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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D2Det | D2Det-master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
gen_attention=dict(
spatial_range=-1, n... | 5,576 | 29.983333 | 79 | py |
D2Det | D2Det-master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
gen_attention=dict(
spatial_range=-1, n... | 5,707 | 30.362637 | 79 | py |
D2Det | D2Det-master/configs/D2Det/D2Det_detection_r101_fpn_2x.py | # model settings
model = dict(
type='D2Det',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, ... | 5,684 | 28.609375 | 77 | py |
D2Det | D2Det-master/configs/D2Det/D2Det_instance_r101_fpn_2x.py | # model settings
model = dict(
type='D2Det',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, ... | 5,731 | 28.699482 | 77 | py |
D2Det | D2Det-master/configs/D2Det/D2Det_detection_r101_fpn_dcn_2x.py | # model settings
model = dict(
type='D2Det',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
dcn=dict(type='DCNv2', deformable_groups=1, fallback_o... | 5,817 | 28.989691 | 78 | py |
D2Det | D2Det-master/configs/D2Det/D2Det_detection_r50_fpn_2x.py | # model settings
model = dict(
type='D2Det',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 51... | 5,681 | 28.59375 | 77 | py |
D2Det | D2Det-master/configs/foveabox/fovea_align_gn_r101_fpn_4gpu_2x.py | # model settings
model = dict(
type='FOVEA',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, ... | 3,678 | 29.404959 | 78 | py |
D2Det | D2Det-master/configs/foveabox/fovea_align_gn_r50_fpn_4gpu_2x.py | # model settings
model = dict(
type='FOVEA',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 51... | 3,675 | 29.380165 | 78 | py |
D2Det | D2Det-master/configs/foveabox/fovea_align_gn_ms_r101_fpn_4gpu_2x.py | # model settings
model = dict(
type='FOVEA',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, ... | 3,773 | 28.952381 | 78 | py |
D2Det | D2Det-master/configs/foveabox/fovea_align_gn_ms_r50_fpn_4gpu_2x.py | # model settings
model = dict(
type='FOVEA',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 51... | 3,770 | 28.928571 | 78 | py |
D2Det | D2Det-master/configs/foveabox/fovea_r50_fpn_4gpu_1x.py | # model settings
model = dict(
type='FOVEA',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 51... | 3,616 | 28.892562 | 78 | py |
D2Det | D2Det-master/configs/double_heads/dh_faster_rcnn_r50_fpn_1x.py | # model settings
model = dict(
type='DoubleHeadRCNN',
pretrained='modelzoo://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[2... | 5,464 | 29.530726 | 78 | py |
D2Det | D2Det-master/configs/wider_face/ssd300_wider_face.py | # model settings
input_size = 300
model = dict(
type='SingleStageDetector',
pretrained='open-mmlab://vgg16_caffe',
backbone=dict(
type='SSDVGG',
input_size=input_size,
depth=16,
with_last_pool=False,
ceil_mode=True,
out_indices=(3, 4),
out_feature_indi... | 3,904 | 27.713235 | 79 | py |
D2Det | D2Det-master/configs/albu_example/mask_rcnn_r50_fpn_1x.py | # model settings
model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256,... | 7,442 | 28.891566 | 78 | py |
D2Det | D2Det-master/configs/grid_rcnn/grid_rcnn_gn_head_r50_fpn_2x.py | # model settings
model = dict(
type='GridRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256,... | 5,630 | 29.112299 | 78 | py |
D2Det | D2Det-master/configs/grid_rcnn/grid_rcnn_gn_head_x101_32x4d_fpn_2x.py | # model settings
model = dict(
type='GridRCNN',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=d... | 5,687 | 29.095238 | 78 | py |
D2Det | D2Det-master/configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=[
dict(
type='FPN',
... | 5,864 | 29.231959 | 78 | py |
D2Det | D2Det-master/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=[
dict(
type='FPN',
... | 5,867 | 29.247423 | 78 | py |
D2Det | D2Det-master/configs/libra_rcnn/libra_fast_rcnn_r50_fpn_1x.py | # model settings
model = dict(
type='FastRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=[
dict(
type='FPN',
... | 4,903 | 30.63871 | 79 | py |
D2Det | D2Det-master/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck... | 5,921 | 29.214286 | 78 | py |
D2Det | D2Det-master/configs/libra_rcnn/libra_retinanet_r50_fpn_1x.py | # model settings
model = dict(
type='RetinaNet',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=[
dict(
type='FPN',
... | 4,134 | 27.715278 | 77 | py |
D2Det | D2Det-master/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x.py | # model settings
model = dict(
type='RetinaNet',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256... | 3,729 | 28.603175 | 77 | py |
D2Det | D2Det-master/configs/free_anchor/retinanet_free_anchor_x101-32x4d_fpn_1x.py | # model settings
model = dict(
type='RetinaNet',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=... | 3,786 | 28.585938 | 77 | py |
D2Det | D2Det-master/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x.py | # model settings
model = dict(
type='RetinaNet',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[2... | 3,732 | 28.626984 | 77 | py |
D2Det | D2Det-master/configs/scratch/scratch_mask_rcnn_r50_fpn_gn_6x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='MaskRCNN',
pretrained=None,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1,
style='pytorch',
zero_init_... | 6,094 | 29.024631 | 78 | py |
D2Det | D2Det-master/configs/scratch/scratch_faster_rcnn_r50_fpn_gn_6x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='FasterRCNN',
pretrained=None,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1,
style='pytorch',
zero_ini... | 5,545 | 28.817204 | 78 | py |
D2Det | D2Det-master/configs/pascal_voc/ssd300_voc.py | # model settings
input_size = 300
model = dict(
type='SingleStageDetector',
pretrained='open-mmlab://vgg16_caffe',
backbone=dict(
type='SSDVGG',
input_size=input_size,
depth=16,
with_last_pool=False,
ceil_mode=True,
out_indices=(3, 4),
out_feature_indi... | 4,105 | 28.539568 | 79 | py |
D2Det | D2Det-master/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[25... | 5,560 | 30.418079 | 78 | py |
D2Det | D2Det-master/configs/pascal_voc/ssd512_voc.py | # model settings
input_size = 512
model = dict(
type='SingleStageDetector',
pretrained='open-mmlab://vgg16_caffe',
backbone=dict(
type='SSDVGG',
input_size=input_size,
depth=16,
with_last_pool=False,
ceil_mode=True,
out_indices=(3, 4),
out_feature_indi... | 4,124 | 28.676259 | 79 | py |
D2Det | D2Det-master/configs/gcnet/mask_rcnn_r50_fpn_sbn_1x.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
n... | 5,907 | 29.453608 | 78 | py |
D2Det | D2Det-master/configs/gcnet/mask_rcnn_r16_gcb_c3-c5_r50_fpn_1x.py | # model settings
model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
gcb=dict(ratio=1. / 16., ),
stage_with_gcb=(F... | 5,899 | 29.729167 | 78 | py |
D2Det | D2Det-master/configs/gcnet/mask_rcnn_r4_gcb_c3-c5_r50_fpn_syncbn_1x.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
g... | 6,008 | 29.658163 | 78 | py |
D2Det | D2Det-master/configs/gcnet/mask_rcnn_r4_gcb_c3-c5_r50_fpn_1x.py | # model settings
model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
gcb=dict(ratio=1. / 4., ),
stage_with_gcb=(Fa... | 5,897 | 29.71875 | 78 | py |
D2Det | D2Det-master/configs/gcnet/mask_rcnn_r16_gcb_c3-c5_r50_fpn_syncbn_1x.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
g... | 6,010 | 29.668367 | 78 | py |
D2Det | D2Det-master/configs/instaboost/ssd300_coco_instaboost_4x.py | # model settings
input_size = 300
model = dict(
type='SingleStageDetector',
pretrained='open-mmlab://vgg16_caffe',
backbone=dict(
type='SSDVGG',
input_size=input_size,
depth=16,
with_last_pool=False,
ceil_mode=True,
out_indices=(3, 4),
out_feature_indi... | 4,276 | 28.095238 | 79 | py |
D2Det | D2Det-master/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
... | 8,291 | 30.290566 | 78 | py |
D2Det | D2Det-master/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x.py | # model settings
model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256,... | 6,086 | 29.283582 | 78 | py |
D2Det | D2Det-master/configs/atss/atss_r50_fpn_1x.py | # model settings
model = dict(
type='ATSS',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 512... | 3,844 | 28.806202 | 77 | py |
D2Det | D2Det-master/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws_2x.py | # model settings
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://jhu/resnext101_32x4d_gn_ws',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_st... | 6,246 | 29.622549 | 78 | py |
D2Det | D2Det-master/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws_2x.py | # model settings
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://jhu/resnet50_gn_ws',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
f... | 6,188 | 29.638614 | 78 | py |
D2Det | D2Det-master/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws_20_23_24e.py | # model settings
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://jhu/resnet50_gn_ws',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
f... | 6,195 | 29.673267 | 78 | py |
D2Det | D2Det-master/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws_1x.py | # model settings
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://jhu/resnet50_gn_ws',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
... | 5,589 | 29.546448 | 78 | py |
D2Det | D2Det-master/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x.py | # model settings
model = dict(
type='RPN',
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
... | 4,844 | 29.471698 | 75 | py |
D2Det | D2Det-master/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x.py | # model settings
model = dict(
type='FastRCNN',
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
... | 4,485 | 31.507246 | 78 | py |
D2Det | D2Det-master/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='RPN',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
... | 4,815 | 29.289308 | 77 | py |
D2Det | D2Det-master/configs/guided_anchoring/ga_rpn_r101_caffe_rpn_1x.py | # model settings
model = dict(
type='RPN',
pretrained='open-mmlab://resnet101_caffe',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
... | 4,847 | 29.490566 | 75 | py |
D2Det | D2Det-master/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x.py | # model settings
model = dict(
type='RetinaNet',
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
... | 4,679 | 28.620253 | 75 | py |
D2Det | D2Det-master/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='RetinaNet',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=... | 4,650 | 28.436709 | 77 | py |
D2Det | D2Det-master/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
... | 6,178 | 29.741294 | 76 | py |
D2Det | D2Det-master/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck... | 6,149 | 29.597015 | 77 | py |
D2Det | D2Det-master/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py | # model settings
model = dict(
type='FasterRCNN',
pretrained=None,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],... | 5,883 | 30.634409 | 136 | py |
D2Det | D2Det-master/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py | # model settings
model = dict(
type='MaskRCNN',
pretrained=None,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
... | 6,306 | 30.535 | 134 | py |
D2Det | D2Det-master/configs/gn/mask_rcnn_r101_fpn_gn_2x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://detectron/resnet101_gn',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
s... | 6,051 | 29.565657 | 78 | py |
D2Det | D2Det-master/configs/gn/mask_rcnn_r50_fpn_gn_2x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://detectron/resnet50_gn',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
sty... | 6,048 | 29.550505 | 78 | py |
D2Det | D2Det-master/configs/gn/mask_rcnn_r50_fpn_gn_contrib_2x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://contrib/resnet50_gn',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style... | 6,056 | 29.590909 | 78 | py |
D2Det | D2Det-master/docs/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 2,343 | 32.014085 | 79 | py |
D2Det | D2Det-master/mmdet/apis/inference.py | import warnings
import matplotlib.pyplot as plt
import mmcv
import numpy as np
import pycocotools.mask as maskUtils
import torch
from mmcv.parallel import collate, scatter
from mmcv.runner import load_checkpoint
from mmdet.core import get_classes
from mmdet.datasets.pipelines import Compose
from mmdet.models import b... | 7,346 | 33.819905 | 108 | py |
D2Det | D2Det-master/mmdet/apis/test.py | import os.path as osp
import pickle
import shutil
import tempfile
import mmcv
import torch
import torch.distributed as dist
from mmcv.runner import get_dist_info
def single_gpu_test(model, data_loader, show=False):
model.eval()
results = []
dataset = data_loader.dataset
prog_bar = mmcv.ProgressBar(le... | 5,100 | 33.466216 | 79 | py |
D2Det | D2Det-master/mmdet/apis/train.py | import random
from collections import OrderedDict
import numpy as np
import torch
import torch.distributed as dist
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import DistSamplerSeedHook, Runner
from mmdet.core import (DistEvalHook, DistOptimizerHook, Fp16OptimizerHook,
... | 7,499 | 31.051282 | 77 | py |
D2Det | D2Det-master/mmdet/core/evaluation/eval_hooks.py | import os.path as osp
from mmcv.runner import Hook
from torch.utils.data import DataLoader
class DistEvalHook(Hook):
"""Distributed evaluation hook.
Attributes:
dataloader (DataLoader): A PyTorch dataloader.
interval (int): Evaluation interval (by epochs). Default: 1.
tmpdir (str | N... | 1,759 | 32.846154 | 77 | py |
D2Det | D2Det-master/mmdet/core/post_processing/merge_augs.py | import numpy as np
import torch
from mmdet.ops import nms
from ..bbox import bbox_mapping_back
def merge_aug_proposals(aug_proposals, img_metas, rpn_test_cfg):
"""Merge augmented proposals (multiscale, flip, etc.)
Args:
aug_proposals (list[Tensor]): proposals from different testing
schem... | 3,573 | 34.039216 | 78 | py |
D2Det | D2Det-master/mmdet/core/post_processing/bbox_nms.py | import torch
from mmdet.ops.nms import nms_wrapper
def multiclass_nms(multi_bboxes,
multi_scores,
score_thr,
nms_cfg,
max_num=-1,
score_factors=None):
"""NMS for multi-class bboxes.
Args:
multi_bboxes (Ten... | 4,693 | 35.387597 | 78 | py |
D2Det | D2Det-master/mmdet/core/mask/mask_target.py | import mmcv
import numpy as np
import torch
from torch.nn.modules.utils import _pair
def mask_target(pos_proposals_list, pos_assigned_gt_inds_list, gt_masks_list,
cfg):
cfg_list = [cfg for _ in range(len(pos_proposals_list))]
mask_targets = map(mask_target_single, pos_proposals_list,
... | 1,700 | 39.5 | 79 | py |
D2Det | D2Det-master/mmdet/core/fp16/hooks.py | import copy
import torch
import torch.nn as nn
from mmcv.runner import OptimizerHook
from ..utils.dist_utils import allreduce_grads
from .utils import cast_tensor_type
class Fp16OptimizerHook(OptimizerHook):
"""FP16 optimizer hook.
The steps of fp16 optimizer is as follows.
1. Scale the loss value.
... | 4,564 | 34.387597 | 79 | py |
D2Det | D2Det-master/mmdet/core/fp16/utils.py | from collections import abc
import numpy as np
import torch
def cast_tensor_type(inputs, src_type, dst_type):
if isinstance(inputs, torch.Tensor):
return inputs.to(dst_type)
elif isinstance(inputs, str):
return inputs
elif isinstance(inputs, np.ndarray):
return inputs
elif isi... | 664 | 26.708333 | 74 | py |
D2Det | D2Det-master/mmdet/core/fp16/decorators.py | import functools
from inspect import getfullargspec
import torch
from .utils import cast_tensor_type
def auto_fp16(apply_to=None, out_fp32=False):
"""Decorator to enable fp16 training automatically.
This decorator is useful when you write custom modules and want to support
mixed precision training. If ... | 6,211 | 37.583851 | 79 | py |
D2Det | D2Det-master/mmdet/core/bbox/bbox_target.py | import torch
from ..utils import multi_apply
from .transforms import bbox2delta
def bbox_target(pos_bboxes_list,
neg_bboxes_list,
pos_gt_bboxes_list,
pos_gt_labels_list,
cfg,
reg_classes=1,
target_means=[.0, .0, .0, .0],
... | 2,717 | 35.72973 | 78 | py |
D2Det | D2Det-master/mmdet/core/bbox/demodata.py | import numpy as np
import torch
def ensure_rng(rng=None):
"""
Simple version of the ``kwarray.ensure_rng``
Args:
rng (int | numpy.random.RandomState | None):
if None, then defaults to the global rng. Otherwise this can be an
integer or a RandomState class
Returns:
... | 1,758 | 25.651515 | 101 | py |
D2Det | D2Det-master/mmdet/core/bbox/geometry.py | import torch
def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False):
"""Calculate overlap between two set of bboxes.
If ``is_aligned`` is ``False``, then calculate the ious between each bbox
of bboxes1 and bboxes2, otherwise the ious between each aligned pair of
bboxes1 and bboxes2.
A... | 3,077 | 33.58427 | 79 | py |
D2Det | D2Det-master/mmdet/core/bbox/transforms.py | import mmcv
import numpy as np
import torch
def bbox2delta(proposals, gt, means=[0, 0, 0, 0], stds=[1, 1, 1, 1]):
assert proposals.size() == gt.size()
proposals = proposals.float()
gt = gt.float()
px = (proposals[..., 0] + proposals[..., 2]) * 0.5
py = (proposals[..., 1] + proposals[..., 3]) * 0.... | 8,734 | 33.525692 | 79 | py |
D2Det | D2Det-master/mmdet/core/bbox/assigners/assign_result.py | import torch
from mmdet.utils import util_mixins
class AssignResult(util_mixins.NiceRepr):
"""
Stores assignments between predicted and truth boxes.
Attributes:
num_gts (int): the number of truth boxes considered when computing this
assignment
gt_inds (LongTensor): for each ... | 7,027 | 35.414508 | 79 | py |
D2Det | D2Det-master/mmdet/core/bbox/assigners/atss_assigner.py | import torch
from ..geometry import bbox_overlaps
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
class ATSSAssigner(BaseAssigner):
"""Assign a corresponding gt bbox or background to each bbox.
Each proposals will be assigned with `0` or a positive integer
indicating the ... | 6,818 | 41.61875 | 87 | py |
D2Det | D2Det-master/mmdet/core/bbox/assigners/point_assigner.py | import torch
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
class PointAssigner(BaseAssigner):
"""Assign a corresponding gt bbox or background to each point.
Each proposals will be assigned with `0`, or a positive integer
indicating the ground truth index.
- 0: nega... | 5,742 | 42.839695 | 79 | py |
D2Det | D2Det-master/mmdet/core/bbox/assigners/approx_max_iou_assigner.py | import torch
from ..geometry import bbox_overlaps
from .max_iou_assigner import MaxIoUAssigner
class ApproxMaxIoUAssigner(MaxIoUAssigner):
"""Assign a corresponding gt bbox or background to each bbox.
Each proposals will be assigned with `-1`, `0`, or a positive integer
indicating the ground truth index... | 6,111 | 42.657143 | 79 | py |
D2Det | D2Det-master/mmdet/core/bbox/assigners/max_iou_assigner.py | import torch
from ..geometry import bbox_overlaps
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
class MaxIoUAssigner(BaseAssigner):
"""Assign a corresponding gt bbox or background to each bbox.
Each proposals will be assigned with `-1`, `0`, or a positive integer
indica... | 8,572 | 42.739796 | 79 | py |
D2Det | D2Det-master/mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py | import numpy as np
import torch
from .random_sampler import RandomSampler
class InstanceBalancedPosSampler(RandomSampler):
def _sample_pos(self, assign_result, num_expected, **kwargs):
pos_inds = torch.nonzero(assign_result.gt_inds > 0)
if pos_inds.numel() != 0:
pos_inds = pos_inds.s... | 1,765 | 41.047619 | 77 | py |
D2Det | D2Det-master/mmdet/core/bbox/samplers/base_sampler.py | from abc import ABCMeta, abstractmethod
import torch
from .sampling_result import SamplingResult
class BaseSampler(metaclass=ABCMeta):
def __init__(self,
num,
pos_fraction,
neg_pos_ub=-1,
add_gt_as_proposals=True,
**kwargs):
... | 3,760 | 36.989899 | 79 | py |
D2Det | D2Det-master/mmdet/core/bbox/samplers/random_sampler.py | import torch
from .base_sampler import BaseSampler
class RandomSampler(BaseSampler):
def __init__(self,
num,
pos_fraction,
neg_pos_ub=-1,
add_gt_as_proposals=True,
**kwargs):
from mmdet.core.bbox import demodata
... | 2,231 | 33.875 | 78 | py |
D2Det | D2Det-master/mmdet/core/bbox/samplers/ohem_sampler.py | import torch
from ..transforms import bbox2roi
from .base_sampler import BaseSampler
class OHEMSampler(BaseSampler):
"""
Online Hard Example Mining Sampler described in [1]_.
References:
.. [1] https://arxiv.org/pdf/1604.03540.pdf
"""
def __init__(self,
num,
... | 2,912 | 35.4125 | 77 | py |
D2Det | D2Det-master/mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py | import numpy as np
import torch
from .random_sampler import RandomSampler
class IoUBalancedNegSampler(RandomSampler):
"""IoU Balanced Sampling
arXiv: https://arxiv.org/pdf/1904.02701.pdf (CVPR 2019)
Sampling proposals according to their IoU. `floor_fraction` of needed RoIs
are sampled from proposal... | 5,956 | 42.801471 | 79 | py |
D2Det | D2Det-master/mmdet/core/bbox/samplers/sampling_result.py | import torch
from mmdet.utils import util_mixins
class SamplingResult(util_mixins.NiceRepr):
"""
Example:
>>> # xdoctest: +IGNORE_WANT
>>> from mmdet.core.bbox.samplers.sampling_result import * # NOQA
>>> self = SamplingResult.random(rng=10)
>>> print('self = {}'.format(self)... | 5,193 | 32.509677 | 81 | py |
D2Det | D2Det-master/mmdet/core/bbox/samplers/pseudo_sampler.py | import torch
from .base_sampler import BaseSampler
from .sampling_result import SamplingResult
class PseudoSampler(BaseSampler):
def __init__(self, **kwargs):
pass
def _sample_pos(self, **kwargs):
raise NotImplementedError
def _sample_neg(self, **kwargs):
raise NotImplementedEr... | 829 | 29.740741 | 79 | py |
D2Det | D2Det-master/mmdet/core/utils/dist_utils.py | from collections import OrderedDict
import torch.distributed as dist
from mmcv.runner import OptimizerHook
from torch._utils import (_flatten_dense_tensors, _take_tensors,
_unflatten_dense_tensors)
def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1):
if bucket_size_mb > 0:
... | 1,856 | 31.578947 | 73 | py |
D2Det | D2Det-master/mmdet/core/optimizer/registry.py | import inspect
import torch
from mmdet.utils import Registry
OPTIMIZERS = Registry('optimizer')
def register_torch_optimizers():
torch_optimizers = []
for module_name in dir(torch.optim):
if module_name.startswith('__'):
continue
_optim = getattr(torch.optim, module_name)
... | 619 | 24.833333 | 73 | py |
D2Det | D2Det-master/mmdet/core/optimizer/copy_of_sgd.py | from torch.optim import SGD
from .registry import OPTIMIZERS
@OPTIMIZERS.register_module
class CopyOfSGD(SGD):
"""A clone of torch.optim.SGD.
A customized optimizer could be defined like CopyOfSGD.
You may derive from built-in optimizers in torch.optim,
or directly implement a new optimizer.
"""... | 321 | 22 | 59 | py |
D2Det | D2Det-master/mmdet/core/optimizer/builder.py | import re
import torch
from mmdet.utils import build_from_cfg
from .registry import OPTIMIZERS
def build_optimizer(model, optimizer_cfg):
"""Build optimizer from configs.
Args:
model (:obj:`nn.Module`): The model with parameters to be optimized.
optimizer_cfg (dict): The config dict of the ... | 4,565 | 43.764706 | 78 | py |
D2Det | D2Det-master/mmdet/core/anchor/anchor_target.py | import torch
from ..bbox import PseudoSampler, assign_and_sample, bbox2delta, build_assigner
from ..utils import multi_apply
def anchor_target(anchor_list,
valid_flag_list,
gt_bboxes_list,
img_metas,
target_means,
target_stds,
... | 7,369 | 37.994709 | 79 | py |
D2Det | D2Det-master/mmdet/core/anchor/guided_anchor_target.py | import torch
from ..bbox import PseudoSampler, build_assigner, build_sampler
from ..utils import multi_apply, unmap
def calc_region(bbox, ratio, featmap_size=None):
"""Calculate a proportional bbox region.
The bbox center are fixed and the new h' and w' is h * ratio and w * ratio.
Args:
bbox (T... | 11,809 | 40.006944 | 79 | py |
D2Det | D2Det-master/mmdet/core/anchor/point_generator.py | import torch
class PointGenerator(object):
def _meshgrid(self, x, y, row_major=True):
xx = x.repeat(len(y))
yy = y.view(-1, 1).repeat(1, len(x)).view(-1)
if row_major:
return xx, yy
else:
return yy, xx
def grid_points(self, featmap_size, stride=16, dev... | 1,287 | 35.8 | 71 | py |
D2Det | D2Det-master/mmdet/core/anchor/anchor_generator.py | import torch
class AnchorGenerator(object):
"""
Examples:
>>> from mmdet.core import AnchorGenerator
>>> self = AnchorGenerator(9, [1.], [1.])
>>> all_anchors = self.grid_anchors((2, 2), device='cpu')
>>> print(all_anchors)
tensor([[ 0., 0., 8., 8.],
... | 3,589 | 35.262626 | 79 | py |
D2Det | D2Det-master/mmdet/core/anchor/point_target.py | import torch
from ..bbox import PseudoSampler, assign_and_sample, build_assigner
from ..utils import multi_apply
def point_target(proposals_list,
valid_flag_list,
gt_bboxes_list,
img_metas,
cfg,
gt_bboxes_ignore_list=None,
... | 6,441 | 37.807229 | 79 | py |
D2Det | D2Det-master/mmdet/models/builder.py | from torch import nn
from mmdet.utils import build_from_cfg
from .registry import (BACKBONES, DETECTORS, HEADS, LOSSES, NECKS,
ROI_EXTRACTORS, SHARED_HEADS)
def build(cfg, registry, default_args=None):
if isinstance(cfg, list):
modules = [
build_from_cfg(cfg_, registry,... | 959 | 20.818182 | 78 | py |
D2Det | D2Det-master/mmdet/models/detectors/two_stage.py | import torch
import torch.nn as nn
from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler
from .. import builder
from ..registry import DETECTORS
from .base import BaseDetector
from .test_mixins import BBoxTestMixin, MaskTestMixin, RPNTestMixin
@DETECTORS.register_module
class TwoStageDetector(Ba... | 13,565 | 38.208092 | 79 | py |
D2Det | D2Det-master/mmdet/models/detectors/base.py | from abc import ABCMeta, abstractmethod
import mmcv
import numpy as np
import pycocotools.mask as maskUtils
import torch.nn as nn
from mmdet.core import auto_fp16, get_classes, tensor2imgs
from mmdet.utils import print_log
class BaseDetector(nn.Module, metaclass=ABCMeta):
"""Base class for detectors"""
def... | 7,166 | 35.943299 | 94 | py |
D2Det | D2Det-master/mmdet/models/detectors/single_stage.py | import torch.nn as nn
from mmdet.core import bbox2result
from .. import builder
from ..registry import DETECTORS
from .base import BaseDetector
@DETECTORS.register_module
class SingleStageDetector(BaseDetector):
"""Base class for single-stage detectors.
Single-stage detectors directly and densely predict bo... | 2,821 | 31.436782 | 78 | py |
D2Det | D2Det-master/mmdet/models/detectors/reppoints_detector.py | import torch
from mmdet.core import bbox2result, bbox_mapping_back, multiclass_nms
from ..registry import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module
class RepPointsDetector(SingleStageDetector):
"""RepPoints: Point Set Representation for Object Detection.
This det... | 3,089 | 36.682927 | 79 | py |
D2Det | D2Det-master/mmdet/models/detectors/cascade_rcnn.py | from __future__ import division
import torch
import torch.nn as nn
from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, build_assigner,
build_sampler, merge_aug_bboxes, merge_aug_masks,
multiclass_nms)
from .. import builder
from ..registry import DETECTORS
from... | 21,759 | 40.846154 | 79 | py |
D2Det | D2Det-master/mmdet/models/detectors/grid_rcnn.py | import torch
from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler
from .. import builder
from ..registry import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module
class GridRCNN(TwoStageDetector):
"""Grid R-CNN.
This detector is the implementation of:
- G... | 9,225 | 39.113043 | 79 | py |
D2Det | D2Det-master/mmdet/models/detectors/double_head_rcnn.py | import torch
from mmdet.core import bbox2roi, build_assigner, build_sampler
from ..registry import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module
class DoubleHeadRCNN(TwoStageDetector):
def __init__(self, reg_roi_scale_factor, **kwargs):
super().__init__(**kwargs)
s... | 7,453 | 40.642458 | 77 | py |
D2Det | D2Det-master/mmdet/models/detectors/D2Det.py | import torch
from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler, multiclass_nms1, bbox2roi_expand
from .. import builder
from ..registry import DETECTORS
from .two_stage import TwoStageDetector
import numpy as np
import mmcv
import pycocotools.mask as mask_util
@DETECTORS.register_module
clas... | 17,077 | 45.156757 | 175 | py |
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