id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
9,949 | from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
from maskrcnn_benchmark.structures.bounding_box import BoxList
import json
import numpy as np
import os.path as osp
import os
from prettytable import PrettyTable
import xml.etree.ElementTree as ET
from collections import defaultdict
from pathlib import P... | Parses the xml files in the Flickr30K Entities dataset input: filename - full file path to the annotations file to parse output: dictionary with the following fields: scene - list of identifiers which were annotated as pertaining to the whole scene nobox - list of identifiers which were annotated as not being visible i... |
9,950 | from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
from maskrcnn_benchmark.structures.bounding_box import BoxList
import json
import numpy as np
import os.path as osp
import os
from prettytable import PrettyTable
import xml.etree.ElementTree as ET
from collections import defaultdict
from pathlib import P... | Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with ``0 <= x1 < x2`` and ``0 <= y1 < y2``. Args: boxes1 (Tensor[N, 4]) boxes2 (Tensor[M, 4]) Returns: iou (Tensor[N, M]): the NxM matrix containing the pairwise IoU values for every element in ... |
9,951 | from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
from maskrcnn_benchmark.structures.bounding_box import BoxList
import json
import numpy as np
import os.path as osp
import os
from prettytable import PrettyTable
import xml.etree.ElementTree as ET
from collections import defaultdict
from pathlib import P... | Return the boxes corresponding to the smallest enclosing box containing all the provided boxes The boxes are expected in [x1, y1, x2, y2] format |
9,952 | import logging
import tempfile
import os
import torch
import numpy as np
import json
from collections import OrderedDict
from tqdm import tqdm
from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.bo... | null |
9,953 | import logging
import tempfile
import os
import torch
import numpy as np
import json
from collections import OrderedDict
from tqdm import tqdm
from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.bo... | null |
9,954 | import copy
import datetime
import json
import os
from collections import OrderedDict, defaultdict
import numpy as np
import pycocotools.mask as mask_util
import torch
import torch._six
import maskrcnn_benchmark.utils.mdetr_dist as dist
from maskrcnn_benchmark.utils.mdetr_dist import all_gather
from .lvis import LVIS
... | null |
9,955 | import copy
import datetime
import json
import os
from collections import OrderedDict, defaultdict
import numpy as np
import pycocotools.mask as mask_util
import torch
import torch._six
import maskrcnn_benchmark.utils.mdetr_dist as dist
from maskrcnn_benchmark.utils.mdetr_dist import all_gather
from .lvis import LVIS
... | null |
9,956 | import copy
import datetime
import json
import os
from collections import OrderedDict, defaultdict
import numpy as np
import pycocotools.mask as mask_util
import torch
import torch._six
import maskrcnn_benchmark.utils.mdetr_dist as dist
from maskrcnn_benchmark.utils.mdetr_dist import all_gather
from .lvis import LVIS
... | null |
9,957 | import copy
import datetime
import json
import os
from collections import OrderedDict, defaultdict
import numpy as np
import pycocotools.mask as mask_util
import torch
import torch._six
import maskrcnn_benchmark.utils.mdetr_dist as dist
from maskrcnn_benchmark.utils.mdetr_dist import all_gather
from .lvis import LVIS
... | null |
9,958 | import json
import os
import time
from collections import defaultdict
import pycocotools.mask as mask_utils
import torchvision
from PIL import Image
def _isArrayLike(obj):
return hasattr(obj, "__iter__") and hasattr(obj, "__len__") | null |
9,959 | from __future__ import division
import os
from collections import OrderedDict
import numpy as np
import torch
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou, getUnionBBox
def evaluate_box_proposals(
predictions, dataset, thresholds=No... | null |
9,960 | from __future__ import division
import os
from collections import OrderedDict
import numpy as np
import torch
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou, getUnionBBox
The provided code snippet includes necessary dependencies for impl... | Calculate average precisions based on evaluation code of PASCAL VOC. This function calculates average precisions from given precisions and recalls. The code is based on the evaluation code used in PASCAL VOC Challenge. Args: prec (list of numpy.array): A list of arrays. :obj:`prec[l]` indicates precision for class :mat... |
9,961 | import torch
import numpy as np
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.data import transforms as T
from maskrcnn_benchmark.structures.image_list import to_image_list
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlis... | null |
9,962 | import collections
import json
import os.path as op
import numpy as np
import torch
from .tsv import TSVYamlDataset, find_file_path_in_yaml
from .box_label_loader import BoxLabelLoader
from maskrcnn_benchmark.data.datasets.coco_dt import CocoDetectionTSV
def sort_key_by_val(dic):
sorted_dic = sorted(dic.items(), k... | null |
9,963 | import collections
import json
import os.path as op
import numpy as np
import torch
from .tsv import TSVYamlDataset, find_file_path_in_yaml
from .box_label_loader import BoxLabelLoader
from maskrcnn_benchmark.data.datasets.coco_dt import CocoDetectionTSV
def bbox_overlaps(anchors, gt_boxes):
"""
anchors: (N, 4)... | Only include boxes that overlap as possible relations. If no overlapping boxes, use all of them. |
9,964 | import bisect
import copy
import logging
import os
import torch.utils.data
import torch.distributed as dist
from maskrcnn_benchmark.utils.comm import get_world_size
from maskrcnn_benchmark.utils.imports import import_file
from . import datasets as D
from . import samplers
from .collate_batch import BatchCollator, BBoxA... | null |
9,965 | import torch
def cat(tensors, dim=0):
def permute_and_flatten(layer, N, A, C, H, W):
def concat_box_prediction_layers(box_regression, box_cls=None, token_logits=None):
box_regression_flattened = []
box_cls_flattened = []
token_logit_flattened = []
# for each feature level, permute the outputs to make ... | null |
9,966 | import torch
def round_channels(channels, divisor=8):
rounded_channels = max(int(channels + divisor / 2.0) // divisor * divisor, divisor)
if float(rounded_channels) < 0.9 * channels:
rounded_channels += divisor
return rounded_channels | null |
9,967 | import torch
import re
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import logging
import cv2
import math
import itertools
import collections
from torchvision.ops import nms
class Conv2dStaticSamePadding(nn.Module):
"""
created by Zylo117
The real keras/tensorflow conv2d with sam... | Chooses static padding if you have specified an image size, and dynamic padding otherwise. Static padding is necessary for ONNX exporting of models. |
9,968 | import torch
import re
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import logging
import cv2
import math
import itertools
import collections
from torchvision.ops import nms
The provided code snippet includes necessary dependencies for implementing the `round_filters` function. Write a Pyth... | Calculate and round number of filters based on depth multiplier. |
9,969 | import torch
import re
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import logging
import cv2
import math
import itertools
import collections
from torchvision.ops import nms
The provided code snippet includes necessary dependencies for implementing the `round_repeats` function. Write a Pyth... | Round number of filters based on depth multiplier. |
9,970 | import torch
import re
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import logging
import cv2
import math
import itertools
import collections
from torchvision.ops import nms
The provided code snippet includes necessary dependencies for implementing the `drop_connect` function. Write a Pytho... | Drop connect. |
9,971 | import torch
import re
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import logging
import cv2
import math
import itertools
import collections
from torchvision.ops import nms
def efficientnet(width_coefficient=None, depth_coefficient=None, dropout_rate=0.2,
drop_connect_rate=... | Get the block args and global params for a given model |
9,972 | import torch
import re
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import logging
import cv2
import math
import itertools
import collections
from torchvision.ops import nms
url_map = {
'efficientnet-b0': 'https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b0-355c32eb.p... | Loads pretrained weights, and downloads if loading for the first time. |
9,973 | import torch
import re
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import logging
import cv2
import math
import itertools
import collections
from torchvision.ops import nms
def init_weights(model):
for name, module in model.named_modules():
is_conv_layer = isinstance(module, nn... | null |
9,974 | import torch
import re
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import logging
import cv2
import math
import itertools
import collections
from torchvision.ops import nms
def calc_iou(a, b):
# a(anchor) [boxes, (y1, x1, y2, x2)]
# b(gt, coco-style) [boxes, (x1, y1, x2, y2)]
... | null |
9,975 | import torch
import re
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import logging
import cv2
import math
import itertools
import collections
from torchvision.ops import nms
def postprocess2(x, anchors, regression, classification,
transformed_anchors, threshold, iou_thresho... | null |
9,976 | import torch
import re
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import logging
import cv2
import math
import itertools
import collections
from torchvision.ops import nms
def postprocess(x, anchors, regression, classification, regressBoxes, clipBoxes, threshold, iou_threshold):
ancho... | null |
9,977 | import torch
import re
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import logging
import cv2
import math
import itertools
import collections
from torchvision.ops import nms
def display(preds, imgs, obj_list, imshow=True, imwrite=False):
for i in range(len(imgs)):
if len(preds[i... | null |
9,978 | import torch
import re
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import logging
import cv2
import math
import itertools
import collections
from torchvision.ops import nms
sigmoid_focal_loss_cuda = _SigmoidFocalLoss.apply
def calculate_focal_loss2(classification, target_list, alpha, gamm... | null |
9,979 | import torch
import re
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import logging
import cv2
import math
import itertools
import collections
from torchvision.ops import nms
def calculate_focal_loss(classification, targets, alpha, gamma):
classification = classification.sigmoid()
de... | null |
9,980 | import torch
import re
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import logging
import cv2
import math
import itertools
import collections
from torchvision.ops import nms
def calculate_giou(pred, gt):
ax1, ay1, ax2, ay2 = pred[:, 0], pred[:, 1], pred[:, 2], pred[:, 3]
bx1, by1, b... | null |
9,981 | from __future__ import absolute_import, division, print_function, unicode_literals
import copy
import logging
import math
from collections import OrderedDict
import torch
import torch.nn as nn
from torch.nn import BatchNorm2d, SyncBatchNorm
from maskrcnn_benchmark.layers import Conv2d, interpolate
from maskrcnn_benchma... | null |
9,982 | from __future__ import absolute_import, division, print_function, unicode_literals
import copy
import logging
import math
from collections import OrderedDict
import torch
import torch.nn as nn
from torch.nn import BatchNorm2d, SyncBatchNorm
from maskrcnn_benchmark.layers import Conv2d, interpolate
from maskrcnn_benchma... | null |
9,983 | import os
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from maskrcnn_benchmark.layers import SEBlock, swish
The provided code snippet includes necessary dependencies for implementing the `calc_tf_padding` function. Write a Python function `def calc_tf_padding(x, ke... | Calculate TF-same like padding size. Parameters: ---------- x : tensor Input tensor. kernel_size : int Convolution window size. stride : int, default 1 Strides of the convolution. dilation : int, default 1 Dilation value for convolution layer. Returns ------- tuple of 4 int The size of the padding. |
9,984 | import os
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from maskrcnn_benchmark.layers import SEBlock, swish
class ConvBlock(nn.Module):
"""
Standard convolution block with Batch normalization and activation.
Parameters:
----------
in_channels : int
Number of... | 1x1 version of the standard convolution block. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. stride : int or tuple/list of 2 int, default 1 Strides of the convolution. padding : int, or tuple/list of 2 int, or tuple/list of 4 int, default 0 Padding valu... |
9,985 | import os
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from maskrcnn_benchmark.layers import SEBlock, swish
class ConvBlock(nn.Module):
"""
Standard convolution block with Batch normalization and activation.
Parameters:
----------
in_channels : int
Number of... | 3x3 version of the standard convolution block. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. stride : int or tuple/list of 2 int, default 1 Strides of the convolution. padding : int, or tuple/list of 2 int, or tuple/list of 4 int, default 1 Padding valu... |
9,986 | import os
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from maskrcnn_benchmark.layers import SEBlock, swish
class ConvBlock(nn.Module):
"""
Standard convolution block with Batch normalization and activation.
Parameters:
----------
in_channels : int
Number of... | 3x3 depthwise version of the standard convolution block. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. stride : int or tuple/list of 2 int, default 1 Strides of the convolution. padding : int, or tuple/list of 2 int, or tuple/list of 4 int, default 1 Pa... |
9,987 | import os
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from maskrcnn_benchmark.layers import SEBlock, swish
class ConvBlock(nn.Module):
"""
Standard convolution block with Batch normalization and activation.
Parameters:
----------
in_channels : int
Number of... | 5x5 depthwise version of the standard convolution block. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. stride : int or tuple/list of 2 int, default 1 Strides of the convolution. padding : int, or tuple/list of 2 int, or tuple/list of 4 int, default 2 Pa... |
9,988 | import os
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from maskrcnn_benchmark.layers import SEBlock, swish
def round_channels(channels,
divisor=8):
"""
Round weighted channel number (make divisible operation).
Parameters:
----------
channels : in... | null |
9,989 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import numpy as np
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
The provided code snippet includes necessary dependencies for implementing the `window_partition` function. Write a Python ... | Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C) |
9,990 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import numpy as np
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
The provided code snippet includes necessary dependencies for implementing the `window_reverse` function. Write a Python fu... | Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C) |
9,991 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import numpy as np
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
class SwinTransformer(nn.Module):
""" Swin Transformer backbone.
A PyTorch impl of : `Swin Transformer: Hierarch... | Create a SwinT instance from config. Returns: VoVNet: a :class:`VoVNet` instance. |
9,992 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `conv7x7` function. Write a Python function `def conv7x7(in_planes, out_planes, stride=1, groups=1, dilation=1)` to solve the following problem:
7x7 convolution ... | 7x7 convolution with padding |
9,993 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `conv5x5` function. Write a Python function `def conv5x5(in_planes, out_planes, stride=1, groups=1, dilation=1)` to solve the following problem:
5x5 convolution ... | 5x5 convolution with padding |
9,994 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `conv3x3` function. Write a Python function `def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1)` to solve the following problem:
3x3 convolution ... | 3x3 convolution with padding |
9,995 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `conv1x1` function. Write a Python function `def conv1x1(in_planes, out_planes, stride=1)` to solve the following problem:
1x1 convolution
Here is the function:... | 1x1 convolution |
9,996 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def maxpool(**kwargs):
return nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | null |
9,997 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def avgpool(**kwargs):
return nn.AvgPool2d(kernel_size=3, stride=2, padding=1) | null |
9,998 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def dropout(prob):
return nn.Dropout(prob) | null |
9,999 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import numpy as np
from einops import rearrange
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
The provided code snippet includes necessary dependencies for implementing the `window_partiti... | Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C) |
10,000 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import numpy as np
from einops import rearrange
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
The provided code snippet includes necessary dependencies for implementing the `window_reverse... | Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C) |
10,001 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import numpy as np
from einops import rearrange
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
class SwinTransformer(nn.Module):
""" Swin Transformer backbone.
A PyTorch impl of ... | Create a SwinT instance from config. Returns: VoVNet: a :class:`VoVNet` instance. |
10,002 | import torch.nn as nn
from .ops import *
import torch
import torch.nn as nn
import torch.nn.functional as F
def channel_shuffle(x, groups):
batchsize, num_channels, height, width = x.data.size()
channels_per_group = num_channels // groups
# reshape
x = x.view(batchsize, groups, channels_per_group, hei... | null |
10,003 | from collections import namedtuple
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import BatchNorm2d, SyncBatchNorm
from maskrcnn_benchmark.layers import FrozenBatchNorm2d, NaiveSyncBatchNorm2d
from maskrcnn_benchmark.layers import Conv2d, DFConv2d, SELayer
from maskrcnn_benchmark.model... | null |
10,006 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import numpy as np
from einops import rearrange
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
class SwinTransformer(nn.Module):
""" Swin Transformer backbone.
A PyTorch impl of ... | Create a SwinT instance from config. Returns: VoVNet: a :class:`VoVNet` instance. |
10,009 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import numpy as np
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
class SwinTransformer(nn.Module):
""" Swin Transformer backbone.
A PyTorch impl of : `Swin Transformer: Hierarch... | Create a SwinT instance from config. Returns: VoVNet: a :class:`VoVNet` instance. |
10,010 | import torch
from torch import nn
from torch.nn import functional as F
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.layers import Conv2d, DYReLU
from maskrcnn_benchmark.modeling.poolers import Pooler
def group_norm(out_channels, affine=True, divisor=1):
out_channels = out_channels // divisor
... | null |
10,011 | import torch
from torch import nn
from torch.nn import functional as F
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.layers import Conv2d, DYReLU
from maskrcnn_benchmark.modeling.poolers import Pooler
def group_norm(out_channels, affine=True, divisor=1):
out_channels = out_channels // divisor
... | Caffe2 implementation uses XavierFill, which in fact corresponds to kaiming_uniform_ in PyTorch |
10,012 | import torch
from torch import nn
from torch.nn import functional as F
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.layers import Conv2d, DYReLU
from maskrcnn_benchmark.modeling.poolers import Pooler
def group_norm(out_channels, affine=True, divisor=1):
out_channels = out_channels // divisor
... | null |
10,013 | import torch
import torch.nn.functional as F
from torch import nn, Tensor
import copy
from typing import Optional, List
def _get_clones(module, N):
return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) | null |
10,014 | import torch
import torch.nn.functional as F
from torch import nn, Tensor
import copy
from typing import Optional, List
The provided code snippet includes necessary dependencies for implementing the `_get_activation_fn` function. Write a Python function `def _get_activation_fn(activation)` to solve the following probl... | Return an activation function given a string |
10,015 | import logging
import torch
from maskrcnn_benchmark.modeling.box_coder import BoxCoder
from maskrcnn_benchmark.structures.bounding_box import BoxList, _onnx_clip_boxes_to_image
from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_nms
from maskrc... | null |
10,016 | import logging
import torch
from maskrcnn_benchmark.modeling.box_coder import BoxCoder
from maskrcnn_benchmark.structures.bounding_box import BoxList, _onnx_clip_boxes_to_image
from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_nms
from maskrc... | null |
10,017 | import logging
import torch
from maskrcnn_benchmark.modeling.box_coder import BoxCoder
from maskrcnn_benchmark.structures.bounding_box import BoxList, _onnx_clip_boxes_to_image
from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_nms
from maskrc... | null |
10,018 | import logging
import torch
from maskrcnn_benchmark.modeling.box_coder import BoxCoder
from maskrcnn_benchmark.structures.bounding_box import BoxList, _onnx_clip_boxes_to_image
from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_nms
from maskrc... | null |
10,019 | import logging
import torch
from maskrcnn_benchmark.modeling.box_coder import BoxCoder
from maskrcnn_benchmark.structures.bounding_box import BoxList, _onnx_clip_boxes_to_image
from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_nms
from maskrc... | null |
10,020 | import logging
import torch
from maskrcnn_benchmark.modeling.box_coder import BoxCoder
from maskrcnn_benchmark.structures.bounding_box import BoxList, _onnx_clip_boxes_to_image
from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_nms
from maskrc... | null |
10,021 | import math
import numpy as np
import torch
from torch import nn
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.image_list import ImageList
from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
class AnchorGenerator(nn.Module):
"""
For a set of imag... | null |
10,022 | import math
import numpy as np
import torch
from torch import nn
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.image_list import ImageList
from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
class AnchorGenerator(nn.Module):
"""
For a set of imag... | null |
10,023 | import math
import numpy as np
import torch
from torch import nn
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.image_list import ImageList
from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
class CenterAnchorGenerator(nn.Module):
"""
For a set o... | null |
10,024 | import math
import numpy as np
import torch
from torch import nn
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.image_list import ImageList
from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
def _generate_anchors(base_size, scales, aspect_ratios):
""... | Generates a matrix of anchor boxes in (x1, y1, x2, y2) format. Anchors are centered on stride / 2, have (approximate) sqrt areas of the specified sizes, and aspect ratios as given. |
10,025 | import torch
from torch import nn
from torch.nn import functional as F
from ..balanced_positive_negative_sampler import BalancedPositiveNegativeSampler
from ..utils import cat, concat_box_prediction_layers
from maskrcnn_benchmark.layers import smooth_l1_loss
from maskrcnn_benchmark.modeling.matcher import Matcher
from ... | null |
10,026 | import torch
from torch import nn
from torch.nn import functional as F
from ..balanced_positive_negative_sampler import BalancedPositiveNegativeSampler
from ..utils import cat, concat_box_prediction_layers
from maskrcnn_benchmark.layers import smooth_l1_loss
from maskrcnn_benchmark.modeling.matcher import Matcher
from ... | null |
10,027 | import torch
from torch import nn
from torch.nn import functional as F
from ..balanced_positive_negative_sampler import BalancedPositiveNegativeSampler
from ..utils import cat, concat_box_prediction_layers
from maskrcnn_benchmark.layers import smooth_l1_loss
from maskrcnn_benchmark.modeling.matcher import Matcher
from ... | null |
10,028 | import torch
from torch import nn
from torch.nn import functional as F
from ..balanced_positive_negative_sampler import BalancedPositiveNegativeSampler
from ..utils import cat, concat_box_prediction_layers
from maskrcnn_benchmark.layers import smooth_l1_loss
from maskrcnn_benchmark.modeling.matcher import Matcher
from ... | null |
10,029 | import math
import os
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.activations import ACT2FN
import pdb
from transformers.modeling_u... | null |
10,030 | import torch
from torch import nn
from torch.nn import functional as F
from maskrcnn_benchmark.modeling import registry
from maskrcnn_benchmark.modeling.backbone import resnet
from maskrcnn_benchmark.modeling.poolers import Pooler
from maskrcnn_benchmark.modeling.make_layers import group_norm
from maskrcnn_benchmark.mo... | null |
10,031 | import torch
import torch.nn.functional as F
from torch import nn
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_nms
from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
from maskrcnn_benchmark.modeling.box_coder import BoxCoder
... | null |
10,032 | import torch
from torch.nn import functional as F
from maskrcnn_benchmark.layers import smooth_l1_loss
from maskrcnn_benchmark.modeling.box_coder import BoxCoder
from maskrcnn_benchmark.modeling.matcher import Matcher
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
from maskrcnn_benchmark.modeling.bal... | null |
10,033 | from torch import nn
_ROI_BOX_PREDICTOR = {
"FastRCNNPredictor": FastRCNNPredictor,
"FPNPredictor": FPNPredictor,
}
def make_roi_box_predictor(cfg):
func = _ROI_BOX_PREDICTOR[cfg.MODEL.ROI_BOX_HEAD.PREDICTOR]
return func(cfg) | null |
10,034 | import torch
from torch import nn
from .roi_box_feature_extractors import make_roi_box_feature_extractor
from .roi_box_predictors import make_roi_box_predictor
from .inference import make_roi_box_post_processor
from .loss import make_roi_box_loss_evaluator
from maskrcnn_benchmark.utils.amp import custom_fwd, custom_bwd... | Constructs a new box head. By default, uses ROIBoxHead, but if it turns out not to be enough, just register a new class and make it a parameter in the config |
10,035 | import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from maskrcnn_benchmark.structures.bounding_box import BoxList
def convert_mask_grounding_to_od_logits(logits, positive_map_label_to_token, num_classes):
od_logits = torch.zeros(logits.shape[0], num_classes + 1, logits.shape[2], l... | null |
10,036 | import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from maskrcnn_benchmark.structures.bounding_box import BoxList
def expand_boxes(boxes, scale):
w_half = (boxes[:, 2] - boxes[:, 0]) * .5
h_half = (boxes[:, 3] - boxes[:, 1]) * .5
x_c = (boxes[:, 2] + boxes[:, 0]) * .5
y... | null |
10,037 | import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from maskrcnn_benchmark.structures.bounding_box import BoxList
class MaskPostProcessor(nn.Module):
"""
From the results of the CNN, post process the masks
by taking the mask corresponding to the class with max
probabili... | null |
10,038 | import torch
from torch import nn
from maskrcnn_benchmark.structures.bounding_box import BoxList
from .roi_mask_feature_extractors import make_roi_mask_feature_extractor
from .roi_mask_predictors import make_roi_mask_predictor
from .inference import make_roi_mask_post_processor
from .loss import make_roi_mask_loss_eval... | Given a set of BoxList containing the `labels` field, return a set of BoxList for which `labels > 0`. Arguments: boxes (list of BoxList) |
10,039 | import torch
from torch import nn
from maskrcnn_benchmark.structures.bounding_box import BoxList
from .roi_mask_feature_extractors import make_roi_mask_feature_extractor
from .roi_mask_predictors import make_roi_mask_predictor
from .inference import make_roi_mask_post_processor
from .loss import make_roi_mask_loss_eval... | null |
10,040 | import torch
from torch.nn import functional as F
from maskrcnn_benchmark.layers import smooth_l1_loss
from maskrcnn_benchmark.modeling.matcher import Matcher
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
from maskrcnn_benchmark.modeling.utils import cat
The provided code snippet includes necessary... | Given segmentation masks and the bounding boxes corresponding to the location of the masks in the image, this function crops and resizes the masks in the position defined by the boxes. This prepares the masks for them to be fed to the loss computation as the targets. Arguments: segmentation_masks: an instance of Segmen... |
10,041 | import torch
from torch.nn import functional as F
from maskrcnn_benchmark.layers import smooth_l1_loss
from maskrcnn_benchmark.modeling.matcher import Matcher
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
from maskrcnn_benchmark.modeling.utils import cat
class MaskRCNNLossComputation(object):
de... | null |
10,042 | import torch
from torch import nn
from torch.nn import functional as F
from maskrcnn_benchmark.layers import Conv2d, _NewEmptyTensorOp
from maskrcnn_benchmark.layers import ConvTranspose2d
from ...utils import permute_and_flatten
_ROI_MASK_PREDICTOR = {"MaskRCNNC4Predictor": MaskRCNNC4Predictor,
... | null |
10,043 | from torch import nn
from torch.nn import functional as F
from .hourglass import Hourglass
from ..box_head.roi_box_feature_extractors import ResNet50Conv5ROIFeatureExtractor
from maskrcnn_benchmark.modeling.poolers import Pooler
from maskrcnn_benchmark.layers import Conv2d
from maskrcnn_benchmark.modeling.make_layers i... | null |
10,044 | import torch
from .roi_keypoint_feature_extractors import make_roi_keypoint_feature_extractor
from .roi_keypoint_predictors import make_roi_keypoint_predictor
from .inference import make_roi_keypoint_post_processor
from .loss import make_roi_keypoint_loss_evaluator
class ROIKeypointHead(torch.nn.Module):
def __init... | null |
10,045 | import cv2
import numpy as np
import torch
from torch import nn
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.keypoint import PersonKeypoints
The provided code snippet includes necessary dependencies for implementing the `heatmaps_to_keypoints` function. Write a Pyth... | Extract predicted keypoint locations from heatmaps. Output has shape (#rois, 4, #keypoints) with the 4 rows corresponding to (x, y, logit, prob) for each keypoint. |
10,046 | import cv2
import numpy as np
import torch
from torch import nn
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.keypoint import PersonKeypoints
class KeypointPostProcessor(nn.Module):
def __init__(self, keypointer=None):
super(KeypointPostProcessor, self).__... | null |
10,047 | from torch import nn
from torch.nn import functional as F
from maskrcnn_benchmark.modeling.poolers import Pooler
from maskrcnn_benchmark.layers import Conv2d
from maskrcnn_benchmark.layers import ConvTranspose2d
_ROI_KEYPOINT_FEATURE_EXTRACTORS = {
"KeypointRCNNFeatureExtractor": KeypointRCNNFeatureExtractor,
"... | null |
10,048 | from torch import nn
from torch.nn import functional as F
from maskrcnn_benchmark import layers
_ROI_KEYPOINT_PREDICTOR = {"KeypointRCNNPredictor": KeypointRCNNPredictor}
def make_roi_keypoint_predictor(cfg):
func = _ROI_KEYPOINT_PREDICTOR[cfg.MODEL.ROI_KEYPOINT_HEAD.PREDICTOR]
return func(cfg) | null |
10,049 | import torch
from torch.nn import functional as F
from maskrcnn_benchmark.modeling.matcher import Matcher
from maskrcnn_benchmark.modeling.balanced_positive_negative_sampler import (
BalancedPositiveNegativeSampler,
)
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
from maskrcnn_benchmark.modeling... | null |
10,050 | import torch
from torch.nn import functional as F
from maskrcnn_benchmark.modeling.matcher import Matcher
from maskrcnn_benchmark.modeling.balanced_positive_negative_sampler import (
BalancedPositiveNegativeSampler,
)
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
from maskrcnn_benchmark.modeling... | null |
10,051 | import torch
from torch.nn import functional as F
from maskrcnn_benchmark.modeling.matcher import Matcher
from maskrcnn_benchmark.modeling.balanced_positive_negative_sampler import (
BalancedPositiveNegativeSampler,
)
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
from maskrcnn_benchmark.modeling... | Validate which keypoints are contained inside a given box. points: NxKx2 boxes: Nx4 output: NxK |
10,052 | import torch
from torch.nn import functional as F
from maskrcnn_benchmark.modeling.matcher import Matcher
from maskrcnn_benchmark.modeling.balanced_positive_negative_sampler import (
BalancedPositiveNegativeSampler,
)
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
from maskrcnn_benchmark.modeling... | null |
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