repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/data/datasets/evaluation/voc/voc_eval.py | # A modification version from chainercv repository.
# (See https://github.com/chainer/chainercv/blob/master/chainercv/evaluations/eval_detection_voc.py)
from __future__ import division
import os
from collections import defaultdict
import numpy as np
from maskrcnn_benchmark.structures.bounding_box import BoxList
from m... | 8,099 | 36.674419 | 100 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/data/datasets/evaluation/voc/__init__.py | import logging
from .voc_eval import do_voc_evaluation
def voc_evaluation(dataset, predictions, output_folder, box_only, **_):
logger = logging.getLogger("maskrcnn_benchmark.inference")
if box_only:
logger.warning("voc evaluation doesn't support box_only, ignored.")
logger.info("performing voc ev... | 505 | 28.764706 | 75 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/data/datasets/evaluation/coco/__init__.py | from .coco_eval import do_coco_evaluation
def coco_evaluation(
dataset,
predictions,
output_folder,
box_only,
iou_types,
expected_results,
expected_results_sigma_tol,
):
return do_coco_evaluation(
dataset=dataset,
predictions=predictions,
box_only=box_only,
... | 494 | 21.5 | 62 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/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 |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/pair_matcher.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
class PairMatcher(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 assign... | 5,155 | 44.628319 | 88 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/registry.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from lib.scene_parser.rcnn.utils.registry import Registry
BACKBONES = Registry()
RPN_HEADS = Registry()
ROI_BOX_FEATURE_EXTRACTORS = Registry()
ROI_BOX_PREDICTOR = Registry()
ROI_KEYPOINT_FEATURE_EXTRACTORS = Registry()
ROI_KEYPOINT_PREDICTOR = R... | 573 | 32.764706 | 71 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/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 |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/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 ..config import cfg
from ..layers import Conv2d
from .poolers import Pooler
def get_group_gn(dim, dim_per_gp, num_groups):
""... | 3,496 | 27.430894 | 78 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/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 |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/balanced_positive_negative_pair_sampler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# object pair sampler, implemented by Jianwei Yang
import torch
class BalancedPositiveNegativePairSampler(object):
"""
This class samples batches, ensuring that they contain a fixed proportion of positives
"""
def __init__(self, ... | 2,773 | 38.628571 | 90 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/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 ..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
on the heuristi... | 4,544 | 32.91791 | 90 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/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):
"""
... | 2,718 | 38.405797 | 90 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/__init__.py | 0 | 0 | 0 | py | |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/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 |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/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",
... | 14,321 | 30.476923 | 85 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/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 lib.scene_parser.rcnn.layers import (
BatchNorm2d,
Conv2d,
FrozenBatchNorm2d,
... | 24,970 | 29.085542 | 88 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/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 lib.scene_parser.rcnn.modeling import registry
from ... | 7,854 | 30.047431 | 83 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/backbone/backbone.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from collections import OrderedDict
from torch import nn
from .. import registry
from lib.scene_parser.rcnn.modeling.make_layers import conv_with_kaiming_uniform
from . import fpn as fpn_module
from . import resnet
@registry.BACKBONES.register(... | 2,868 | 33.566265 | 81 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/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 |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/backbone/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from .backbone import build_backbone
from . import fbnet
| 129 | 31.5 | 71 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/backbone/fbnet_modeldef.py | from __future__ import absolute_import, division, print_function, unicode_literals
def add_archs(archs):
global MODEL_ARCH
for x in archs:
assert x not in MODEL_ARCH, "Duplicated model name {} existed".format(x)
MODEL_ARCH[x] = archs[x]
MODEL_ARCH = {
"default": {
"block_op_type"... | 5,985 | 26.333333 | 82 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/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 lib.scene_parser.rcnn.structures.image_list import to_image_list
from ..backbone import build_backbone
from ..rpn.rpn import build_rpn
from ..roi_heads.roi... | 2,233 | 33.369231 | 87 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/detector/__init__.py | 0 | 0 | 0 | py | |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/inference.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torch.nn.functional as F
from torch import nn
from lib.scene_parser.rcnn.structures.bounding_box import BoxList
from lib.scene_parser.rcnn.structures.boxlist_ops import boxlist_nms
from lib.scene_parser.rcnn.structures.boxlist_... | 6,176 | 37.12963 | 88 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/roi_relation_feature_extractors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from torch.nn import functional as F
from lib.scene_parser.rcnn.modeling import registry
from lib.scene_parser.rcnn.modeling.backbone import resnet
from lib.scene_parser.rcnn.modeling.poolers import Pooler
from li... | 6,349 | 36.797619 | 95 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/relation_heads.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Relation head for predicting relationship between object pairs.
# Written by Jianwei Yang (jw2yang@gatech.edu).
import numpy as np
import torch
from torch import nn
from lib.scene_parser.rcnn.structures.bounding_box_pair import BoxPairList
from l... | 11,698 | 47.745833 | 136 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/roi_relation_box_predictors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from lib.scene_parser.rcnn.modeling import registry
from torch import nn
@registry.ROI_RELATION_BOX_PREDICTOR.register("FastRCNNPredictor")
class FastRCNNPredictor(nn.Module):
def __init__(self, config, in_channels):
super(FastRCNNPre... | 2,371 | 36.0625 | 89 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch.nn import functional as F
from lib.scene_parser.rcnn.layers import smooth_l1_loss
from lib.scene_parser.rcnn.modeling.box_coder import BoxCoder
from lib.scene_parser.rcnn.modeling.matcher import Matcher
from lib.scene_parse... | 13,391 | 41.514286 | 136 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/roi_relation_predictors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from lib.scene_parser.rcnn.modeling import registry
import torch
from torch import nn
@registry.ROI_RELATION_PREDICTOR.register("FastRCNNRelationPredictor")
class FastRCNNPredictor(nn.Module):
def __init__(self, config, in_channels):
... | 2,129 | 35.724138 | 94 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/__init__.py | 0 | 0 | 0 | py | |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/roi_relation_box_feature_extractors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from torch.nn import functional as F
from lib.scene_parser.rcnn.modeling import registry
from lib.scene_parser.rcnn.modeling.backbone import resnet
from lib.scene_parser.rcnn.modeling.poolers import Pooler
from li... | 5,464 | 34.953947 | 90 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/sparse_targets.py | import torch
import torch.nn as nn
class FrequencyBias(nn.Module):
"""
The goal of this is to provide a simplified way of computing
P(predicate | obj1, obj2, img).
"""
def __init__(self, pred_dist):
# pred_dist: [num_classes, num_classes, num_preds] numpy array
super(FrequencyBias... | 3,594 | 35.313131 | 87 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/imp/imp.py | # Scene Graph Generation by Iterative Message Passing
# Reimplemented by Jianwei Yang (jw2yang@gatech.edu)
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
from ..roi_relation_feature_extractors import make_roi_relation_feature_extractor
from ..roi_re... | 6,009 | 49.083333 | 129 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/imp/__init__.py | 0 | 0 | 0 | py | |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/baseline/__init__.py | 0 | 0 | 0 | py | |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/baseline/baseline.py | # Scene Graph Generation with baseline (vanilla) model
# Reimnplemetned by Jianwei Yang (jw2yang@gatech.edu)
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn import Parameter
from ..roi_relation_feature_extractors import make_roi_rel... | 1,668 | 41.794872 | 99 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/grcnn/__init__.py | 0 | 0 | 0 | py | |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/grcnn/grcnn.py | # Graph R-CNN for scene graph generation
# Reimnplemetned by Jianwei Yang (jw2yang@gatech.edu)
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
from ..roi_relation_feature_extractors import make_roi_relation_feature_extractor
from ..roi_relation_box_f... | 7,173 | 49.521127 | 106 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/grcnn/agcn/agcn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math
import time
def normal_init(m, mean, stddev, truncated=False):
if truncated:
m.weight.data.normal_().fmod_(2).mul_(stddev).add_(mean) # not a perfect approximation
else:
m.weight.data.normal_(mean,... | 3,579 | 43.75 | 110 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/grcnn/agcn/__init__.py | 0 | 0 | 0 | py | |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/reldn/semantic.py | 0 | 0 | 0 | py | |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/reldn/reldn.py | # Scene Graph Generation by Iterative Message Passing
# Reimnplemetned by Jianwei Yang (jw2yang@gatech.edu)
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
from ..roi_relation_feature_extractors import make_roi_relation_feature_extractor
from ..roi_r... | 6,871 | 47.055944 | 133 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/reldn/__init__.py | 0 | 0 | 0 | py | |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/reldn/spatial.py | import torch
import torch.nn as nn
import numpy as np
from lib.scene_parser.rcnn.utils.boxes import bbox_transform_inv, boxes_union
class SpatialFeature(nn.Module):
def __init__(self, cfg, dim):
super(SpatialFeature, self).__init__()
self.model = nn.Sequential(
nn.Linear(28, 64), nn.Lea... | 2,141 | 41.84 | 142 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/reldn/visual.py | import torch
import torch.nn as nn
class VisualFeature(nn.Module):
def __init__(self, dim):
self.subj_branch = nn.Sequential(nn.Linear())
def forward(self, subj_feat, obj_feat, rel_feat):
pass
def build_visual_feature(cfg, in_channels):
return VisualFeature(cfg, in_channels)
| 307 | 22.692308 | 53 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/msdn/msdn_base.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn import Parameter
import pdb
class Message_Passing_Unit_v2(nn.Module):
def __init__(self, fea_size, filter_size = 128):
super(Message_Passing_Unit_v2, self).__init__()
self.w = n... | 4,594 | 37.291667 | 106 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/msdn/__init__.py | 0 | 0 | 0 | py | |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/msdn/msdn.py | # MSDN for scene graph generation
# Reimnplemetned by Jianwei Yang (jw2yang@gatech.edu)
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn import Parameter
from .msdn_base import MSDN_BASE
from ..roi_relation_feature_extractors import... | 4,365 | 42.227723 | 123 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/relpn/utils.py | import torch
def box_pos_encoder(bboxes, width, height):
"""
bounding box encoding
"""
bboxes_enc = bboxes.clone()
dim0 = bboxes_enc[:, 0] / width
dim1 = bboxes_enc[:, 1] / height
dim2 = bboxes_enc[:, 2] / width
dim3 = bboxes_enc[:, 3] / height
dim4 = (bboxes_enc[:, 2] - bboxes_enc... | 538 | 30.705882 | 105 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/relpn/relpn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from lib.scene_parser.rcnn.modeling.box_coder import BoxCoder
from lib.scene_parser.rcnn.modeling.matcher import Matcher
from lib.scene_parser.rcnn.modeling.pair_matcher import PairMatcher
from lib.scene_parser.rcnn.structures.boxlist_ops import boxlist... | 17,740 | 45.080519 | 142 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/relpn/relationshipness.py | import torch
import torch.nn as nn
from .utils import box_pos_encoder
from ..auxilary.multi_head_att import MultiHeadAttention
class Relationshipness(nn.Module):
"""
compute relationshipness between subjects and objects
"""
def __init__(self, dim, pos_encoding=False):
super(Relationshipness, se... | 3,681 | 31.298246 | 86 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/relpn/__init__.py | 0 | 0 | 0 | py | |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/auxilary/multi_head_att.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask == 0, -1e9)
s... | 1,719 | 28.152542 | 78 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/auxilary/__init__.py | 0 | 0 | 0 | py | |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/rpn/inference.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from lib.scene_parser.rcnn.modeling.box_coder import BoxCoder
from lib.scene_parser.rcnn.structures.bounding_box import BoxList
from lib.scene_parser.rcnn.structures.boxlist_ops import cat_boxlist
from lib.scene_parser.rcnn.structures... | 7,773 | 36.555556 | 87 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/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 lib.scene_parser.rcnn.structures.bounding_box import BoxList
class BufferList(nn.Module):
"""
Similar to nn.ParameterList, but for buffers
"""
def __init__(s... | 9,951 | 33.317241 | 88 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/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... | 5,780 | 35.588608 | 87 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/rpn/utils.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Utility functions minipulating the prediction layers
"""
from ..utils import cat
import torch
def permute_and_flatten(layer, N, A, C, H, W):
layer = layer.view(N, -1, C, H, W)
layer = layer.permute(0, 3, 4, 1, 2)
layer = layer.re... | 1,679 | 35.521739 | 80 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/rpn/rpn.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torch.nn.functional as F
from torch import nn
from lib.scene_parser.rcnn.modeling import registry
from lib.scene_parser.rcnn.modeling.box_coder import BoxCoder
from lib.scene_parser.rcnn.modeling.rpn.retinanet.retinanet import ... | 7,624 | 35.658654 | 88 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/rpn/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# from .rpn import build_rpn
| 101 | 33 | 71 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/rpn/retinanet/inference.py | import torch
from ..inference import RPNPostProcessor
from ..utils import permute_and_flatten
from lib.scene_parser.rcnn.modeling.box_coder import BoxCoder
from lib.scene_parser.rcnn.modeling.utils import cat
from lib.scene_parser.rcnn.structures.bounding_box import BoxList
from lib.scene_parser.rcnn.structures.boxli... | 6,937 | 34.579487 | 79 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/rpn/retinanet/loss.py | """
This file contains specific functions for computing losses on the RetinaNet
file
"""
import torch
from torch.nn import functional as F
from ..utils import concat_box_prediction_layers
from lib.scene_parser.rcnn.layers import smooth_l1_loss
from lib.scene_parser.rcnn.layers import SigmoidFocalLoss
from lib.scene_... | 3,505 | 31.462963 | 83 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/rpn/retinanet/retinanet.py | import math
import torch
import torch.nn.functional as F
from torch import nn
from .inference import make_retinanet_postprocessor
from .loss import make_retinanet_loss_evaluator
from ..anchor_generator import make_anchor_generator_retinanet
from lib.scene_parser.rcnn.modeling.box_coder import BoxCoder
class Retina... | 5,303 | 33.666667 | 88 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/rpn/retinanet/__init__.py | 0 | 0 | 0 | py | |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/roi_heads/roi_heads.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .box_head.box_head import build_roi_box_head
class CombinedROIHeads(torch.nn.ModuleDict):
"""
Combines a set of individual heads (for box prediction or masks) into a single
head.
"""
def __init__(self, cfg,... | 1,436 | 33.214286 | 93 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/roi_heads/__init__.py | 0 | 0 | 0 | py | |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/roi_heads/box_head/inference.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torch.nn.functional as F
from torch import nn
from lib.scene_parser.rcnn.structures.bounding_box import BoxList
from lib.scene_parser.rcnn.structures.boxlist_ops import boxlist_nms
from lib.scene_parser.rcnn.structures.boxlist_... | 12,133 | 41.575439 | 108 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/roi_heads/box_head/roi_box_feature_extractors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from torch.nn import functional as F
from lib.scene_parser.rcnn.modeling import registry
from lib.scene_parser.rcnn.modeling.backbone import resnet
from lib.scene_parser.rcnn.modeling.poolers import Pooler
from li... | 5,419 | 34.657895 | 81 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/roi_heads/box_head/box_head.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from .roi_box_feature_extractors import make_roi_box_feature_extractor
from .roi_box_predictors import make_roi_box_predictor
from .inference import make_roi_box_post_processor
from .loss import make_roi_box_loss_... | 3,495 | 39.651163 | 96 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/roi_heads/box_head/loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch.nn import functional as F
from lib.scene_parser.rcnn.layers import smooth_l1_loss
from lib.scene_parser.rcnn.modeling.box_coder import BoxCoder
from lib.scene_parser.rcnn.modeling.matcher import Matcher
from lib.scene_parse... | 8,001 | 35.538813 | 102 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/roi_heads/box_head/roi_box_predictors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from lib.scene_parser.rcnn.modeling import registry
from torch import nn
@registry.ROI_BOX_PREDICTOR.register("FastRCNNPredictor")
class FastRCNNPredictor(nn.Module):
def __init__(self, config, in_channels):
super(FastRCNNPredictor, s... | 2,298 | 35.492063 | 87 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/roi_heads/box_head/__init__.py | 0 | 0 | 0 | py | |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/structures/image_list.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from __future__ import division
import torch
class ImageList(object):
"""
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,
and storing in... | 2,485 | 33.054795 | 87 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/structures/segmentation_mask.py | import cv2
import copy
import torch
import numpy as np
from maskrcnn_benchmark.layers.misc import interpolate
from maskrcnn_benchmark.utils import cv2_util
import pycocotools.mask as mask_utils
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
""" ABSTRACT
Segmentations come in either:
1) Binary masks
2) Polygons
... | 18,637 | 31.357639 | 94 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/structures/bounding_box.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
class BoxList(object):
"""
This class represents a set of bounding boxes.
The bounding boxes are represented as a Nx4 Tensor.
In order to uniquely determine the bou... | 9,645 | 35.127341 | 92 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/structures/bounding_box_pair.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .bounding_box import BoxList
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
class BoxPairList(object):
"""
This class represents a set of bounding boxes.
The bounding boxes are represented as a Nx4 Tensor.
I... | 10,466 | 35.34375 | 97 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/structures/boxlist_ops.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .bounding_box import BoxList
from ..layers import nms as _box_nms
def boxlist_nms(boxlist, nms_thresh, max_proposals=-1, score_field="scores"):
"""
Performs non-maximum suppression on a boxlist, with scores specified
... | 3,703 | 27.492308 | 97 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/structures/__init__.py | 0 | 0 | 0 | py | |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/structures/keypoint.py | import torch
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
class Keypoints(object):
def __init__(self, keypoints, size, mode=None):
# FIXME remove check once we have better integration with device
# in my version this would consistently return a CPU tensor
device = keypoints.device ... | 6,555 | 33.687831 | 97 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/utils/box.py | import numpy as np
import torch
def bbox_overlaps(anchors, gt_boxes):
"""
anchors: (N, 4) ndarray of float
gt_boxes: (K, 4) ndarray of float
overlaps: (N, K) ndarray of overlap between boxes and query_boxes
"""
N = anchors.size(0)
K = gt_boxes.size(0)
gt_boxes_area = ((gt_boxes[:,2] - ... | 998 | 28.382353 | 69 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/utils/pytorch_misc.py | """
Miscellaneous functions that might be useful for pytorch
"""
import h5py
import numpy as np
import torch
from torch.autograd import Variable
import os
import dill as pkl
from itertools import tee
from torch import nn
def optimistic_restore(network, state_dict):
mismatch = False
own_state = network.state_d... | 14,457 | 30.430435 | 110 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/utils/logger.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import logging
import os
import sys
# def setup_logger(name, save_dir, distributed_rank, filename="log.txt"):
def setup_logger(name, save_dir, filename="log.txt"):
logger = logging.getLogger(name)
logger.setLevel(logging.DEBUG)
# don'... | 843 | 30.259259 | 84 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/utils/__init__.py | 0 | 0 | 0 | py | |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/utils/miscellaneous.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import errno
import json
import logging
import os
# from .comm import is_main_process
def mkdir(path):
try:
os.makedirs(path)
except OSError as e:
if e.errno != errno.EEXIST:
raise
def save_labels(dataset_lis... | 1,157 | 27.95 | 116 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/voc_eval.py | # --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Bharath Hariharan
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import pr... | 6,658 | 30.559242 | 76 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/vg_hdf5.py | import os
from collections import defaultdict
import numpy as np
import copy
import pickle
import scipy.sparse
from PIL import Image
import h5py, json
import torch
from pycocotools.coco import COCO
from torch.utils.data import Dataset
from lib.scene_parser.rcnn.structures.bounding_box import BoxList
from lib.utils.box ... | 12,110 | 40.618557 | 129 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/factory.py | from .vg_hdf5 import vg_hdf5
def build_data_loader(cfg, split="train", num_im=0):
if cfg.DATASET.NAME == "vg_bm":
return vg_hdf5(cfg, split=split, num_im=num_im)
else:
raise NotImplementedError("Unsupported dataset {}.".format(dataset))
# cfg.data_dir = "data/vg"
| 297 | 32.111111 | 76 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/vg_eval.py | # --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Bharath Hariharan
# --------------------------------------------------------
import xml.etree.ElementTree as ET
import os
import numpy as np
from .voc_eval import voc_ap
d... | 4,124 | 32.536585 | 111 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/collate_batch.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from lib.scene_parser.rcnn.structures.image_list import to_image_list
class BatchCollator(object):
"""
From a list of samples from the dataset,
returns the batched images and targets.
This should be passed to the DataLoader
"""... | 943 | 29.451613 | 72 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/build.py | import copy
import bisect
import torch
from torch.utils import data
from .vg_hdf5 import vg_hdf5
from . import samplers
from .transforms import build_transforms
from .collate_batch import BatchCollator
from lib.scene_parser.rcnn.utils.comm import get_world_size, get_rank
def make_data_sampler(dataset, shuffle, distrib... | 3,399 | 40.463415 | 108 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/evaluation/__init__.py | from .coco import coco_evaluation
from .voc import voc_evaluation
from .sg import sg_evaluation
from ..vg_hdf5 import vg_hdf5
def evaluate(dataset, predictions, output_folder, **kwargs):
"""evaluate dataset using different methods based on dataset type.
Args:
dataset: Dataset object
prediction... | 1,908 | 37.18 | 122 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/evaluation/gqa_coco/gqa_coco_eval.py | import logging
import tempfile
import os
import torch
from collections import OrderedDict
from tqdm import tqdm
from lib.scene_parser.mask_rcnn.modeling.roi_heads.mask_head.inference import Masker
from lib.scene_parser.mask_rcnn.structures.bounding_box import BoxList
from lib.scene_parser.mask_rcnn.structures.boxlist_... | 10,982 | 34.201923 | 91 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/evaluation/gqa_coco/__init__.py | from .gqa_coco_eval import do_gqa_coco_evaluation
def gqa_coco_evaluation(
dataset,
predictions,
output_folder,
box_only,
iou_types,
expected_results,
expected_results_sigma_tol,
):
return do_gqa_coco_evaluation(
dataset=dataset,
predictions=predictions,
box_onl... | 510 | 22.227273 | 62 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/evaluation/gqa_voc/gqa_voc_eval.py | # A modification version from chainercv repository.
# (See https://github.com/chainer/chainercv/blob/master/chainercv/evaluations/eval_detection_voc.py)
from __future__ import division
import os
from collections import defaultdict
import numpy as np
from lib.scene_parser.mask_rcnn.structures.bounding_box import BoxLis... | 8,143 | 36.87907 | 100 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/evaluation/gqa_voc/__init__.py | import logging
from .gqa_voc_eval import do_gqa_voc_evaluation
def gqa_voc_evaluation(dataset, predictions, output_folder, box_only, **_):
logger = logging.getLogger("graph_reasoning_machine.inference")
if box_only:
logger.warning("voc evaluation doesn't support box_only, ignored.")
logger.info("... | 526 | 30 | 75 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/evaluation/sg/sg_eval.py | import numpy as np
import torch
from .evaluator import BasicSceneGraphEvaluator
def do_sg_evaluation(dataset, predictions, predictions_pred, output_folder, logger):
"""
scene graph generation evaluation
"""
evaluator = BasicSceneGraphEvaluator.all_modes(multiple_preds=False)
top_Ns = [20, 50, 100... | 13,320 | 39.244713 | 108 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/evaluation/sg/evaluator.py | """
Adapted from Danfei Xu. In particular, slow code was removed
"""
import torch
import numpy as np
from functools import reduce
from lib.utils.pytorch_misc import intersect_2d, argsort_desc
from lib.utils.box import bbox_overlaps
MODES = ('sgdet', 'sgcls', 'predcls')
np.set_printoptions(precision=3)
class BasicSce... | 12,230 | 40.744027 | 139 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/evaluation/sg/__init__.py | import logging
from .sg_eval import do_sg_evaluation
def sg_evaluation(dataset, predictions, predictions_pred, output_folder, box_only, **_):
logger = logging.getLogger("scene_graph_generation.inference")
logger.info("performing scene graph evaluation.")
return do_sg_evaluation(
dataset=dataset,
... | 462 | 27.9375 | 88 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/evaluation/voc/voc_eval.py | # A modification version from chainercv repository.
# (See https://github.com/chainer/chainercv/blob/master/chainercv/evaluations/eval_detection_voc.py)
from __future__ import division
import os
from collections import defaultdict
import numpy as np
from lib.scene_parser.rcnn.structures.bounding_box import BoxList
fro... | 8,105 | 36.702326 | 100 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/evaluation/voc/__init__.py | import logging
from .voc_eval import do_voc_evaluation
def voc_evaluation(dataset, predictions, output_folder, box_only, **_):
logger = logging.getLogger("scene_graph_generation.inference")
if box_only:
logger.warning("voc evaluation doesn't support box_only, ignored.")
logger.info("performing vo... | 509 | 29 | 75 | py |
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