
from mmdet.registry import TRANSFORMS
The provided code snippet includes necessary dependencies for implementing the `get_loading_pipeline` function. Write a Python function `def get_loading_pip... | Only keep loading image and annotations related configuration. Args: pipeline (list[dict]): Data pipeline configs. Returns: list[dict]: The new pipeline list with only keep loading image and annotations related configuration. Examples: >>> pipelines = [ ... dict(type='LoadImageFromFile'), ... dict(type='LoadAnnotations... |
14,774 | from typing import List, Optional, Union
import numpy as np
from mmcv.transforms import RandomChoice
from mmcv.transforms.utils import cache_randomness
from mmengine.config import ConfigDict
from mmdet.registry import TRANSFORMS
AUTOAUG_POLICIES_V0 = [
[('Equalize', 0.8, 1), ('ShearY', 0.8, 4)],
[('Color', 0.4,... | Autoaugment policies that was used in AutoAugment Paper. |
14,775 | from typing import List, Optional, Union
import numpy as np
from mmcv.transforms import RandomChoice
from mmcv.transforms.utils import cache_randomness
from mmengine.config import ConfigDict
from mmdet.registry import TRANSFORMS
_MAX_LEVEL = 10
The provided code snippet includes necessary dependencies for implementing... | Map from level to magnitude. |
14,776 | from typing import Optional
import torch
import torch.nn as nn
from torch import Tensor
from mmdet.registry import MODELS
from .utils import weighted_loss
The provided code snippet includes necessary dependencies for implementing the `smooth_l1_loss` function. Write a Python function `def smooth_l1_loss(pred: Tensor, ... | Smooth L1 loss. Args: pred (Tensor): The prediction. target (Tensor): The learning target of the prediction. beta (float, optional): The threshold in the piecewise function. Defaults to 1.0. Returns: Tensor: Calculated loss |
14,777 | from typing import Optional
import torch
import torch.nn as nn
from torch import Tensor
from mmdet.registry import MODELS
from .utils import weighted_loss
The provided code snippet includes necessary dependencies for implementing the `l1_loss` function. Write a Python function `def l1_loss(pred: Tensor, target: Tensor... | L1 loss. Args: pred (Tensor): The prediction. target (Tensor): The learning target of the prediction. Returns: Tensor: Calculated loss |
14,778 | import functools
from typing import Callable, Optional
import torch
import torch.nn.functional as F
from torch import Tensor
def weight_reduce_loss(loss: Tensor,
weight: Optional[Tensor] = None,
reduction: str = 'mean',
avg_factor: Optional[float] = N... | 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... |
14,779 | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.registry import MODELS
The provided code snippet includes necessary dependencies for implementing the `ae_loss_per_image` function. Write a Python function `def ae_loss_per_image(tl_preds, br_preds, match)` to solve the following problem:
As... | Associative Embedding Loss in one image. Associative Embedding Loss including two parts: pull loss and push loss. Pull loss makes embedding vectors from same object closer to each other. Push loss distinguish embedding vector from different objects, and makes the gap between them is large enough. During computing, usua... |
14,780 | from typing import Optional
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from mmdet.registry import MODELS
from .utils import weighted_loss
The provided code snippet includes necessary dependencies for implementing the `mse_loss` function. Write a Python function `def mse_loss(pred: T... | A Wrapper of MSE loss. Args: pred (Tensor): The prediction. target (Tensor): The learning target of the prediction. Returns: Tensor: loss Tensor |
14,781 | from typing import List, Optional, Tuple
import torch
import torch.nn as nn
from torch import Tensor
from mmdet.structures.bbox import bbox_overlaps
from ..task_modules.coders import BaseBBoxCoder
from ..task_modules.samplers import SamplingResult
The provided code snippet includes necessary dependencies for implement... | Importance-based Sample Reweighting (ISR_P), positive part. Args: cls_score (Tensor): Predicted classification scores. bbox_pred (Tensor): Predicted bbox deltas. bbox_targets (tuple[Tensor]): A tuple of bbox targets, the are labels, label_weights, bbox_targets, bbox_weights, respectively. rois (Tensor): Anchors (single... |
14,782 | from typing import List, Optional, Tuple
import torch
import torch.nn as nn
from torch import Tensor
from mmdet.structures.bbox import bbox_overlaps
from ..task_modules.coders import BaseBBoxCoder
from ..task_modules.samplers import SamplingResult
The provided code snippet includes necessary dependencies for implement... | Classification-Aware Regression Loss (CARL). Args: cls_score (Tensor): Predicted classification scores. labels (Tensor): Targets of classification. bbox_pred (Tensor): Predicted bbox deltas. bbox_targets (Tensor): Target of bbox regression. loss_bbox (func): Regression loss func of the head. bbox_coder (obj): BBox code... |
14,783 | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.ops import sigmoid_focal_loss as _sigmoid_focal_loss
from mmdet.registry import MODELS
from .utils import weight_reduce_loss
def weight_reduce_loss(loss: Tensor,
weight: Optional[Tensor] = None,
r... | PyTorch version of `Focal Loss <https://arxiv.org/abs/1708.02002>`_. Args: pred (torch.Tensor): The prediction with shape (N, C), C is the number of classes target (torch.Tensor): The learning label of the prediction. weight (torch.Tensor, optional): Sample-wise loss weight. gamma (float, optional): The gamma for calcu... |
14,784 | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.ops import sigmoid_focal_loss as _sigmoid_focal_loss
from mmdet.registry import MODELS
from .utils import weight_reduce_loss
def weight_reduce_loss(loss: Tensor,
weight: Optional[Tensor] = None,
r... | PyTorch version of `Focal Loss <https://arxiv.org/abs/1708.02002>`_. Different from `py_sigmoid_focal_loss`, this function accepts probability as input. Args: pred (torch.Tensor): The prediction probability with shape (N, C), C is the number of classes. target (torch.Tensor): The learning label of the prediction. The t... |
14,785 | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.ops import sigmoid_focal_loss as _sigmoid_focal_loss
from mmdet.registry import MODELS
from .utils import weight_reduce_loss
def weight_reduce_loss(loss: Tensor,
weight: Optional[Tensor] = None,
r... | r"""A wrapper of cuda version `Focal Loss <https://arxiv.org/abs/1708.02002>`_. Args: pred (torch.Tensor): The prediction with shape (N, C), C is the number of classes. target (torch.Tensor): The learning label of the prediction. weight (torch.Tensor, optional): Sample-wise loss weight. gamma (float, optional): The gam... |
14,786 | from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.models.losses.utils import weighted_loss
from mmdet.registry import MODELS
The provided code snippet includes necessary dependencies for implementing the `quality_focal_loss` function. Write a Python function `d... | r"""Quality Focal Loss (QFL) is from `Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection <https://arxiv.org/abs/2006.04388>`_. Args: pred (torch.Tensor): Predicted joint representation of classification and quality (IoU) estimation with shape (N, C), C is the number of ... |
14,787 | from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.models.losses.utils import weighted_loss
from mmdet.registry import MODELS
The provided code snippet includes necessary dependencies for implementing the `quality_focal_loss_tensor_target` function. Write a Pyth... | `QualityFocal Loss <https://arxiv.org/abs/2008.13367>`_ Args: pred (torch.Tensor): The prediction with shape (N, C), C is the number of classes target (torch.Tensor): The learning target of the iou-aware classification score with shape (N, C), C is the number of classes. beta (float): The beta parameter for calculating... |
14,788 | from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.models.losses.utils import weighted_loss
from mmdet.registry import MODELS
The provided code snippet includes necessary dependencies for implementing the `quality_focal_loss_with_prob` function. Write a Python f... | r"""Quality Focal Loss (QFL) is from `Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection <https://arxiv.org/abs/2006.04388>`_. Different from `quality_focal_loss`, this function accepts probability as input. Args: pred (torch.Tensor): Predicted joint representation of c... |
14,789 | from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.models.losses.utils import weighted_loss
from mmdet.registry import MODELS
The provided code snippet includes necessary dependencies for implementing the `distribution_focal_loss` function. Write a Python functi... | r"""Distribution Focal Loss (DFL) is from `Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection <https://arxiv.org/abs/2006.04388>`_. Args: pred (torch.Tensor): Predicted general distribution of bounding boxes (before softmax) with shape (N, n+1), n is the max value of th... |
14,790 | from typing import Optional
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from mmdet.registry import MODELS
from .utils import weight_reduce_loss
def weight_reduce_loss(loss: Tensor,
weight: Optional[Tensor] = None,
reduction: str = 'mean',... | `Varifocal Loss <https://arxiv.org/abs/2008.13367>`_ Args: pred (Tensor): The prediction with shape (N, C), C is the number of classes. target (Tensor): The learning target of the iou-aware classification score with shape (N, C), C is the number of classes. weight (Tensor, optional): The weight of loss for each predict... |
14,791 | from typing import Optional
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from mmdet.registry import MODELS
from .utils import weighted_loss
The provided code snippet includes necessary dependencies for implementing the `knowledge_distillation_kl_div_loss` function. Write a Python func... | r"""Loss function for knowledge distilling using KL divergence. Args: pred (Tensor): Predicted logits with shape (N, n + 1). soft_label (Tensor): Target logits with shape (N, N + 1). T (int): Temperature for distillation. detach_target (bool): Remove soft_label from automatic differentiation Returns: Tensor: Loss tenso... |
14,792 | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.registry import MODELS
from .utils import weight_reduce_loss
def _expand_onehot_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
inds = torch.nonzero(
(labels >=... | null |
14,793 | import numpy as np
import torch
import torch.nn as nn
from mmdet.registry import MODELS
from .utils import weighted_loss
The provided code snippet includes necessary dependencies for implementing the `balanced_l1_loss` function. Write a Python function `def balanced_l1_loss(pred, target, ... | Calculate balanced L1 loss. Please see the `Libra R-CNN <https://arxiv.org/pdf/1904.02701.pdf>`_ Args: pred (torch.Tensor): The prediction with shape (N, 4). target (torch.Tensor): The learning target of the prediction with shape (N, 4). beta (float): The loss is a piecewise function of prediction and target and ``beta... |
14,794 | import torch.nn as nn
The provided code snippet includes necessary dependencies for implementing the `accuracy` function. Write a Python function `def accuracy(pred, target, topk=1, thresh=None)` to solve the following problem:
Calculate accuracy according to the prediction and target. Args: pred (torch.Tensor): The m... | Calculate accuracy according to the prediction and target. Args: pred (torch.Tensor): The model prediction, shape (N, num_class) target (torch.Tensor): The target of each prediction, shape (N, ) topk (int | tuple[int], optional): If the predictions in ``topk`` matches the target, the predictions will be regarded as cor... |
14,795 | from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from mmdet.registry import MODELS
from .accuracy import accuracy
from .cross_entropy_loss import cross_entropy
from .utils import weight_reduce_loss
def cross_entropy(pred,
... | Calculate the Seesaw CrossEntropy loss. Args: cls_score (Tensor): The prediction with shape (N, C), C is the number of classes. labels (Tensor): The learning label of the prediction. label_weights (Tensor): Sample-wise loss weight. cum_samples (Tensor): Cumulative samples for each category. num_classes (int): The numbe... |
14,796 | import math
import warnings
from typing import Optional
import torch
import torch.nn as nn
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures.bbox import bbox_overlaps
from .utils import weighted_loss
The provided code snippet includes necessary dependencies for implementing the `iou_loss... | IoU loss. Computing the IoU loss between a set of predicted bboxes and target bboxes. The loss is calculated as negative log of IoU. Args: pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2), shape (n, 4). target (Tensor): Corresponding gt bboxes, shape (n, 4). linear (bool, optional): If True, use linear scale ... |
14,797 | import math
import warnings
from typing import Optional
import torch
import torch.nn as nn
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures.bbox import bbox_overlaps
from .utils import weighted_loss
The provided code snippet includes necessary dependencies for implementing the `bounded_... | BIoULoss. This is an implementation of paper `Improving Object Localization with Fitness NMS and Bounded IoU Loss. <https://arxiv.org/abs/1711.00164>`_. Args: pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2), shape (n, 4). target (Tensor): Corresponding gt bboxes, shape (n, 4). beta (float, optional): Beta pa... |
14,798 | import math
import warnings
from typing import Optional
import torch
import torch.nn as nn
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures.bbox import bbox_overlaps
from .utils import weighted_loss
The provided code snippet includes necessary dependencies for implementing the `giou_los... | r"""`Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression <https://arxiv.org/abs/1902.09630>`_. Args: pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2), shape (n, 4). target (Tensor): Corresponding gt bboxes, shape (n, 4). eps (float): Epsilon to avoid log(0). Return: Tensor: Lo... |
14,799 | import math
import warnings
from typing import Optional
import torch
import torch.nn as nn
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures.bbox import bbox_overlaps
from .utils import weighted_loss
The provided code snippet includes necessary dependencies for implementing the `diou_los... | r"""Implementation of `Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression https://arxiv.org/abs/1911.08287`_. Code is modified from https://github.com/Zzh-tju/DIoU. Args: pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2), shape (n, 4). target (Tensor): Corresponding gt bboxes, shape (n, ... |
14,800 | import math
import warnings
from typing import Optional
import torch
import torch.nn as nn
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures.bbox import bbox_overlaps
from .utils import weighted_loss
The provided code snippet includes necessary dependencies for implementing the `ciou_los... | r"""`Implementation of paper `Enhancing Geometric Factors into Model Learning and Inference for Object Detection and Instance Segmentation <https://arxiv.org/abs/2005.03572>`_. Code is modified from https://github.com/Zzh-tju/CIoU. Args: pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2), shape (n, 4). target (... |
14,801 | import math
import warnings
from typing import Optional
import torch
import torch.nn as nn
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures.bbox import bbox_overlaps
from .utils import weighted_loss
The provided code snippet includes necessary dependencies for implementing the `eiou_los... | r"""Implementation of paper `Extended-IoU Loss: A Systematic IoU-Related Method: Beyond Simplified Regression for Better Localization <https://ieeexplore.ieee.org/abstract/document/9429909>`_ Code is modified from https://github.com//ShiqiYu/libfacedetection.train. Args: pred (Tensor): Predicted bboxes of format (x1, y... |
14,802 | import torch
import torch.nn as nn
from mmdet.registry import MODELS
from .utils import weight_reduce_loss
def weight_reduce_loss(loss: Tensor,
weight: Optional[Tensor] = None,
reduction: str = 'mean',
avg_factor: Optional[float] = None) -> Tensor:
... | Calculate dice loss, there are two forms of dice loss is supported: - the one proposed in `V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation <https://arxiv.org/abs/1606.04797>`_. - the dice loss in which the power of the number in the denominator is the first power instead of the seco... |
14,803 | import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.registry import MODELS
from .utils import weight_reduce_loss
def _expand_onehot_labels(labels, label_weights, label_channels, ignore_index):
"""Expand onehot labels to match the size of prediction."""
bin_labels = labe... | Calculate the binary CrossEntropy loss. Args: pred (torch.Tensor): The prediction with shape (N, 1) or (N, ). When the shape of pred is (N, 1), label will be expanded to one-hot format, and when the shape of pred is (N, ), label will not be expanded to one-hot format. label (torch.Tensor): The learning label of the pre... |
14,804 | import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.registry import MODELS
from .utils import weight_reduce_loss
The provided code snippet includes necessary dependencies for implementing the `mask_cross_entropy` function. Write a Python function `def mask_cross_entropy(pred, ... | Calculate the CrossEntropy loss for masks. Args: pred (torch.Tensor): The prediction with shape (N, C, *), C is the number of classes. The trailing * indicates arbitrary shape. target (torch.Tensor): The learning label of the prediction. label (torch.Tensor): ``label`` indicates the class label of the mask correspondin... |
14,805 | from typing import Optional, Union
import torch.nn as nn
from torch import Tensor
from mmdet.registry import MODELS
from .utils import weight_reduce_loss, weighted_loss
The provided code snippet includes necessary dependencies for implementing the `gaussian_focal_loss` function. Write a Python function `def gaussian_f... | `Focal Loss <https://arxiv.org/abs/1708.02002>`_ for targets in gaussian distribution. Args: pred (torch.Tensor): The prediction. gaussian_target (torch.Tensor): The learning target of the prediction in gaussian distribution. alpha (float, optional): A balanced form for Focal Loss. Defaults to 2.0. gamma (float, option... |
14,806 | from typing import Optional, Union
import torch.nn as nn
from torch import Tensor
from mmdet.registry import MODELS
from .utils import weight_reduce_loss, weighted_loss
def weight_reduce_loss(loss: Tensor,
weight: Optional[Tensor] = None,
reduction: str = 'mean',
... | `Focal Loss <https://arxiv.org/abs/1708.02002>`_ for targets in gaussian distribution. Note: The index with a value of 1 in ``gaussian_target`` in the ``gaussian_focal_loss`` function is a positive sample, but in ``gaussian_focal_loss_with_pos_inds`` the positive sample is passed in through the ``pos_inds`` parameter. ... |
14,807 | from typing import Optional, Tuple, Union
import torch
from mmcv.ops.nms import batched_nms
from torch import Tensor
from mmdet.structures.bbox import bbox_overlaps
from mmdet.utils import ConfigType
The provided code snippet includes necessary dependencies for implementing the `multiclass_nms` function. Write a Pytho... | NMS for multi-class bboxes. Args: multi_bboxes (Tensor): shape (n, #class*4) or (n, 4) multi_scores (Tensor): shape (n, #class), where the last column contains scores of the background class, but this will be ignored. score_thr (float): bbox threshold, bboxes with scores lower than it will not be considered. nms_cfg (U... |
14,808 | from typing import Optional, Tuple, Union
import torch
from mmcv.ops.nms import batched_nms
from torch import Tensor
from mmdet.structures.bbox import bbox_overlaps
from mmdet.utils import ConfigType
The provided code snippet includes necessary dependencies for implementing the `fast_nms` function. Write a Python func... | Fast NMS in `YOLACT <https://arxiv.org/abs/1904.02689>`_. Fast NMS allows already-removed detections to suppress other detections so that every instance can be decided to be kept or discarded in parallel, which is not possible in traditional NMS. This relaxation allows us to implement Fast NMS entirely in standard GPU-... |
14,809 | import torch
The provided code snippet includes necessary dependencies for implementing the `mask_matrix_nms` function. Write a Python function `def mask_matrix_nms(masks, labels, scores, filter_thr=-1, nms_pre=-1, max_... | Matrix NMS for multi-class masks. Args: masks (Tensor): Has shape (num_instances, h, w) labels (Tensor): Labels of corresponding masks, has shape (num_instances,). scores (Tensor): Mask scores of corresponding masks, has shape (num_instances). filter_thr (float): Score threshold to filter the masks after matrix nms. De... |
14,810 | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn.bricks.wrappers import NewEmptyTensorOp, obsolete_torch_version
The provided code snippet includes necessary dependencies for implementing the `adaptive_avg_pool2d` function. Write a Python function `def adaptive_avg_pool2d(input, output_... | Handle empty batch dimension to adaptive_avg_pool2d. Args: input (tensor): 4D tensor. output_size (int, tuple[int,int]): the target output size. |
14,811 | import math
import warnings
from typing import Optional, Sequence, Tuple, Union
import torch
import torch.nn.functional as F
from mmcv.cnn import (Linear, build_activation_layer, build_conv_layer,
build_norm_layer)
from mmcv.cnn.bricks.drop import Dropout
from mmengine.model import BaseModule, Mod... | Convert [N, L, C] shape tensor to [N, C, H, W] shape tensor. Args: x (Tensor): The input tensor of shape [N, L, C] before conversion. hw_shape (Sequence[int]): The height and width of output feature map. Returns: Tensor: The output tensor of shape [N, C, H, W] after conversion. |
14,812 | import math
import warnings
from typing import Optional, Sequence, Tuple, Union
import torch
import torch.nn.functional as F
from mmcv.cnn import (Linear, build_activation_layer, build_conv_layer,
build_norm_layer)
from mmcv.cnn.bricks.drop import Dropout
from mmengine.model import BaseModule, Mod... | Flatten [N, C, H, W] shape tensor to [N, L, C] shape tensor. Args: x (Tensor): The input tensor of shape [N, C, H, W] before conversion. Returns: Tensor: The output tensor of shape [N, L, C] after conversion. |
14,813 | import math
import warnings
from typing import Optional, Sequence, Tuple, Union
import torch
import torch.nn.functional as F
from mmcv.cnn import (Linear, build_activation_layer, build_conv_layer,
build_norm_layer)
from mmcv.cnn.bricks.drop import Dropout
from mmengine.model import BaseModule, Mod... | Convert coordinate tensor to positional encoding. Args: coord_tensor (Tensor): Coordinate tensor to be converted to positional encoding. With the last dimension as 2 or 4. num_feats (int, optional): The feature dimension for each position along x-axis or y-axis. Note the final returned dimension for each position is 2 ... |
14,814 | import math
import warnings
from typing import Optional, Sequence, Tuple, Union
import torch
import torch.nn.functional as F
from mmcv.cnn import (Linear, build_activation_layer, build_conv_layer,
build_norm_layer)
from mmcv.cnn.bricks.drop import Dropout
from mmengine.model import BaseModule, Mod... | Inverse function of sigmoid. Args: x (Tensor): The tensor to do the inverse. eps (float): EPS avoid numerical overflow. Defaults 1e-5. Returns: Tensor: The x has passed the inverse function of sigmoid, has the same shape with input. |
14,815 | from typing import Dict, List, Optional, Sequence, Tuple
import torch
import torch.nn as nn
from mmcv.cnn import Scale
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures.bbox import bbox2distance
from mmdet.utils import (ConfigType, InstanceList... | This function is used to transpose image first tensors to level first ones. |
14,816 | import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from timm.models.vision_transformer import _cfg
from mmdet.vtpack.layers import DynamicGrainedEncoder
from mm... | convert patch embedding weight from manual patchify + linear proj to conv |
14,817 | import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from timm.models.vision_transformer import _cfg
from mmdet.vtpack.layers import DynamicGrainedEncoder
from mm... | null |
14,818 | import time
import warnings
from collections import OrderedDict
from copy import deepcopy
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn import build_norm_layer
from mmengine.model.weight_init import (constant_init, trunc_normal_,
... | null |
14,819 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmengine.model import BaseModule
from torch.nn.modules.utils import _pair
from mmdet.models.backbones.resnet import Bottleneck, ResNet
from mmdet.registry i... | Build Trident Res Layers. |
14,820 | import copy
import math
from functools import partial
import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn.bricks import ConvModule, DropPath
from mmengine.model import BaseModule, Sequential
from mmdet.registry import MODELS
from ..layers import InvertedResidual, SELayer
from ..utils im... | Scaling operation to the layer's parameters according to the arch_setting. |
14,821 | import warnings
from collections import OrderedDict
from copy import deepcopy
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn import build_norm_layer
from mmcv.cnn.bricks.transformer import FFN, build_dropout
from mmengine.logging import MMLogger
from... | null |
14,822 | import math
import warnings
from collections import OrderedDict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import Conv2d, build_activation_layer, build_norm_layer
from mmcv.cnn.bricks.drop import build_dropout
from mmcv.cnn.bricks.transformer import MultiheadAtte... | null |
14,823 | import time
import warnings
from collections import OrderedDict
from copy import deepcopy
from typing import Any, Callable, List, Optional, Type, Union
from torch import Tensor
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn import build_norm_layer
fr... | 3x3 convolution with padding |
14,824 | import time
import warnings
from collections import OrderedDict
from copy import deepcopy
from typing import Any, Callable, List, Optional, Type, Union
from torch import Tensor
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn import build_norm_layer
fr... | 1x1 convolution |
14,825 | from typing import Optional, Tuple
import torch
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.registry import TASK_UTILS
from mmdet.utils import ConfigType
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
The provided code snippet includes necessary dep... | Expand an array of boxes by a given scale. Args: bboxes (Tensor): Shape (m, 4) scale (float): The scale factor of bboxes Returns: Tensor: Shape (m, 4). Scaled bboxes |
14,826 | from typing import Optional, Tuple
import torch
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.registry import TASK_UTILS
from mmdet.utils import ConfigType
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
The provided code snippet includes necessary dep... | Are points located in bboxes. Args: points (Tensor): Points, shape: (m, 2). bboxes (Tensor): Bounding boxes, shape: (n, 4). Return: Tensor: Flags indicating if points are located in bboxes, shape: (m, n). |
14,827 | from typing import Optional, Tuple
import torch
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.registry import TASK_UTILS
from mmdet.utils import ConfigType
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
The provided code snippet includes necessary dep... | Compute the area of an array of bboxes. Args: bboxes (Tensor): The coordinates ox bboxes. Shape: (m, 4) Returns: Tensor: Area of the bboxes. Shape: (m, ) |
14,828 | import warnings
from typing import List, Optional
import torch
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.registry import TASK_UTILS
from mmdet.utils import ConfigType
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
The provided code snippet include... | Compute the center distance between bboxes and priors. Args: bboxes (Tensor): Shape (n, 4) for , "xyxy" format. priors (Tensor): Shape (n, 4) for priors, "xyxy" format. Returns: Tensor: Center distances between bboxes and priors. |
14,829 | from typing import Optional, Tuple
import torch
import torch.nn.functional as F
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import BaseBoxes
from mmdet.utils import ConfigType
from .assign_result import AssignResult
from .base_as... | Compute the masks center of mass. Args: masks: Mask tensor, has shape (num_masks, H, W). eps: a small number to avoid normalizer to be zero. Defaults to 1e-7. Returns: Tensor: The masks center of mass. Has shape (num_masks, 2). |
14,830 | from typing import List, Optional, Tuple
import torch
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.registry import TASK_UTILS
from ..prior_generators import anchor_inside_flags
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
The provided code snippet ... | Calculate region of the box defined by the ratio, the ratio is from the center of the box to every edge. |
14,831 | from typing import List, Optional, Tuple
import torch
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.registry import TASK_UTILS
from ..prior_generators import anchor_inside_flags
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
The provided code snippet ... | Get the flag indicate whether anchor centers are inside regions. |
14,832 | import torch
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import bbox_overlaps, get_box_tensor
def cast_tensor_type(x, scale=1., dtype=None):
if dtype == 'fp16':
# scale is for preventing overflows
x = (x / scale).half()
return x | null |
14,833 | from typing import Optional, Tuple
import torch
from torch import Tensor
from mmdet.structures.bbox import BaseBoxes
The provided code snippet includes necessary dependencies for implementing the `anchor_inside_flags` function. Write a Python function `def anchor_inside_flags(flat_anchors: Tensor, ... | Check whether the anchors are inside the border. Args: flat_anchors (torch.Tensor): Flatten anchors, shape (n, 4). valid_flags (torch.Tensor): An existing valid flags of anchors. img_shape (tuple(int)): Shape of current image. allowed_border (int): The border to allow the valid anchor. Defaults to 0. Returns: torch.Ten... |
14,834 | from typing import Optional, Tuple
import torch
from torch import Tensor
from mmdet.structures.bbox import BaseBoxes
The provided code snippet includes necessary dependencies for implementing the `calc_region` function. Write a Python function `def calc_region(bbox: Tensor, ratio: float, ... | Calculate a proportional bbox region. The bbox center are fixed and the new h' and w' is h * ratio and w * ratio. Args: bbox (Tensor): Bboxes to calculate regions, shape (n, 4). ratio (float): Ratio of the output region. featmap_size (tuple, Optional): Feature map size in (height, width) order used for clipping the bou... |
14,835 | import warnings
import numpy as np
import torch
from torch import Tensor
from mmdet.structures.bbox import BaseBoxes, cat_boxes
from mmdet.utils import util_mixins
from mmdet.utils.util_random import ensure_rng
from ..assigners import AssignResult
The provided code snippet includes necessary dependencies for implement... | Simple version of ``kwimage.Boxes.random`` Returns: Tensor: shape (n, 4) in x1, y1, x2, y2 format. References: https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390 Example: >>> num = 3 >>> scale = 512 >>> rng = 0 >>> boxes = random_boxes(num, scale, rng) >>> print(boxes) tensor(... |
14,836 | import numpy as np
import torch
import torch.nn.functional as F
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import HorizontalBoxes, bbox_rescale, get_box_tensor
from .base_bbox_coder import BaseBBoxCoder
def generat_buckets(proposals, num_buckets, scale_factor=1.0):
"""Generate buckets w.r.t bu... | Generate buckets estimation and fine regression targets. Args: proposals (Tensor): Shape (n, 4) gt (Tensor): Shape (n, 4) num_buckets (int): Number of buckets. scale_factor (float): Scale factor to rescale proposals. offset_topk (int): Topk buckets are used to generate bucket fine regression targets. Defaults to 2. off... |
14,837 | import numpy as np
import torch
import torch.nn.functional as F
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import HorizontalBoxes, bbox_rescale, get_box_tensor
from .base_bbox_coder import BaseBBoxCoder
The provided code snippet includes necessary dependencies for implementing the `bucket2bbox` f... | Apply bucketing estimation (cls preds) and fine regression (offset preds) to generate det bboxes. Args: proposals (Tensor): Boxes to be transformed. Shape (n, 4) cls_preds (Tensor): bucketing estimation. Shape (n, num_buckets*2). offset_preds (Tensor): fine regression. Shape (n, num_buckets*2). num_buckets (int): Numbe... |
14,838 | import warnings
import numpy as np
import torch
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import HorizontalBoxes, get_box_tensor
from .base_bbox_coder import BaseBBoxCoder
The provided code snippet includes necessary dependencies for implementing the `bbox2delta` function. Write a Python functio... | Compute deltas of proposals w.r.t. gt. We usually compute the deltas of x, y, w, h of proposals w.r.t ground truth bboxes to get regression target. This is the inverse function of :func:`delta2bbox`. Args: proposals (Tensor): Boxes to be transformed, shape (N, ..., 4) gt (Tensor): Gt bboxes to be used as base, shape (N... |
14,839 | import warnings
import numpy as np
import torch
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import HorizontalBoxes, get_box_tensor
from .base_bbox_coder import BaseBBoxCoder
The provided code snippet includes necessary dependencies for implementing the `delta2bbox` function. Write a Python functio... | Apply deltas to shift/scale base boxes. Typically the rois are anchor or proposed bounding boxes and the deltas are network outputs used to shift/scale those boxes. This is the inverse function of :func:`bbox2delta`. Args: rois (Tensor): Boxes to be transformed. Has shape (N, 4). deltas (Tensor): Encoded offsets relati... |
14,840 | import warnings
import numpy as np
import torch
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import HorizontalBoxes, get_box_tensor
from .base_bbox_coder import BaseBBoxCoder
The provided code snippet includes necessary dependencies for implementing the `onnx_delta2bbox` function. Write a Python fu... | Apply deltas to shift/scale base boxes. Typically the rois are anchor or proposed bounding boxes and the deltas are network outputs used to shift/scale those boxes. This is the inverse function of :func:`bbox2delta`. Args: rois (Tensor): Boxes to be transformed. Has shape (N, 4) or (B, N, 4) deltas (Tensor): Encoded of... |
14,841 | import torch
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import HorizontalBoxes, get_box_tensor
from .base_bbox_coder import BaseBBoxCoder
The provided code snippet includes necessary dependencies for implementing the `bboxes2tblr` function. Write a Python function `def bboxes2tblr(priors, gts, no... | Encode ground truth boxes to tblr coordinate. It first convert the gt coordinate to tblr format, (top, bottom, left, right), relative to prior box centers. The tblr coordinate may be normalized by the side length of prior bboxes if `normalize_by_wh` is specified as True, and it is then normalized by the `normalizer` fa... |
14,842 | import torch
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import HorizontalBoxes, get_box_tensor
from .base_bbox_coder import BaseBBoxCoder
The provided code snippet includes necessary dependencies for implementing the `tblr2bboxes` function. Write a Python function `def tblr2bboxes(priors, ... | Decode tblr outputs to prediction boxes. The process includes 3 steps: 1) De-normalize tblr coordinates by multiplying it with `normalizer`; 2) De-normalize tblr coordinates by the prior bbox width and height if `normalize_by_wh` is `True`; 3) Convert tblr (top, bottom, left, right) pair relative to the center of prior... |
14,843 | import numpy as np
import torch
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import HorizontalBoxes, get_box_tensor
from .base_bbox_coder import BaseBBoxCoder
The provided code snippet includes necessary dependencies for implementing the `legacy_bbox2delta` function. Write a Python function `def le... | Compute deltas of proposals w.r.t. gt in the MMDet V1.x manner. We usually compute the deltas of x, y, w, h of proposals w.r.t ground truth bboxes to get regression target. This is the inverse function of `delta2bbox()` Args: proposals (Tensor): Boxes to be transformed, shape (N, ..., 4) gt (Tensor): Gt bboxes to be us... |
14,844 | import numpy as np
import torch
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import HorizontalBoxes, get_box_tensor
from .base_bbox_coder import BaseBBoxCoder
The provided code snippet includes necessary dependencies for implementing the `legacy_delta2bbox` function. Write a Python function `def le... | Apply deltas to shift/scale base boxes in the MMDet V1.x manner. Typically the rois are anchor or proposed bounding boxes and the deltas are network outputs used to shift/scale those boxes. This is the inverse function of `bbox2delta()` Args: rois (Tensor): Boxes to be transformed. Has shape (N, 4) deltas (Tensor): Enc... |
14,845 | import warnings
from mmdet.registry import TASK_UTILS
The provided code snippet includes necessary dependencies for implementing the `build_bbox_coder` function. Write a Python function `def build_bbox_coder(cfg, **default_args)` to solve the following problem:
Builder of box coder.
Here is the function:
def build_b... | Builder of box coder. |
14,846 | import warnings
from mmdet.registry import TASK_UTILS
The provided code snippet includes necessary dependencies for implementing the `build_iou_calculator` function. Write a Python function `def build_iou_calculator(cfg, default_args=None)` to solve the following problem:
Builder of IoU calculator.
Here is the functi... | Builder of IoU calculator. |
14,847 | import warnings
from mmdet.registry import TASK_UTILS
The provided code snippet includes necessary dependencies for implementing the `build_match_cost` function. Write a Python function `def build_match_cost(cfg, default_args=None)` to solve the following problem:
Builder of IoU calculator.
Here is the function:
def... | Builder of IoU calculator. |
14,848 | import warnings
from mmdet.registry import TASK_UTILS
The provided code snippet includes necessary dependencies for implementing the `build_assigner` function. Write a Python function `def build_assigner(cfg, **default_args)` to solve the following problem:
Builder of box assigner.
Here is the function:
def build_as... | Builder of box assigner. |
14,849 | import warnings
from mmdet.registry import TASK_UTILS
The provided code snippet includes necessary dependencies for implementing the `build_sampler` function. Write a Python function `def build_sampler(cfg, **default_args)` to solve the following problem:
Builder of box sampler.
Here is the function:
def build_sampl... | Builder of box sampler. |
14,850 | import warnings
from mmdet.registry import TASK_UTILS
def build_prior_generator(cfg, default_args=None):
warnings.warn(
'``build_prior_generator`` would be deprecated soon, please use '
'``mmdet.registry.TASK_UTILS.build()`` ')
return TASK_UTILS.build(cfg, default_args=default_args) | null |
14,851 | import warnings
from mmdet.registry import TASK_UTILS
def build_anchor_generator(cfg, default_args=None):
warnings.warn(
'``build_anchor_generator`` would be deprecated soon, please use '
'``mmdet.registry.TASK_UTILS.build()`` ')
return TASK_UTILS.build(cfg, default_args=default_args) | null |
14,852 | from typing import List, Tuple
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, build_conv_layer, build_upsample_layer
from mmcv.ops.carafe import CARAFEPack
from mmengine.config import ConfigDict
from mmengine.model import BaseModule, ModuleList
fro... | Paste instance masks according to boxes. This implementation is modified from https://github.com/facebookresearch/detectron2/ Args: masks (Tensor): N, 1, H, W boxes (Tensor): N, 4 img_h (int): Height of the image to be pasted. img_w (int): Width of the image to be pasted. skip_empty (bool): Only paste masks within the ... |
14,853 | from typing import Union
from mmengine.config import ConfigDict
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures import SampleList
from mmdet.structures.bbox import BaseBoxes
from mmdet.structures.mask import BitmapMasks, PolygonMasks
from mmd... | Convert the key and value of mmengine.ConfigDict into a list. Args: cfg (ConfigDict): The detectron2 model config. config_list (list): A list contains the key and value of ConfigDict. Defaults to []. father_name (str): The father name add before the key. Defaults to "MODEL". Returns: tuple: - config_list: A list contai... |
14,854 | from typing import Union
from mmengine.config import ConfigDict
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures import SampleList
from mmdet.structures.bbox import BaseBoxes
from mmdet.structures.mask import BitmapMasks, PolygonMasks
from mmd... | Convert the Detectron2's result to DetDataSample. Args: data_samples (list[:obj:`DetDataSample`]): The batch data samples. It usually includes information such as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`. d2_results_list (list): The list of the results of Detectron2's model. Returns: list[:obj:`DetDataSample`... |
14,855 | 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 ... | Interpolate the `source` to the shape of the `target`. The `source` must be a Tensor, but the `target` can be a Tensor or a np.ndarray with the shape (..., target_h, target_w). Args: source (Tensor): A 3D/4D Tensor with the shape (N, H, W) or (N, C, H, W). target (Tensor | np.ndarray): The interpolation target with the... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.