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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s2anet | s2anet-master/configs/reppoints/reppoints_moment_r101_fpn_2x_mt.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
styl... | 4,289 | 28.383562 | 79 | py |
s2anet | s2anet-master/configs/reppoints/reppoints_moment_x101_dcn_fpn_2x_mt.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0,... | 4,533 | 28.441558 | 79 | py |
s2anet | s2anet-master/configs/reppoints/reppoints_minmax_r50_fpn_1x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style=... | 4,209 | 28.647887 | 79 | py |
s2anet | s2anet-master/configs/reppoints/reppoints_partial_minmax_r50_fpn_1x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style=... | 4,225 | 28.760563 | 79 | py |
s2anet | s2anet-master/configs/reppoints/bbox_r50_grid_center_fpn_1x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style=... | 4,239 | 28.65035 | 79 | py |
s2anet | s2anet-master/configs/reppoints/reppoints_moment_r101_dcn_fpn_2x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
styl... | 4,362 | 29.089655 | 79 | py |
s2anet | s2anet-master/configs/reppoints/reppoints_moment_r50_fpn_2x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style=... | 4,210 | 28.65493 | 79 | py |
s2anet | s2anet-master/configs/reppoints/reppoints_moment_r101_dcn_fpn_2x_mt.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
styl... | 4,438 | 28.791946 | 79 | py |
s2anet | s2anet-master/configs/reppoints/reppoints_moment_r101_fpn_2x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
styl... | 4,213 | 28.676056 | 79 | py |
s2anet | s2anet-master/configs/reppoints/reppoints_moment_r50_fpn_1x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style=... | 4,209 | 28.647887 | 79 | py |
s2anet | s2anet-master/configs/reppoints/reppoints_moment_r50_fpn_2x_mt.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style=... | 4,286 | 28.363014 | 79 | py |
s2anet | s2anet-master/configs/fp16/faster_rcnn_r50_fpn_fp16_1x.py | # fp16 settings
fp16 = dict(loss_scale=512.)
# 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=dic... | 5,379 | 29.224719 | 78 | py |
s2anet | s2anet-master/configs/fp16/retinanet_r50_fpn_fp16_1x.py | # fp16 settings
fp16 = dict(loss_scale=512.)
# 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... | 3,850 | 27.954887 | 77 | py |
s2anet | s2anet-master/configs/fp16/mask_rcnn_r50_fpn_fp16_1x.py | # fp16 settings
fp16 = dict(loss_scale=512.)
# 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(... | 5,825 | 29.186528 | 78 | py |
s2anet | s2anet-master/configs/fcos/fcos_mstrain_640_800_x101_64x4d_fpn_gn_2x.py | # model settings
model = dict(
type='FCOS',
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=dict(... | 3,982 | 27.45 | 77 | py |
s2anet | s2anet-master/configs/fcos/fcos_mstrain_640_800_r101_caffe_fpn_gn_2x_4gpu.py | # model settings
model = dict(
type='FCOS',
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),
style='caffe'),
... | 3,995 | 27.748201 | 75 | py |
s2anet | s2anet-master/configs/fcos/fcos_r50_caffe_fpn_gn_1x_4gpu.py | # model settings
model = dict(
type='FCOS',
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),
style='caffe'),
ne... | 3,901 | 27.903704 | 75 | py |
s2anet | s2anet-master/configs/hrsc2016/retinanet_obb_r50_fpn_6x_hrsc2016.py | PI = 3.141592653
# 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',
... | 4,338 | 29.556338 | 89 | py |
s2anet | s2anet-master/configs/hrsc2016/cascade_s2anet_2s_r50_fpn_3x_hrsc2016.py | # model settings
model = dict(
type='CascadeS2ANetDetector',
pretrained='torchvision://resnet50',
num_stages=2,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='... | 5,813 | 30.597826 | 89 | py |
s2anet | s2anet-master/configs/hrsc2016/cascade_s2anet_1s_r50_fpn_4x_hrsc2016.py | # model settings
model = dict(
type='CascadeS2ANetDetector',
pretrained='torchvision://resnet50',
num_stages=1,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='... | 4,488 | 29.331081 | 89 | py |
s2anet | s2anet-master/configs/hrsc2016/s2anet_r101_fpn_3x_hrsc2016.py | # model settings
model = dict(
type='S2ANetDetector',
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_channe... | 5,011 | 30.325 | 89 | py |
s2anet | s2anet-master/configs/hrsc2016/s2anet_r50_fpn_3x_hrsc2016.py | # model settings
model = dict(
type='S2ANetDetector',
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... | 5,009 | 30.3125 | 89 | py |
s2anet | s2anet-master/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x.py | # model settings
model = dict(
type='MaskScoringRCNN',
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),
style='ca... | 6,072 | 29.365 | 78 | py |
s2anet | s2anet-master/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='MaskScoringRCNN',
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'),
... | 6,064 | 29.174129 | 78 | py |
s2anet | s2anet-master/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x.py | # model settings
model = dict(
type='MaskScoringRCNN',
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),
style='caff... | 6,069 | 29.35 | 78 | py |
s2anet | s2anet-master/configs/rotated_iou/retinanet_obb_r50_fpn_6x_hrsc2016_iouloss.py | PI = 3.141592653
# 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',
... | 4,454 | 29.513699 | 89 | py |
s2anet | s2anet-master/configs/rotated_iou/cascade_s2anet_2s_r50_fpn_1x_dota_iouloss.py | # model settings
model = dict(
type='CascadeS2ANetDetector',
pretrained='torchvision://resnet50',
num_stages=2,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='... | 6,444 | 32.221649 | 85 | py |
s2anet | s2anet-master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_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... | 5,680 | 30.214286 | 79 | py |
s2anet | s2anet-master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_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... | 5,531 | 29.905028 | 79 | py |
s2anet | s2anet-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... | 5,531 | 29.905028 | 79 | py |
s2anet | s2anet-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... | 5,680 | 30.214286 | 79 | py |
s2anet | s2anet-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, ... | 3,633 | 29.283333 | 78 | py |
s2anet | s2anet-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... | 3,630 | 29.258333 | 78 | py |
s2anet | s2anet-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, ... | 3,728 | 28.832 | 78 | py |
s2anet | s2anet-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... | 3,725 | 28.808 | 78 | py |
s2anet | s2anet-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... | 3,571 | 28.766667 | 78 | py |
s2anet | s2anet-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... | 5,419 | 29.449438 | 78 | py |
s2anet | s2anet-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... | 3,903 | 27.705882 | 79 | py |
s2anet | s2anet-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,... | 7,417 | 28.791165 | 78 | py |
s2anet | s2anet-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,... | 5,585 | 29.032258 | 78 | py |
s2anet | s2anet-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... | 5,642 | 29.015957 | 78 | py |
s2anet | s2anet-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',
... | 5,819 | 29.15544 | 78 | py |
s2anet | s2anet-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',
... | 5,822 | 29.170984 | 78 | py |
s2anet | s2anet-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',
... | 4,858 | 30.551948 | 79 | py |
s2anet | s2anet-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... | 5,876 | 29.138462 | 78 | py |
s2anet | s2anet-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',
... | 4,184 | 27.469388 | 77 | py |
s2anet | s2anet-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_... | 6,039 | 28.90099 | 78 | py |
s2anet | s2anet-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... | 5,500 | 28.735135 | 78 | py |
s2anet | s2anet-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... | 4,061 | 28.434783 | 79 | py |
s2anet | s2anet-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... | 5,516 | 30.346591 | 78 | py |
s2anet | s2anet-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... | 4,080 | 28.572464 | 79 | py |
s2anet | s2anet-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... | 5,852 | 29.326425 | 78 | py |
s2anet | s2anet-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... | 5,844 | 29.602094 | 78 | py |
s2anet | s2anet-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... | 5,953 | 29.533333 | 78 | py |
s2anet | s2anet-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... | 5,842 | 29.591623 | 78 | py |
s2anet | s2anet-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... | 5,955 | 29.54359 | 78 | py |
s2anet | s2anet-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,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch'),
neck=d... | 3,897 | 29.217054 | 77 | py |
s2anet | s2anet-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... | 6,191 | 29.502463 | 78 | py |
s2anet | s2anet-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... | 6,133 | 29.517413 | 78 | py |
s2anet | s2anet-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... | 6,140 | 29.552239 | 78 | py |
s2anet | s2anet-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),
... | 5,544 | 29.467033 | 78 | py |
s2anet | s2anet-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,
... | 4,790 | 29.322785 | 75 | py |
s2anet | s2anet-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,
... | 4,440 | 31.416058 | 78 | py |
s2anet | s2anet-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(
... | 4,761 | 29.139241 | 77 | py |
s2anet | s2anet-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,
... | 4,793 | 29.341772 | 75 | py |
s2anet | s2anet-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,
... | 4,634 | 28.522293 | 75 | py |
s2anet | s2anet-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=... | 4,605 | 28.33758 | 77 | py |
s2anet | s2anet-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,
... | 6,133 | 29.67 | 76 | py |
s2anet | s2anet-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... | 6,104 | 29.525 | 77 | py |
s2anet | s2anet-master/configs/dota/faster_rcnn_hbb_obb_r50_fpn_1x_dota.py | # model settings
model = dict(
type='FasterRCNNHBBOBB',
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_channe... | 5,387 | 29.788571 | 84 | py |
s2anet | s2anet-master/configs/dota/cascade_s2anet_2s_r50_fpn_1x_dota.py | # model settings
model = dict(
type='CascadeS2ANetDetector',
pretrained='torchvision://resnet50',
num_stages=2,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='... | 5,856 | 31.359116 | 84 | py |
s2anet | s2anet-master/configs/dota/retinanet_obb_r50_fpn_1x_dota.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... | 4,261 | 29.884058 | 80 | py |
s2anet | s2anet-master/configs/dota/s2anet_r50_fpn_1x_dota.py | # model settings
model = dict(
type='S2ANetDetector',
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... | 5,076 | 30.930818 | 83 | py |
s2anet | s2anet-master/configs/dota/cascade_s2anet_1s_r50_fpn_1x_dota.py | # model settings
model = dict(
type='CascadeS2ANetDetector',
pretrained='torchvision://resnet50',
num_stages=1,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='... | 4,529 | 30.241379 | 83 | py |
s2anet | s2anet-master/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py | # model settings
model = dict(
type='FasterRCNN',
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=[256, ... | 5,593 | 29.568306 | 79 | py |
s2anet | s2anet-master/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py | # model settings
model = dict(
type='MaskRCNN',
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=[256, 51... | 6,008 | 29.502538 | 79 | py |
s2anet | s2anet-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... | 5,996 | 29.441624 | 78 | py |
s2anet | s2anet-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... | 5,993 | 29.426396 | 78 | py |
s2anet | s2anet-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... | 6,001 | 29.467005 | 78 | py |
s2anet | s2anet-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... | 5,973 | 33.732558 | 79 | py |
s2anet | s2anet-master/mmdet/apis/train.py | from __future__ import division
import re
from collections import OrderedDict
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import DistSamplerSeedHook, Runner, obj_from_dict
from mmdet import datasets
from mmdet.core import (CocoDistEvalmAPHook, CocoDistEvalRecallHo... | 9,069 | 37.927039 | 78 | py |
s2anet | s2anet-master/mmdet/apis/env.py | import logging
import os
import random
import subprocess
import numpy as np
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from mmcv.runner import get_dist_info
def init_dist(launcher, backend='nccl', **kwargs):
if mp.get_start_method(allow_none=True) is None:
mp.set_sta... | 2,041 | 28.171429 | 70 | py |
s2anet | s2anet-master/mmdet/core/evaluation/eval_hooks.py | import os
import os.path as osp
import mmcv
import numpy as np
import torch
import torch.distributed as dist
from mmcv.parallel import collate, scatter
from mmcv.runner import Hook
from pycocotools.cocoeval import COCOeval
from torch.utils.data import Dataset
from mmdet import datasets
from .coco_utils import fast_ev... | 6,301 | 35.853801 | 79 | py |
s2anet | s2anet-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 |
s2anet | s2anet-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 (Tens... | 2,808 | 36.959459 | 78 | py |
s2anet | s2anet-master/mmdet/core/post_processing/bbox_nms_rotated.py | import torch
from mmdet.ops import ml_nms_rotated
def multiclass_nms_rotated(multi_bboxes,
multi_scores,
score_thr,
nms_cfg,
max_num=-1,
score_factors=None):
"""NMS for multi-cla... | 2,309 | 34.538462 | 78 | py |
s2anet | s2anet-master/mmdet/core/post_processing/merge_augs_rotated.py | import torch
from mmdet.ops import nms_rotated
from ..bbox import bbox_mapping_back_rotated
def merge_aug_proposals_rotated(aug_proposals, img_metas, rpn_test_cfg):
"""Merge augmented proposals (multiscale, flip, etc.)
Args:
aug_proposals (list[Tensor]): proposals from different testing
... | 2,679 | 37.285714 | 83 | py |
s2anet | s2anet-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,
... | 1,501 | 37.512821 | 77 | py |
s2anet | s2anet-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.
... | 4,482 | 34.023438 | 79 | py |
s2anet | s2anet-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 |
s2anet | s2anet-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 |
s2anet | s2anet-master/mmdet/core/bbox/bbox_target.py | import torch
from .transforms import bbox2delta
from ..utils import multi_apply
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],
... | 2,716 | 36.219178 | 78 | py |
s2anet | s2anet-master/mmdet/core/bbox/bbox_target_rotated.py | import torch
from .transforms_rotated import bbox2delta_rotated
from ..utils import multi_apply
def bbox_target_rotated(pos_bboxes_list,
neg_bboxes_list,
pos_gt_bboxes_list,
pos_gt_labels_list,
cfg,
... | 2,284 | 36.459016 | 86 | py |
s2anet | s2anet-master/mmdet/core/bbox/transforms_rotated.py | import math
import numpy as np
import torch
def norm_angle(angle, range=[-np.pi / 4, np.pi]):
return (angle - range[0]) % range[1] + range[0]
def bbox2delta_rotated(proposals, gt, means=(0., 0., 0., 0., 0.), stds=(1., 1., 1., 1., 1.)):
"""Compute deltas of proposals w.r.t. gt.
We usually compute the d... | 16,728 | 32.65996 | 107 | py |
s2anet | s2anet-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.... | 7,766 | 33.986486 | 79 | py |
s2anet | s2anet-master/mmdet/core/bbox/assigners/assign_result.py | import torch
class AssignResult(object):
def __init__(self, num_gts, gt_inds, max_overlaps, labels=None):
self.num_gts = num_gts
self.gt_inds = gt_inds
self.max_overlaps = max_overlaps
self.labels = labels
def add_gt_(self, gt_labels):
self_inds = torch.arange(
... | 664 | 32.25 | 77 | py |
s2anet | s2anet-master/mmdet/core/bbox/assigners/point_assigner.py | import torch
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
from ..builder import BBOX_ASSIGNERS
@BBOX_ASSIGNERS.register_module
class PointAssigner(BaseAssigner):
"""Assign a corresponding gt bbox or background to each point.
Each proposals will be assigned with `0`, or a p... | 5,249 | 43.117647 | 79 | py |
s2anet | s2anet-master/mmdet/core/bbox/assigners/approx_max_iou_assigner.py | import torch
from .max_iou_assigner import MaxIoUAssigner
from ..builder import BBOX_ASSIGNERS
from ..iou_calculators import build_iou_calculator
@BBOX_ASSIGNERS.register_module
class ApproxMaxIoUAssigner(MaxIoUAssigner):
"""Assign a corresponding gt bbox or background to each bbox.
Each proposals will be a... | 5,088 | 41.408333 | 79 | py |
s2anet | s2anet-master/mmdet/core/bbox/assigners/max_iou_assigner.py | import torch
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
from ..builder import BBOX_ASSIGNERS
from ..iou_calculators import build_iou_calculator
@BBOX_ASSIGNERS.register_module
class MaxIoUAssigner(BaseAssigner):
"""Assign a corresponding gt bbox or background to each bbox.
... | 6,735 | 41.904459 | 79 | py |
s2anet | s2anet-master/mmdet/core/bbox/coder/pseudo_bbox_coder.py | from ..builder import BBOX_CODERS
from .base_bbox_coder import BaseBBoxCoder
@BBOX_CODERS.register_module
class PseudoBBoxCoder(BaseBBoxCoder):
"""Pseudo bounding box coder."""
def __init__(self, **kwargs):
super(BaseBBoxCoder, self).__init__(**kwargs)
def encode(self, bboxes, gt_bboxes):
... | 527 | 26.789474 | 60 | py |
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