instruction stringclasses 100
values | code stringlengths 78 193k | response stringlengths 259 170k | file stringlengths 59 203 |
|---|---|---|---|
Annotate my code with docstrings | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
import torch
import torch.nn.functional as F
from fvcore.transforms.transform import (
CropTransform,
HFlipTransform,
NoOpTransform,
Transform,
TransformList,
)
from PIL import Image
try:
import cv2 ... | --- +++ @@ -1,6 +1,10 @@ # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
+"""
+See "Data Augmentation" tutorial for an overview of the system:
+https://detectron2.readthedocs.io/tutorials/augmentation.html
+"""
import numpy as np
import torch
@@ -30,8 +34,23 @@
class ExtentTransfor... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/data/transforms/transform.py |
Write docstrings for data processing functions | # Copyright (c) Facebook, Inc. and its affiliates.
import contextlib
import io
import itertools
import json
import logging
import numpy as np
import os
import tempfile
from collections import OrderedDict
from typing import Optional
from PIL import Image
from tabulate import tabulate
from detectron2.data import Metadat... | --- +++ @@ -22,8 +22,19 @@
class COCOPanopticEvaluator(DatasetEvaluator):
+ """
+ Evaluate Panoptic Quality metrics on COCO using PanopticAPI.
+ It saves panoptic segmentation prediction in `output_dir`
+
+ It contains a synchronize call and has to be called from all workers.
+ """
def __init_... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/evaluation/panoptic_evaluation.py |
Generate descriptive docstrings automatically | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
# All coco categories, together with their nice-looking visualization colors
# It's from https://github.com/cocodataset/panopticapi/blob/master/panoptic_coco_categories.json
COCO_CATEGORIES = [
{"color": [220, 20, 60], "isthing": 1, "id":... | --- +++ @@ -1,6 +1,18 @@ # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
+"""
+Note:
+For your custom dataset, there is no need to hard-code metadata anywhere in the code.
+For example, for COCO-format dataset, metadata will be obtained automatically
+when calling `load_coco_json`. For oth... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/data/datasets/builtin_meta.py |
Document helper functions with docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
import itertools
import json
import numpy as np
import os
import torch
from pycocotools.cocoeval import COCOeval, maskUtils
from detectron2.structures import BoxMode, RotatedBoxes, pairwise_iou_rotated
from detectron2.utils.file_io import PathManager
from .coco_evalu... | --- +++ @@ -97,8 +97,21 @@
class RotatedCOCOEvaluator(COCOEvaluator):
+ """
+ Evaluate object proposal/instance detection outputs using COCO-like metrics and APIs,
+ with rotated boxes support.
+ Note: this uses IOU only and does not consider angle differences.
+ """
def process(self, inputs, ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/evaluation/rotated_coco_evaluation.py |
Document helper functions with docstrings | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
import sys
from numpy import random
from typing import Tuple
import torch
from fvcore.transforms.transform import (
BlendTransform,
CropTransform,
HFlipTransform,
NoOpTransform,
PadTransform,
Transform,... | --- +++ @@ -1,5 +1,8 @@ # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
+"""
+Implement many useful :class:`Augmentation`.
+"""
import numpy as np
import sys
from numpy import random
@@ -43,8 +46,19 @@
class RandomApply(Augmentation):
+ """
+ Randomly apply an augmentation with ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/data/transforms/augmentation_impl.py |
Generate NumPy-style docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
from torch.utils.data.sampler import BatchSampler, Sampler
class GroupedBatchSampler(BatchSampler):
def __init__(self, sampler, group_ids, batch_size):
if not isinstance(sampler, Sampler):
raise ValueError(
... | --- +++ @@ -4,8 +4,22 @@
class GroupedBatchSampler(BatchSampler):
+ """
+ Wraps another sampler to yield a mini-batch of indices.
+ It enforces that the batch only contain elements from the same group.
+ It also tries to provide mini-batches which follows an ordering which is
+ as close as possible t... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/data/samplers/grouped_batch_sampler.py |
Write proper docstrings for these functions | # Copyright (c) Facebook, Inc. and its affiliates.
import copy
import logging
import numpy as np
import time
from pycocotools.cocoeval import COCOeval
from detectron2 import _C
logger = logging.getLogger(__name__)
class COCOeval_opt(COCOeval):
def evaluate(self):
tic = time.time()
p = self.par... | --- +++ @@ -11,8 +11,19 @@
class COCOeval_opt(COCOeval):
+ """
+ This is a slightly modified version of the original COCO API, where the functions evaluateImg()
+ and accumulate() are implemented in C++ to speedup evaluation
+ """
def evaluate(self):
+ """
+ Run per image evaluation... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/evaluation/fast_eval_api.py |
Document all public functions with docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
import copy
import logging
import re
from typing import Dict, List
import torch
def convert_basic_c2_names(original_keys):
layer_keys = copy.deepcopy(original_keys)
layer_keys = [
{"pred_b": "linear_b", "pred_w": "linear_w"}.get(k, k) for k in layer_k... | --- +++ @@ -7,6 +7,15 @@
def convert_basic_c2_names(original_keys):
+ """
+ Apply some basic name conversion to names in C2 weights.
+ It only deals with typical backbone models.
+
+ Args:
+ original_keys (list[str]):
+ Returns:
+ list[str]: The same number of strings matching those in ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/checkpoint/c2_model_loading.py |
Add structured docstrings to improve clarity | # Copyright (c) Facebook, Inc. and its affiliates.
import copy
import itertools
import json
import logging
import os
import pickle
from collections import OrderedDict
import torch
import detectron2.utils.comm as comm
from detectron2.config import CfgNode
from detectron2.data import MetadataCatalog
from detectron2.stru... | --- +++ @@ -20,6 +20,10 @@
class LVISEvaluator(DatasetEvaluator):
+ """
+ Evaluate object proposal and instance detection/segmentation outputs using
+ LVIS's metrics and evaluation API.
+ """
def __init__(
self,
@@ -30,6 +34,20 @@ *,
max_dets_per_image=None,
):
+ ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/evaluation/lvis_evaluation.py |
Document all endpoints with docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
import logging
import os
from fvcore.common.timer import Timer
from detectron2.data import DatasetCatalog, MetadataCatalog
from detectron2.structures import BoxMode
from detectron2.utils.file_io import PathManager
from .builtin_meta import _get_coco_instances_meta
fr... | --- +++ @@ -23,6 +23,15 @@
def register_lvis_instances(name, metadata, json_file, image_root):
+ """
+ Register a dataset in LVIS's json annotation format for instance detection and segmentation.
+
+ Args:
+ name (str): a name that identifies the dataset, e.g. "lvis_v0.5_train".
+ metadata (d... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/data/datasets/lvis.py |
Create docstrings for all classes and functions | # Copyright (c) Facebook, Inc. and its affiliates.
import itertools
import json
import logging
import numpy as np
import os
from collections import OrderedDict
from typing import Optional, Union
import pycocotools.mask as mask_util
import torch
from PIL import Image
from detectron2.data import DatasetCatalog, Metadata... | --- +++ @@ -34,6 +34,9 @@
class SemSegEvaluator(DatasetEvaluator):
+ """
+ Evaluate semantic segmentation metrics.
+ """
def __init__(
self,
@@ -45,6 +48,16 @@ num_classes=None,
ignore_label=None,
):
+ """
+ Args:
+ dataset_name (str): name of ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/evaluation/sem_seg_evaluation.py |
Write docstrings for utility functions | # Copyright (c) Facebook, Inc. and its affiliates.
import itertools
import logging
import math
from collections import defaultdict
from typing import Optional
import torch
from torch.utils.data.sampler import Sampler
from detectron2.utils import comm
logger = logging.getLogger(__name__)
class TrainingSampler(Sample... | --- +++ @@ -13,8 +13,35 @@
class TrainingSampler(Sampler):
+ """
+ In training, we only care about the "infinite stream" of training data.
+ So this sampler produces an infinite stream of indices and
+ all workers cooperate to correctly shuffle the indices and sample different indices.
+
+ The sample... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/data/samplers/distributed_sampler.py |
Annotate my code with docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
import collections.abc as abc
import dataclasses
import logging
from typing import Any
from detectron2.utils.registry import _convert_target_to_string, locate
__all__ = ["dump_dataclass", "instantiate"]
def dump_dataclass(obj: Any):
assert dataclasses.is_datac... | --- +++ @@ -11,6 +11,15 @@
def dump_dataclass(obj: Any):
+ """
+ Dump a dataclass recursively into a dict that can be later instantiated.
+
+ Args:
+ obj: a dataclass object
+
+ Returns:
+ dict
+ """
assert dataclasses.is_dataclass(obj) and not isinstance(
obj, type
)... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/config/instantiate.py |
Generate docstrings with examples | # Copyright (c) Facebook, Inc. and its affiliates.
import math
from typing import Dict
import torch
import torch.nn.functional as F
from detectron2.layers import ShapeSpec, cat
from detectron2.layers.roi_align_rotated import ROIAlignRotated
from detectron2.modeling import poolers
from detectron2.modeling.proposal_gen... | --- +++ @@ -21,6 +21,11 @@
class Caffe2Boxes(Boxes):
+ """
+ Representing a list of detectron2.structures.Boxes from minibatch, each box
+ is represented by a 5d vector (batch index + 4 coordinates), or a 6d vector
+ (batch index + 5 coordinates) for RotatedBoxes.
+ """
def __init__(self, tens... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/export/c10.py |
Please document this code using docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
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 Boxe... | --- +++ @@ -25,6 +25,18 @@
def assemble_rcnn_outputs_by_name(image_sizes, tensor_outputs, force_mask_on=False):
+ """
+ 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 d... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/export/caffe2_modeling.py |
Add docstrings to existing functions | # Copyright (c) Facebook, Inc. and its affiliates.
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_modelin... | --- +++ @@ -20,8 +20,39 @@
class Caffe2Tracer:
+ """
+ Make a detectron2 model traceable with Caffe2 operators.
+ This class creates a traceable version of a detectron2 model which:
+
+ 1. Rewrite parts of the model using ops in Caffe2. Note that some ops do
+ not have GPU implementation in Caffe2... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/export/api.py |
Fill in missing docstrings in my code | # Copyright (c) Facebook, Inc. and its affiliates.
import logging
import numpy as np
from itertools import count
import torch
from caffe2.proto import caffe2_pb2
from caffe2.python import core
from .caffe2_modeling import META_ARCH_CAFFE2_EXPORT_TYPE_MAP, convert_batched_inputs_to_c2_format
from .shared import Scoped... | --- +++ @@ -15,6 +15,11 @@
# ===== ref: mobile-vision predictor's 'Caffe2Wrapper' class ======
class ProtobufModel(torch.nn.Module):
+ """
+ Wrapper of a caffe2's protobuf model.
+ It works just like nn.Module, but running caffe2 under the hood.
+ Input/Output are tuple[tensor] that match the caffe2 net'... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/export/caffe2_inference.py |
Create simple docstrings for beginners | # Copyright (c) Facebook, Inc. and its affiliates.
import copy
import io
import logging
import numpy as np
from typing import List
import onnx
import onnx.optimizer
import torch
from caffe2.proto import caffe2_pb2
from caffe2.python import core
from caffe2.python.onnx.backend import Caffe2Backend
from tabulate import ... | --- +++ @@ -32,6 +32,16 @@
def export_onnx_model(model, inputs):
+ """
+ Trace and export a model to onnx format.
+
+ Args:
+ model (nn.Module):
+ inputs (tuple[args]): the model will be called by `model(*inputs)`
+
+ Returns:
+ an onnx model
+ """
assert isinstance(model, t... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/export/caffe2_export.py |
Generate consistent documentation across files | # Copyright (c) Facebook, Inc. and its affiliates.
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, loca... | --- +++ @@ -13,6 +13,23 @@
@dataclass
class Schema:
+ """
+ A Schema defines how to flatten a possibly hierarchical object into tuple of
+ primitive objects, so it can be used as inputs/outputs of PyTorch's tracing.
+
+ PyTorch does not support tracing a function that produces rich output
+ structures... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/export/flatten.py |
Annotate my code with docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
import collections
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... | --- +++ @@ -22,6 +22,11 @@
def to_device(t, device_str):
+ """
+ This function is a replacement of .to(another_device) such that it allows the
+ casting to be traced properly by explicitly calling the underlying copy ops.
+ It also avoids introducing unncessary op when casting to the same device.
+ "... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/export/shared.py |
Write docstrings for this repository | # Copyright (c) Facebook, Inc. and its affiliates.
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 imp... | --- +++ @@ -27,6 +27,10 @@
class Caffe2CompatibleConverter:
+ """
+ A GenericUpdater which implements the `create_from` interface, by modifying
+ module object and assign it with another class replaceCls.
+ """
def __init__(self, replaceCls):
self.replaceCls = replaceCls
@@ -54,6 +58,11 ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/export/caffe2_patch.py |
Add docstrings to existing functions | # Copyright (c) Facebook, Inc. and its affiliates.
import os
import torch
from detectron2.utils.file_io import PathManager
from .torchscript_patch import freeze_training_mode, patch_instances
__all__ = ["scripting_with_instances", "dump_torchscript_IR"]
def scripting_with_instances(model, fields):
assert (
... | --- +++ @@ -11,6 +11,42 @@
def scripting_with_instances(model, fields):
+ """
+ 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. Thi... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/export/torchscript.py |
Write reusable docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
from torch import nn
from torchvision.ops import roi_align
# NOTE: torchvision's RoIAlign has a different default aligned=False
class ROIAlign(nn.Module):
def __init__(self, output_size, spatial_scale, sampling_ratio, aligned=True):
super().__init__()
... | --- +++ @@ -6,6 +6,34 @@ # NOTE: torchvision's RoIAlign has a different default aligned=False
class ROIAlign(nn.Module):
def __init__(self, output_size, spatial_scale, sampling_ratio, aligned=True):
+ """
+ Args:
+ output_size (tuple): h, w
+ spatial_scale (float): scale the in... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/layers/roi_align.py |
Write docstrings for utility functions | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import fvcore.nn.weight_init as weight_init
from torch import nn
from .batch_norm import FrozenBatchNorm2d, get_norm
from .wrappers import Conv2d
"""
CNN building blocks.
"""
class CNNBlockBase(nn.Module):
def __init__(self, in_channe... | --- +++ @@ -14,14 +14,41 @@
class CNNBlockBase(nn.Module):
+ """
+ A CNN block is assumed to have input channels, output channels and a stride.
+ The input and output of `forward()` method must be NCHW tensors.
+ The method can perform arbitrary computation but must match the given
+ channels and str... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/layers/blocks.py |
Document all endpoints with docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
import math
from functools import lru_cache
import torch
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from torchvision.ops import deform_conv2d
from detectron2... | --- +++ @@ -163,6 +163,15 @@ @staticmethod
@lru_cache(maxsize=128)
def _cal_im2col_step(input_size, default_size):
+ """
+ Calculate proper im2col step size, which should be divisible by input_size and not larger
+ than prefer_size. Meanwhile the step size should be as large as possib... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/layers/deform_conv.py |
Fully document this Python code with docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
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.M... | --- +++ @@ -11,6 +11,24 @@
class FrozenBatchNorm2d(nn.Module):
+ """
+ BatchNorm2d where the batch statistics and the affine parameters are fixed.
+
+ It contains non-trainable buffers called
+ "weight" and "bias", "running_mean", "running_var",
+ initialized to perform identity transformation.
+
+ ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/layers/batch_norm.py |
Add docstrings that explain logic | # Copyright (c) Facebook, Inc. and its affiliates.
import copy
import json
import os
from detectron2.data import DatasetCatalog, MetadataCatalog
from detectron2.utils.file_io import PathManager
from .coco import load_coco_json, load_sem_seg
__all__ = ["register_coco_panoptic", "register_coco_panoptic_separated"]
d... | --- +++ @@ -12,6 +12,16 @@
def load_coco_panoptic_json(json_file, image_dir, gt_dir, meta):
+ """
+ Args:
+ image_dir (str): path to the raw dataset. e.g., "~/coco/train2017".
+ gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017".
+ json_file (str): path to the js... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/data/datasets/coco_panoptic.py |
Generate documentation strings for clarity | # Copyright (c) Facebook, Inc. and its affiliates.
import torch
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from detectron2.layers.wrappers import disable_torch_compiler
class _ROIAlignRotated(Function):
... | --- +++ @@ -50,12 +50,32 @@
class ROIAlignRotated(nn.Module):
def __init__(self, output_size, spatial_scale, sampling_ratio):
+ """
+ Args:
+ output_size (tuple): h, w
+ spatial_scale (float): scale the input boxes by this number
+ sampling_ratio (int): number of in... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/layers/roi_align_rotated.py |
Document this module using docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
from typing import Tuple
import torch
from PIL import Image
from torch.nn import functional as F
__all__ = ["paste_masks_in_image"]
BYTES_PER_FLOAT = 4
# TODO: This memory limit may be too much or too little. It would be better to
# determine it b... | --- +++ @@ -15,6 +15,20 @@
def _do_paste_mask(masks, boxes, img_h: int, img_w: int, skip_empty: bool = True):
+ """
+ Args:
+ masks: N, 1, H, W
+ boxes: N, 4
+ img_h, img_w (int):
+ skip_empty (bool): only paste masks within the region that
+ tightly bound all boxes, and... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/layers/mask_ops.py |
Add missing documentation to my Python functions | # Copyright (c) Facebook, Inc. and its affiliates.
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
# need some explicit imports due to https://github.com/pytorch/pytorch/issues/38964
import dete... | --- +++ @@ -26,10 +26,16 @@
def _add_instances_conversion_methods(newInstances):
+ """
+ Add from_instances methods to the scripted Instances class.
+ """
cls_name = newInstances.__name__
@torch.jit.unused
def from_instances(instances: Instances):
+ """
+ Create scripted Inst... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/export/torchscript_patch.py |
Create docstrings for reusable components | # Copyright (c) Facebook, Inc. and its affiliates.
from copy import deepcopy
import fvcore.nn.weight_init as weight_init
import torch
from torch import nn
from torch.nn import functional as F
from .batch_norm import get_norm
from .blocks import DepthwiseSeparableConv2d
from .wrappers import Conv2d
class ASPP(nn.Mod... | --- +++ @@ -12,6 +12,9 @@
class ASPP(nn.Module):
+ """
+ Atrous Spatial Pyramid Pooling (ASPP).
+ """
def __init__(
self,
@@ -25,6 +28,30 @@ dropout: float = 0.0,
use_depthwise_separable_conv=False,
):
+ """
+ Args:
+ in_channels (int): number ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/layers/aspp.py |
Document this script properly | # Copyright (c) Facebook, Inc. and its affiliates.
import math
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn.functional as F
from torch import nn
from detectron2.layers import Conv2d, ShapeSpec, get_norm
from .backbone import Backbone
from .build import BACKBONE_REGISTRY
from .resnet import... | --- +++ @@ -15,6 +15,10 @@
class FPN(Backbone):
+ """
+ This module implements :paper:`FPN`.
+ It creates pyramid features built on top of some input feature maps.
+ """
_fuse_type: torch.jit.Final[str]
@@ -28,6 +32,30 @@ fuse_type="sum",
square_pad=0,
):
+ """
+ ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/backbone/fpn.py |
Add standardized docstrings across the file | import logging
import numpy as np
import torch
import torch.nn as nn
from .backbone import Backbone
from .utils import (
PatchEmbed,
add_decomposed_rel_pos,
get_abs_pos,
window_partition,
window_unpartition,
)
logger = logging.getLogger(__name__)
__all__ = ["MViT"]
def attention_pool(x, pool, ... | --- +++ @@ -31,6 +31,7 @@
class MultiScaleAttention(nn.Module):
+ """Multiscale Multi-head Attention block."""
def __init__(
self,
@@ -48,6 +49,21 @@ rel_pos_zero_init=True,
input_size=None,
):
+ """
+ Args:
+ dim (int): Number of input channels.
+ ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/backbone/mvit.py |
Annotate my code with docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
import functools
import warnings
from typing import List, Optional
import torch
from torch.nn import functional as F
from detectron2.utils.env import TORCH_VERSION
def shapes_to_tensor(x: List[int], device: Optional[torch.device] = None) -> torch.Tensor:
if tor... | --- +++ @@ -1,4 +1,12 @@ # Copyright (c) Facebook, Inc. and its affiliates.
+"""
+Wrappers around on some nn functions, mainly to support empty tensors.
+
+Ideally, add support directly in PyTorch to empty tensors in those functions.
+
+These can be removed once https://github.com/pytorch/pytorch/issues/12013
+is imple... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/layers/wrappers.py |
Turn comments into proper docstrings | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import torch
from torchvision.ops import boxes as box_ops
from torchvision.ops import nms # noqa . for compatibility
from detectron2.layers.wrappers import disable_torch_compiler
def batched_nms(
boxes: torch.Tensor, scores: torch.Tenso... | --- +++ @@ -11,6 +11,9 @@ def batched_nms(
boxes: torch.Tensor, scores: torch.Tensor, idxs: torch.Tensor, iou_threshold: float
):
+ """
+ Same as torchvision.ops.boxes.batched_nms, but with float().
+ """
assert boxes.shape[-1] == 4
# Note: Torchvision already has a strategy (https://github.com... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/layers/nms.py |
Fully document this Python code with docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
import collections
import math
from typing import List
import torch
from torch import nn
from detectron2.config import configurable
from detectron2.layers import ShapeSpec, move_device_like
from detectron2.structures import Boxes, RotatedBoxes
from detectron2.utils.re... | --- +++ @@ -19,6 +19,9 @@
class BufferList(nn.Module):
+ """
+ Similar to nn.ParameterList, but for buffers
+ """
def __init__(self, buffers):
super().__init__()
@@ -53,6 +56,17 @@
def _broadcast_params(params, num_features, name):
+ """
+ If one size (or aspect ratio) is specifie... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/anchor_generator.py |
Document functions with clear intent | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from detectron2.modeling.backbone.backbone import Backbone
_to_2tuple = nn.modules.utils._ntuple(2)
class Mlp(nn.M... | --- +++ @@ -1,4 +1,16 @@ # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+Implementation of Swin models from :paper:`swin`.
+
+This code is adapted from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py with minimal mo... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/backbone/swin.py |
Add docstrings for better understanding | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import numpy as np
from torch import nn
from detectron2.layers import CNNBlockBase, ShapeSpec, get_norm
from .backbone import Backbone
__all__ = [
"AnyNet",
"RegNet",
"ResStem",
"SimpleStem",
"VanillaBlock",
"ResBasicBloc... | --- +++ @@ -1,4 +1,11 @@ # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+Implementation of RegNet models from :paper:`dds` and :paper:`scaling`.
+
+This code is adapted from https://github.com/facebookresearch/pycls with minimal modifications.
+Some code duplication exists between RegNet an... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/backbone/regnet.py |
Add well-formatted docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
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
class _ModelZooUrls:
... | --- +++ @@ -10,6 +10,9 @@
class _ModelZooUrls:
+ """
+ Mapping from names to officially released Detectron2 pre-trained models.
+ """
S3_PREFIX = "https://dl.fbaipublicfiles.com/detectron2/"
@@ -94,6 +97,10 @@
@staticmethod
def query(config_path: str) -> Optional[str]:
+ """
+ ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/model_zoo/model_zoo.py |
Add docstrings to improve readability | # Copyright (c) Facebook, Inc. and its affiliates.
from typing import List
import torch
from detectron2.layers import nonzero_tuple
# TODO: the name is too general
class Matcher:
def __init__(
self, thresholds: List[float], labels: List[int], allow_low_quality_matches: bool = False
):
# Add ... | --- +++ @@ -7,10 +7,45 @@
# TODO: the name is too general
class Matcher:
+ """
+ This class assigns to each predicted "element" (e.g., a box) a ground-truth
+ element. Each predicted element will have exactly zero or one matches; each
+ ground-truth element may be matched to zero or more predicted elemen... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/matcher.py |
Can you add docstrings to this Python file? | # Copyright (c) Facebook, Inc. and its affiliates.
import math
from typing import List, Tuple, Union
import torch
from fvcore.nn import giou_loss, smooth_l1_loss
from torch.nn import functional as F
from detectron2.layers import cat, ciou_loss, diou_loss
from detectron2.structures import Boxes
# Value for clamping la... | --- +++ @@ -19,14 +19,39 @@
@torch.jit.script
class Box2BoxTransform:
+ """
+ The box-to-box transform defined in R-CNN. The transformation is parameterized
+ by 4 deltas: (dx, dy, dw, dh). The transformation scales the box's width and height
+ by exp(dw), exp(dh) and shifts a box's center by the offset ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/box_regression.py |
Generate docstrings with parameter types | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import logging
from typing import Dict, List
import torch
from torch import nn
from detectron2.config import configurable
from detectron2.structures import ImageList
from ..postprocessing import detector_postprocess, sem_seg_postprocess
from ... | --- +++ @@ -19,6 +19,9 @@
@META_ARCH_REGISTRY.register()
class PanopticFPN(GeneralizedRCNN):
+ """
+ Implement the paper :paper:`PanopticFPN`.
+ """
@configurable
def __init__(
@@ -30,6 +33,19 @@ combine_instances_score_thresh: float = 0.5,
**kwargs,
):
+ """
+ ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/meta_arch/panoptic_fpn.py |
Write docstrings for this repository | # Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
from typing import Callable, Dict, Optional, Tuple, Union
import fvcore.nn.weight_init as weight_init
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.layers import Con... | --- +++ @@ -32,6 +32,9 @@
@META_ARCH_REGISTRY.register()
class SemanticSegmentor(nn.Module):
+ """
+ Main class for semantic segmentation architectures.
+ """
@configurable
def __init__(
@@ -42,6 +45,13 @@ pixel_mean: Tuple[float],
pixel_std: Tuple[float],
):
+ """
+... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/meta_arch/semantic_seg.py |
Add docstrings following best practices | # Copyright (c) Facebook, Inc. and its affiliates.
import ast
import builtins
import collections.abc as abc
import importlib
import inspect
import logging
import os
import uuid
from contextlib import contextmanager
from copy import deepcopy
from dataclasses import is_dataclass
from typing import List, Tuple, Union
imp... | --- +++ @@ -23,6 +23,21 @@
class LazyCall:
+ """
+ Wrap a callable so that when it's called, the call will not be executed,
+ but returns a dict that describes the call.
+
+ LazyCall object has to be called with only keyword arguments. Positional
+ arguments are not yet supported.
+
+ Examples:
+ ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/config/lazy.py |
Annotate my code with docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn.functional as F
from torch import nn
from detectron2.layers import (
CNNBlockBase,
Conv2d,
DeformConv,
ModulatedDeformConv,
ShapeSpec,
get_norm,
)
from... | --- +++ @@ -30,8 +30,20 @@
class BasicBlock(CNNBlockBase):
+ """
+ The basic residual block for ResNet-18 and ResNet-34 defined in :paper:`ResNet`,
+ with two 3x3 conv layers and a projection shortcut if needed.
+ """
def __init__(self, in_channels, out_channels, *, stride=1, norm="BN"):
+ ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/backbone/resnet.py |
Generate docstrings for script automation | # Copyright (c) Facebook, Inc. and its affiliates.
import logging
import math
from typing import List, Tuple
import torch
from fvcore.nn import sigmoid_focal_loss_jit
from torch import Tensor, nn
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.layers import CycleBatchNor... | --- +++ @@ -27,6 +27,9 @@
@META_ARCH_REGISTRY.register()
class RetinaNet(DenseDetector):
+ """
+ Implement RetinaNet in :paper:`RetinaNet`.
+ """
@configurable
def __init__(
@@ -52,6 +55,39 @@ vis_period=0,
input_format="BGR",
):
+ """
+ NOTE: this interface i... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/meta_arch/retinanet.py |
Generate docstrings with parameter types | import logging
import math
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn as nn
from detectron2.layers import CNNBlockBase, Conv2d, get_norm
from detectron2.modeling.backbone.fpn import _assert_strides_are_log2_contiguous
from .backbone import Backbone
from .utils import (
PatchEmbed,
... | --- +++ @@ -23,6 +23,7 @@
class Attention(nn.Module):
+ """Multi-head Attention block with relative position embeddings."""
def __init__(
self,
@@ -33,6 +34,16 @@ rel_pos_zero_init=True,
input_size=None,
):
+ """
+ Args:
+ dim (int): Number of input ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/backbone/vit.py |
Create simple docstrings for beginners | # Copyright (c) Facebook, Inc. and its affiliates.
import logging
import numpy as np
from typing import Dict, List, Optional, Tuple
import torch
from torch import nn
from detectron2.config import configurable
from detectron2.data.detection_utils import convert_image_to_rgb
from detectron2.layers import move_device_lik... | --- +++ @@ -23,6 +23,12 @@
@META_ARCH_REGISTRY.register()
class GeneralizedRCNN(nn.Module):
+ """
+ Generalized R-CNN. Any models that contains the following three components:
+ 1. Per-image feature extraction (aka backbone)
+ 2. Region proposal generation
+ 3. Per-region feature extraction and predic... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/meta_arch/rcnn.py |
Document my Python code with docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
import itertools
import logging
import numpy as np
from collections import OrderedDict
from collections.abc import Mapping
from typing import Dict, List, Optional, Tuple, Union
import torch
from omegaconf import DictConfig, OmegaConf
from torch import Tensor, nn
from ... | --- +++ @@ -19,6 +19,10 @@
def _to_container(cfg):
+ """
+ mmdet will assert the type of dict/list.
+ So convert omegaconf objects to dict/list.
+ """
if isinstance(cfg, DictConfig):
cfg = OmegaConf.to_container(cfg, resolve=True)
from mmcv.utils import ConfigDict
@@ -27,6 +31,14 @@
... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/mmdet_wrapper.py |
Create documentation strings for testing functions | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = [
"window_partition",
"window_unpartition",
"add_decomposed_rel_pos",
"get_abs_pos",
"PatchEmbed",
]
def window_partition(x, window_size)... | --- +++ @@ -14,6 +14,16 @@
def window_partition(x, window_size):
+ """
+ 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 * n... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/backbone/utils.py |
Add docstrings to improve collaboration | # Copyright (c) Facebook, Inc. and its affiliates.
import contextlib
import datetime
import io
import json
import logging
import numpy as np
import os
import shutil
import pycocotools.mask as mask_util
from fvcore.common.timer import Timer
from iopath.common.file_io import file_lock
from PIL import Image
from detectro... | --- +++ @@ -33,6 +33,39 @@
def load_coco_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None):
+ """
+ Load a json file with COCO's instances annotation format.
+ Currently supports instance detection, instance segmentation,
+ and person keypoints annotations.
+
+ Args:
+ ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/data/datasets/coco.py |
Can you add docstrings to this Python file? | # Copyright (c) Facebook, Inc. and its affiliates.
import logging
import math
from typing import List, Tuple, Union
import torch
from detectron2.layers import batched_nms, cat, move_device_like
from detectron2.structures import Boxes, Instances
logger = logging.getLogger(__name__)
def _is_tracing():
# (fixed in... | --- +++ @@ -29,6 +29,34 @@ min_box_size: float,
training: bool,
):
+ """
+ For each feature map, select the `pre_nms_topk` highest scoring proposals,
+ apply NMS, clip proposals, and remove small boxes. Return the `post_nms_topk`
+ highest scoring proposals among all the feature maps for each imag... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/proposal_generator/proposal_utils.py |
Create docstrings for all classes and functions | # Copyright (c) Facebook, Inc. and its affiliates.
from typing import Dict, List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
from torch import nn
from detectron2.config import configurable
from detectron2.layers import Conv2d, ShapeSpec, cat
from detectron2.structures import Boxes, ImageList, ... | --- +++ @@ -56,17 +56,42 @@
def build_rpn_head(cfg, input_shape):
+ """
+ Build an RPN head defined by `cfg.MODEL.RPN.HEAD_NAME`.
+ """
name = cfg.MODEL.RPN.HEAD_NAME
return RPN_HEAD_REGISTRY.get(name)(cfg, input_shape)
@RPN_HEAD_REGISTRY.register()
class StandardRPNHead(nn.Module):
+ ""... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/proposal_generator/rpn.py |
Create documentation strings for testing functions | # Copyright (c) Facebook, Inc. and its affiliates.
import logging
from typing import List, Optional, Tuple
import torch
from fvcore.nn import sigmoid_focal_loss_jit
from torch import nn
from torch.nn import functional as F
from detectron2.layers import ShapeSpec, batched_nms
from detectron2.structures import Boxes, I... | --- +++ @@ -23,6 +23,9 @@
class FCOS(DenseDetector):
+ """
+ Implement FCOS in :paper:`fcos`.
+ """
def __init__(
self,
@@ -42,6 +45,12 @@ pixel_mean,
pixel_std,
):
+ """
+ Args:
+ center_sampling_radius: radius of the "center" of a groundtruth... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/meta_arch/fcos.py |
Help me comply with documentation standards | # Copyright (c) Facebook, Inc. and its affiliates.
import torch
from torch.nn import functional as F
from detectron2.structures import Instances, ROIMasks
# perhaps should rename to "resize_instance"
def detector_postprocess(
results: Instances, output_height: int, output_width: int, mask_threshold: float = 0.5
... | --- +++ @@ -9,6 +9,23 @@ def detector_postprocess(
results: Instances, output_height: int, output_width: int, mask_threshold: float = 0.5
):
+ """
+ Resize the output instances.
+ The input images are often resized when entering an object detector.
+ As a result, we often need the outputs of the detec... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/postprocessing.py |
Add well-formatted docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
import logging
from typing import Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.data.detection_utils import get_fed_loss_cls_weights
from d... | --- +++ @@ -51,6 +51,31 @@ nms_thresh: float,
topk_per_image: int,
):
+ """
+ Call `fast_rcnn_inference_single_image` for all images.
+
+ Args:
+ boxes (list[Tensor]): A list of Tensors of predicted class-specific or class-agnostic
+ boxes for each image. Element i has shape (Ri, K ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/roi_heads/fast_rcnn.py |
Add docstrings to my Python code | # Copyright (c) Facebook, Inc. and its affiliates.
import math
from typing import List, Optional
import torch
from torch import nn
from torchvision.ops import RoIPool
from detectron2.layers import ROIAlign, ROIAlignRotated, cat, nonzero_tuple, shapes_to_tensor
from detectron2.structures import Boxes
from detectron2.ut... | --- +++ @@ -27,6 +27,27 @@ canonical_box_size: int,
canonical_level: int,
):
+ """
+ Map each box in `box_lists` to a feature map level index and return the assignment
+ vector.
+
+ Args:
+ box_lists (list[Boxes] | list[RotatedBoxes]): A list of N Boxes or N RotatedBoxes,
+ where... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/poolers.py |
Turn comments into proper docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
from typing import List
import torch
from torch import nn
from torch.autograd.function import Function
from detectron2.config import configurable
from detectron2.layers import ShapeSpec
from detectron2.structures import Boxes, Instances, pairwise_iou
from detectron2.u... | --- +++ @@ -30,6 +30,9 @@
@ROI_HEADS_REGISTRY.register()
class CascadeROIHeads(StandardROIHeads):
+ """
+ The ROI heads that implement :paper:`Cascade R-CNN`.
+ """
@configurable
def __init__(
@@ -42,6 +45,18 @@ proposal_matchers: List[Matcher],
**kwargs,
):
+ """
+ ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/roi_heads/cascade_rcnn.py |
Replace inline comments with docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
import itertools
import logging
from typing import Dict, List
import torch
from detectron2.config import configurable
from detectron2.layers import ShapeSpec, batched_nms_rotated, cat
from detectron2.structures import Instances, RotatedBoxes, pairwise_iou_rotated
from... | --- +++ @@ -27,6 +27,35 @@ min_box_size,
training,
):
+ """
+ For each feature map, select the `pre_nms_topk` highest scoring proposals,
+ apply NMS, clip proposals, and remove small boxes. Return the `post_nms_topk`
+ highest scoring proposals among all the feature maps if `training` is True,
+ ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/proposal_generator/rrpn.py |
Create docstrings for all classes and functions | # Copyright (c) Facebook, Inc. and its affiliates.
import inspect
import logging
import numpy as np
from typing import Dict, List, Optional, Tuple
import torch
from torch import nn
from detectron2.config import configurable
from detectron2.layers import ShapeSpec, nonzero_tuple
from detectron2.structures import Boxes,... | --- +++ @@ -36,6 +36,9 @@
def build_roi_heads(cfg, input_shape):
+ """
+ Build ROIHeads defined by `cfg.MODEL.ROI_HEADS.NAME`.
+ """
name = cfg.MODEL.ROI_HEADS.NAME
return ROI_HEADS_REGISTRY.get(name)(cfg, input_shape)
@@ -43,6 +46,21 @@ def select_foreground_proposals(
proposals: List[Ins... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/roi_heads/roi_heads.py |
Add docstrings to meet PEP guidelines | # Copyright (c) Facebook, Inc. and its affiliates.
import logging
import numpy as np
import torch
from detectron2.config import configurable
from detectron2.layers import ShapeSpec, batched_nms_rotated
from detectron2.structures import Instances, RotatedBoxes, pairwise_iou_rotated
from detectron2.utils.events import g... | --- +++ @@ -46,6 +46,31 @@ def fast_rcnn_inference_rotated(
boxes, scores, image_shapes, score_thresh, nms_thresh, topk_per_image
):
+ """
+ Call `fast_rcnn_inference_single_image_rotated` for all images.
+
+ Args:
+ boxes (list[Tensor]): A list of Tensors of predicted class-specific or class-agno... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/roi_heads/rotated_fast_rcnn.py |
Add docstrings that explain logic | # Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
from typing import List
import fvcore.nn.weight_init as weight_init
import torch
from torch import nn
from detectron2.config import configurable
from detectron2.layers import Conv2d, ShapeSpec, get_norm
from detectron2.utils.registry import Registry... | --- +++ @@ -24,11 +24,25 @@ # added in the order they will be used in forward().
@ROI_BOX_HEAD_REGISTRY.register()
class FastRCNNConvFCHead(nn.Sequential):
+ """
+ A head with several 3x3 conv layers (each followed by norm & relu) and then
+ several fc layers (each followed by relu).
+ """
@configu... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/roi_heads/box_head.py |
Add docstrings with type hints explained | # Copyright (c) Facebook, Inc. and its affiliates.
from typing import List
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.layers import Conv2d, ConvTranspose2d, cat, interpolate
from detectron2.structures import Instances, heatmaps_to_k... | --- +++ @@ -30,11 +30,29 @@
def build_keypoint_head(cfg, input_shape):
+ """
+ Build a keypoint head from `cfg.MODEL.ROI_KEYPOINT_HEAD.NAME`.
+ """
name = cfg.MODEL.ROI_KEYPOINT_HEAD.NAME
return ROI_KEYPOINT_HEAD_REGISTRY.get(name)(cfg, input_shape)
def keypoint_rcnn_loss(pred_keypoint_logit... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/roi_heads/keypoint_head.py |
Add docstrings to meet PEP guidelines | # Copyright (c) Facebook, Inc. and its affiliates.
from __future__ import division
from typing import Any, Dict, List, Optional, Tuple
import torch
from torch import device
from torch.nn import functional as F
from detectron2.layers.wrappers import move_device_like, shapes_to_tensor
from detectron2.utils.torch_version... | --- +++ @@ -10,8 +10,24 @@
class ImageList:
+ """
+ Structure that holds a list of images (of possibly
+ varying sizes) as a single tensor.
+ This works by padding the images to the same size.
+ The original sizes of each image is stored in `image_sizes`.
+
+ Attributes:
+ image_sizes (list... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/structures/image_list.py |
Generate docstrings for exported functions | # Copyright (c) Facebook, Inc. and its affiliates.
from typing import List
import fvcore.nn.weight_init as weight_init
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.layers import Conv2d, ConvTranspose2d, ShapeSpec, cat, get_norm
from d... | --- +++ @@ -31,6 +31,23 @@
@torch.jit.unused
def mask_rcnn_loss(pred_mask_logits: torch.Tensor, instances: List[Instances], vis_period: int = 0):
+ """
+ Compute the mask prediction loss defined in the Mask R-CNN paper.
+
+ Args:
+ pred_mask_logits (Tensor): A tensor of shape (B, C, Hmask, Wmask) or ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/roi_heads/mask_head.py |
Generate docstrings with parameter types | # Copyright (c) Facebook, Inc. and its affiliates.
import itertools
import warnings
from typing import Any, Dict, List, Tuple, Union
import torch
class Instances:
def __init__(self, image_size: Tuple[int, int], **kwargs: Any):
self._image_size = image_size
self._fields: Dict[str, Any] = {}
... | --- +++ @@ -6,8 +6,42 @@
class Instances:
+ """
+ This class represents a list of instances in an image.
+ It stores the attributes of instances (e.g., boxes, masks, labels, scores) as "fields".
+ All fields must have the same ``__len__`` which is the number of instances.
+
+ All other (non-field) at... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/structures/instances.py |
Add docstrings to incomplete code | # Copyright (c) Facebook, Inc. and its affiliates.
import copy
import itertools
import logging
from collections import defaultdict
from enum import Enum
from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Type, Union
import torch
from fvcore.common.param_scheduler import (
CosineParamScheduler,
... | --- +++ @@ -27,6 +27,10 @@
def _create_gradient_clipper(cfg: CfgNode) -> _GradientClipper:
+ """
+ Creates gradient clipping closure to clip by value or by norm,
+ according to the provided config.
+ """
cfg = copy.deepcopy(cfg)
def clip_grad_norm(p: _GradientClipperInput):
@@ -48,6 +52,10 @... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/solver/build.py |
Add documentation for all methods | #!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import copy
import numpy as np
from typing import Dict
import torch
from scipy.optimize import linear_sum_assignment
from detectron2.config import configurable
from detectron2.structures import Boxes, Instances
from ..config.config import ... | --- +++ @@ -14,6 +14,9 @@
class BaseHungarianTracker(BaseTracker):
+ """
+ A base class for all Hungarian trackers
+ """
@configurable
def __init__(
@@ -26,6 +29,19 @@ min_instance_period: int = 1,
**kwargs
):
+ """
+ Args:
+ video_height: height the v... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/tracking/hungarian_tracker.py |
Create docstrings for reusable components | # Copyright (c) Facebook, Inc. and its affiliates.
import logging
import math
from bisect import bisect_right
from typing import List
import torch
from fvcore.common.param_scheduler import (
CompositeParamScheduler,
ConstantParamScheduler,
LinearParamScheduler,
ParamScheduler,
)
try:
from torch.opt... | --- +++ @@ -20,6 +20,9 @@
class WarmupParamScheduler(CompositeParamScheduler):
+ """
+ Add an initial warmup stage to another scheduler.
+ """
def __init__(
self,
@@ -29,6 +32,16 @@ warmup_method: str = "linear",
rescale_interval: bool = False,
):
+ """
+ ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/solver/lr_scheduler.py |
Add docstrings that explain inputs and outputs | #!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import copy
import numpy as np
from typing import List
import torch
from detectron2.config import configurable
from detectron2.structures import Boxes, Instances
from detectron2.structures.boxes import pairwise_iou
from ..config.config imp... | --- +++ @@ -15,6 +15,9 @@
@TRACKER_HEADS_REGISTRY.register()
class BBoxIOUTracker(BaseTracker):
+ """
+ A bounding box tracker to assign ID based on IoU between current and previous instances
+ """
@configurable
def __init__(
@@ -29,6 +32,21 @@ track_iou_threshold: float = 0.5,
... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/tracking/bbox_iou_tracker.py |
Document this code for team use | # Copyright (c) Facebook, Inc. and its affiliates.
import math
import numpy as np
from enum import IntEnum, unique
from typing import List, Tuple, Union
import torch
from torch import device
_RawBoxType = Union[List[float], Tuple[float, ...], torch.Tensor, np.ndarray]
@unique
class BoxMode(IntEnum):
XYXY_ABS = ... | --- +++ @@ -11,6 +11,9 @@
@unique
class BoxMode(IntEnum):
+ """
+ Enum of different ways to represent a box.
+ """
XYXY_ABS = 0
"""
@@ -39,6 +42,14 @@
@staticmethod
def convert(box: _RawBoxType, from_mode: "BoxMode", to_mode: "BoxMode") -> _RawBoxType:
+ """
+ Args:
+ ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/structures/boxes.py |
Write documentation strings for class attributes | #!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import numpy as np
from typing import List
from detectron2.config import CfgNode as CfgNode_
from detectron2.config import configurable
from .base_tracker import TRACKER_HEADS_REGISTRY
from .vanilla_hungarian_bbox_iou_tracker import Vanil... | --- +++ @@ -13,6 +13,10 @@
@TRACKER_HEADS_REGISTRY.register()
class IOUWeightedHungarianBBoxIOUTracker(VanillaHungarianBBoxIOUTracker):
+ """
+ A tracker using IoU as weight in Hungarian algorithm, also known
+ as Munkres or Kuhn-Munkres algorithm
+ """
@configurable
def __init__(
@@ -27,6 +3... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/tracking/iou_weighted_hungarian_bbox_iou_tracker.py |
Write docstrings for data processing functions | # Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
import random
__all__ = ["colormap", "random_color", "random_colors"]
# fmt: off
# RGB:
_COLORS = np.array(
[
0.000, 0.447, 0.741,
0.850, 0.325, 0.098,
0.929, 0.694, 0.125,
0.494, 0.184, 0.556,
0.466, 0... | --- +++ @@ -1,5 +1,9 @@ # Copyright (c) Facebook, Inc. and its affiliates.
+"""
+An awesome colormap for really neat visualizations.
+Copied from Detectron, and removed gray colors.
+"""
import numpy as np
import random
@@ -90,6 +94,14 @@
def colormap(rgb=False, maximum=255):
+ """
+ Args:
+ rgb ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/utils/colormap.py |
Generate docstrings for exported functions | # Copyright (c) Facebook, Inc. and its affiliates.
import glob
import logging
import numpy as np
import os
import tempfile
from collections import OrderedDict
import torch
from PIL import Image
from detectron2.data import MetadataCatalog
from detectron2.utils import comm
from detectron2.utils.file_io import PathManage... | --- +++ @@ -16,8 +16,17 @@
class CityscapesEvaluator(DatasetEvaluator):
+ """
+ Base class for evaluation using cityscapes API.
+ """
def __init__(self, dataset_name):
+ """
+ Args:
+ dataset_name (str): the name of the dataset.
+ It must have the following me... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/evaluation/cityscapes_evaluation.py |
Create Google-style docstrings for my code | # Copyright (c) Facebook, Inc. and its affiliates.
# adapted from https://github.com/tensorpack/tensorpack/blob/master/tensorpack/utils/develop.py
def create_dummy_class(klass, dependency, message=""):
err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, klass)
if message:
e... | --- +++ @@ -1,8 +1,22 @@ # Copyright (c) Facebook, Inc. and its affiliates.
+"""Utilities for developers only.
+These are not visible to users (not automatically imported). And should not
+appeared in docs."""
# adapted from https://github.com/tensorpack/tensorpack/blob/master/tensorpack/utils/develop.py
def crea... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/utils/develop.py |
Generate missing documentation strings | # Copyright (c) Facebook, Inc. and its affiliates.
# -*- coding: utf-8 -*-
import typing
from typing import Any, List
import fvcore
from fvcore.nn import activation_count, flop_count, parameter_count, parameter_count_table
from torch import nn
from detectron2.export import TracingAdapter
__all__ = [
"activation_... | --- +++ @@ -53,14 +53,46 @@
class FlopCountAnalysis(fvcore.nn.FlopCountAnalysis):
+ """
+ Same as :class:`fvcore.nn.FlopCountAnalysis`, but supports detectron2 models.
+ """
def __init__(self, model, inputs):
+ """
+ Args:
+ model (nn.Module):
+ inputs (Any): inpu... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/utils/analysis.py |
Write proper docstrings for these functions | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import concurrent.futures
import logging
import numpy as np
import time
import weakref
from typing import List, Mapping, Optional
import torch
from torch.nn.parallel import DataParallel, DistributedDataParallel
import detectron2.utils.comm as c... | --- +++ @@ -17,6 +17,36 @@
class HookBase:
+ """
+ Base class for hooks that can be registered with :class:`TrainerBase`.
+
+ Each hook can implement 4 methods. The way they are called is demonstrated
+ in the following snippet:
+ ::
+ hook.before_train()
+ for iter in range(start_iter,... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/engine/train_loop.py |
Generate NumPy-style docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
import importlib
import importlib.util
import logging
import numpy as np
import os
import random
import sys
from datetime import datetime
import torch
__all__ = ["seed_all_rng"]
TORCH_VERSION = tuple(int(x) for x in torch.__version__.split(".")[:2])
"""
PyTorch vers... | --- +++ @@ -25,6 +25,12 @@
def seed_all_rng(seed=None):
+ """
+ Set the random seed for the RNG in torch, numpy and python.
+
+ Args:
+ seed (int): if None, will use a strong random seed.
+ """
if seed is None:
seed = (
os.getpid()
@@ -51,6 +57,9 @@
def _configure_l... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/utils/env.py |
Add docstrings that explain inputs and outputs | #!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import numpy as np
from typing import List
from detectron2.config import CfgNode as CfgNode_
from detectron2.config import configurable
from detectron2.structures import Instances
from detectron2.structures.boxes import pairwise_iou
from d... | --- +++ @@ -16,6 +16,9 @@
@TRACKER_HEADS_REGISTRY.register()
class VanillaHungarianBBoxIOUTracker(BaseHungarianTracker):
+ """
+ Hungarian algo based tracker using bbox iou as metric
+ """
@configurable
def __init__(
@@ -30,6 +33,21 @@ track_iou_threshold: float = 0.5,
**kwargs,... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/tracking/vanilla_hungarian_bbox_iou_tracker.py |
Add well-formatted docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
from typing import List
import pycocotools.mask as mask_util
from detectron2.structures import Instances
from detectron2.utils.visualizer import (
ColorMode,
Visualizer,
_create_text_labels,
_PanopticPrediction,
)
from .colormap imp... | --- +++ @@ -15,6 +15,18 @@
class _DetectedInstance:
+ """
+ Used to store data about detected objects in video frame,
+ in order to transfer color to objects in the future frames.
+
+ Attributes:
+ label (int):
+ bbox (tuple[float]):
+ mask_rle (dict):
+ color (tuple[float]):... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/utils/video_visualizer.py |
Fully document this Python code with docstrings | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
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,
... | --- +++ @@ -1,6 +1,10 @@ # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
+"""
+Common data processing utilities that are used in a
+typical object detection data pipeline.
+"""
import logging
import numpy as np
from typing import List, Union
@@ -40,6 +44,9 @@
class SizeMismatchError... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/data/detection_utils.py |
Help me write clear docstrings | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import datetime
import itertools
import logging
import math
import operator
import os
import tempfile
import time
import warnings
from collections import Counter
import torch
from fvcore.common.checkpoint import Checkpointer
from fvcore.common.... | --- +++ @@ -48,8 +48,14 @@
class CallbackHook(HookBase):
+ """
+ Create a hook using callback functions provided by the user.
+ """
def __init__(self, *, before_train=None, after_train=None, before_step=None, after_step=None):
+ """
+ Each argument is a function that takes one argument... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/engine/hooks.py |
Generate docstrings for this script | # Copyright (c) Facebook, Inc. and its affiliates.
import functools
import numpy as np
import torch
import torch.distributed as dist
_LOCAL_PROCESS_GROUP = None
_MISSING_LOCAL_PG_ERROR = (
"Local process group is not yet created! Please use detectron2's `launch()` "
"to start processes and initialize pytorch ... | --- +++ @@ -1,4 +1,8 @@ # Copyright (c) Facebook, Inc. and its affiliates.
+"""
+This file contains primitives for multi-gpu communication.
+This is useful when doing distributed training.
+"""
import functools
import numpy as np
@@ -32,6 +36,19 @@
@functools.lru_cache()
def create_local_process_group(num_worker... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/utils/comm.py |
Add professional docstrings to my codebase | # Copyright (c) Facebook, Inc. and its affiliates.
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):
def __init__(self, tensor: torch.Tensor):
device = tensor.device if isinstance(... | --- +++ @@ -9,8 +9,207 @@
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`, `nonempty`, etc),
+ and also behaves like a Tensor
+ (support indexing, `to(device)`, `.device`, and it... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/structures/rotated_boxes.py |
Document all endpoints with docstrings | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
# flake8: noqa
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path ... | --- +++ @@ -28,6 +28,9 @@
class GithubURLDomain(Domain):
+ """
+ Resolve certain links in markdown files to github source.
+ """
name = "githuburl"
ROOT = "https://github.com/facebookresearch/detectron2/blob/main/"
@@ -356,6 +359,9 @@ options: Dict = {},
content: List[str] = [],
):
+ ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/docs/conf.py |
Generate docstrings with parameter types | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import os
import detectron2.data.transforms as T
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import DatasetMapper, MetadataCatalog, build_detection_train_loader
from detect... | --- +++ @@ -1,6 +1,11 @@ #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
+"""
+DeepLab Training Script.
+
+This script is a simplified version of the training script in detectron2/tools.
+"""
import os
@@ -35,9 +40,21 @@
class Trainer(DefaultTrainer):
+ """
+ We use the "Defa... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/projects/DeepLab/train_net.py |
Add docstrings to clarify complex logic | # -*- coding = utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
# pyre-ignore-all-errors
from detectron2.config import CfgNode as CN
def add_dataset_category_config(cfg: CN) -> None:
_C = cfg
_C.DATASETS.CATEGORY_MAPS = CN(new_allowed=True)
_C.DATASETS.WHITELISTED_CATEGORIES = CN(new_allowed=... | --- +++ @@ -6,6 +6,11 @@
def add_dataset_category_config(cfg: CN) -> None:
+ """
+ Add config for additional category-related dataset options
+ - category whitelisting
+ - category mapping
+ """
_C = cfg
_C.DATASETS.CATEGORY_MAPS = CN(new_allowed=True)
_C.DATASETS.WHITELISTED_CATEGOR... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/projects/DensePose/densepose/config.py |
Generate documentation strings for clarity | # Copyright (c) Facebook, Inc. and its affiliates.
from abc import ABCMeta, abstractmethod
from typing import Dict
import torch.nn as nn
from detectron2.layers import ShapeSpec
__all__ = ["Backbone"]
class Backbone(nn.Module, metaclass=ABCMeta):
def __init__(self):
super().__init__()
@abstractmeth... | --- +++ @@ -9,27 +9,66 @@
class Backbone(nn.Module, metaclass=ABCMeta):
+ """
+ Abstract base class for network backbones.
+ """
def __init__(self):
+ """
+ The `__init__` method of any subclass can specify its own set of arguments.
+ """
super().__init__()
@abst... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/backbone/backbone.py |
Write reusable docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
from typing import Callable, Dict, List, Optional, Tuple, Union
import fvcore.nn.weight_init as weight_init
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.layers import ASPP, Conv2d, De... | --- +++ @@ -14,6 +14,9 @@
@SEM_SEG_HEADS_REGISTRY.register()
class DeepLabV3PlusHead(nn.Module):
+ """
+ A semantic segmentation head described in :paper:`DeepLabV3+`.
+ """
@configurable
def __init__(
@@ -33,6 +36,35 @@ num_classes: Optional[int] = None,
use_depthwise_separable... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/projects/DeepLab/deeplab/semantic_seg.py |
Document my Python code with docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
# pyre-unsafe
from typing import Any, Tuple, Type
import torch
class BaseConverter:
@classmethod
def register(cls, from_type: Type, converter: Any = None):
if converter is not None:
cls._do_register(from_type, converter)
def w... | --- +++ @@ -7,9 +7,25 @@
class BaseConverter:
+ """
+ Converter base class to be reused by various converters.
+ Converter allows one to convert data from various source types to a particular
+ destination type. Each source type needs to register its converter. The
+ registration for each source type... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/projects/DensePose/densepose/converters/base.py |
Document this module using docstrings | # Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
from typing import Any, List, Tuple, Union
import torch
from torch.nn import functional as F
class Keypoints:
def __init__(self, keypoints: Union[torch.Tensor, np.ndarray, List[List[float]]]):
device = keypoints.device if isinstance(ke... | --- +++ @@ -6,8 +6,25 @@
class Keypoints:
+ """
+ Stores keypoint **annotation** data. GT Instances have a `gt_keypoints` property
+ containing the x,y location and visibility flag of each keypoint. This tensor has shape
+ (N, K, 3) where N is the number of instances and K is the number of keypoints per... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/structures/keypoints.py |
Write docstrings for algorithm functions | import numpy as np
from typing import Dict, List, Optional, Tuple
import torch
from torch import Tensor, nn
from detectron2.data.detection_utils import convert_image_to_rgb
from detectron2.layers import move_device_like
from detectron2.modeling import Backbone
from detectron2.structures import Boxes, ImageList, Instan... | --- +++ @@ -13,6 +13,9 @@
def permute_to_N_HWA_K(tensor, K: int):
+ """
+ Transpose/reshape a tensor from (N, (Ai x K), H, W) to (N, (HxWxAi), K)
+ """
assert tensor.dim() == 4, tensor.shape
N, _, H, W = tensor.shape
tensor = tensor.view(N, -1, K, H, W)
@@ -22,6 +25,10 @@
class DenseDete... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/modeling/meta_arch/dense_detector.py |
Write Python docstrings for this snippet | # Copyright (c) Facebook, Inc. and its affiliates.
import fvcore.nn.weight_init as weight_init
import torch.nn.functional as F
from detectron2.layers import CNNBlockBase, Conv2d, get_norm
from detectron2.modeling import BACKBONE_REGISTRY
from detectron2.modeling.backbone.resnet import (
BasicStem,
BottleneckBl... | --- +++ @@ -13,8 +13,16 @@
class DeepLabStem(CNNBlockBase):
+ """
+ The DeepLab ResNet stem (layers before the first residual block).
+ """
def __init__(self, in_channels=3, out_channels=128, norm="BN"):
+ """
+ Args:
+ norm (str or callable): norm after the first conv layer... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/projects/DeepLab/deeplab/resnet.py |
Create Google-style docstrings for my code | # Copyright (c) Facebook, Inc. and its affiliates.
import datetime
import logging
import time
from collections import OrderedDict, abc
from contextlib import ExitStack, contextmanager
from typing import List, Union
import torch
from torch import nn
from detectron2.utils.comm import get_world_size, is_main_process
from... | --- +++ @@ -13,20 +13,69 @@
class DatasetEvaluator:
+ """
+ Base class for a dataset evaluator.
+
+ The function :func:`inference_on_dataset` runs the model over
+ all samples in the dataset, and have a DatasetEvaluator to process the inputs/outputs.
+
+ This class will accumulate information of the ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/evaluation/evaluator.py |
Generate helpful docstrings for debugging | # Copyright (c) Facebook, Inc. and its affiliates.
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_... | --- +++ @@ -20,6 +20,14 @@
def polygons_to_bitmask(polygons: List[np.ndarray], height: int, width: int) -> np.ndarray:
+ """
+ Args:
+ polygons (list[ndarray]): each array has shape (Nx2,)
+ height, width (int)
+
+ Returns:
+ ndarray: a bool mask of shape (height, width)
+ """
... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/structures/masks.py |
Generate NumPy-style docstrings | #!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
from detectron2.config import configurable
from detectron2.utils.registry import Registry
from ..config.config import CfgNode as CfgNode_
from ..structures import Instances
TRACKER_HEADS_REGISTRY = Registry("TRACKER_HEADS")
TRACKER_HEADS_R... | --- +++ @@ -13,6 +13,9 @@
class BaseTracker:
+ """
+ A parent class for all trackers
+ """
@configurable
def __init__(self, **kwargs):
@@ -27,10 +30,35 @@ raise NotImplementedError("Calling BaseTracker::from_config")
def update(self, predictions: Instances) -> Instances:
+ ... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/tracking/base_tracker.py |
Add docstrings to clarify complex logic | # Copyright (c) Facebook, Inc. and its affiliates.
# pyre-unsafe
from typing import Any
from detectron2.structures import Boxes
from ..structures import DensePoseChartResult, DensePoseChartResultWithConfidences
from .base import BaseConverter
class ToChartResultConverter(BaseConverter):
registry = {}
dst... | --- +++ @@ -11,6 +11,10 @@
class ToChartResultConverter(BaseConverter):
+ """
+ Converts various DensePose predictor outputs to DensePose results.
+ Each DensePose predictor output type has to register its convertion strategy.
+ """
registry = {}
dst_type = DensePoseChartResult
@@ -19,10 +23... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/projects/DensePose/densepose/converters/to_chart_result.py |
Document functions with detailed explanations | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import logging
import numpy as np
import os
import tempfile
import xml.etree.ElementTree as ET
from collections import OrderedDict, defaultdict
from functools import lru_cache
import torch
from detectron2.data import MetadataCatalog
from detec... | --- +++ @@ -18,8 +18,21 @@
class PascalVOCDetectionEvaluator(DatasetEvaluator):
+ """
+ Evaluate Pascal VOC style AP for Pascal VOC dataset.
+ It contains a synchronization, therefore has to be called from all ranks.
+
+ Note that the concept of AP can be implemented in different ways and may not
+ p... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/evaluation/pascal_voc_evaluation.py |
Write docstrings including parameters and return values | import math
import torch
def diou_loss(
boxes1: torch.Tensor,
boxes2: torch.Tensor,
reduction: str = "none",
eps: float = 1e-7,
) -> torch.Tensor:
x1, y1, x2, y2 = boxes1.unbind(dim=-1)
x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1)
# TODO: use torch._assert_async() when pytorch 1.8 support ... | --- +++ @@ -8,6 +8,17 @@ reduction: str = "none",
eps: float = 1e-7,
) -> torch.Tensor:
+ """
+ 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,).
+ red... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/detectron2/layers/losses.py |
Write docstrings for algorithm functions | # Copyright (c) Facebook, Inc. and its affiliates.
# pyre-unsafe
from typing import Any
import torch
from torch.nn import functional as F
from detectron2.structures import BitMasks, Boxes, BoxMode
from .base import IntTupleBox, make_int_box
from .to_mask import ImageSizeType
def resample_coarse_segm_tensor_to_bbo... | --- +++ @@ -13,6 +13,17 @@
def resample_coarse_segm_tensor_to_bbox(coarse_segm: torch.Tensor, box_xywh_abs: IntTupleBox):
+ """
+ Resample coarse segmentation tensor to the given
+ bounding box and derive labels for each pixel of the bounding box
+
+ Args:
+ coarse_segm: float tensor of shape [1,... | https://raw.githubusercontent.com/facebookresearch/detectron2/HEAD/projects/DensePose/densepose/converters/segm_to_mask.py |
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