id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
7,349 | import torch
from mmcv.ops import convex_iou, points_in_polygons
from mmdet.core.bbox.assigners.assign_result import AssignResult
from mmdet.core.bbox.assigners.base_assigner import BaseAssigner
from ..builder import ROTATED_BBOX_ASSIGNERS
The provided code snippet includes necessary dependencies for implementing the ... | Compute overlaps between polygons and points. Args: gt_rbboxes (torch.Tensor): Groundtruth polygons, shape (k, 8). points (torch.Tensor): Points to be assigned, shape(n, 18). Returns: overlaps (torch.Tensor): Overlaps between k gt_bboxes and n bboxes, shape(k, n). |
7,350 | import torch
from mmcv.ops import convex_iou, points_in_polygons
from mmdet.core.bbox.assigners.assign_result import AssignResult
from mmdet.core.bbox.assigners.base_assigner import BaseAssigner
from ..builder import ROTATED_BBOX_ASSIGNERS
The provided code snippet includes necessary dependencies for implementing the ... | Get horizontal bboxes from polygons. Args: gt_rbboxes (torch.Tensor): Groundtruth polygons, shape (k, 8). Returns: gt_rect_bboxes (torch.Tensor): The horizontal bboxes, shape (k, 4). |
7,351 | import torch
from mmcv.ops import convex_iou, points_in_polygons
from mmdet.core.bbox.assigners.assign_result import AssignResult
from mmdet.core.bbox.assigners.base_assigner import BaseAssigner
from ..builder import ROTATED_BBOX_ASSIGNERS
The provided code snippet includes necessary dependencies for implementing the ... | Compute the aspect ratio of all gts. Args: gt_rbboxes (torch.Tensor): Groundtruth polygons, shape (k, 8). Returns: ratios (torch.Tensor): The aspect ratio of gt_rbboxes, shape (k, 1). |
7,352 | from mmcv.ops import box_iou_rotated
from .builder import ROTATED_IOU_CALCULATORS
The provided code snippet includes necessary dependencies for implementing the `rbbox_overlaps` function. Write a Python function `def rbbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False)` to solve the following problem:
Calcul... | Calculate overlap between two set of bboxes. Args: bboxes1 (torch.Tensor): shape (B, m, 5) in <cx, cy, w, h, a> format or empty. bboxes2 (torch.Tensor): shape (B, n, 5) in <cx, cy, w, h, a> format or empty. mode (str): "iou" (intersection over union), "iof" (intersection over foreground) or "giou" (generalized intersec... |
7,353 | from mmcv.utils import build_from_cfg
from mmdet.core.bbox.iou_calculators.builder import IOU_CALCULATORS
ROTATED_IOU_CALCULATORS = IOU_CALCULATORS
The provided code snippet includes necessary dependencies for implementing the `build_iou_calculator` function. Write a Python function `def build_iou_calculator(cfg, defa... | Builder of IoU calculator. |
7,354 | from mmcv.utils import build_from_cfg
from mmdet.core.bbox.builder import BBOX_ASSIGNERS, BBOX_CODERS, BBOX_SAMPLERS
ROTATED_BBOX_ASSIGNERS = BBOX_ASSIGNERS
The provided code snippet includes necessary dependencies for implementing the `build_assigner` function. Write a Python function `def build_assigner(cfg, **defau... | Builder of box assigner. |
7,355 | from mmcv.utils import build_from_cfg
from mmdet.core.bbox.builder import BBOX_ASSIGNERS, BBOX_CODERS, BBOX_SAMPLERS
ROTATED_BBOX_SAMPLERS = BBOX_SAMPLERS
The provided code snippet includes necessary dependencies for implementing the `build_sampler` function. Write a Python function `def build_sampler(cfg, **default_a... | Builder of box sampler. |
7,356 | from mmcv.utils import build_from_cfg
from mmdet.core.bbox.builder import BBOX_ASSIGNERS, BBOX_CODERS, BBOX_SAMPLERS
ROTATED_BBOX_CODERS = BBOX_CODERS
The provided code snippet includes necessary dependencies for implementing the `build_bbox_coder` function. Write a Python function `def build_bbox_coder(cfg, **default... | Builder of box coder. |
7,357 |
The provided code snippet includes necessary dependencies for implementing the `rotated_anchor_inside_flags` function. Write a Python function `def rotated_anchor_inside_flags(flat_anchors, valid_flags, img_shape, allowed_... | Check whether the rotated anchors are inside the border. Args: flat_anchors (torch.Tensor): Flatten anchors, shape (n, 5). valid_flags (torch.Tensor): An existing valid flags of anchors. img_shape (tuple(int)): Shape of current image. allowed_border (int, optional): The border to allow the valid anchor. Defaults to 0. ... |
7,358 | from mmcv.utils import build_from_cfg
from mmdet.core.anchor.builder import ANCHOR_GENERATORS
ROTATED_ANCHOR_GENERATORS = ANCHOR_GENERATORS
def build_prior_generator(cfg, default_args=None):
return build_from_cfg(cfg, ROTATED_ANCHOR_GENERATORS, default_args) | null |
7,359 | import numpy as np
import torch
from mmcv.ops import nms, nms_rotated
def translate_bboxes(bboxes, offset):
"""Translate bboxes according to its shape.
If the bbox shape is (n, 5), the bboxes are regarded as horizontal bboxes
and in (x, y, x, y, score) format. If the bbox shape is (n, 6), the bboxes
are... | Merge patch results via nms. Args: results (list[np.ndarray] | list[tuple]): A list of patches results. offsets (np.ndarray): Positions of the left top points of patches. img_shape (tuple): A tuple of the huge image's width and height. iou_thr (float): The IoU threshold of NMS. device (str): The device to call nms. Ret... |
7,360 | from itertools import product
from math import ceil
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `get_multiscale_patch` function. Write a Python function `def get_multiscale_patch(sizes, steps, ratios)` to solve the following problem:
Get multiscale patch sizes and ... | 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... |
7,361 | from itertools import product
from math import ceil
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `slide_window` function. Write a Python function `def slide_window(width, height, sizes, steps, img_rate_thr=0.6)` to solve the following problem:
Slide windows in image... | Slide windows in images and get window position. Args: width (int): The width of the image. height (int): The height of the image. sizes (list): List of window's sizes. steps (list): List of window's steps. img_rate_thr (float): Threshold of window area divided by image area. Returns: np.ndarray: Information of valid w... |
7,362 | import cv2
import matplotlib.pyplot as plt
import mmcv
import numpy as np
from matplotlib.collections import PatchCollection
from matplotlib.patches import Polygon
from mmdet.core.visualization import palette_val
from mmdet.core.visualization.image import draw_labels, draw_masks
from mmrotate.core.visualization.palette... | Draw bboxes and class labels (with scores) on an image. Args: img (str | ndarray): The image to be displayed. bboxes (ndarray): Bounding boxes (with scores), shaped (n, 5) or (n, 6). labels (ndarray): Labels of bboxes. segms (ndarray | None): Masks, shaped (n,h,w) or None. class_names (list[str]): Names of each classes... |
7,363 | from multiprocessing import get_context
import numpy as np
import torch
from mmcv.ops import box_iou_rotated
from mmcv.utils import print_log
from mmdet.core import average_precision
from terminaltables import AsciiTable
def tpfp_default(det_bboxes,
gt_bboxes,
gt_bboxes_ignore=None,
... | Evaluate mAP of a rotated 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: - `bbo... |
7,364 | import torch
from mmcv.ops import nms_rotated
The provided code snippet includes necessary dependencies for implementing the `multiclass_nms_rotated` function. Write a Python function `def multiclass_nms_rotated(multi_bboxes, multi_scores, score_thr, ... | NMS for multi-class bboxes. Args: multi_bboxes (torch.Tensor): shape (n, #class*5) or (n, 5) multi_scores (torch.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... |
7,365 | import torch
from mmcv.ops import nms_rotated
The provided code snippet includes necessary dependencies for implementing the `aug_multiclass_nms_rotated` function. Write a Python function `def aug_multiclass_nms_rotated(merged_bboxes, merged_labels, score_thr, nms, max_num, classes)` to ... | NMS for aug multi-class bboxes. Args: multi_bboxes (torch.Tensor): shape (n, #class*5) or (n, 5) multi_scores (torch.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 consid... |
7,366 | import copy
import platform
from mmcv.utils import build_from_cfg
from mmdet.datasets import DATASETS, PIPELINES
from mmdet.datasets.builder import _concat_dataset
ROTATED_DATASETS = DATASETS
def build_dataset(cfg, default_args=None):
from mmdet.datasets.dataset_wrappers import (ClassBalancedDataset,
... | null |
7,367 | import glob
import os
import os.path as osp
import re
import tempfile
import time
import warnings
import zipfile
from collections import defaultdict
from functools import partial
import mmcv
import numpy as np
import torch
from mmcv.ops import nms_rotated
from mmdet.datasets.custom import CustomDataset
from mmrotate.co... | Merging patch bboxes into full image. Args: CLASSES (list): Label category. iou_thr (float): Threshold of IoU. |
7,368 | import torch
import torch.nn as nn
from mmdet.models.losses.utils import weighted_loss
from mmrotate.core import GaussianMixture, gt2gaussian
from ..builder import ROTATED_LOSSES
def kld_single2single(g1, g2):
"""Compute Kullback-Leibler Divergence.
Args:
g1 (dict[str, torch.Tensor]): Gaussian distribut... | Kullback-Leibler Divergence loss. Args: pred (torch.Tensor): Convexes with shape (N, 9, 2). target (torch.Tensor): Polygons with shape (N, 4, 2). eps (float): Defaults to 1e-6. Returns: torch.Tensor: Kullback-Leibler Divergence loss. |
7,369 | from copy import deepcopy
import torch
from mmdet.models.losses.utils import weighted_loss
from torch import nn
from ..builder import ROTATED_LOSSES
The provided code snippet includes necessary dependencies for implementing the `xy_wh_r_2_xy_sigma` function. Write a Python function `def xy_wh_r_2_xy_sigma(xywhr)` to s... | Convert oriented bounding box to 2-D Gaussian distribution. Args: xywhr (torch.Tensor): rbboxes with shape (N, 5). Returns: xy (torch.Tensor): center point of 2-D Gaussian distribution with shape (N, 2). sigma (torch.Tensor): covariance matrix of 2-D Gaussian distribution with shape (N, 2, 2). |
7,370 | from copy import deepcopy
import torch
from mmdet.models.losses.utils import weighted_loss
from torch import nn
from ..builder import ROTATED_LOSSES
The provided code snippet includes necessary dependencies for implementing the `xy_stddev_pearson_2_xy_sigma` function. Write a Python function `def xy_stddev_pearson_2_x... | Convert oriented bounding box from the Pearson coordinate system to 2-D Gaussian distribution. Args: xy_stddev_pearson (torch.Tensor): rbboxes with shape (N, 5). Returns: xy (torch.Tensor): center point of 2-D Gaussian distribution with shape (N, 2). sigma (torch.Tensor): covariance matrix of 2-D Gaussian distribution ... |
7,371 | from copy import deepcopy
import torch
from mmdet.models.losses.utils import weighted_loss
from torch import nn
from ..builder import ROTATED_LOSSES
def postprocess(distance, fun='log1p', tau=1.0):
"""Convert distance to loss.
Args:
distance (torch.Tensor)
fun (str, optional): The function appli... | Gaussian Wasserstein distance loss. Derivation and simplification: Given any positive-definite symmetrical 2*2 matrix Z: :math:`Tr(Z^{1/2}) = λ_1^{1/2} + λ_2^{1/2}` where :math:`λ_1` and :math:`λ_2` are the eigen values of Z Meanwhile we have: :math:`Tr(Z) = λ_1 + λ_2` :math:`det(Z) = λ_1 * λ_2` Combination with follow... |
7,372 | from copy import deepcopy
import torch
from mmdet.models.losses.utils import weighted_loss
from torch import nn
from ..builder import ROTATED_LOSSES
def postprocess(distance, fun='log1p', tau=1.0):
"""Convert distance to loss.
Args:
distance (torch.Tensor)
fun (str, optional): The function appli... | Symmetrical Kullback-Leibler Divergence loss. Args: pred (torch.Tensor): Predicted bboxes. target (torch.Tensor): Corresponding gt bboxes. fun (str): The function applied to distance. Defaults to 'log1p'. tau (float): Defaults to 1.0. alpha (float): Defaults to 1.0. sqrt (bool): Whether to sqrt the distance. Defaults t... |
7,373 | from copy import deepcopy
import torch
from mmdet.models.losses.utils import weighted_loss
from torch import nn
from ..builder import ROTATED_LOSSES
def postprocess(distance, fun='log1p', tau=1.0):
"""Convert distance to loss.
Args:
distance (torch.Tensor)
fun (str, optional): The function appli... | Symmetrical Max Kullback-Leibler Divergence loss. Args: pred (torch.Tensor): Predicted bboxes. target (torch.Tensor): Corresponding gt bboxes. fun (str): The function applied to distance. Defaults to 'log1p'. tau (float): Defaults to 1.0. alpha (float): Defaults to 1.0. sqrt (bool): Whether to sqrt the distance. Defaul... |
7,374 | from copy import deepcopy
import torch
from mmdet.models.losses.utils import weighted_loss
from torch import nn
from ..builder import ROTATED_LOSSES
def postprocess(distance, fun='log1p', tau=1.0):
"""Convert distance to loss.
Args:
distance (torch.Tensor)
fun (str, optional): The function appli... | Symmetrical Min Kullback-Leibler Divergence loss. Args: pred (torch.Tensor): Predicted bboxes. target (torch.Tensor): Corresponding gt bboxes. fun (str): The function applied to distance. Defaults to 'log1p'. tau (float): Defaults to 1.0. alpha (float): Defaults to 1.0. sqrt (bool): Whether to sqrt the distance. Defaul... |
7,375 | import torch
import torch.nn as nn
from mmcv.ops import convex_giou
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from ..builder import ROTATED_LOSSES
The provided code snippet includes necessary dependencies for implementing the `AspectRatio` function. Write a Python func... | Compute the aspect ratio of all gts. Args: gt_rbboxes (torch.Tensor): Groundtruth polygons, shape (k, 8). Returns: ratios (torch.Tensor): The aspect ratio of gt_rbboxes, shape (k, 1). |
7,376 | import warnings
import torch
import torch.nn as nn
from mmdet.models.losses.utils import weighted_loss
from ..builder import ROTATED_LOSSES
The provided code snippet includes necessary dependencies for implementing the `rotated_iou_loss` function. Write a Python function `def rotated_iou_loss(pred, target, linear=Fals... | Rotated IoU loss. Computing the IoU loss between a set of predicted rbboxes and target rbboxes. The loss is calculated as negative log of IoU. Args: pred (torch.Tensor): Predicted bboxes of format (x, y, h, w, angle), shape (n, 5). target (torch.Tensor): Corresponding gt bboxes, shape (n, 5). linear (bool, optional): I... |
7,377 | import torch
import torch.nn as nn
from mmcv.ops import points_in_polygons
from ..builder import ROTATED_LOSSES
def spatial_border_loss(pts, gt_bboxes):
"""The loss is used to penalize the learning points out of the assigned
ground truth boxes (polygon by default).
Args:
pts (torch.Tensor): point se... | Weghted spatial border loss. Args: pts (torch.Tensor): point sets with shape (N, 9*2). gt_bboxes (torch.Tensor): gt_bboxes with polygon form with shape(N, 8) weight (torch.Tensor): weights for point sets with shape (N) Returns: loss (torch.Tensor) |
7,378 | import torch
from mmdet.models.losses.utils import weighted_loss
from torch import nn
from ..builder import ROTATED_LOSSES
def xy_wh_r_2_xy_sigma(xywhr):
"""Convert oriented bounding box to 2-D Gaussian distribution.
Args:
xywhr (torch.Tensor): rbboxes with shape (N, 5).
Returns:
xy (torch.T... | Kalman filter IoU loss. Args: pred (torch.Tensor): Predicted bboxes. target (torch.Tensor): Corresponding gt bboxes. pred_decode (torch.Tensor): Predicted decode bboxes. targets_decode (torch.Tensor): Corresponding gt decode bboxes. fun (str): The function applied to distance. Defaults to None. beta (float): Defaults t... |
7,379 | from copy import deepcopy
import torch
from torch import nn
from ..builder import ROTATED_LOSSES
The provided code snippet includes necessary dependencies for implementing the `xy_wh_r_2_xy_sigma` function. Write a Python function `def xy_wh_r_2_xy_sigma(xywhr)` to solve the following problem:
Convert oriented boundin... | Convert oriented bounding box to 2-D Gaussian distribution. Args: xywhr (torch.Tensor): rbboxes with shape (N, 5). Returns: xy (torch.Tensor): center point of 2-D Gaussian distribution with shape (N, 2). sigma (torch.Tensor): covariance matrix of 2-D Gaussian distribution with shape (N, 2, 2). |
7,380 | from copy import deepcopy
import torch
from torch import nn
from ..builder import ROTATED_LOSSES
The provided code snippet includes necessary dependencies for implementing the `gwd_loss` function. Write a Python function `def gwd_loss(pred, target, fun='sqrt', tau=2.0)` to solve the following problem:
Gaussian Wassers... | Gaussian Wasserstein distance loss. Args: pred (torch.Tensor): Predicted bboxes. target (torch.Tensor): Corresponding gt bboxes. fun (str): The function applied to distance. Defaults to 'log1p'. tau (float): Defaults to 1.0. Returns: loss (torch.Tensor) |
7,381 | from copy import deepcopy
import torch
from torch import nn
from ..builder import ROTATED_LOSSES
The provided code snippet includes necessary dependencies for implementing the `bcd_loss` function. Write a Python function `def bcd_loss(pred, target, fun='log1p', tau=1.0)` to solve the following problem:
Bhatacharyya di... | Bhatacharyya distance loss. Args: pred (torch.Tensor): Predicted bboxes. target (torch.Tensor): Corresponding gt bboxes. fun (str): The function applied to distance. Defaults to 'log1p'. tau (float): Defaults to 1.0. Returns: loss (torch.Tensor) |
7,382 | from copy import deepcopy
import torch
from torch import nn
from ..builder import ROTATED_LOSSES
The provided code snippet includes necessary dependencies for implementing the `kld_loss` function. Write a Python function `def kld_loss(pred, target, fun='log1p', tau=1.0)` to solve the following problem:
Kullback-Leible... | Kullback-Leibler Divergence loss. Args: pred (torch.Tensor): Predicted bboxes. target (torch.Tensor): Corresponding gt bboxes. fun (str): The function applied to distance. Defaults to 'log1p'. tau (float): Defaults to 1.0. Returns: loss (torch.Tensor) |
7,383 | import torch.nn as nn
import torch.nn.functional as F
from mmdet.models import weight_reduce_loss
from ..builder import ROTATED_LOSSES
The provided code snippet includes necessary dependencies for implementing the `smooth_focal_loss` function. Write a Python function `def smooth_focal_loss(pred, ... | Smooth Focal Loss proposed in Circular Smooth Label (CSL). Args: pred (torch.Tensor): The prediction. target (torch.Tensor): The learning label of the prediction. weight (torch.Tensor, optional): The weight of loss for each prediction. Defaults to None. gamma (float, optional): The gamma for calculating the modulating ... |
7,384 | import math
import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.ops import DeformConv2d, chamfer_distance, min_area_polygons
from mmcv.runner import force_fp32
from mmdet.core import images_to_levels, multi_apply, unmap
from mmdet.core.anchor.point_generator import MlvlPointG... | Compute the Chamfer distance between two point sets. Args: point_set_1 (torch.tensor): point set 1 with shape (N_pointsets, N_points, 2) point_set_2 (torch.tensor): point set 2 with shape (N_pointsets, N_points, 2) Returns: dist (torch.tensor): chamfer distance between two point sets with shape (N_pointsets,) |
7,385 | import torch
from mmcv.ops import convex_iou
The provided code snippet includes necessary dependencies for implementing the `points_center_pts` function. Write a Python function `def points_center_pts(RPoints, y_first=True)` to solve the following problem:
Compute center point of Pointsets. Args: RPoints (torch.Tensor... | Compute center point of Pointsets. Args: RPoints (torch.Tensor): the lists of Pointsets, shape (k, 18). y_first (bool, optional): if True, the sequence of Pointsets is (y,x). Returns: center_pts (torch.Tensor): the mean_center coordination of Pointsets, shape (k, 18). |
7,386 | import torch
from mmcv.ops import convex_iou
The provided code snippet includes necessary dependencies for implementing the `convex_overlaps` function. Write a Python function `def convex_overlaps(gt_bboxes, points)` to solve the following problem:
Compute overlaps between polygons and points. Args: gt_rbboxes (torch.... | Compute overlaps between polygons and points. Args: gt_rbboxes (torch.Tensor): Groundtruth polygons, shape (k, 8). points (torch.Tensor): Points to be assigned, shape(n, 18). Returns: overlaps (torch.Tensor): Overlaps between k gt_bboxes and n bboxes, shape(k, n). |
7,387 | import torch
from mmcv.ops import convex_iou
The provided code snippet includes necessary dependencies for implementing the `levels_to_images` function. Write a Python function `def levels_to_images(mlvl_tensor, flatten=False)` to solve the following problem:
Concat multi-level feature maps by image. [feature_level0, ... | 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... |
7,388 | import torch
from mmcv.ops import convex_iou
The provided code snippet includes necessary dependencies for implementing the `get_num_level_anchors_inside` function. Write a Python function `def get_num_level_anchors_inside(num_level_anchors, inside_flags)` to solve the following problem:
Get number of every level anch... | Get number of every level anchors inside. Args: num_level_anchors (List[int]): List of number of every level's anchors. inside_flags (torch.Tensor): Flags of all anchors. Returns: List[int]: List of number of inside anchors. |
7,389 | import warnings
import e2cnn.nn as enn
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.runner import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import ROTATED_BACKBONES
from ..utils import (build_enn_divide_feature, build_enn_norm_layer,
build_en... | Get the expansion of a residual block. The block expansion will be obtained by the following order: 1. If ``expansion`` is given, just return it. 2. If ``block`` has the attribute ``expansion``, then return ``block.expansion``. 3. Return the default value according the the block type: 1 for ``BasicBlock`` and 4 for ``B... |
7,390 | import warnings
from mmdet.models.builder import MODELS
ROTATED_BACKBONES = MODELS
The provided code snippet includes necessary dependencies for implementing the `build_backbone` function. Write a Python function `def build_backbone(cfg)` to solve the following problem:
Build backbone.
Here is the function:
def buil... | Build backbone. |
7,391 | import warnings
from mmdet.models.builder import MODELS
ROTATED_NECKS = MODELS
The provided code snippet includes necessary dependencies for implementing the `build_neck` function. Write a Python function `def build_neck(cfg)` to solve the following problem:
Build neck.
Here is the function:
def build_neck(cfg):
... | Build neck. |
7,392 | import warnings
from mmdet.models.builder import MODELS
ROTATED_ROI_EXTRACTORS = MODELS
The provided code snippet includes necessary dependencies for implementing the `build_roi_extractor` function. Write a Python function `def build_roi_extractor(cfg)` to solve the following problem:
Build roi extractor.
Here is the... | Build roi extractor. |
7,393 | import warnings
from mmdet.models.builder import MODELS
ROTATED_SHARED_HEADS = MODELS
The provided code snippet includes necessary dependencies for implementing the `build_shared_head` function. Write a Python function `def build_shared_head(cfg)` to solve the following problem:
Build shared head.
Here is the functio... | Build shared head. |
7,394 | import warnings
from mmdet.models.builder import MODELS
ROTATED_HEADS = MODELS
The provided code snippet includes necessary dependencies for implementing the `build_head` function. Write a Python function `def build_head(cfg)` to solve the following problem:
Build head.
Here is the function:
def build_head(cfg):
... | Build head. |
7,395 | import warnings
from mmdet.models.builder import MODELS
ROTATED_LOSSES = MODELS
The provided code snippet includes necessary dependencies for implementing the `build_loss` function. Write a Python function `def build_loss(cfg)` to solve the following problem:
Build loss.
Here is the function:
def build_loss(cfg):
... | Build loss. |
7,396 | import warnings
from mmdet.models.builder import MODELS
ROTATED_DETECTORS = MODELS
The provided code snippet includes necessary dependencies for implementing the `build_detector` function. Write a Python function `def build_detector(cfg, train_cfg=None, test_cfg=None)` to solve the following problem:
Build detector.
... | Build detector. |
7,397 | import e2cnn.nn as enn
from e2cnn import gspaces
gspace = gspaces.Rot2dOnR2(N=N)
The provided code snippet includes necessary dependencies for implementing the `build_enn_feature` function. Write a Python function `def build_enn_feature(planes)` to solve the following problem:
build a enn regular feature map with the ... | build a enn regular feature map with the specified number of channels. |
7,398 | import e2cnn.nn as enn
from e2cnn import gspaces
def build_enn_divide_feature(planes):
"""build a enn regular feature map with the specified number of channels
divided by N."""
assert gspace.fibergroup.order() > 0
N = gspace.fibergroup.order()
planes = planes / N
planes = int(planes)
return ... | build an enn normalizion layer. |
7,399 | import e2cnn.nn as enn
from e2cnn import gspaces
def build_enn_divide_feature(planes):
"""build a enn regular feature map with the specified number of channels
divided by N."""
assert gspace.fibergroup.order() > 0
N = gspace.fibergroup.order()
planes = planes / N
planes = int(planes)
return ... | enn convolution. Args: in_channels (List[int]): Number of input channels per scale. out_channels (int): Number of output channels (used at each scale). kernel_size (int, optional): The size of kernel. stride (int, optional): Stride of the convolution. Default: 1. padding (int or tuple): Zero-padding added to both sides... |
7,400 | import e2cnn.nn as enn
from e2cnn import gspaces
def build_enn_divide_feature(planes):
"""build a enn regular feature map with the specified number of channels
divided by N."""
assert gspace.fibergroup.order() > 0
N = gspace.fibergroup.order()
planes = planes / N
planes = int(planes)
return ... | enn convolution with trivial input featurn. Args: in_channels (List[int]): Number of input channels per scale. out_channels (int): Number of output channels (used at each scale). kernel_size (int, optional): The size of kernel. stride (int, optional): Stride of the convolution. Default: 1. padding (int or tuple): Zero-... |
7,401 | import e2cnn.nn as enn
from e2cnn import gspaces
def build_enn_divide_feature(planes):
"""build a enn regular feature map with the specified number of channels
divided by N."""
assert gspace.fibergroup.order() > 0
N = gspace.fibergroup.order()
planes = planes / N
planes = int(planes)
return ... | enn ReLU. |
7,402 | import e2cnn.nn as enn
from e2cnn import gspaces
def build_enn_divide_feature(planes):
"""build a enn regular feature map with the specified number of channels
divided by N."""
assert gspace.fibergroup.order() > 0
N = gspace.fibergroup.order()
planes = planes / N
planes = int(planes)
return ... | enn Average Pooling. Args: inplanes (int): The number of input channel. kernel_size (int, optional): The size of kernel. stride (int, optional): Stride of the convolution. Default: 1. padding (int or tuple): Zero-padding added to both sides of the input. Default: 0. ceil_mode (bool, optional): if True, keep information... |
7,403 | import e2cnn.nn as enn
from e2cnn import gspaces
def build_enn_divide_feature(planes):
"""build a enn regular feature map with the specified number of channels
divided by N."""
assert gspace.fibergroup.order() > 0
N = gspace.fibergroup.order()
planes = planes / N
planes = int(planes)
return ... | enn Max Pooling. |
7,404 | import e2cnn.nn as enn
from e2cnn import gspaces
def build_enn_divide_feature(planes):
"""build a enn regular feature map with the specified number of channels
divided by N."""
assert gspace.fibergroup.order() > 0
N = gspace.fibergroup.order()
planes = planes / N
planes = int(planes)
return ... | enn Interpolate. |
7,405 | import os
import platform
import warnings
import cv2
import torch.multiprocessing as mp
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 the following problem:
Setup multi-processing enviro... | Setup multi-processing environment variables. |
7,406 | from mmcv.utils import collect_env as collect_basic_env
from mmcv.utils import get_git_hash
import mmrotate
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 environment information.... | Collect environment information. |
7,407 | import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
dp_factory = {'cuda': MMDataParallel, 'cpu': MMDataParallel}
The provided code snippet includes necessary dependencies for implementing the `build_dp` function. Write a Python function `def build_dp(model, device='cuda', dim=0, *args, **k... | build DataParallel module by device type. if device is cuda, return a MMDataParallel model; if device is mlu, return a MLUDataParallel model. Args: model (:class:`nn.Module`): model to be parallelized. device (str): device type, cuda, cpu or mlu. Defaults to cuda. dim (int): Dimension used to scatter the data. Defaults... |
7,408 | import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
ddp_factory = {'cuda': MMDistributedDataParallel}
The provided code snippet includes necessary dependencies for implementing the `build_ddp` function. Write a Python function `def build_ddp(model, device='cuda', *args, **kwargs)` to solve... | Build DistributedDataParallel module by device type. If device is cuda, return a MMDistributedDataParallel model; if device is mlu, return a MLUDistributedDataParallel model. Args: model (:class:`nn.Module`): module to be parallelized. device (str): device type, mlu or cuda. Returns: :class:`nn.Module`: the module to b... |
7,409 | import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
def is_npu_available():
"""Returns a bool indicating if NPU is currently available."""
return hasattr(torch, 'npu') and torch.npu.is_available()
The provided code snippet includes necessary dependencies for implementing the `get_d... | Returns an available device, cpu, cuda. |
7,410 | import glob
import os.path as osp
import warnings
The provided code snippet includes necessary dependencies for implementing the `find_latest_checkpoint` function. Write a Python function `def find_latest_checkpoint(path, suffix='pth')` to solve the following problem:
Find the latest checkpoint from the working direct... | 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 |
7,411 | import copy
import warnings
from mmcv 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 `runner` s... | 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. |
7,412 | import logging
from mmcv.utils import get_logger
The provided code snippet includes necessary dependencies for implementing the `get_root_logger` function. Write a Python function `def get_root_logger(log_file=None, log_level=logging.INFO)` to solve the following problem:
Get root logger. Args: log_file (str, optional... | Get root logger. Args: log_file (str, optional): File path of log. Defaults to None. log_level (int, optional): The level of logger. Defaults to logging.INFO. Returns: :obj:`logging.Logger`: The obtained logger |
7,413 | from argparse import ArgumentParser
from mmdet.apis import inference_detector, init_detector, show_result_pyplot
import mmrotate
def parse_args():
parser = ArgumentParser()
parser.add_argument('img', help='Image file')
parser.add_argument('config', help='Config file')
parser.add_argument('checkpoint', ... | null |
7,414 | from argparse import ArgumentParser
from mmdet.apis import init_detector, show_result_pyplot
from mmrotate.apis import inference_detector_by_patches
def parse_args():
parser = ArgumentParser()
parser.add_argument('img', help='Image file')
parser.add_argument('config', help='Config file')
parser.add_arg... | null |
7,415 | import argparse
import copy
import os
import os.path as osp
import time
import warnings
import mmcv
import torch
import torch.distributed as dist
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist
from mmcv.utils import get_git_hash
from mmdet import __version__
from mmdet.apis import ... | null |
7,416 | import argparse
import numpy as np
import torch
from mmcv import Config, DictAction
from mmrotate.models import build_detector
def parse_args():
parser = argparse.ArgumentParser(description='Train a detector')
parser.add_argument('config', help='train config file path')
parser.add_argument(
'--shap... | null |
7,417 | import argparse
import os
import matplotlib.pyplot as plt
import mmcv
import numpy as np
import torch
from matplotlib.ticker import MultipleLocator
from mmcv import Config, DictAction
from mmcv.ops import nms_rotated
from mmdet.datasets import build_dataset
from mmrotate.core.bbox import rbbox_overlaps
def parse_args(... | null |
7,418 | import argparse
import os
import matplotlib.pyplot as plt
import mmcv
import numpy as np
import torch
from matplotlib.ticker import MultipleLocator
from mmcv import Config, DictAction
from mmcv.ops import nms_rotated
from mmdet.datasets import build_dataset
from mmrotate.core.bbox import rbbox_overlaps
def analyze_per_... | 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... |
7,419 | import argparse
import os
import matplotlib.pyplot as plt
import mmcv
import numpy as np
import torch
from matplotlib.ticker import MultipleLocator
from mmcv import Config, DictAction
from mmcv.ops import nms_rotated
from mmdet.datasets import build_dataset
from mmrotate.core.bbox import rbbox_overlaps
The provided co... | 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. ... |
7,420 | import argparse
import json
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `cal_train_time` function. Write a Python function `def cal_train_time(log_dicts, args)` to solve the following problem:
calc... | calculate the training time. |
7,421 | import argparse
import json
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `plot_curve` function. Write a Python function `def plot_curve(log_dicts, args)` to solve the following problem:
Plot curve.
... | Plot curve. |
7,422 | import argparse
import json
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
def add_plot_parser(subparsers):
"""Add plot parser."""
parser_plt = subparsers.add_parser(
'plot_curve', help='parser for plotting curves')
parser_plt.add_argument(
'json_logs'... | Parse parameters. |
7,423 | import argparse
import json
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `load_json_logs` function. Write a Python function `def load_json_logs(json_logs)` to solve the following problem:
Load and c... | 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 value of sub dict is a list of corresponding values of all iterations. Args: json_logs (str): json file of logs. Returns: dict: dict of logs. |
7,424 | import argparse
import copy
import os
import time
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDistributedDataParallel
from mmcv.runner import init_dist, load_checkpoint, wrap_fp16_model
from mmdet.datasets import build_dataloader, replace_ImageToTensor
... | null |
7,425 | import argparse
import copy
import os
import time
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDistributedDataParallel
from mmcv.runner import init_dist, load_checkpoint, wrap_fp16_model
from mmdet.datasets import build_dataloader, replace_ImageToTensor
... | Repeat to inference several times and take the average. Args: cfg (object): Test config object. checkpoint (str): Checkpoint file path. max_iter (int): Num of max iter. log_interval (int): Interval of logging. is_fuse_conv_bn (bool): Whether to fuse conv and bn, this will slightly increase the inference speed use_fp16 ... |
7,426 | import argparse
import subprocess
import torch
The provided code snippet includes necessary dependencies for implementing the `parse_args` function. Write a Python function `def parse_args()` to solve the following problem:
Parse parameters.
Here is the function:
def parse_args():
"""Parse parameters."""
par... | Parse parameters. |
7,427 | import argparse
import subprocess
import torch
The provided code snippet includes necessary dependencies for implementing the `process_checkpoint` function. Write a Python function `def process_checkpoint(in_file, out_file)` to solve the following problem:
Only inference related parameters are retained. Args: in_file ... | Only inference related parameters are retained. Args: in_file (str): Filename of input checkpoint. out_file (str): Filename of output checkpoint. |
7,428 | import argparse
import codecs
import datetime
import itertools
import json
import logging
import os
import os.path as osp
import time
from functools import partial, reduce
from math import ceil
from multiprocessing import Manager, Pool
import cv2
import numpy as np
from PIL import Image
def add_parser(parser):
"""A... | Parse arguments. |
7,429 | import argparse
import codecs
import datetime
import itertools
import json
import logging
import os
import os.path as osp
import time
from functools import partial, reduce
from math import ceil
from multiprocessing import Manager, Pool
import cv2
import numpy as np
from PIL import Image
def get_sliding_window(info, siz... | Args: arguments (object): Parameters. sizes (list): List of window's sizes. gaps (list): List of window's gaps. img_rate_thr (float): Threshold of window area divided by image area. iof_thr (float): Threshold of overlaps between bbox and window. no_padding (bool): If True, no padding. padding_value (tuple[int|float]): ... |
7,430 | import argparse
import codecs
import datetime
import itertools
import json
import logging
import os
import os.path as osp
import time
from functools import partial, reduce
from math import ceil
from multiprocessing import Manager, Pool
import cv2
import numpy as np
from PIL import Image
The provided code snippet inclu... | Setup logger. Args: log_path (str): Path of log. Returns: object: Logger. |
7,431 | import argparse
import codecs
import datetime
import itertools
import json
import logging
import os
import os.path as osp
import time
from functools import partial, reduce
from math import ceil
from multiprocessing import Manager, Pool
import cv2
import numpy as np
from PIL import Image
def _load_dota_single(imgfile, i... | Load DOTA dataset. Args: img_dir (str): Path of images. ann_dir (str): Path of annotations. nproc (int): number of processes. Returns: list: Dataset's contents. |
7,432 | import argparse
import os
from collections import Sequence
from pathlib import Path
import mmcv
from mmcv import Config, DictAction
from mmdet.datasets.builder import build_dataset
from mmrotate.core.visualization import imshow_det_rbboxes
def parse_args():
parser = argparse.ArgumentParser(description='Browse a da... | null |
7,433 | import argparse
import os
from collections import Sequence
from pathlib import Path
import mmcv
from mmcv import Config, DictAction
from mmdet.datasets.builder import build_dataset
from mmrotate.core.visualization import imshow_det_rbboxes
The provided code snippet includes necessary dependencies for implementing the ... | Retrieve the dataset config file. Args: config_path (str): Path of the config file. skip_type (list[str]): List of the useless pipeline to skip. cfg_options (dict): dict of configs to merge from. |
7,434 | import argparse
import warnings
from mmcv import Config, DictAction
The provided code snippet includes necessary dependencies for implementing the `parse_args` function. Write a Python function `def parse_args()` to solve the following problem:
Parse arguments.
Here is the function:
def parse_args():
"""Parse ar... | Parse arguments. |
7,435 | from argparse import ArgumentParser, Namespace
from pathlib import Path
from tempfile import TemporaryDirectory
import mmcv
The provided code snippet includes necessary dependencies for implementing the `mmrotate2torchserve` function. Write a Python function `def mmrotate2torchserve( config_file: str, checkpoi... | Converts MMRotate model (config + checkpoint) to TorchServe `.mar`. Args: config_file: In MMRotate config format. The contents vary for each task repository. checkpoint_file: In MMRotate checkpoint format. The contents vary for each task repository. output_folder: Folder where `{model_name}.mar` will be created. The fi... |
7,436 | from argparse import ArgumentParser, Namespace
from pathlib import Path
from tempfile import TemporaryDirectory
import mmcv
def parse_args():
parser = ArgumentParser(
description='Convert MMRotate models to TorchServe `.mar` format.')
parser.add_argument('config', type=str, help='config file path')
... | null |
7,437 | import os
import gc
import time
import base64
from contextlib import asynccontextmanager
from typing import List, Literal, Union, Tuple, Optional
import torch
import uvicorn
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from loguru import logger
from pydantic import BaseM... | An asynchronous context manager for managing the lifecycle of the FastAPI app. It ensures that GPU memory is cleared after the app's lifecycle ends, which is essential for efficient resource management in GPU environments. |
7,438 | import os
import gc
import time
import base64
from contextlib import asynccontextmanager
from typing import List, Literal, Union, Tuple, Optional
import torch
import uvicorn
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from loguru import logger
from pydantic import BaseM... | An endpoint to list available models. It returns a list of model cards. This is useful for clients to query and understand what models are available for use. |
7,439 | import os
import gc
import time
import base64
from contextlib import asynccontextmanager
from typing import List, Literal, Union, Tuple, Optional
import torch
import uvicorn
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from loguru import logger
from pydantic import BaseM... | null |
7,440 | import requests
import json
import base64
def create_chat_completion(model, messages, temperature=0.8, max_tokens=2048, top_p=0.8, use_stream=False):
"""
This function sends a request to the chat API to generate a response based on the given messages.
Args:
model (str): The name of the model to use ... | Facilitates a simple chat interaction involving an image. Args: use_stream (bool): Specifies whether to use streaming for chat responses. img_path (str): Path to the image file to be included in the chat. This function encodes the specified image and constructs a predefined conversation involving the image. It then cal... |
7,441 | import gradio as gr
import os, sys
from PIL import Image
import torch
import time
from sat.model.mixins import CachedAutoregressiveMixin
from sat.mpu import get_model_parallel_world_size
from sat.model import AutoModel
from utils.utils import chat, llama2_tokenizer, llama2_text_processor_inference, get_image_processor,... | null |
7,442 | import gradio as gr
import os, sys
from PIL import Image
import torch
import time
from sat.model.mixins import CachedAutoregressiveMixin
from sat.mpu import get_model_parallel_world_size
from sat.model import AutoModel
from utils.utils import chat, llama2_tokenizer, llama2_text_processor_inference, get_image_processor,... | null |
7,443 | import gradio as gr
import os, sys
from PIL import Image
import torch
import time
from sat.model.mixins import CachedAutoregressiveMixin
from sat.mpu import get_model_parallel_world_size
from sat.model import AutoModel
from utils.utils import chat, llama2_tokenizer, llama2_text_processor_inference, get_image_processor,... | null |
7,444 | import gradio as gr
import os, sys
from PIL import Image
import torch
import time
from sat.model.mixins import CachedAutoregressiveMixin
from sat.mpu import get_model_parallel_world_size
from sat.model import AutoModel
from utils.utils import chat, llama2_tokenizer, llama2_text_processor_inference, get_image_processor,... | null |
7,445 | import base64
from io import BytesIO
from PIL import Image
The provided code snippet includes necessary dependencies for implementing the `images_are_same` function. Write a Python function `def images_are_same(img1: Image, img2: Image) -> bool` to solve the following problem:
Compare two PIL images.
Here is the func... | Compare two PIL images. |
7,446 | import base64
from io import BytesIO
from PIL import Image
The provided code snippet includes necessary dependencies for implementing the `encode_file_to_base64` function. Write a Python function `def encode_file_to_base64(file)` to solve the following problem:
Convert a file to base64.
Here is the function:
def enc... | Convert a file to base64. |
7,447 | import requests
import re
import streamlit as st
from dataclasses import dataclass
from enum import auto, Enum
from PIL.Image import Image
from PIL import ImageDraw
from streamlit.delta_generator import DeltaGenerator
class Role(Enum):
"""
CogVLM | CogAgent Only have 2 roles: USER, ASSISTANT
Represents the ... | Prepares the conversation history for processing by concatenating the content of each turn. Args: history (list[Conversation]): The conversation history, a list of Conversation objects. Returns: str: A single string that concatenates the content of each conversation turn, followed by the ASSISTANT role indicator. This ... |
7,448 | import requests
import re
import streamlit as st
from dataclasses import dataclass
from enum import auto, Enum
from PIL.Image import Image
from PIL import ImageDraw
from streamlit.delta_generator import DeltaGenerator
The provided code snippet includes necessary dependencies for implementing the `postprocess_text` fun... | Post-processes the generated text by incorporating it into a given template. Args: template (str): A template string containing a placeholder for the generated text. text (str): The generated text to be incorporated into the template. Returns: str: The template with the generated text replacing the placeholder. |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.