repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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SA-AutoAug | SA-AutoAug-master/detectron2/projects/DensePose/densepose/structures/cse.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from dataclasses import dataclass
from typing import Union
import torch
@dataclass
class DensePoseEmbeddingPredictorOutput:
"""
Predictor output that contains embedding and coarse segmentation data:
* embedding: float tensor of size ... | 1,704 | 31.169811 | 94 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/DensePose/densepose/structures/transform_data.py | # Copyright (c) Facebook, Inc. and its affiliates.
from typing import BinaryIO, Dict, Union
import torch
def normalized_coords_transform(x0, y0, w, h):
"""
Coordinates transform that maps top left corner to (-1, -1) and bottom
right corner to (1, 1). Used for torch.grid_sample to initialize the
grid
... | 2,794 | 37.819444 | 128 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/DensePose/densepose/structures/mesh.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import pickle
from functools import lru_cache
from typing import Dict, Optional, Tuple
import torch
from detectron2.utils.file_io import PathManager
from densepose.data.meshes.catalog import MeshCatalog, MeshInfo
def _maybe_copy_to_device(
... | 6,330 | 36.023392 | 98 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/DensePose/densepose/structures/chart_result.py | # Copyright (c) Facebook, Inc. and its affiliates.
from dataclasses import dataclass
from typing import Any, Optional, Tuple
import torch
@dataclass
class DensePoseChartResult:
"""
DensePose results for chart-based methods represented by labels and inner
coordinates (U, V) of individual charts. Each char... | 6,914 | 36.581522 | 99 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/DensePose/densepose/structures/chart.py | # Copyright (c) Facebook, Inc. and its affiliates.
from dataclasses import dataclass
from typing import Union
import torch
@dataclass
class DensePoseChartPredictorOutput:
"""
Predictor output that contains segmentation and inner coordinates predictions for predefined
body parts:
* coarse segmentatio... | 2,320 | 31.690141 | 100 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/DensePose/densepose/structures/list.py | # Copyright (c) Facebook, Inc. and its affiliates.
import torch
from densepose.structures.data_relative import DensePoseDataRelative
class DensePoseList(object):
_TORCH_DEVICE_CPU = torch.device("cpu")
def __init__(self, densepose_datas, boxes_xyxy_abs, image_size_hw, device=_TORCH_DEVICE_CPU):
ass... | 2,912 | 40.028169 | 99 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/DensePose/tests/test_model_e2e.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
from detectron2.structures import BitMasks, Boxes, Instances
from .common import get_model
# TODO(plabatut): Modularize detectron2 tests and re-use
def make_model_inputs(image, instances=None):
if instances is None:
return ... | 1,137 | 24.863636 | 87 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/DensePose/tests/test_image_resize_transform.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
from densepose.data.transform import ImageResizeTransform
class TestImageResizeTransform(unittest.TestCase):
def test_image_resize_1(self):
images_batch = torch.ones((3, 100, 100, 3), dtype=torch.uint8) * 100
transfo... | 637 | 36.529412 | 91 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/DensePose/tests/common.py | # Copyright (c) Facebook, Inc. and its affiliates.
import os
import torch
from detectron2.config import get_cfg
from detectron2.engine import default_setup
from detectron2.modeling import build_model
from densepose import add_densepose_config
_BASE_CONFIG_DIR = "configs"
_EVOLUTION_CONFIG_SUB_DIR = "evolution"
_HRN... | 3,475 | 26.808 | 98 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/DensePose/tests/test_cse_annotations_accumulator.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import unittest
import torch
from detectron2.structures import Boxes, BoxMode, Instances
from densepose.modeling.losses.embed_utils import CseAnnotationsAccumulator
from densepose.structures import DensePoseDataRelative, DensePoseList
class Tes... | 8,664 | 34.954357 | 100 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/DensePose/tests/test_tensor_storage.py | # Copyright (c) Facebook, Inc. and its affiliates.
import io
import tempfile
import unittest
from contextlib import ExitStack
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from detectron2.utils import comm
from densepose.evaluation.tensor_storage import (
SingleProcessFileTenso... | 10,863 | 41.272374 | 97 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/DensePose/tests/test_chart_based_annotations_accumulator.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
from detectron2.structures import Boxes, BoxMode, Instances
from densepose.modeling.losses.utils import ChartBasedAnnotationsAccumulator
from densepose.structures import DensePoseDataRelative, DensePoseList
image_shape = (100, 100)
inst... | 3,535 | 44.922078 | 98 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/DensePose/tests/test_video_keyframe_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
import contextlib
import os
import random
import tempfile
import unittest
import torch
import torchvision.io as io
from densepose.data.transform import ImageResizeTransform
from densepose.data.video import RandomKFramesSelector, VideoKeyframeDataset
try:
import ... | 3,464 | 36.258065 | 95 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/TridentNet/tridentnet/trident_backbone.py | # Copyright (c) Facebook, Inc. and its affiliates.
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn.functional as F
from detectron2.layers import Conv2d, FrozenBatchNorm2d, get_norm
from detectron2.modeling import BACKBONE_REGISTRY, ResNet, ResNetBlockBase
from detectron2.modeling.backbone.resn... | 7,846 | 34.506787 | 97 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/TridentNet/tridentnet/trident_rpn.py | # Copyright (c) Facebook, Inc. and its affiliates.
import torch
from detectron2.modeling import PROPOSAL_GENERATOR_REGISTRY
from detectron2.modeling.proposal_generator.rpn import RPN
from detectron2.structures import ImageList
@PROPOSAL_GENERATOR_REGISTRY.register()
class TridentRPN(RPN):
"""
Trident RPN sub... | 1,150 | 33.878788 | 90 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/TridentNet/tridentnet/trident_conv.py | # Copyright (c) Facebook, Inc. and its affiliates.
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.modules.utils import _pair
from detectron2.layers.wrappers import _NewEmptyTensorOp
class TridentConv(nn.Module):
def __init__(
self,
in_channels,
out_ch... | 3,868 | 34.824074 | 100 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/DeepLab/train_net.py | #!/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
import torch
import detectron2.data.transforms as T
import detectron2.utils.comm as comm
from detectron2.checkpoint imp... | 4,636 | 32.121429 | 98 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/DeepLab/deeplab/lr_scheduler.py | # Copyright (c) Facebook, Inc. and its affiliates.
import math
from typing import List
import torch
from detectron2.solver.lr_scheduler import _get_warmup_factor_at_iter
# NOTE: PyTorch's LR scheduler interface uses names that assume the LR changes
# only on epoch boundaries. We typically use iteration based schedule... | 2,398 | 37.079365 | 148 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/DeepLab/deeplab/build_solver.py | # Copyright (c) Facebook, Inc. and its affiliates.
import torch
from detectron2.config import CfgNode
from detectron2.solver import build_lr_scheduler as build_d2_lr_scheduler
from .lr_scheduler import WarmupPolyLR
def build_lr_scheduler(
cfg: CfgNode, optimizer: torch.optim.Optimizer
) -> torch.optim.lr_schedu... | 883 | 29.482759 | 73 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/DeepLab/deeplab/resnet.py | # 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... | 5,797 | 35.465409 | 92 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/DeepLab/deeplab/loss.py | # Copyright (c) Facebook, Inc. and its affiliates.
import torch
import torch.nn as nn
class DeepLabCE(nn.Module):
"""
Hard pixel mining with cross entropy loss, for semantic segmentation.
This is used in TensorFlow DeepLab frameworks.
Paper: DeeperLab: Single-Shot Image Parser
Reference: https://g... | 1,776 | 42.341463 | 147 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/DeepLab/deeplab/semantic_seg.py | # 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... | 14,826 | 41.484241 | 98 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/TensorMask/setup.py | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
import glob
import os
from setuptools import find_packages, setup
import torch
from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension
def get_extensions():
this_dir = os.path.dirname(os.path.abspath(__file__))
exte... | 2,040 | 28.157143 | 100 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/TensorMask/tensormask/arch.py | # Copyright (c) Facebook, Inc. and its affiliates.
import copy
import math
from typing import List
import torch
import torch.nn.functional as F
from fvcore.nn import sigmoid_focal_loss_star_jit, smooth_l1_loss
from torch import nn
from detectron2.layers import ShapeSpec, batched_nms, cat, paste_masks_in_image
from det... | 42,127 | 45.091904 | 100 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/TensorMask/tensormask/layers/swap_align2nat.py | # Copyright (c) Facebook, Inc. and its affiliates.
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from tensormask import _C
class _SwapAlign2Nat(Function):
@staticmethod
def forward(ctx, X, lambda_val, pad_val):
ctx.lambda_val = lambda... | 2,083 | 32.612903 | 89 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/TensorMask/tests/test_swap_align2nat.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
from torch.autograd import gradcheck
from tensormask.layers.swap_align2nat import SwapAlign2Nat
class SwapAlign2NatTest(unittest.TestCase):
@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")... | 1,048 | 30.787879 | 84 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/Panoptic-DeepLab/train_net.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
"""
Panoptic-DeepLab Training Script.
This script is a simplified version of the training script in detectron2/tools.
"""
import os
import torch
import detectron2.data.transforms as T
import detectron2.utils.comm as comm
from detectron2.checkp... | 6,312 | 34.869318 | 97 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/Panoptic-DeepLab/panoptic_deeplab/post_processing.py | # Copyright (c) Facebook, Inc. and its affiliates.
# Reference: https://github.com/bowenc0221/panoptic-deeplab/blob/master/segmentation/model/post_processing/instance_post_processing.py # noqa
from collections import Counter
import torch
import torch.nn.functional as F
def find_instance_center(center_heatmap, thres... | 9,600 | 39.855319 | 142 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/Panoptic-DeepLab/panoptic_deeplab/dataset_mapper.py | # Copyright (c) Facebook, Inc. and its affiliates.
import copy
import logging
import numpy as np
from typing import Callable, List, Union
import torch
from panopticapi.utils import rgb2id
from detectron2.config import configurable
from detectron2.data import MetadataCatalog
from detectron2.data import detection_utils ... | 4,456 | 37.094017 | 99 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/Panoptic-DeepLab/panoptic_deeplab/target_generator.py | # Copyright (c) Facebook, Inc. and its affiliates.
# Reference: https://github.com/bowenc0221/panoptic-deeplab/blob/aa934324b55a34ce95fea143aea1cb7a6dbe04bd/segmentation/data/transforms/target_transforms.py#L11 # noqa
import numpy as np
import torch
class PanopticDeepLabTargetGenerator(object):
"""
Generates... | 7,653 | 48.064103 | 167 | py |
SA-AutoAug | SA-AutoAug-master/detectron2/projects/Panoptic-DeepLab/panoptic_deeplab/panoptic_seg.py | # Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
from typing import Callable, Dict, List, 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.data import MetadataCatalog
... | 23,513 | 40.036649 | 100 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/setup.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#!/usr/bin/env python
import glob
import os
import torch
from setuptools import find_packages
from setuptools import setup
from torch.utils.cpp_extension import CUDA_HOME
from torch.utils.cpp_extension import CppExtension
from torch.utils.cpp_ext... | 2,084 | 28.785714 | 100 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/tools/test_net.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Set up custom environment before nearly anything else is imported
# NOTE: this should be the first import (no not reorder)
from maskrcnn_benchmark.utils.env import setup_environment # noqa F401 isort:skip
import argparse
import os
import torch... | 4,178 | 35.657895 | 119 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/tools/search.py | import os
import sys
import time
import glob
import numpy as np
import pickle
import torch
import logging
import argparse
import functools
import random
from maskrcnn_benchmark.modeling.detector.generalized_rcnn import GeneralizedRCNN
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.utils.logger impor... | 11,946 | 35.535168 | 214 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/tools/train_net.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
r"""
Basic training script for PyTorch
"""
# Set up custom environment before nearly anything else is imported
# NOTE: this should be the first import (no not reorder)
from maskrcnn_benchmark.utils.env import setup_environment # noqa F401 isort:s... | 6,719 | 31.463768 | 119 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/solver/lr_scheduler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from bisect import bisect_right
import torch
# FIXME ideally this would be achieved with a CombinedLRScheduler,
# separating MultiStepLR with WarmupLR
# but the current LRScheduler design doesn't allow it
class WarmupMultiStepLR(torch.optim.lr_s... | 1,817 | 33.301887 | 80 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/solver/build.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .lr_scheduler import WarmupMultiStepLR
def make_optimizer(cfg, model):
params = []
for key, value in model.named_parameters():
if not value.requires_grad:
continue
lr = cfg.SOLVER.BASE_LR
... | 976 | 29.53125 | 79 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/layers/batch_norm.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
class FrozenBatchNorm2d(nn.Module):
"""
BatchNorm2d where the batch statistics and the affine parameters
are fixed
"""
def __init__(self, n):
super(FrozenBatchNorm2d, self).__init__()... | 1,094 | 33.21875 | 71 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/layers/iou_loss.py | # GIoU and Linear IoU are added by following
# https://github.com/yqyao/FCOS_PLUS/blob/master/maskrcnn_benchmark/layers/iou_loss.py.
import torch
from torch import nn
class IOULoss(nn.Module):
def __init__(self, loss_type="iou"):
super(IOULoss, self).__init__()
self.loss_type = loss_type
def ... | 1,961 | 36.730769 | 95 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/layers/roi_pool.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
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 maskrcnn_benchmark import _C
from apex import amp
class _ROIPool(Function... | 1,900 | 27.80303 | 74 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/layers/roi_align.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
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 maskrcnn_benchmark import _C
from apex import amp
class _ROIAlign(Functio... | 2,154 | 29.785714 | 85 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/layers/smooth_l1_loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
# TODO maybe push this to nn?
def smooth_l1_loss(input, target, beta=1. / 9, size_average=True, return_loss_vec=False):
"""
very similar to the smooth_l1_loss from pytorch, but with
the extra beta parameter
"""
n ... | 615 | 25.782609 | 89 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/layers/sigmoid_focal_loss.py | import torch
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from maskrcnn_benchmark import _C
# TODO: Use JIT to replace CUDA implementation in the future.
class _SigmoidFocalLoss(Function):
@staticmethod
def forward(ctx, logits, targets, gamma... | 2,388 | 29.628205 | 118 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/layers/_utils.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import glob
import os.path
import torch
try:
from torch.utils.cpp_extension import load as load_ext
from torch.utils.cpp_extension import CUDA_HOME
except ImportError:
raise ImportError("The cpp layer extensions requires PyTorch 0.4 o... | 1,165 | 28.15 | 80 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/layers/scale.py | import torch
from torch import nn
class Scale(nn.Module):
def __init__(self, init_value=1.0):
super(Scale, self).__init__()
self.scale = nn.Parameter(torch.FloatTensor([init_value]))
def forward(self, input):
return input * self.scale
| 270 | 21.583333 | 66 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/layers/misc.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
helper class that supports empty tensors on some nn functions.
Ideally, add support directly in PyTorch to empty tensors in
those functions.
This can be removed once https://github.com/pytorch/pytorch/issues/12013
is implemented
"""
import m... | 6,499 | 31.663317 | 88 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/layers/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .batch_norm import FrozenBatchNorm2d
from .misc import Conv2d
from .misc import DFConv2d
from .misc import ConvTranspose2d
from .misc import BatchNorm2d
from .misc import interpolate
from .nms import nms, ml_nms
from .roi_align i... | 1,431 | 26.018868 | 105 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/layers/dcn/deform_conv_func.py | import torch
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from maskrcnn_benchmark import _C
class DeformConvFunction(Function):
@staticmethod
def forward(
ctx,
input,
offset,
weight,
... | 8,309 | 30.596958 | 83 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/layers/dcn/deform_pool_func.py | import torch
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from maskrcnn_benchmark import _C
class DeformRoIPoolingFunction(Function):
@staticmethod
def forward(
ctx,
data,
rois,
offset,
spatial_scale,
out_size,
... | 2,595 | 26.041667 | 99 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/layers/dcn/deform_pool_module.py | from torch import nn
from .deform_pool_func import deform_roi_pooling
class DeformRoIPooling(nn.Module):
def __init__(self,
spatial_scale,
out_size,
out_channels,
no_trans,
group_size=1,
part_size=None,
... | 6,307 | 40.774834 | 79 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/layers/dcn/deform_conv_module.py | import math
import torch
import torch.nn as nn
from torch.nn.modules.utils import _pair
from .deform_conv_func import deform_conv, modulated_deform_conv
class DeformConv(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
... | 6,102 | 31.811828 | 78 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/augmentations/scale_aware_aug.py | import copy
import torch
import torchvision
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.augmentations.image_level_augs.img_level_augs import Img_augs
from maskrcnn_benchmark.augmentations.box_level_augs.box_level_augs import Box_augs
from maskrcnn_benchmark.augmentations.box_level_augs.color_augs ... | 2,851 | 46.533333 | 185 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/augmentations/image_level_augs/zoom_out.py | import math
import torch
import random
import numpy as np
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask
from maskrcnn_benchmark.augmentations.image_level_augs.scale_jitter import scale_jitter
class Zoom_out(object):
def ... | 3,626 | 46.723684 | 183 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/augmentations/image_level_augs/scale_jitter.py | import torch
def scale_jitter(tensor, target, jitter_factor):
if isinstance(jitter_factor, tuple):
new_h, new_w = jitter_factor
elif isinstance(jitter_factor, float):
_, h, w = tensor.shape
new_h, new_w = int(h * jitter_factor), int(w * jitter_factor)
else:
return tensor, t... | 530 | 32.1875 | 117 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/augmentations/image_level_augs/zoom_in.py | import torch
import numpy as np
from maskrcnn_benchmark.augmentations.image_level_augs.scale_jitter import scale_jitter
class Zoom_in(object):
def __init__(self, ratio=1.0, iou_threshold=0.5):
self.ratio = ratio
self.iou_threshold = iou_threshold
def __call__(self, tensor, target):
if... | 1,454 | 38.324324 | 110 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/augmentations/box_level_augs/gaussian_maps.py | import math
import torch
def _gaussian_map(img, boxes, scale_splits=None, scale_ratios=None):
g_maps = torch.zeros(*img.shape[1:]).to(img.device)
height, width = img.shape[1], img.shape[2]
x_range = torch.arange(0, height, 1).to(img.device)
y_range = torch.arange(0, width, 1).to(img.device)
xx, y... | 1,968 | 40.020833 | 111 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/augmentations/box_level_augs/geometric_augs.py | import copy
import random
import torch
import torchvision.transforms as transforms
from maskrcnn_benchmark.config import cfg
import numpy as np
from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask
from maskrcnn_benchmark.augmentations.box_level_augs.gaussian_maps import _gaussian_map
_MAX_LEVEL... | 5,612 | 49.116071 | 210 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/augmentations/box_level_augs/box_level_augs.py | import torch
import random
import numpy as np
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.augmentations.box_level_augs.color_augs import color_aug_func
from maskrcnn_benchmark.augmentations.box_level_augs.geometric_augs import geometric_aug_func
pixel_mean = cfg.INPUT.PIXEL_MEAN
def _box_sample... | 2,849 | 40.304348 | 244 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/augmentations/box_level_augs/color_augs.py | import random
import torch
import torch.nn.functional as F
from maskrcnn_benchmark.augmentations.box_level_augs.gaussian_maps import _merge_gaussian
_MAX_LEVEL = 10.0
def blend(image1, image2, factor):
"""Blend image1 and image2 using 'factor'.
Factor can be above 0.0. A value of 0.0 means only image1 is us... | 7,942 | 38.321782 | 203 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/engine/inference.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import logging
import time
import os
import torch
from tqdm import tqdm
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.data.datasets.evaluation import evaluate
from ..utils.comm import is_main_process, get_world_size
from ..uti... | 4,297 | 33.111111 | 96 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/engine/bbox_aug.py | import torch
import torchvision.transforms as TT
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.data import transforms as T
from maskrcnn_benchmark.structures.image_list import to_image_list
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.modeling.roi_heads.box... | 4,440 | 36.319328 | 98 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/engine/trainer.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import datetime
import logging
import os
import time
import torch
import torch.distributed as dist
from tqdm import tqdm
from maskrcnn_benchmark.data import make_data_loader
from maskrcnn_benchmark.utils.comm import get_world_size, synchronize
fr... | 7,762 | 37.430693 | 146 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/engine/bbox_aug_vote.py | import torch
import torchvision.transforms as TT
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.data import transforms as T
from maskrcnn_benchmark.structures.image_list import to_image_list
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.boxlist_ops... | 12,092 | 37.759615 | 120 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/utils/c2_model_loading.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import logging
import pickle
from collections import OrderedDict
import torch
from maskrcnn_benchmark.utils.model_serialization import load_state_dict
from maskrcnn_benchmark.utils.registry import Registry
def _rename_basic_resnet_weights(layer... | 8,386 | 39.129187 | 129 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/utils/metric_logger.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from collections import defaultdict
from collections import deque
import torch
class SmoothedValue(object):
"""Track a series of values and provide access to smoothed values over a
window or the global series average.
"""
def __... | 1,862 | 26.80597 | 82 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/utils/checkpoint.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import logging
import os
import torch
from maskrcnn_benchmark.utils.model_serialization import load_state_dict
from maskrcnn_benchmark.utils.c2_model_loading import load_c2_format
from maskrcnn_benchmark.utils.imports import import_file
from mask... | 4,836 | 33.55 | 87 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/utils/comm.py | """
This file contains primitives for multi-gpu communication.
This is useful when doing distributed training.
"""
import pickle
import time
import torch
import torch.distributed as dist
def get_world_size():
if not dist.is_available():
return 1
if not dist.is_initialized():
return 1
ret... | 3,372 | 27.584746 | 84 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/utils/model_zoo.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import os
import sys
try:
from torch.hub import _download_url_to_file
from torch.hub import urlparse
from torch.hub import HASH_REGEX
except ImportError:
from torch.utils.model_zoo import _download_url_to_file
from torch.utils.... | 3,045 | 48.129032 | 135 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/utils/collect_env.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import PIL
from torch.utils.collect_env import get_pretty_env_info
def get_pil_version():
return "\n Pillow ({})".format(PIL.__version__)
def collect_env_info():
env_str = get_pretty_env_info()
env_str += get_pil_version()
... | 338 | 21.6 | 71 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/utils/model_serialization.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from collections import OrderedDict
import logging
import torch
from maskrcnn_benchmark.utils.imports import import_file
def align_and_update_state_dicts(model_state_dict, loaded_state_dict):
"""
Strategy: suppose that the models that w... | 3,464 | 41.777778 | 91 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/utils/imports.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
if torch._six.PY3:
import importlib
import importlib.util
import sys
# from https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path?utm_medium=organic&utm_source=google_rich_qa&utm_campai... | 843 | 34.166667 | 168 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/data/build.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import bisect
import copy
import logging
import torch.utils.data
from maskrcnn_benchmark.utils.comm import get_world_size
from maskrcnn_benchmark.utils.imports import import_file
from maskrcnn_benchmark.utils.miscellaneous import save_labels
from... | 7,156 | 38.10929 | 143 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/data/datasets/voc.py | import os
import torch
import torch.utils.data
from PIL import Image
import sys
if sys.version_info[0] == 2:
import xml.etree.cElementTree as ET
else:
import xml.etree.ElementTree as ET
from maskrcnn_benchmark.structures.bounding_box import BoxList
class PascalVOCDataset(torch.utils.data.Dataset):
CL... | 4,168 | 29.654412 | 118 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/data/datasets/concat_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import bisect
from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
class ConcatDataset(_ConcatDataset):
"""
Same as torch.utils.data.dataset.ConcatDataset, but exposes an extra
method for querying the sizes of the ima... | 766 | 30.958333 | 72 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/data/datasets/cityscapes.py | import os
import glob
import json
from PIL import Image
import numpy as np
import torch
import torchvision
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask
from .abstract import AbstractDataset
from cityscapesscripts.helpers... | 7,644 | 31.257384 | 85 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/data/datasets/abstract.py | import torch
class AbstractDataset(torch.utils.data.Dataset):
"""
Serves as a common interface to reduce boilerplate and help dataset
customization
A generic Dataset for the maskrcnn_benchmark must have the following
non-trivial fields / methods implemented:
CLASSES - list/tuple:
... | 2,309 | 32.478261 | 80 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/data/datasets/coco.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torchvision
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask
from maskrcnn_benchmark.structures.keypoint import PersonKeypoints
min_ke... | 3,783 | 35.038095 | 85 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/data/datasets/evaluation/coco/abs_to_coco.py | # COCO style evaluation for custom datasets derived from AbstractDataset
# Warning! area is computed using binary maps, therefore results may differ
# because of the precomputed COCO areas
# by botcs@github
import numpy as np
import torch
import pycocotools.mask as mask_util
from maskrcnn_benchmark.data.datasets.abst... | 6,795 | 33.150754 | 81 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/data/datasets/evaluation/coco/coco_eval.py | import logging
import tempfile
import os
import torch
from collections import OrderedDict
from tqdm import tqdm
from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
... | 14,055 | 34.405542 | 88 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/data/datasets/evaluation/cityscapes/eval_instances.py | #!/usr/bin/python
#
# The evaluation script for instance-level semantic labeling.
# We use this script to evaluate your approach on the test set.
# You can use the script to evaluate on the validation set.
#
# Please check the description of the "getPrediction" method below
# and set the required environment variables ... | 37,178 | 39.90099 | 126 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/data/datasets/evaluation/cityscapes/cityscapes_eval.py | import logging
import tempfile
import os
import torch
from collections import OrderedDict
from tqdm import tqdm
from copy import deepcopy
import torch
import numpy as np
from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker
from maskrcnn_benchmark.structures.bounding_box import BoxList
from mas... | 3,502 | 32.682692 | 81 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/data/samplers/grouped_batch_sampler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import itertools
import torch
from torch.utils.data.sampler import BatchSampler
from torch.utils.data.sampler import Sampler
class GroupedBatchSampler(BatchSampler):
"""
Wraps another sampler to yield a mini-batch of indices.
It enfo... | 4,845 | 40.775862 | 88 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/data/samplers/iteration_based_batch_sampler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from torch.utils.data.sampler import BatchSampler
class IterationBasedBatchSampler(BatchSampler):
"""
Wraps a BatchSampler, resampling from it until
a specified number of iterations have been sampled
"""
def __init__(self, ba... | 1,164 | 35.40625 | 71 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/data/samplers/distributed.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Code is copy-pasted exactly as in torch.utils.data.distributed.
# FIXME remove this once c10d fixes the bug it has
import math
import torch
import torch.distributed as dist
from torch.utils.data.sampler import Sampler
class DistributedSampler(S... | 2,569 | 37.358209 | 86 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/data/transforms/transforms.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import random
import torch
import torchvision
from torchvision.transforms import functional as F
class Compose(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, image, target):
for ... | 3,477 | 27.508197 | 83 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/modeling/matcher.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
class Matcher(object):
"""
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 assigned t... | 5,129 | 44.39823 | 88 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/modeling/make_layers.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Miscellaneous utility functions
"""
import torch
from torch import nn
from torch.nn import functional as F
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.layers import Conv2d
from maskrcnn_benchmark.modeling.poolers import P... | 3,557 | 27.926829 | 78 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/modeling/utils.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Miscellaneous utility functions
"""
import torch
def cat(tensors, dim=0):
"""
Efficient version of torch.cat that avoids a copy if there is only a single element in a list
"""
assert isinstance(tensors, (list, tuple))
if ... | 400 | 22.588235 | 97 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/modeling/poolers.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torch.nn.functional as F
from torch import nn
from maskrcnn_benchmark.layers import ROIAlign
from .utils import cat
class LevelMapper(object):
"""Determine which FPN level each RoI in a set of RoIs should map to based
... | 4,561 | 33.044776 | 90 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/modeling/balanced_positive_negative_sampler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
class BalancedPositiveNegativeSampler(object):
"""
This class samples batches, ensuring that they contain a fixed proportion of positives
"""
def __init__(self, batch_size_per_image, positive_fraction):
"""
... | 2,716 | 38.376812 | 90 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/modeling/box_coder.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import math
import torch
class BoxCoder(object):
"""
This class encodes and decodes a set of bounding boxes into
the representation used for training the regressors.
"""
def __init__(self, weights, bbox_xform_clip=math.log(1... | 3,367 | 34.083333 | 86 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/modeling/backbone/resnet.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Variant of the resnet module that takes cfg as an argument.
Example usage. Strings may be specified in the config file.
model = ResNet(
"StemWithFixedBatchNorm",
"BottleneckWithFixedBatchNorm",
"ResNet50StagesTo4",
... | 14,182 | 30.378319 | 85 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/modeling/backbone/fbnet_builder.py | """
FBNet model builder
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import copy
import logging
import math
from collections import OrderedDict
import torch
import torch.nn as nn
from maskrcnn_benchmark.layers import (
BatchNorm2d,
Conv2d,
FrozenBatchNorm2d,
... | 24,964 | 29.078313 | 88 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/modeling/backbone/fbnet.py | from __future__ import absolute_import, division, print_function, unicode_literals
import copy
import json
import logging
from collections import OrderedDict
from . import (
fbnet_builder as mbuilder,
fbnet_modeldef as modeldef,
)
import torch.nn as nn
from maskrcnn_benchmark.modeling import registry
from mas... | 7,845 | 30.011858 | 83 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/modeling/backbone/backbone.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from collections import OrderedDict
from torch import nn
from maskrcnn_benchmark.modeling import registry
from maskrcnn_benchmark.modeling.make_layers import conv_with_kaiming_uniform
from . import fpn as fpn_module
from . import resnet
@regist... | 2,759 | 33.5 | 81 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/modeling/backbone/fpn.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torch.nn.functional as F
from torch import nn
class FPN(nn.Module):
"""
Module that adds FPN on top of a list of feature maps.
The feature maps are currently supposed to be in increasing depth
order, and must b... | 3,939 | 38.4 | 86 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Implements the Generalized R-CNN framework
"""
import torch
from torch import nn
from maskrcnn_benchmark.structures.image_list import to_image_list
from ..backbone import build_backbone
from ..rpn.rpn import build_rpn
from ..roi_heads.roi_he... | 2,473 | 33.361111 | 101 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/modeling/rpn/inference.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from maskrcnn_benchmark.modeling.box_coder import BoxCoder
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
from maskrcnn_benchmark.structures.boxlist_ops... | 7,758 | 36.483092 | 87 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/modeling/rpn/anchor_generator.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import math
import numpy as np
import torch
from torch import nn
from maskrcnn_benchmark.structures.bounding_box import BoxList
class BufferList(nn.Module):
"""
Similar to nn.ParameterList, but for buffers
"""
def __init__(self... | 10,890 | 33.795527 | 88 | py |
SA-AutoAug | SA-AutoAug-master/maskrcnn-benchmark/maskrcnn_benchmark/modeling/rpn/loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
This file contains specific functions for computing losses on the RPN
file
"""
import torch
from torch.nn import functional as F
from .utils import concat_box_prediction_layers
from ..balanced_positive_negative_sampler import BalancedPositiv... | 6,055 | 36.153374 | 87 | py |
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