id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
3,466 | import importlib
import math
from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List, Any, Generator
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.cuda.amp import autocast
from torch.nn import CrossEntropyLoss
from transformers import PreTrainedTokenizer, Generat... | null |
3,467 | import base64
import logging
import os
import requests
import unicodedata
from typing import Collection, Dict, List, Set, Tuple, Union, Any, Callable, Optional
import tiktoken
import numpy as np
from PIL import Image
from PIL import ImageFont
from PIL import ImageDraw
from transformers import PreTrainedTokenizer, Added... | null |
3,468 | import base64
import logging
import os
import requests
import unicodedata
from typing import Collection, Dict, List, Set, Tuple, Union, Any, Callable, Optional
import tiktoken
import numpy as np
from PIL import Image
from PIL import ImageFont
from PIL import ImageDraw
from transformers import PreTrainedTokenizer, Added... | null |
3,472 | from collections import OrderedDict
import math
import requests
from io import BytesIO
from functools import partial
from PIL import Image
from typing import Callable, Optional, Sequence, Tuple, List
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.init import trun... | null |
3,473 | from collections import OrderedDict
import math
import requests
from io import BytesIO
from functools import partial
from PIL import Image
from typing import Callable, Optional, Sequence, Tuple, List
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.init import trun... | null |
3,474 | from collections import OrderedDict
import math
import requests
from io import BytesIO
from functools import partial
from PIL import Image
from typing import Callable, Optional, Sequence, Tuple, List
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.init import trun... | null |
3,475 | from collections import OrderedDict
import math
import requests
from io import BytesIO
from functools import partial
from PIL import Image
from typing import Callable, Optional, Sequence, Tuple, List
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.init import trun... | grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) |
3,476 | import json
from argparse import ArgumentParser
def open_json(path):
with open(path,"r") as f:
data=json.load(f)
return data | null |
3,477 | import json
from argparse import ArgumentParser
def save_json(json_list,save_path):
with open(save_path, 'w') as file:
json.dump(json_list, file, indent=4) | null |
3,478 | import json
from argparse import ArgumentParser
def caculate_IOU(box1,box2):
Ax1=box1[0]
Ay1=box1[1]
Ax2=box1[2]
Ay2=box1[3]
Bx1=box2[0]
By1=box2[1]
Bx2=box2[2]
By2=box2[3]
Ix1 = max(Ax1, Bx1)
Iy1 = max(Ay1, By1)
Ix2 = min(Ax2, Bx2)
Iy2 = min(Ay2, By2)
Intersection... | null |
3,479 | import json
from argparse import ArgumentParser
def _get_args():
parser = ArgumentParser()
parser.add_argument("--blip2_caption", type=str, default="./outputs/blip2_cap.json")
parser.add_argument("--ori_caption", type=str, default=None)
parser.add_argument("--grit", type=str, default="./outputs/grit_sc... | null |
3,480 | from typing import Any
import torch
from PIL import Image
from argparse import ArgumentParser
from lavis.models import load_model_and_preprocess
import os
import json
from tqdm import tqdm
import re
def save_json(json_list,save_path):
with open(save_path, 'w') as file:
json.dump(json_list, file, indent=4) | null |
3,481 | from typing import Any
import torch
from PIL import Image
from argparse import ArgumentParser
from lavis.models import load_model_and_preprocess
import os
import json
from tqdm import tqdm
import re
def _get_args():
parser = ArgumentParser()
parser.add_argument("--image_folder", type=str, default="./images")
... | null |
3,482 | import cv2
from segment_anything import SamAutomaticMaskGenerator, sam_model_registry
import argparse
import json
import os
from typing import Any, Dict, List
from tqdm import tqdm
from PIL import Image
def write_masks_to_folder(masks: List[Dict[str, Any]], path: str) -> None:
header = "id,area,bbox_x0,bbox_y0,bb... | null |
3,483 | import cv2
from segment_anything import SamAutomaticMaskGenerator, sam_model_registry
import argparse
import json
import os
from typing import Any, Dict, List
from tqdm import tqdm
from PIL import Image
def get_amg_kwargs(args):
amg_kwargs = {
"points_per_side": args.points_per_side,
"points_per_b... | null |
3,484 | import cv2
from segment_anything import SamAutomaticMaskGenerator, sam_model_registry
import argparse
import json
import os
from typing import Any, Dict, List
from tqdm import tqdm
from PIL import Image
def get_image_files(folder_path):
image_files = []
for root, dirs, files in os.walk(folder_path):
... | null |
3,485 | from detectron2.data import transforms as T
from .transforms.custom_augmentation_impl import EfficientDetResizeCrop
class EfficientDetResizeCrop(Augmentation):
"""
Scale the shorter edge to the given size, with a limit of `max_size` on the longer edge.
If `max_size` is reached, then downscale so that the l... | Create a list of default :class:`Augmentation` from config. Now it includes resizing and flipping. Returns: list[Augmentation] |
3,486 | import logging
import os
from fvcore.common.timer import Timer
from detectron2.structures import BoxMode
from fvcore.common.file_io import PathManager
from detectron2.data import DatasetCatalog, MetadataCatalog
from lvis import LVIS
def load_GRiTcoco_json(json_file, image_root, dataset_name=None):
'''
Load COCO... | null |
3,487 | import logging
import os
from fvcore.common.timer import Timer
from detectron2.structures import BoxMode
from fvcore.common.file_io import PathManager
from detectron2.data import DatasetCatalog, MetadataCatalog
from lvis import LVIS
for key, (image_root, json_file) in _CUSTOM_SPLITS_LVIS.items():
register_GRiTcoco_... | null |
3,488 | import logging
import os
from fvcore.common.timer import Timer
from detectron2.structures import BoxMode
from fvcore.common.file_io import PathManager
from detectron2.data import DatasetCatalog, MetadataCatalog
from lvis import LVIS
def load_vg_json(json_file, image_root, dataset_name=None):
json_file = PathManager... | null |
3,489 | import logging
import os
from fvcore.common.timer import Timer
from detectron2.structures import BoxMode
from fvcore.common.file_io import PathManager
from detectron2.data import DatasetCatalog, MetadataCatalog
from lvis import LVIS
for key, (image_root, json_file) in _CUSTOM_SPLITS_LVIS.items():
register_vg_instan... | null |
3,490 | import logging
import os
from fvcore.common.timer import Timer
from detectron2.structures import BoxMode
from fvcore.common.file_io import PathManager
from detectron2.data import DatasetCatalog, MetadataCatalog
from lvis import LVIS
def load_o365_json(json_file, image_root, dataset_name=None):
def register_o365_instan... | null |
3,491 | import logging
import os
from fvcore.common.timer import Timer
from detectron2.structures import BoxMode
from fvcore.common.file_io import PathManager
from detectron2.data import DatasetCatalog, MetadataCatalog
from lvis import LVIS
for key, (image_root, json_file) in _CUSTOM_SPLITS_LVIS.items():
register_o365_inst... | null |
3,492 | import operator
import torch
import torch.utils.data
from detectron2.utils.comm import get_world_size
from detectron2.config import configurable
from torch.utils.data.sampler import BatchSampler, Sampler
from detectron2.data.common import DatasetFromList, MapDataset
from detectron2.data.dataset_mapper import DatasetMap... | null |
3,493 | import operator
import torch
import torch.utils.data
from detectron2.utils.comm import get_world_size
from detectron2.config import configurable
from torch.utils.data.sampler import BatchSampler, Sampler
from detectron2.data.common import DatasetFromList, MapDataset
from detectron2.data.dataset_mapper import DatasetMap... | null |
3,494 | 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 `window_partition` function. Write a Python function `def window_partition(x, window_size)` to solve the following problem:
Partition into non-overlapping window... | Partition into non-overlapping windows with padding if needed. Args: x (tensor): input tokens with [B, H, W, C]. window_size (int): window size. Returns: windows: windows after partition with [B * num_windows, window_size, window_size, C]. (Hp, Wp): padded height and width before partition |
3,495 | 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 `window_unpartition` function. Write a Python function `def window_unpartition(windows, window_size, pad_hw, hw)` to solve the following problem:
Window unpartit... | Window unpartition into original sequences and removing padding. Args: x (tensor): input tokens with [B * num_windows, window_size, window_size, C]. window_size (int): window size. pad_hw (Tuple): padded height and width (Hp, Wp). hw (Tuple): original height and width (H, W) before padding. Returns: x: unpartitioned se... |
3,496 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def get_rel_pos(q_size, k_size, rel_pos):
"""
Get relative positional embeddings according to the relative positions of
query and key sizes.
Args:
q_size (int): size of query q.
k_size (int): size of key k... | Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950 Args: attn (Tensor): attention map. q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C). rel_pos_h (Te... |
3,497 | 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 `get_abs_pos` function. Write a Python function `def get_abs_pos(abs_pos, has_cls_token, hw)` to solve the following problem:
Calculate absolute positional embed... | Calculate absolute positional embeddings. If needed, resize embeddings and remove cls_token dimension for the original embeddings. Args: abs_pos (Tensor): absolute positional embeddings with (1, num_position, C). has_cls_token (bool): If true, has 1 embedding in abs_pos for cls token. hw (Tuple): size of input image to... |
3,498 | import logging
import math
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn as nn
from functools import partial
from detectron2.layers import CNNBlockBase, Conv2d, get_norm
from detectron2.modeling.backbone.build import BACKBONE_REGISTRY
from detectron2.layers import ShapeSpec
import sys
from ce... | null |
3,499 | import logging
import math
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn as nn
from functools import partial
from detectron2.layers import CNNBlockBase, Conv2d, get_norm
from detectron2.modeling.backbone.build import BACKBONE_REGISTRY
from detectron2.layers import ShapeSpec
import sys
from ce... | null |
3,500 | import logging
import math
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn as nn
from functools import partial
from detectron2.layers import CNNBlockBase, Conv2d, get_norm
from detectron2.modeling.backbone.build import BACKBONE_REGISTRY
from detectron2.layers import ShapeSpec
import sys
from ce... | null |
3,501 | from __future__ import absolute_import, division, print_function, unicode_literals
import sys
import json
import logging
import os
import shutil
import tempfile
import fnmatch
from functools import wraps
from hashlib import sha256
from io import open
import boto3
import requests
from botocore.exceptions import ClientEr... | Return the url and etag (which may be ``None``) stored for `filename`. Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist. |
3,502 | from __future__ import absolute_import, division, print_function, unicode_literals
import sys
import json
import logging
import os
import shutil
import tempfile
import fnmatch
from functools import wraps
from hashlib import sha256
from io import open
import boto3
import requests
from botocore.exceptions import ClientEr... | Given something that might be a URL (or might be a local path), determine which. If it's a URL, download the file and cache it, and return the path to the cached file. If it's already a local path, make sure the file exists and then return the path. |
3,503 | from __future__ import absolute_import, division, print_function, unicode_literals
import sys
import json
import logging
import os
import shutil
import tempfile
import fnmatch
from functools import wraps
from hashlib import sha256
from io import open
import boto3
import requests
from botocore.exceptions import ClientEr... | Wrapper function for s3 requests in order to create more helpful error messages. |
3,504 | from torch import nn
import torch
import functools
from torch.nn import functional as F
import warnings
class BertEncoderAsDecoder(nn.Module):
def __init__(self, encoder):
super().__init__()
self.encoder = encoder
def forward(self, tgt, memory,
tgt_mask=None,
tgt_... | null |
3,505 | from __future__ import absolute_import, division, print_function, unicode_literals
import copy
import os
import json
import logging
import math
import sys
from io import open
import torch
from torch import nn
import torch.utils.checkpoint as checkpoint
from .file_utils import cached_path
def qk2attn(query, key, attent... | null |
3,506 | from __future__ import absolute_import, division, print_function, unicode_literals
import copy
import os
import json
import logging
import math
import sys
from io import open
import torch
from torch import nn
import torch.utils.checkpoint as checkpoint
from .file_utils import cached_path
def _gelu_python(x):
retu... | null |
3,507 | from typing import Dict, List, Optional, Tuple
import torch
from detectron2.config import configurable
from detectron2.structures import ImageList, Instances, Boxes
from detectron2.modeling.meta_arch.build import META_ARCH_REGISTRY
from detectron2.modeling.meta_arch.rcnn import GeneralizedRCNN
from detectron2.structure... | Rescale the output instances to the target size. |
3,508 | from typing import Dict, List, Optional, Tuple
import torch
from detectron2.config import configurable
from detectron2.structures import ImageList, Instances, Boxes
from detectron2.modeling.meta_arch.build import META_ARCH_REGISTRY
from detectron2.modeling.meta_arch.rcnn import GeneralizedRCNN
from detectron2.structure... | null |
3,509 | import torch
from detectron2.structures import Boxes, RotatedBoxes, pairwise_iou, pairwise_iou_rotated
def soft_nms(boxes, scores, method, gaussian_sigma, linear_threshold, prune_threshold):
"""
Performs soft non-maximum suppression algorithm on axis aligned boxes
Args:
boxes (Tensor[N, 5]):
... | Performs soft non-maximum suppression in a batched fashion. Each index value correspond to a category, and NMS will not be applied between elements of different categories. Args: boxes (Tensor[N, 4]): boxes where NMS will be performed. They are expected to be in (x1, y1, x2, y2) format scores (Tensor[N]): scores for ea... |
3,510 | from detectron2.config import CfgNode as CN
def add_grit_config(cfg):
_C = cfg
_C.MODEL.BEAM_SIZE = 1
_C.MODEL.TRAIN_TASK = ["ObjectDet", "DenseCap"]
_C.MODEL.TEST_TASK = "DenseCap" # This can be varied if the model is jointly trained on multiple tasks
_C.MODEL.ROI_BOX_HEAD.USE_BIAS = 0.0 # >= 0... | null |
3,511 | import itertools
from typing import Any, Callable, Dict, Iterable, List, Set, Type, Union
import torch
from detectron2.config import CfgNode
from detectron2.solver.build import maybe_add_gradient_clipping
def get_vit_lr_decay_rate(name, lr_decay_rate=1.0, num_layers=12):
"""
Calculate lr decay rate for differen... | null |
3,512 | import itertools
import json
import os
from detectron2.structures import Boxes, BoxMode, pairwise_iou
from detectron2.utils.file_io import PathManager
import numpy as np
import pycocotools.mask as mask_util
from detectron2.evaluation.coco_evaluation import COCOEvaluator
from detectron2.evaluation.coco_evaluation import... | Add object_descriptions and logit (if applicable) to detectron2's instances_to_coco_json |
3,513 | import glob
import os
import shutil
from os import path
from setuptools import find_packages, setup
from typing import List
import torch
from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension
def get_version():
init_py_path = path.join(path.abspath(path.dirname(__file__)), "detectron2", "__in... | null |
3,514 | import glob
import os
import shutil
from os import path
from setuptools import find_packages, setup
from typing import List
import torch
from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension
torch_ver = [int(x) for x in torch.__version__.split(".")[:2]]
assert torch_ver >= [1, 8], "Requires PyTor... | null |
3,515 | import glob
import os
import shutil
from os import path
from setuptools import find_packages, setup
from typing import List
import torch
from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension
The provided code snippet includes necessary dependencies for implementing the `get_model_zoo_configs` fu... | Return a list of configs to include in package for model zoo. Copy over these configs inside detectron2/model_zoo. |
3,516 | import os
import sys
from unittest import mock
from sphinx.domains import Domain
from typing import Dict, List, Tuple
import sphinx_rtd_theme
class GithubURLDomain(Domain):
"""
Resolve certain links in markdown files to github source.
"""
name = "githuburl"
ROOT = "https://github.com/facebookresearc... | null |
3,517 | import copy
import logging
import re
from typing import Dict, List
import torch
from tabulate import tabulate
def convert_c2_detectron_names(weights):
"""
Map Caffe2 Detectron weight names to Detectron2 names.
Args:
weights (dict): name -> tensor
Returns:
dict: detectron2 names -> tensor... | Match names between the two state-dict, and returns a new chkpt_state_dict with names converted to match model_state_dict with heuristics. The returned dict can be later loaded with fvcore checkpointer. If `c2_conversion==True`, `ckpt_state_dict` is assumed to be a Caffe2 model and will be renamed at first. Strategy: s... |
3,518 | import numpy as np
from typing import Any, List, Tuple, Union
import torch
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `_keypoints_to_heatmap` function. Write a Python function `def _keypoints_to_heatmap( keypoints: torch.Tensor, rois: torch.T... | Encode keypoint locations into a target heatmap for use in SoftmaxWithLoss across space. Maps keypoints from the half-open interval [x1, x2) on continuous image coordinates to the closed interval [0, heatmap_size - 1] on discrete image coordinates. We use the continuous-discrete conversion from Heckbert 1990 ("What is ... |
3,519 | import numpy as np
from typing import Any, List, Tuple, Union
import torch
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `heatmaps_to_keypoints` function. Write a Python function `def heatmaps_to_keypoints(maps: torch.Tensor, rois: torch.Tensor) -> ... | Extract predicted keypoint locations from heatmaps. Args: maps (Tensor): (#ROIs, #keypoints, POOL_H, POOL_W). The predicted heatmap of logits for each ROI and each keypoint. rois (Tensor): (#ROIs, 4). The box of each ROI. Returns: Tensor of shape (#ROIs, #keypoints, 4) with the last dimension corresponding to (x, y, lo... |
3,520 | import copy
import itertools
import numpy as np
from typing import Any, Iterator, List, Union
import pycocotools.mask as mask_util
import torch
from torch import device
from detectron2.layers.roi_align import ROIAlign
from detectron2.utils.memory import retry_if_cuda_oom
from .boxes import Boxes
def polygon_area(x, y)... | null |
3,521 | import copy
import itertools
import numpy as np
from typing import Any, Iterator, List, Union
import pycocotools.mask as mask_util
import torch
from torch import device
from detectron2.layers.roi_align import ROIAlign
from detectron2.utils.memory import retry_if_cuda_oom
from .boxes import Boxes
def polygons_to_bitmask... | Rasterize the polygons into a mask image and crop the mask content in the given box. The cropped mask is resized to (mask_size, mask_size). This function is used when generating training targets for mask head in Mask R-CNN. Given original ground-truth masks for an image, new ground-truth mask training targets in the si... |
3,522 | import math
from typing import List, Tuple
import torch
from detectron2.layers.rotated_boxes import pairwise_iou_rotated
from .boxes import Boxes
class RotatedBoxes(Boxes):
"""
This structure stores a list of rotated boxes as a Nx5 torch.Tensor.
It supports some common methods about boxes
(`area`, `clip... | Given two lists of rotated boxes of size N and M, compute the IoU (intersection over union) between **all** N x M pairs of boxes. The box order must be (x_center, y_center, width, height, angle). Args: boxes1, boxes2 (RotatedBoxes): two `RotatedBoxes`. Contains N & M rotated boxes, respectively. Returns: Tensor: IoU, s... |
3,523 | import math
import numpy as np
from enum import IntEnum, unique
from typing import List, Tuple, Union
import torch
from torch import device
class Boxes:
"""
This structure stores a list of boxes as a Nx4 torch.Tensor.
It supports some common methods about boxes
(`area`, `clip`, `nonempty`, etc),
and... | Given two lists of boxes of size N and M, compute the IoU (intersection over union) between **all** N x M pairs of boxes. The box order must be (xmin, ymin, xmax, ymax). Args: boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively. Returns: Tensor: IoU, sized [N,M]. |
3,524 | import math
import numpy as np
from enum import IntEnum, unique
from typing import List, Tuple, Union
import torch
from torch import device
class Boxes:
"""
This structure stores a list of boxes as a Nx4 torch.Tensor.
It supports some common methods about boxes
(`area`, `clip`, `nonempty`, etc),
and... | Similar to :func:`pariwise_iou` but compute the IoA (intersection over boxes2 area). Args: boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively. Returns: Tensor: IoA, sized [N,M]. |
3,525 | import math
import numpy as np
from enum import IntEnum, unique
from typing import List, Tuple, Union
import torch
from torch import device
class Boxes:
"""
This structure stores a list of boxes as a Nx4 torch.Tensor.
It supports some common methods about boxes
(`area`, `clip`, `nonempty`, etc),
and... | Pairwise distance between N points and M boxes. The distance between a point and a box is represented by the distance from the point to 4 edges of the box. Distances are all positive when the point is inside the box. Args: points: Nx2 coordinates. Each row is (x, y) boxes: M boxes Returns: Tensor: distances of size (N,... |
3,526 | import math
import numpy as np
from enum import IntEnum, unique
from typing import List, Tuple, Union
import torch
from torch import device
class Boxes:
"""
This structure stores a list of boxes as a Nx4 torch.Tensor.
It supports some common methods about boxes
(`area`, `clip`, `nonempty`, etc),
and... | Compute pairwise intersection over union (IOU) of two sets of matched boxes that have the same number of boxes. Similar to :func:`pairwise_iou`, but computes only diagonal elements of the matrix. Args: boxes1 (Boxes): bounding boxes, sized [N,4]. boxes2 (Boxes): same length as boxes1 Returns: Tensor: iou, sized [N]. |
3,527 | import copy
import logging
import os
import torch
from caffe2.proto import caffe2_pb2
from torch import nn
from detectron2.config import CfgNode
from detectron2.utils.file_io import PathManager
from .caffe2_inference import ProtobufDetectionModel
from .caffe2_modeling import META_ARCH_CAFFE2_EXPORT_TYPE_MAP, convert_ba... | null |
3,528 | import contextlib
from unittest import mock
import torch
from detectron2.modeling import poolers
from detectron2.modeling.proposal_generator import rpn
from detectron2.modeling.roi_heads import keypoint_head, mask_head
from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers
from .c10 import (
Caffe... | null |
3,529 | import contextlib
from unittest import mock
import torch
from detectron2.modeling import poolers
from detectron2.modeling.proposal_generator import rpn
from detectron2.modeling.roi_heads import keypoint_head, mask_head
from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers
from .c10 import (
Caffe... | null |
3,530 | import contextlib
from unittest import mock
import torch
from detectron2.modeling import poolers
from detectron2.modeling.proposal_generator import rpn
from detectron2.modeling.roi_heads import keypoint_head, mask_head
from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers
from .c10 import (
Caffe... | null |
3,531 | import contextlib
from unittest import mock
import torch
from detectron2.modeling import poolers
from detectron2.modeling.proposal_generator import rpn
from detectron2.modeling.roi_heads import keypoint_head, mask_head
from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers
from .c10 import (
Caffe... | null |
3,532 | import collections
from dataclasses import dataclass
from typing import Callable, List, Optional, Tuple
import torch
from torch import nn
from detectron2.structures import Boxes, Instances, ROIMasks
from detectron2.utils.registry import _convert_target_to_string, locate
from .torchscript_patch import patch_builtin_len
... | Flatten an object so it can be used for PyTorch tracing. Also returns how to rebuild the original object from the flattened outputs. Returns: res (tuple): the flattened results that can be used as tracing outputs schema: an object with a ``__call__`` method such that ``schema(res) == obj``. It is a pure dataclass that ... |
3,533 | import os
import sys
import tempfile
from contextlib import ExitStack, contextmanager
from copy import deepcopy
from unittest import mock
import torch
from torch import nn
import detectron2
from detectron2.structures import Boxes, Instances
from detectron2.utils.env import _import_file
The provided code snippet inclu... | Patch the builtin len() function of a few detectron2 modules to use __len__ instead, because __len__ does not convert values to integers and therefore is friendly to tracing. Args: modules (list[stsr]): names of extra modules to patch len(), in addition to those in detectron2. |
3,534 | import os
import sys
import tempfile
from contextlib import ExitStack, contextmanager
from copy import deepcopy
from unittest import mock
import torch
from torch import nn
import detectron2
from detectron2.structures import Boxes, Instances
from detectron2.utils.env import _import_file
The provided code snippet inclu... | Apply patches on a few nonscriptable detectron2 classes. Should not have side-effects on eager usage. |
3,535 | import functools
import io
import struct
import types
import torch
from detectron2.modeling import meta_arch
from detectron2.modeling.box_regression import Box2BoxTransform
from detectron2.modeling.roi_heads import keypoint_head
from detectron2.structures import Boxes, ImageList, Instances, RotatedBoxes
from .c10 impor... | A function to assemble caffe2 model's outputs (i.e. Dict[str, Tensor]) to detectron2's format (i.e. list of Instances instance). This only works when the model follows the Caffe2 detectron's naming convention. Args: image_sizes (List[List[int, int]]): [H, W] of every image. tensor_outputs (Dict[str, Tensor]): external_... |
3,536 | import functools
import io
import struct
import types
import torch
from detectron2.modeling import meta_arch
from detectron2.modeling.box_regression import Box2BoxTransform
from detectron2.modeling.roi_heads import keypoint_head
from detectron2.structures import Boxes, ImageList, Instances, RotatedBoxes
from .c10 impor... | null |
3,537 | import functools
import io
import struct
import types
import torch
from detectron2.modeling import meta_arch
from detectron2.modeling.box_regression import Box2BoxTransform
from detectron2.modeling.roi_heads import keypoint_head
from detectron2.structures import Boxes, ImageList, Instances, RotatedBoxes
from .c10 impor... | null |
3,538 | import functools
import io
import struct
import types
import torch
from detectron2.modeling import meta_arch
from detectron2.modeling.box_regression import Box2BoxTransform
from detectron2.modeling.roi_heads import keypoint_head
from detectron2.structures import Boxes, ImageList, Instances, RotatedBoxes
from .c10 impor... | See get_caffe2_inputs() below. |
3,539 | import copy
import io
import logging
import numpy as np
from typing import List
import onnx
import torch
from caffe2.proto import caffe2_pb2
from caffe2.python import core
from caffe2.python.onnx.backend import Caffe2Backend
from tabulate import tabulate
from termcolor import colored
from torch.onnx import OperatorExpo... | Export a caffe2-compatible Detectron2 model to caffe2 format via ONNX. Arg: model: a caffe2-compatible version of detectron2 model, defined in caffe2_modeling.py tensor_inputs: a list of tensors that caffe2 model takes as input. |
3,540 | import copy
import io
import logging
import numpy as np
from typing import List
import onnx
import torch
from caffe2.proto import caffe2_pb2
from caffe2.python import core
from caffe2.python.onnx.backend import Caffe2Backend
from tabulate import tabulate
from termcolor import colored
from torch.onnx import OperatorExpo... | Run the caffe2 model on given inputs, recording the shape and draw the graph. predict_net/init_net: caffe2 model. tensor_inputs: a list of tensors that caffe2 model takes as input. graph_save_path: path for saving graph of exported model. |
3,541 | import collections
import contextlib
import copy
import functools
import logging
import numpy as np
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from unittest import mock
import caffe2.python.utils as putils
import torch
import torch.nn.functional as F
from caffe2.proto import caffe2_p... | null |
3,542 | import collections
import contextlib
import copy
import functools
import logging
import numpy as np
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from unittest import mock
import caffe2.python.utils as putils
import torch
import torch.nn.functional as F
from caffe2.proto import caffe2_p... | null |
3,543 | import collections
import contextlib
import copy
import functools
import logging
import numpy as np
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from unittest import mock
import caffe2.python.utils as putils
import torch
import torch.nn.functional as F
from caffe2.proto import caffe2_p... | null |
3,544 | import collections
import contextlib
import copy
import functools
import logging
import numpy as np
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from unittest import mock
import caffe2.python.utils as putils
import torch
import torch.nn.functional as F
from caffe2.proto import caffe2_p... | null |
3,545 | import collections
import contextlib
import copy
import functools
import logging
import numpy as np
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from unittest import mock
import caffe2.python.utils as putils
import torch
import torch.nn.functional as F
from caffe2.proto import caffe2_p... | null |
3,546 | import collections
import contextlib
import copy
import functools
import logging
import numpy as np
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from unittest import mock
import caffe2.python.utils as putils
import torch
import torch.nn.functional as F
from caffe2.proto import caffe2_p... | null |
3,547 | import os
import torch
from detectron2.utils.file_io import PathManager
from .torchscript_patch import freeze_training_mode, patch_instances
def patch_instances(fields):
"""
A contextmanager, under which the Instances class in detectron2 is replaced
by a statically-typed scriptable class, defined by `field... | Run :func:`torch.jit.script` on a model that uses the :class:`Instances` class. Since attributes of :class:`Instances` are "dynamically" added in eager mode，it is difficult for scripting to support it out of the box. This function is made to support scripting a model that uses :class:`Instances`. It does the following:... |
3,548 | import os
import torch
from detectron2.utils.file_io import PathManager
from .torchscript_patch import freeze_training_mode, patch_instances
PathManager = PathManagerBase()
PathManager.register_handler(HTTPURLHandler())
PathManager.register_handler(OneDrivePathHandler())
PathManager.register_handler(Detectron2Hand... | Dump IR of a TracedModule/ScriptModule/Function in various format (code, graph, inlined graph). Useful for debugging. Args: model (TracedModule/ScriptModule/ScriptFUnction): traced or scripted module dir (str): output directory to dump files. |
3,549 | import os
from typing import Optional
import pkg_resources
import torch
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate
from detectron2.modeling import build_model
def get_config(config_path, trained: bool = False):
"""
Returns a co... | Get a model specified by relative path under Detectron2's official ``configs/`` directory. Args: config_path (str): config file name relative to detectron2's "configs/" directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml" trained (bool): see :func:`get_config`. device (str or None): overwrite the dev... |
3,550 | from typing import List, Optional
import torch
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `shapes_to_tensor` function. Write a Python function `def shapes_to_tensor(x: List[int], device: Optional[torch.device] = None) -> torch.Tensor` to solve th... | Turn a list of integer scalars or integer Tensor scalars into a vector, in a way that's both traceable and scriptable. In tracing, `x` should be a list of scalar Tensor, so the output can trace to the inputs. In scripting or eager, `x` should be a list of int. |
3,551 | from typing import List, Optional
import torch
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `cat` function. Write a Python function `def cat(tensors: List[torch.Tensor], dim: int = 0)` to solve the following problem:
Efficient version of torch.cat ... | Efficient version of torch.cat that avoids a copy if there is only a single element in a list |
3,552 | from typing import List, Optional
import torch
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `cross_entropy` function. Write a Python function `def cross_entropy(input, target, *, reduction="mean", **kwargs)` to solve the following problem:
Same as ... | Same as `torch.nn.functional.cross_entropy`, but returns 0 (instead of nan) for empty inputs. |
3,553 | from typing import List, Optional
import torch
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `nonzero_tuple` function. Write a Python function `def nonzero_tuple(x)` to solve the following problem:
A 'as_tuple=True' version of torch.nonzero to suppo... | A 'as_tuple=True' version of torch.nonzero to support torchscript. because of https://github.com/pytorch/pytorch/issues/38718 |
3,554 | import math
import torch
The provided code snippet includes necessary dependencies for implementing the `diou_loss` function. Write a Python function `def diou_loss( boxes1: torch.Tensor, boxes2: torch.Tensor, reduction: str = "none", eps: float = 1e-7, ) -> torch.Tensor` to solve the following problem... | Distance Intersection over Union Loss (Zhaohui Zheng et. al) https://arxiv.org/abs/1911.08287 Args: boxes1, boxes2 (Tensor): box locations in XYXY format, shape (N, 4) or (4,). reduction: 'none' | 'mean' | 'sum' 'none': No reduction will be applied to the output. 'mean': The output will be averaged. 'sum': The output w... |
3,555 | import math
import torch
The provided code snippet includes necessary dependencies for implementing the `ciou_loss` function. Write a Python function `def ciou_loss( boxes1: torch.Tensor, boxes2: torch.Tensor, reduction: str = "none", eps: float = 1e-7, ) -> torch.Tensor` to solve the following problem... | Complete Intersection over Union Loss (Zhaohui Zheng et. al) https://arxiv.org/abs/1911.08287 Args: boxes1, boxes2 (Tensor): box locations in XYXY format, shape (N, 4) or (4,). reduction: 'none' | 'mean' | 'sum' 'none': No reduction will be applied to the output. 'mean': The output will be averaged. 'sum': The output w... |
3,556 | import torch
from torchvision.ops import boxes as box_ops
from torchvision.ops import nms
The provided code snippet includes necessary dependencies for implementing the `batched_nms` function. Write a Python function `def batched_nms( boxes: torch.Tensor, scores: torch.Tensor, idxs: torch.Tensor, iou_threshold: fl... | Same as torchvision.ops.boxes.batched_nms, but with float(). |
3,557 | import torch
from torchvision.ops import boxes as box_ops
from torchvision.ops import nms
def nms_rotated(boxes, scores, iou_threshold):
"""
Performs non-maximum suppression (NMS) on the rotated boxes according
to their intersection-over-union (IoU).
Rotated NMS iteratively removes lower scoring rotate... | Performs non-maximum suppression in a batched fashion. Each index value correspond to a category, and NMS will not be applied between elements of different categories. Args: boxes (Tensor[N, 5]): boxes where NMS will be performed. They are expected to be in (x_ctr, y_ctr, width, height, angle_degrees) format scores (Te... |
3,558 | import numpy as np
from typing import Tuple
import torch
from PIL import Image
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `paste_mask_in_image_old` function. Write a Python function `def paste_mask_in_image_old(mask, box, img_h, img_w, threshold)... | Paste a single mask in an image. This is a per-box implementation of :func:`paste_masks_in_image`. This function has larger quantization error due to incorrect pixel modeling and is not used any more. Args: mask (Tensor): A tensor of shape (Hmask, Wmask) storing the mask of a single object instance. Values are in [0, 1... |
3,559 | import numpy as np
from typing import Tuple
import torch
from PIL import Image
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `pad_masks` function. Write a Python function `def pad_masks(masks, padding)` to solve the following problem:
Args: masks (t... | Args: masks (tensor): A tensor of shape (B, M, M) representing B masks. padding (int): Number of cells to pad on all sides. Returns: The padded masks and the scale factor of the padding size / original size. |
3,560 | import numpy as np
from typing import Tuple
import torch
from PIL import Image
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `scale_boxes` function. Write a Python function `def scale_boxes(boxes, scale)` to solve the following problem:
Args: boxes ... | Args: boxes (tensor): A tensor of shape (B, 4) representing B boxes with 4 coords representing the corners x0, y0, x1, y1, scale (float): The box scaling factor. Returns: Scaled boxes. |
3,561 | import numpy as np
from typing import Tuple
import torch
from PIL import Image
from torch.nn import functional as F
def paste_masks_in_image(
masks: torch.Tensor, boxes: torch.Tensor, image_shape: Tuple[int, int], threshold: float = 0.5
):
"""
Paste a set of masks that are of a fixed resolution (e.g., 28 x ... | A wrapper of paste_masks_in_image where image_shape is Tensor. During tracing, shapes might be tensors instead of ints. The Tensor->int conversion should be scripted rather than traced. |
3,562 | import torch
import torch.distributed as dist
from fvcore.nn.distributed import differentiable_all_reduce
from torch import nn
from torch.nn import functional as F
from detectron2.utils import comm, env
from .wrappers import BatchNorm2d
class FrozenBatchNorm2d(nn.Module):
"""
BatchNorm2d where the batch statist... | Args: norm (str or callable): either one of BN, SyncBN, FrozenBN, GN; or a callable that takes a channel number and returns the normalization layer as a nn.Module. Returns: nn.Module or None: the normalization layer |
3,563 | import logging
import numpy as np
from itertools import count
from typing import List, Tuple
import torch
import tqdm
from fvcore.common.timer import Timer
from detectron2.utils import comm
from .build import build_batch_data_loader
from .common import DatasetFromList, MapDataset
from .samplers import TrainingSampler
... | Benchmark an iterator/iterable for `num_iter` iterations with an extra `warmup` iterations of warmup. End early if `max_time_seconds` time is spent on iterations. Returns: float: average time (seconds) per iteration list[float]: time spent on each iteration. Sometimes useful for further analysis. |
3,564 | import copy
import itertools
import logging
import numpy as np
import pickle
import random
import torch.utils.data as data
from torch.utils.data.sampler import Sampler
from detectron2.utils.serialize import PicklableWrapper
def _shard_iterator_dataloader_worker(iterable):
# Shard the iterable if we're currently in... | null |
3,565 | import logging
import numpy as np
from typing import List, Union
import pycocotools.mask as mask_util
import torch
from PIL import Image
from detectron2.structures import (
BitMasks,
Boxes,
BoxMode,
Instances,
Keypoints,
PolygonMasks,
RotatedBoxes,
polygons_to_bitmask,
)
from detectron2.... | Convert an image from given format to RGB. Args: image (np.ndarray or Tensor): an HWC image format (str): the format of input image, also see `read_image` Returns: (np.ndarray): (H,W,3) RGB image in 0-255 range, can be either float or uint8 |
3,566 | import logging
import numpy as np
from typing import List, Union
import pycocotools.mask as mask_util
import torch
from PIL import Image
from detectron2.structures import (
BitMasks,
Boxes,
BoxMode,
Instances,
Keypoints,
PolygonMasks,
RotatedBoxes,
polygons_to_bitmask,
)
from detectron2.... | Raise an error if the image does not match the size specified in the dict. |
3,567 | import logging
import numpy as np
from typing import List, Union
import pycocotools.mask as mask_util
import torch
from PIL import Image
from detectron2.structures import (
BitMasks,
Boxes,
BoxMode,
Instances,
Keypoints,
PolygonMasks,
RotatedBoxes,
polygons_to_bitmask,
)
from detectron2.... | Apply transformations to the proposals in dataset_dict, if any. Args: dataset_dict (dict): a dict read from the dataset, possibly contains fields "proposal_boxes", "proposal_objectness_logits", "proposal_bbox_mode" image_shape (tuple): height, width transforms (TransformList): proposal_topk (int): only keep top-K scori... |
3,568 | import logging
import numpy as np
from typing import List, Union
import pycocotools.mask as mask_util
import torch
from PIL import Image
from detectron2.structures import (
BitMasks,
Boxes,
BoxMode,
Instances,
Keypoints,
PolygonMasks,
RotatedBoxes,
polygons_to_bitmask,
)
from detectron2.... | Apply transforms to box, segmentation and keypoints annotations of a single instance. It will use `transforms.apply_box` for the box, and `transforms.apply_coords` for segmentation polygons & keypoints. If you need anything more specially designed for each data structure, you'll need to implement your own version of th... |
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