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
value |
|---|---|---|---|---|---|---|
PseCo | PseCo-master/thirdparty/mmdetection/configs/rpn/rpn_r101_fpn_2x_coco.py | _base_ = './rpn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 191 | 26.428571 | 61 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='DeformableDETR',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False)... | 6,478 | 36.450867 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py | _base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
... | 441 | 28.466667 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py | _base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 221 | 30.714286 | 65 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py | _base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
... | 443 | 28.6 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py | _base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
... | 443 | 28.6 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py | _base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
... | 441 | 28.466667 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py | _base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 219 | 30.428571 | 63 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/yolof/yolof_r50_c5_8x8_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='YOLOF',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(3, ),
frozen_stages=1,
norm_cfg=dict(ty... | 3,279 | 29.943396 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py | _base_ = './mask_rcnn_r50_fpn_instaboost_4x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='... | 430 | 27.733333 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py | _base_ = './mask_rcnn_r50_fpn_instaboost_4x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 208 | 28.857143 | 61 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/instaboost/cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py | _base_ = './cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 217 | 26.25 | 61 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py | _base_ = './cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
... | 438 | 28.266667 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/detr/detr_r50_8x2_150e_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='DETR',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(3, ),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm... | 5,858 | 37.801325 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/atss/atss_r101_fpn_1x_coco.py | _base_ = './atss_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 192 | 26.571429 | 61 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/atss/atss_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='ATSS',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=d... | 1,925 | 29.571429 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py | _base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py']
teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth' # noqa
model = dict(
teacher_config='configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_co... | 1,628 | 35.2 | 187 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py | _base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py']
model = dict(
backbone=dict(
type='ResNet',
depth=34,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_c... | 569 | 27.5 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth' # noqa
model = dict(
type='Kn... | 2,120 | 32.666667 | 163 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py | _base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py']
model = dict(
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),
norm_eval=True,
style='pytorch',
init_c... | 572 | 27.65 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py | _base_ = [
'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvi... | 3,783 | 37.222222 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py | _base_ = './mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 231 | 32.142857 | 75 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py | _base_ = './mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 227 | 31.571429 | 71 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py | _base_ = './mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py' # noqa: E501
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 257 | 35.857143 | 101 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py | _base_ = './mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py' # noqa: E501
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 253 | 35.285714 | 97 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py | _base_ = [
'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvisio... | 4,807 | 35.150376 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/gn+ws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py'
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
type='ResNeXt',
depth=50,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
... | 544 | 27.684211 | 66 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py | _base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py'
# model settings
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices... | 561 | 27.1 | 67 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py'
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
... | 546 | 27.789474 | 67 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py | _base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py'
# model settings
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
type='ResNeXt',
depth=50,
groups=32,
base_width=4,
num_stages=4,
out_indices=... | 559 | 27 | 66 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py | _base_ = './ga_faster_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 419 | 27 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py | _base_ = './ga_faster_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 419 | 27 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py | _base_ = './ga_retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch'... | 422 | 27.2 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py | _base_ = './ga_rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 416 | 26.8 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py | _base_ = './ga_rpn_r50_caffe_fpn_1x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 236 | 25.333333 | 67 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py | _base_ = './ga_retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch'... | 422 | 27.2 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py | _base_ = './ga_faster_r50_caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 222 | 26.875 | 67 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py | _base_ = './ga_rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 416 | 26.8 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py | _base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py'
model = dict(
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,
style='caffe',
in... | 2,407 | 35.484848 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
rpn_head=dict(
_delete_=True,
type='GARPNHead',
in_channels=256,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=8,
scal... | 2,408 | 35.5 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py | _base_ = './ga_retinanet_r50_caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 225 | 27.25 | 67 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py | _base_ = '../retinanet/retinanet_r50_caffe_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
_delete_=True,
type='GARetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
... | 2,055 | 31.634921 | 74 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_mstrain_2x.py | _base_ = '../_base_/default_runtime.py'
# model settings
model = dict(
type='RetinaNet',
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,
... | 5,095 | 28.976471 | 74 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py | _base_ = '../rpn/rpn_r50_caffe_fpn_1x_coco.py'
model = dict(
rpn_head=dict(
_delete_=True,
type='GARPNHead',
in_channels=256,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=8,
scales_per_octave=3,... | 2,028 | 33.389831 | 74 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py | _base_ = './sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 216 | 26.125 | 61 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py | _base_ = './sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 235 | 28.5 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
num_stages = 6
num_proposals = 100
model = dict(
type='SparseRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
... | 3,469 | 35.145833 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/apis/inference.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import mmcv
import numpy as np
import torch
from mmcv.ops import RoIPool
from mmcv.parallel import collate, scatter
from mmcv.runner import load_checkpoint
from mmdet.core import get_classes
from mmdet.datasets import replace_ImageToTensor
from mmdet.dat... | 8,035 | 32.206612 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/apis/test.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import pickle
import shutil
import tempfile
import time
import mmcv
import torch
import torch.distributed as dist
from mmcv.image import tensor2imgs
from mmcv.runner import get_dist_info
from mmdet.core import encode_mask_results
def single_gpu_t... | 6,874 | 34.807292 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/apis/train.py | # Copyright (c) OpenMMLab. All rights reserved.
import random
import warnings
import numpy as np
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner,
Fp16OptimizerHook, OptimizerHook, build_optim... | 6,574 | 36.571429 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/evaluation/eval_hooks.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import torch.distributed as dist
from mmcv.runner import DistEvalHook as BaseDistEvalHook
from mmcv.runner import EvalHook as BaseEvalHook
from torch.nn.modules.batchnorm import _BatchNorm
class EvalHook(BaseEvalHook):
def _do_evaluate(self, ... | 2,211 | 34.677419 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/post_processing/merge_augs.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import warnings
import numpy as np
import torch
from mmcv import ConfigDict
from mmcv.ops import nms
from ..bbox import bbox_mapping_back
def merge_aug_proposals(aug_proposals, img_metas, cfg):
"""Merge augmented proposals (multiscale, flip, etc.)
... | 5,790 | 36.36129 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/post_processing/bbox_nms.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.ops.nms import batched_nms
from mmdet.core.bbox.iou_calculators import bbox_overlaps
def multiclass_nms(multi_bboxes,
multi_scores,
score_thr,
nms_cfg,
max_num=-1,
... | 6,461 | 36.569767 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/mask/structures.py | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
import cv2
import mmcv
import numpy as np
import pycocotools.mask as maskUtils
import torch
from mmcv.ops.roi_align import roi_align
class BaseInstanceMasks(metaclass=ABCMeta):
"""Base class for instance masks."""
@abstr... | 38,817 | 36.360924 | 141 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/mask/mask_target.py | # Copyright (c) OpenMMLab. All rights reserved.
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):
"""Compute mask target for positive proposals in multiple images.
Args:
pos_prop... | 5,115 | 38.96875 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/hook/sync_random_size_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
import random
import torch
from mmcv.runner import get_dist_info
from mmcv.runner.hooks import HOOKS, Hook
from torch import distributed as dist
@HOOKS.register_module()
class SyncRandomSizeHook(Hook):
"""Change and synchronize the random image size across ranks, c... | 2,116 | 35.5 | 75 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/hook/checkloss_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.runner.hooks import HOOKS, Hook
@HOOKS.register_module()
class CheckInvalidLossHook(Hook):
"""Check invalid loss hook.
This hook will regularly check whether the loss is valid
during training.
Args:
interval (int): Checki... | 681 | 26.28 | 66 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/hook/sync_norm_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
from collections import OrderedDict
from mmcv.runner import get_dist_info
from mmcv.runner.hooks import HOOKS, Hook
from torch import nn
from ..utils.dist_utils import all_reduce_dict
def get_norm_states(module):
async_norm_states = OrderedDict()
for name, chi... | 1,728 | 32.901961 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/export/model_wrappers.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import warnings
import numpy as np
import torch
from mmdet.core import bbox2result
from mmdet.models import BaseDetector
class DeployBaseDetector(BaseDetector):
"""DeployBaseDetector."""
def __init__(self, class_names, device_id):
... | 7,472 | 39.61413 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/export/pytorch2onnx.py | # Copyright (c) OpenMMLab. All rights reserved.
from functools import partial
import mmcv
import numpy as np
import torch
from mmcv.runner import load_checkpoint
def generate_inputs_and_wrap_model(config_path,
checkpoint_path,
input_config,
... | 6,194 | 36.77439 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/export/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .onnx_helper import (add_dummy_nms_for_onnx, dynamic_clip_for_onnx,
get_k_for_topk)
from .pytorch2onnx import (build_model_from_cfg,
generate_inputs_and_wrap_model,
preprocess_example_inp... | 505 | 37.923077 | 75 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/export/onnx_helper.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import torch
def dynamic_clip_for_onnx(x1, y1, x2, y2, max_shape):
"""Clip boxes dynamically for onnx.
Since torch.clamp cannot have dynamic `min` and `max`, we scale the
boxes by 1/max_shape and clamp in the range [0, 1].
Args:
x1... | 8,367 | 36.357143 | 98 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/demodata.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from mmdet.utils.util_random import ensure_rng
def random_boxes(num=1, scale=1, rng=None):
"""Simple version of ``kwimage.Boxes.random``
Returns:
Tensor: shape (n, 4) in x1, y1, x2, y2 format.
References:
ht... | 1,181 | 26.488372 | 101 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/transforms.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
def bbox_flip(bboxes, img_shape, direction='horizontal'):
"""Flip bboxes horizontally or vertically.
Args:
bboxes (Tensor): Shape (..., 4*k)
img_shape (tuple): Image shape.
direction (str): Flip direction,... | 8,277 | 32.379032 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/assigners/assign_result.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.utils import util_mixins
import ipdb
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
... | 7,764 | 36.694175 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/assigners/sim_ota_assigner.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch
import torch.nn.functional as F
from ..builder import BBOX_ASSIGNERS
from ..iou_calculators import bbox_overlaps
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
@BBOX_ASSIGNERS.register_module()
class SimOTA... | 11,296 | 43.301961 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/assigners/atss_assigner.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import BBOX_ASSIGNERS
from ..iou_calculators import build_iou_calculator
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
@BBOX_ASSIGNERS.register_module()
class ATSSAssigner(BaseAssigner):
"""Assign a corr... | 7,809 | 42.388889 | 87 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/assigners/center_region_assigner.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import BBOX_ASSIGNERS
from ..iou_calculators import build_iou_calculator
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
def scale_boxes(bboxes, scale):
"""Expand an array of boxes by a given scale.
A... | 15,477 | 44.928783 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/assigners/region_assigner.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.core import anchor_inside_flags
from ..builder import BBOX_ASSIGNERS
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
def calc_region(bbox, ratio, stride, featmap_size=None):
"""Calculate region of the box defi... | 9,473 | 41.484305 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/assigners/grid_assigner.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import BBOX_ASSIGNERS
from ..iou_calculators import build_iou_calculator
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
@BBOX_ASSIGNERS.register_module()
class GridAssigner(BaseAssigner):
"""Assign a corr... | 6,864 | 42.726115 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/assigners/hungarian_assigner.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import BBOX_ASSIGNERS
from ..match_costs import build_match_cost
from ..transforms import bbox_cxcywh_to_xyxy
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
try:
from scipy.optimize import linear_sum_assig... | 6,665 | 44.346939 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/assigners/uniform_assigner.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import BBOX_ASSIGNERS
from ..iou_calculators import build_iou_calculator
from ..transforms import bbox_xyxy_to_cxcywh
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
@BBOX_ASSIGNERS.register_module()
class Uni... | 5,556 | 39.860294 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/assigners/point_assigner.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import BBOX_ASSIGNERS
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
@BBOX_ASSIGNERS.register_module()
class PointAssigner(BaseAssigner):
"""Assign a corresponding gt bbox or background to each point.
... | 5,995 | 43.414815 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/assigners/approx_max_iou_assigner.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import BBOX_ASSIGNERS
from ..iou_calculators import build_iou_calculator
from .max_iou_assigner import MaxIoUAssigner
@BBOX_ASSIGNERS.register_module()
class ApproxMaxIoUAssigner(MaxIoUAssigner):
"""Assign a corresponding gt bbox or back... | 6,697 | 44.564626 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/assigners/max_iou_assigner.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import BBOX_ASSIGNERS
from ..iou_calculators import build_iou_calculator
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
import ipdb
@BBOX_ASSIGNERS.register_module()
class MaxIoUAssigner(BaseAssigner):
""... | 9,810 | 44.845794 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/match_costs/match_cost.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.core.bbox.iou_calculators import bbox_overlaps
from mmdet.core.bbox.transforms import bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh
from .builder import MATCH_COST
@MATCH_COST.register_module()
class BBoxL1Cost:
"""BBoxL1Cost.
Args:
... | 6,342 | 33.102151 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/coder/yolo_bbox_coder.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from ..builder import BBOX_CODERS
from .base_bbox_coder import BaseBBoxCoder
@BBOX_CODERS.register_module()
class YOLOBBoxCoder(BaseBBoxCoder):
"""YOLO BBox coder.
Following `YOLO <https://arxiv.org/abs/1506.02640>`_, this coder divide... | 3,252 | 37.72619 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/coder/bucketing_bbox_coder.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
import torch
import torch.nn.functional as F
from ..builder import BBOX_CODERS
from ..transforms import bbox_rescale
from .base_bbox_coder import BaseBBoxCoder
@BBOX_CODERS.register_module()
class BucketingBBoxCoder(BaseBBoxCoder):
""... | 14,119 | 39.113636 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/coder/pseudo_bbox_coder.py | # Copyright (c) OpenMMLab. All rights reserved.
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... | 577 | 27.9 | 60 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/coder/tblr_bbox_coder.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from ..builder import BBOX_CODERS
from .base_bbox_coder import BaseBBoxCoder
@BBOX_CODERS.register_module()
class TBLRBBoxCoder(BaseBBoxCoder):
"""TBLR BBox coder.
Following the practice in `FSAF <https://arxiv.org/abs/1903.00621>`_,
... | 8,625 | 40.671498 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/coder/legacy_delta_xywh_bbox_coder.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
import torch
from ..builder import BBOX_CODERS
from .base_bbox_coder import BaseBBoxCoder
@BBOX_CODERS.register_module()
class LegacyDeltaXYWHBBoxCoder(BaseBBoxCoder):
"""Legacy Delta XYWH BBox coder used in MMDet V1.x.
Following... | 8,257 | 37.0553 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
import torch
from ..builder import BBOX_CODERS
from .base_bbox_coder import BaseBBoxCoder
@BBOX_CODERS.register_module()
class DeltaXYWHBBoxCoder(BaseBBoxCoder):
"""Delta XYWH BBox coder.
Following the practice in `R-CNN <https:/... | 10,945 | 39.095238 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/iou_calculators/iou2d_calculator.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from .builder import IOU_CALCULATORS
def cast_tensor_type(x, scale=1., dtype=None):
if dtype == 'fp16':
# scale is for preventing overflows
x = (x / scale).half()
return x
def fp16_clamp(x, min=None, max=None):
if not x.is_cud... | 9,678 | 35.942748 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from ..builder import BBOX_SAMPLERS
from .random_sampler import RandomSampler
@BBOX_SAMPLERS.register_module()
class InstanceBalancedPosSampler(RandomSampler):
"""Instance balanced sampler that samples equal number of positive sample... | 2,319 | 39.701754 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/samplers/base_sampler.py | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
import torch
from .sampling_result import SamplingResult
import ipdb
class BaseSampler(metaclass=ABCMeta):
"""Base class of samplers."""
def __init__(self,
num,
pos_fraction,
... | 3,931 | 37.174757 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/samplers/random_sampler.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import BBOX_SAMPLERS
from .base_sampler import BaseSampler
@BBOX_SAMPLERS.register_module()
class RandomSampler(BaseSampler):
"""Random sampler.
Args:
num (int): Number of samples
pos_fraction (float): Fraction of po... | 3,071 | 36.012048 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/samplers/ohem_sampler.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import BBOX_SAMPLERS
from ..transforms import bbox2roi
from .base_sampler import BaseSampler
@BBOX_SAMPLERS.register_module()
class OHEMSampler(BaseSampler):
r"""Online Hard Example Mining Sampler described in `Training Region-based
... | 4,146 | 37.045872 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from ..builder import BBOX_SAMPLERS
from .random_sampler import RandomSampler
@BBOX_SAMPLERS.register_module()
class IoUBalancedNegSampler(RandomSampler):
"""IoU Balanced Sampling.
arXiv: https://arxiv.org/pdf/1904.02701.pdf (CV... | 6,740 | 41.396226 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/samplers/score_hlr_sampler.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.ops import nms_match
from ..builder import BBOX_SAMPLERS
from ..transforms import bbox2roi
from .base_sampler import BaseSampler
from .sampling_result import SamplingResult
@BBOX_SAMPLERS.register_module()
class ScoreHLRSampler(BaseSampler):
... | 11,235 | 41.240602 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/samplers/sampling_result.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.utils import util_mixins
class SamplingResult(util_mixins.NiceRepr):
"""Bbox sampling result.
Example:
>>> # xdoctest: +IGNORE_WANT
>>> from mmdet.core.bbox.samplers.sampling_result import * # NOQA
>>> self = Sa... | 5,382 | 33.954545 | 81 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/bbox/samplers/pseudo_sampler.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import BBOX_SAMPLERS
from .base_sampler import BaseSampler
from .sampling_result import SamplingResult
@BBOX_SAMPLERS.register_module()
class PseudoSampler(BaseSampler):
"""A pseudo sampler that does not do sampling actually."""
def... | 1,463 | 33.046512 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/utils/dist_utils.py | # Copyright (c) OpenMMLab. All rights reserved.
import functools
import pickle
import warnings
from collections import OrderedDict
import torch
import torch.distributed as dist
from mmcv.runner import OptimizerHook, get_dist_info
from torch._utils import (_flatten_dense_tensors, _take_tensors,
... | 4,967 | 32.12 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/utils/misc.py | # Copyright (c) OpenMMLab. All rights reserved.
from functools import partial
import numpy as np
import torch
from six.moves import map, zip
from ..mask.structures import BitmapMasks, PolygonMasks
def multi_apply(func, *args, **kwargs):
"""Apply function to a list of arguments.
Note:
This function ... | 2,663 | 29.976744 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/anchor/point_generator.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from torch.nn.modules.utils import _pair
from .builder import PRIOR_GENERATORS
@PRIOR_GENERATORS.register_module()
class PointGenerator:
def _meshgrid(self, x, y, row_major=True):
xx = x.repeat(len(y))
yy = y.view(-1... | 9,830 | 39.45679 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/anchor/anchor_generator.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import mmcv
import numpy as np
import torch
from torch.nn.modules.utils import _pair
from .builder import PRIOR_GENERATORS
@PRIOR_GENERATORS.register_module()
class AnchorGenerator:
"""Standard anchor generator for 2D anchor-based detectors.
A... | 36,717 | 41.794872 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/core/anchor/utils.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import ipdb
def images_to_levels(target, num_levels):
"""Convert targets by image to targets by feature level.
[target_img0, target_img1] -> [target_level0, target_level1, ...]
"""
target = torch.stack(target, 0)
level_targets = []
s... | 3,029 | 33.431818 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/detectors/two_stage.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch
import mmcv
import cv2
import os
from ..builder import DETECTORS, build_backbone, build_head, build_neck
from .base import BaseDetector
import ipdb
@DETECTORS.register_module()
class TwoStageDetector(BaseDetector):
"""Base class for two... | 8,381 | 36.756757 | 148 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/detectors/base.py | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from collections import OrderedDict
import mmcv
import numpy as np
import torch
import torch.distributed as dist
from mmcv.runner import BaseModule, auto_fp16
from mmdet.core.visualization import imshow_det_bboxes
import os
import... | 15,043 | 39.659459 | 82 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/detectors/single_stage.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch
from mmdet.core import bbox2result
from ..builder import DETECTORS, build_backbone, build_head, build_neck
from .base import BaseDetector
@DETECTORS.register_module()
class SingleStageDetector(BaseDetector):
"""Base class for single-st... | 6,509 | 37.982036 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/detectors/detr.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class DETR(SingleStageDetector):
r"""Implementation of `DETR: End-to-End Object Detection with
Transformers <https://arxiv.o... | 2,483 | 33.985915 | 79 | py |
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