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PseCo
|
PseCo-master/thirdparty/mmdetection/configs/detectors/htc_r50_sac_1x_coco.py
|
_base_ = '../htc/htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_deform=True),
stage_with_sac=(False, True, True, True)))
| 245
| 26.333333
| 50
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/detectors/detectors_htc_r50_1x_coco.py
|
_base_ = '../htc/htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_deform=True),
stage_with_sac=(False, True, True, True),
output_img=True),
neck=dict(
type='RFP',
rfp_steps=2,
aspp_out_channels=64,
aspp_dilations=(1, 3, 6, 1),
rfp_backbone=dict(
rfp_inplanes=256,
type='DetectoRS_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,
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_deform=True),
stage_with_sac=(False, True, True, True),
pretrained='torchvision://resnet50',
style='pytorch')))
| 916
| 30.62069
| 57
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py
|
_base_ = [
'../_base_/models/cascade_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
output_img=True),
neck=dict(
type='RFP',
rfp_steps=2,
aspp_out_channels=64,
aspp_dilations=(1, 3, 6, 1),
rfp_backbone=dict(
rfp_inplanes=256,
type='DetectoRS_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,
conv_cfg=dict(type='ConvAWS'),
pretrained='torchvision://resnet50',
style='pytorch')))
| 851
| 28.37931
| 72
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/detectors/htc_r50_rfp_1x_coco.py
|
_base_ = '../htc/htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
output_img=True),
neck=dict(
type='RFP',
rfp_steps=2,
aspp_out_channels=64,
aspp_dilations=(1, 3, 6, 1),
rfp_backbone=dict(
rfp_inplanes=256,
type='DetectoRS_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,
conv_cfg=dict(type='ConvAWS'),
pretrained='torchvision://resnet50',
style='pytorch')))
| 714
| 27.6
| 57
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py
|
_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')),
bbox_head=dict(
norm_on_bbox=True,
centerness_on_reg=True,
dcn_on_last_conv=True,
center_sampling=True,
conv_bias=True,
loss_bbox=dict(type='GIoULoss', loss_weight=1.0)),
# training and testing settings
test_cfg=dict(nms=dict(type='nms', iou_threshold=0.6)))
# dataset settings
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
optimizer_config = dict(_delete_=True, grad_clip=None)
lr_config = dict(warmup='linear')
| 1,904
| 32.421053
| 74
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py
|
_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
model = dict(bbox_head=dict(center_sampling=True, center_sample_radius=1.5))
| 128
| 42
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py
|
_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py'
model = dict(
backbone=dict(
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')),
bbox_head=dict(
norm_on_bbox=True,
centerness_on_reg=True,
dcn_on_last_conv=False,
center_sampling=True,
conv_bias=True,
loss_bbox=dict(type='GIoULoss', loss_weight=1.0)),
# training and testing settings
test_cfg=dict(nms=dict(type='nms', iou_threshold=0.6)))
# dataset settings
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
optimizer_config = dict(_delete_=True, grad_clip=None)
lr_config = dict(warmup='linear')
| 1,780
| 31.381818
| 72
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py
|
_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
img_norm_cfg = dict(
mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 1,331
| 32.3
| 75
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py
|
_base_ = './fcos_r50_caffe_fpn_gn-head_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),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
# optimizer
optimizer = dict(
lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.))
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 1,966
| 31.245902
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py
|
_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron/resnet101_caffe')))
| 224
| 27.125
| 66
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py
|
_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron/resnet101_caffe')))
img_norm_cfg = dict(
mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 1,550
| 31.3125
| 75
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py
|
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='FCOS',
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',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron/resnet50_caffe')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_output', # use P5
num_outs=5,
relu_before_extra_convs=True),
bbox_head=dict(
type='FCOSHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 16, 32, 64, 128],
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='IoULoss', loss_weight=1.0),
loss_centerness=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False),
test_cfg=dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100))
img_norm_cfg = dict(
mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
# optimizer
optimizer = dict(
lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.))
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
warmup='constant',
warmup_iters=500,
warmup_ratio=1.0 / 3,
step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
| 3,281
| 29.672897
| 75
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py
|
# TODO: Remove this config after benchmarking all related configs
_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py'
data = dict(samples_per_gpu=4, workers_per_gpu=4)
| 166
| 32.4
| 65
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
neck=dict(
type='FPN_CARAFE',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
start_level=0,
end_level=-1,
norm_cfg=None,
act_cfg=None,
order=('conv', 'norm', 'act'),
upsample_cfg=dict(
type='carafe',
up_kernel=5,
up_group=1,
encoder_kernel=3,
encoder_dilation=1,
compressed_channels=64)),
roi_head=dict(
mask_head=dict(
upsample_cfg=dict(
type='carafe',
scale_factor=2,
up_kernel=5,
up_group=1,
encoder_kernel=3,
encoder_dilation=1,
compressed_channels=64))))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=64),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=64),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
| 1,971
| 31.327869
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py
|
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
neck=dict(
type='FPN_CARAFE',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
start_level=0,
end_level=-1,
norm_cfg=None,
act_cfg=None,
order=('conv', 'norm', 'act'),
upsample_cfg=dict(
type='carafe',
up_kernel=5,
up_group=1,
encoder_kernel=3,
encoder_dilation=1,
compressed_channels=64)))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=64),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=64),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
| 1,640
| 31.176471
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/common/mstrain_3x_coco_instance.py
|
_base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
# multiscale_mode='range'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
# Use RepeatDataset to speed up training
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type='RepeatDataset',
times=3,
dataset=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
pipeline=train_pipeline)),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline))
evaluation = dict(interval=1, metric=['bbox', 'segm'])
# optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
# Experiments show that using step=[9, 11] has higher performance
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[9, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
| 2,466
| 31.038961
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/common/mstrain-poly_3x_coco_instance.py
|
_base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
# multiscale_mode='range'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
# Use RepeatDataset to speed up training
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type='RepeatDataset',
times=3,
dataset=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
pipeline=train_pipeline)),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline))
evaluation = dict(interval=1, metric=['bbox', 'segm'])
# optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
# Experiments show that using step=[9, 11] has higher performance
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[9, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
| 2,516
| 30.074074
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/common/mstrain_3x_coco.py
|
_base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
# multiscale_mode='range'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
# Use RepeatDataset to speed up training
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type='RepeatDataset',
times=3,
dataset=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
pipeline=train_pipeline)),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline))
evaluation = dict(interval=1, metric='bbox')
# optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
# Experiments show that using step=[9, 11] has higher performance
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[9, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
| 2,428
| 30.545455
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py
|
_base_ = './panoptic_fpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 200
| 27.714286
| 61
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco.py
|
_base_ = './panoptic_fpn_r50_fpn_1x_coco.py'
# dataset settings
dataset_type = 'CocoPanopticDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
# multiscale_mode='range'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadPanopticAnnotations',
with_bbox=True,
with_mask=True,
with_seg=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='SegRescale', scale_factor=1 / 4),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
# Use RepeatDataset to speed up training
data = dict(
train=dict(
_delete_=True,
type='RepeatDataset',
times=3,
dataset=dict(
type=dataset_type,
ann_file=data_root + 'annotations/panoptic_train2017.json',
img_prefix=data_root + 'train2017/',
seg_prefix=data_root + 'annotations/panoptic_train2017/',
pipeline=train_pipeline)),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
| 1,933
| 30.193548
| 79
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco.py
|
_base_ = './panoptic_fpn_r50_fpn_mstrain_3x_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/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py
|
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_panoptic.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='PanopticFPN',
semantic_head=dict(
type='PanopticFPNHead',
num_classes=54,
in_channels=256,
inner_channels=128,
start_level=0,
end_level=4,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
conv_cfg=None,
loss_seg=dict(
type='CrossEntropyLoss', ignore_index=-1, loss_weight=0.5)),
panoptic_fusion_head=dict(
type='HeuristicFusionHead',
num_things_classes=80,
num_stuff_classes=53),
test_cfg=dict(
panoptic=dict(
score_thr=0.6,
max_per_img=100,
mask_thr_binary=0.5,
mask_overlap=0.5,
nms=dict(type='nms', iou_threshold=0.5, class_agnostic=True),
stuff_area_limit=4096)))
custom_hooks = []
| 997
| 29.242424
| 73
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/scnet/scnet_r101_fpn_20e_coco.py
|
_base_ = './scnet_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 194
| 26.857143
| 61
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py
|
_base_ = './scnet_r50_fpn_20e_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),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 440
| 26.5625
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/scnet/scnet_x101_64x4d_fpn_8x1_20e_coco.py
|
_base_ = './scnet_x101_64x4d_fpn_20e_coco.py'
data = dict(samples_per_gpu=1, workers_per_gpu=1)
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 169
| 41.5
| 72
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/scnet/scnet_r50_fpn_20e_coco.py
|
_base_ = './scnet_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 19])
runner = dict(type='EpochBasedRunner', max_epochs=20)
| 142
| 27.6
| 53
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/scnet/scnet_r50_fpn_1x_coco.py
|
_base_ = '../htc/htc_r50_fpn_1x_coco.py'
# model settings
model = dict(
type='SCNet',
roi_head=dict(
_delete_=True,
type='SCNetRoIHead',
num_stages=3,
stage_loss_weights=[1, 0.5, 0.25],
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=[
dict(
type='SCNetBBoxHead',
num_shared_fcs=2,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='SCNetBBoxHead',
num_shared_fcs=2,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='SCNetBBoxHead',
num_shared_fcs=2,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
],
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
mask_head=dict(
type='SCNetMaskHead',
num_convs=12,
in_channels=256,
conv_out_channels=256,
num_classes=80,
conv_to_res=True,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
semantic_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[8]),
semantic_head=dict(
type='SCNetSemanticHead',
num_ins=5,
fusion_level=1,
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=183,
loss_seg=dict(
type='CrossEntropyLoss', ignore_index=255, loss_weight=0.2),
conv_to_res=True),
glbctx_head=dict(
type='GlobalContextHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=80,
loss_weight=3.0,
conv_to_res=True),
feat_relay_head=dict(
type='FeatureRelayHead',
in_channels=1024,
out_conv_channels=256,
roi_feat_size=7,
scale_factor=2)))
# uncomment below code to enable test time augmentations
# img_norm_cfg = dict(
# mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# test_pipeline = [
# dict(type='LoadImageFromFile'),
# dict(
# type='MultiScaleFlipAug',
# img_scale=[(600, 900), (800, 1200), (1000, 1500), (1200, 1800),
# (1400, 2100)],
# flip=True,
# transforms=[
# dict(type='Resize', keep_ratio=True),
# dict(type='RandomFlip', flip_ratio=0.5),
# dict(type='Normalize', **img_norm_cfg),
# dict(type='Pad', size_divisor=32),
# dict(type='ImageToTensor', keys=['img']),
# dict(type='Collect', keys=['img']),
# ])
# ]
# data = dict(
# val=dict(pipeline=test_pipeline),
# test=dict(pipeline=test_pipeline))
| 5,020
| 35.649635
| 79
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rpn/crpn_r50_caffe_fpn_1x_coco.py
|
_base_ = '../rpn/rpn_r50_caffe_fpn_1x_coco.py'
model = dict(
rpn_head=dict(
_delete_=True,
type='CascadeRPNHead',
num_stages=2,
stages=[
dict(
type='StageCascadeRPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[1.0],
strides=[4, 8, 16, 32, 64]),
adapt_cfg=dict(type='dilation', dilation=3),
bridged_feature=True,
sampling=False,
with_cls=False,
reg_decoded_bbox=True,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=(.0, .0, .0, .0),
target_stds=(0.1, 0.1, 0.5, 0.5)),
loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0)),
dict(
type='StageCascadeRPNHead',
in_channels=256,
feat_channels=256,
adapt_cfg=dict(type='offset'),
bridged_feature=False,
sampling=True,
with_cls=True,
reg_decoded_bbox=True,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=(.0, .0, .0, .0),
target_stds=(0.05, 0.05, 0.1, 0.1)),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True,
loss_weight=1.0),
loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0))
]),
train_cfg=dict(rpn=[
dict(
assigner=dict(
type='RegionAssigner', center_ratio=0.2, ignore_ratio=0.5),
allowed_border=-1,
pos_weight=-1,
debug=False),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.7,
min_pos_iou=0.3,
ignore_iof_thr=-1,
iou_calculator=dict(type='BboxOverlaps2D')),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False)
]),
test_cfg=dict(
rpn=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.8),
min_bbox_size=0)))
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
| 2,750
| 34.269231
| 79
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py
|
_base_ = '../faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py'
rpn_weight = 0.7
model = dict(
rpn_head=dict(
_delete_=True,
type='CascadeRPNHead',
num_stages=2,
stages=[
dict(
type='StageCascadeRPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[1.0],
strides=[4, 8, 16, 32, 64]),
adapt_cfg=dict(type='dilation', dilation=3),
bridged_feature=True,
sampling=False,
with_cls=False,
reg_decoded_bbox=True,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=(.0, .0, .0, .0),
target_stds=(0.1, 0.1, 0.5, 0.5)),
loss_bbox=dict(
type='IoULoss', linear=True,
loss_weight=10.0 * rpn_weight)),
dict(
type='StageCascadeRPNHead',
in_channels=256,
feat_channels=256,
adapt_cfg=dict(type='offset'),
bridged_feature=False,
sampling=True,
with_cls=True,
reg_decoded_bbox=True,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=(.0, .0, .0, .0),
target_stds=(0.05, 0.05, 0.1, 0.1)),
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0 * rpn_weight),
loss_bbox=dict(
type='IoULoss', linear=True,
loss_weight=10.0 * rpn_weight))
]),
roi_head=dict(
bbox_head=dict(
bbox_coder=dict(target_stds=[0.04, 0.04, 0.08, 0.08]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.5),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
# model training and testing settings
train_cfg=dict(
rpn=[
dict(
assigner=dict(
type='RegionAssigner', center_ratio=0.2, ignore_ratio=0.5),
allowed_border=-1,
pos_weight=-1,
debug=False),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.7,
min_pos_iou=0.3,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False)
],
rpn_proposal=dict(max_per_img=300, nms=dict(iou_threshold=0.8)),
rcnn=dict(
assigner=dict(
pos_iou_thr=0.65, neg_iou_thr=0.65, min_pos_iou=0.65),
sampler=dict(type='RandomSampler', num=256))),
test_cfg=dict(
rpn=dict(max_per_img=300, nms=dict(iou_threshold=0.8)),
rcnn=dict(score_thr=1e-3)))
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
| 3,490
| 36.537634
| 79
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rpn/crpn_fast_rcnn_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',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')),
roi_head=dict(
bbox_head=dict(
bbox_coder=dict(target_stds=[0.04, 0.04, 0.08, 0.08]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.5),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
# model training and testing settings
train_cfg=dict(
rcnn=dict(
assigner=dict(
pos_iou_thr=0.65, neg_iou_thr=0.65, min_pos_iou=0.65),
sampler=dict(num=256))),
test_cfg=dict(rcnn=dict(score_thr=1e-3)))
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadProposals', num_max_proposals=300),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadProposals', num_max_proposals=300),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='ToTensor', keys=['proposals']),
dict(
type='ToDataContainer',
fields=[dict(key='proposals', stack=False)]),
dict(type='Collect', keys=['img', 'proposals']),
])
]
data = dict(
train=dict(
proposal_file=data_root +
'proposals/crpn_r50_caffe_fpn_1x_train2017.pkl',
pipeline=train_pipeline),
val=dict(
proposal_file=data_root +
'proposals/crpn_r50_caffe_fpn_1x_val2017.pkl',
pipeline=test_pipeline),
test=dict(
proposal_file=data_root +
'proposals/crpn_r50_caffe_fpn_1x_val2017.pkl',
pipeline=test_pipeline))
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
| 2,833
| 35.333333
| 78
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/legacy_1.x/faster_rcnn_r50_fpn_1x_coco_v1.py
|
_base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='FasterRCNN',
backbone=dict(
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
rpn_head=dict(
type='RPNHead',
anchor_generator=dict(
type='LegacyAnchorGenerator',
center_offset=0.5,
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
roi_head=dict(
type='StandardRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
type='RoIAlign',
output_size=7,
sampling_ratio=2,
aligned=False),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
# model training and testing settings
train_cfg=dict(
rpn_proposal=dict(max_per_img=2000),
rcnn=dict(assigner=dict(match_low_quality=True))))
| 1,385
| 34.538462
| 79
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py
|
_base_ = [
'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='CascadeRCNN',
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_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
anchor_generator=dict(type='LegacyAnchorGenerator', center_offset=0.5),
bbox_coder=dict(
type='LegacyDeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0])),
roi_head=dict(
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
type='RoIAlign',
output_size=7,
sampling_ratio=2,
aligned=False)),
bbox_head=[
dict(
type='Shared2FCBBoxHead',
reg_class_agnostic=True,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='LegacyDeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2])),
dict(
type='Shared2FCBBoxHead',
reg_class_agnostic=True,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='LegacyDeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1])),
dict(
type='Shared2FCBBoxHead',
reg_class_agnostic=True,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='LegacyDeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067])),
],
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
type='RoIAlign',
output_size=14,
sampling_ratio=2,
aligned=False))))
dist_params = dict(backend='nccl', port=29515)
| 2,791
| 33.9
| 79
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/legacy_1.x/mask_rcnn_r50_fpn_1x_coco_v1.py
|
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
rpn_head=dict(
anchor_generator=dict(type='LegacyAnchorGenerator', center_offset=0.5),
bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
roi_head=dict(
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
type='RoIAlign',
output_size=7,
sampling_ratio=2,
aligned=False)),
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
type='RoIAlign',
output_size=14,
sampling_ratio=2,
aligned=False)),
bbox_head=dict(
bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
# model training and testing settings
train_cfg=dict(
rpn_proposal=dict(max_per_img=2000),
rcnn=dict(assigner=dict(match_low_quality=True))))
| 1,238
| 34.4
| 79
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py
|
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
bbox_head=dict(
type='RetinaHead',
anchor_generator=dict(
type='LegacyAnchorGenerator',
center_offset=0.5,
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'),
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)))
| 617
| 33.333333
| 73
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/legacy_1.x/retinanet_r50_caffe_fpn_1x_coco_v1.py
|
_base_ = './retinanet_r50_fpn_1x_coco_v1.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron/resnet50_caffe')))
# use caffe img_norm
img_norm_cfg = dict(
mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
| 1,413
| 32.666667
| 75
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/legacy_1.x/ssd300_coco_v1.py
|
_base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
input_size = 300
model = dict(
bbox_head=dict(
type='SSDHead',
anchor_generator=dict(
type='LegacySSDAnchorGenerator',
scale_major=False,
input_size=input_size,
basesize_ratio_range=(0.15, 0.9),
strides=[8, 16, 32, 64, 100, 300],
ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]),
bbox_coder=dict(
type='LegacyDeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[0.1, 0.1, 0.2, 0.2])))
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile', to_float32=True),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PhotoMetricDistortion',
brightness_delta=32,
contrast_range=(0.5, 1.5),
saturation_range=(0.5, 1.5),
hue_delta=18),
dict(
type='Expand',
mean=img_norm_cfg['mean'],
to_rgb=img_norm_cfg['to_rgb'],
ratio_range=(1, 4)),
dict(
type='MinIoURandomCrop',
min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
min_crop_size=0.3),
dict(type='Resize', img_scale=(300, 300), keep_ratio=False),
dict(type='Normalize', **img_norm_cfg),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(300, 300),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=False),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=8,
workers_per_gpu=3,
train=dict(
_delete_=True,
type='RepeatDataset',
times=5,
dataset=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
pipeline=train_pipeline)),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
# optimizer
optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4)
optimizer_config = dict(_delete_=True)
dist_params = dict(backend='nccl', port=29555)
| 2,659
| 32.25
| 79
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
type='MaskScoringRCNN',
roi_head=dict(
type='MaskScoringRoIHead',
mask_iou_head=dict(
type='MaskIoUHead',
num_convs=4,
num_fcs=2,
roi_feat_size=14,
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
num_classes=80)),
# model training and testing settings
train_cfg=dict(rcnn=dict(mask_thr_binary=0.5)))
| 515
| 29.352941
| 58
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/ms_rcnn/ms_rcnn_r50_fpn_1x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
type='MaskScoringRCNN',
roi_head=dict(
type='MaskScoringRoIHead',
mask_iou_head=dict(
type='MaskIoUHead',
num_convs=4,
num_fcs=2,
roi_feat_size=14,
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
num_classes=80)),
# model training and testing settings
train_cfg=dict(rcnn=dict(mask_thr_binary=0.5)))
| 509
| 29
| 52
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py
|
_base_ = './ms_rcnn_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',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 417
| 26.866667
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py
|
_base_ = './ms_rcnn_x101_64x4d_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 151
| 29.4
| 53
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py
|
_base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 150
| 29.2
| 53
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py
|
_base_ = './ms_rcnn_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',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 417
| 26.866667
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py
|
_base_ = './ms_rcnn_r101_caffe_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 151
| 29.4
| 53
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py
|
_base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 220
| 26.625
| 67
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py
|
_base_ = './fast_rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197
| 27.285714
| 61
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/fast_rcnn/fast_rcnn_r50_fpn_2x_coco.py
|
_base_ = './fast_rcnn_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 147
| 23.666667
| 53
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py
|
_base_ = './fast_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197
| 27.285714
| 61
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py
|
_base_ = './fast_rcnn_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/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py
|
_base_ = './fast_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='BN', requires_grad=False),
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadProposals', num_max_proposals=2000),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadProposals', num_max_proposals=None),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='ToTensor', keys=['proposals']),
dict(
type='ToDataContainer',
fields=[dict(key='proposals', stack=False)]),
dict(type='Collect', keys=['img', 'proposals']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
| 1,710
| 33.918367
| 78
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py
|
_base_ = [
'../_base_/models/fast_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadProposals', num_max_proposals=2000),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadProposals', num_max_proposals=None),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='ToTensor', keys=['proposals']),
dict(
type='ToDataContainer',
fields=[dict(key='proposals', stack=False)]),
dict(type='Collect', keys=['img', 'proposals']),
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_train2017.pkl',
pipeline=train_pipeline),
val=dict(
proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_val2017.pkl',
pipeline=test_pipeline),
test=dict(
proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_val2017.pkl',
pipeline=test_pipeline))
| 1,944
| 35.698113
| 78
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py
|
_base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py'
# model settings
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')),
neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))
| 482
| 36.153846
| 78
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py
|
_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(32, 64)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(32, 64, 128)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(32, 64, 128, 256))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')),
neck=dict(
_delete_=True,
type='HRFPN',
in_channels=[32, 64, 128, 256],
out_channels=256))
# learning policy
lr_config = dict(step=[16, 19])
runner = dict(type='EpochBasedRunner', max_epochs=20)
| 1,296
| 30.634146
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(32, 64)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(32, 64, 128)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(32, 64, 128, 256))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')),
neck=dict(
_delete_=True,
type='HRFPN',
in_channels=[32, 64, 128, 256],
out_channels=256))
| 1,181
| 30.105263
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py
|
_base_ = './faster_rcnn_hrnetv2p_w18_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 154
| 24.833333
| 53
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py
|
_base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py'
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')),
neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))
| 461
| 37.5
| 78
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py
|
_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 158
| 30.8
| 53
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py
|
_base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py'
# model settings
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')),
neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
| 457
| 37.166667
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py
|
_base_ = './mask_rcnn_hrnetv2p_w40_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 151
| 29.4
| 53
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py
|
_base_ = '../fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(32, 64)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(32, 64, 128)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(32, 64, 128, 256))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')),
neck=dict(
_delete_=True,
type='HRFPN',
in_channels=[32, 64, 128, 256],
out_channels=256,
stride=2,
num_outs=5))
img_norm_cfg = dict(
mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
| 2,333
| 31.873239
| 78
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py
|
_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py'
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')),
neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))
| 463
| 37.666667
| 78
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py
|
_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(32, 64)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(32, 64, 128)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(32, 64, 128, 256))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')),
neck=dict(
_delete_=True,
type='HRFPN',
in_channels=[32, 64, 128, 256],
out_channels=256))
# learning policy
lr_config = dict(step=[16, 19])
runner = dict(type='EpochBasedRunner', max_epochs=20)
| 1,291
| 30.512195
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py
|
_base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 151
| 29.4
| 53
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py
|
_base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py'
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')),
neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
| 459
| 40.818182
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py
|
_base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py'
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')),
neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))
| 484
| 39.416667
| 78
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/htc_x101_64x4d_fpn_16x1_28e_coco.py
|
_base_ = '../htc/htc_x101_64x4d_fpn_16x1_20e_coco.py'
# learning policy
lr_config = dict(step=[24, 27])
runner = dict(type='EpochBasedRunner', max_epochs=28)
| 158
| 30.8
| 53
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py
|
_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 153
| 29.8
| 53
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py
|
_base_ = './cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py'
# model settings
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')),
neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
| 462
| 37.583333
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py
|
_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')),
neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
| 443
| 39.363636
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py
|
_base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 151
| 29.4
| 53
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py
|
_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py'
# model settings
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')),
neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
| 455
| 37
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py
|
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(32, 64)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(32, 64, 128)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(32, 64, 128, 256))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')),
neck=dict(
_delete_=True,
type='HRFPN',
in_channels=[32, 64, 128, 256],
out_channels=256))
| 1,185
| 30.210526
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py
|
_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
img_norm_cfg = dict(
mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 1,337
| 32.45
| 78
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py
|
_base_ = './fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 158
| 30.8
| 53
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py
|
_base_ = './htc_hrnetv2p_w32_20e_coco.py'
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')),
neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
| 431
| 38.272727
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py
|
_base_ = './cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py'
# model settings
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')),
neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))
| 487
| 36.538462
| 78
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/htc_hrnetv2p_w40_28e_coco.py
|
_base_ = './htc_hrnetv2p_w40_20e_coco.py'
# learning policy
lr_config = dict(step=[24, 27])
runner = dict(type='EpochBasedRunner', max_epochs=28)
| 146
| 28.4
| 53
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py
|
_base_ = './faster_rcnn_hrnetv2p_w40_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 153
| 29.8
| 53
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py
|
_base_ = '../htc/htc_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(32, 64)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(32, 64, 128)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(32, 64, 128, 256))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')),
neck=dict(
_delete_=True,
type='HRFPN',
in_channels=[32, 64, 128, 256],
out_channels=256))
| 1,170
| 29.815789
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py
|
_base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py'
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')),
neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
| 436
| 38.727273
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py
|
_base_ = './htc_hrnetv2p_w32_20e_coco.py'
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')),
neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))
| 456
| 37.083333
| 78
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py
|
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='1111',
kv_stride=2),
stages=(False, False, True, True),
position='after_conv2')
]))
| 403
| 27.857143
| 56
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py
|
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='1111',
kv_stride=2),
stages=(False, False, True, True),
position='after_conv2')
],
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 575
| 32.882353
| 72
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py
|
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='0010',
kv_stride=2),
stages=(False, False, True, True),
position='after_conv2')
],
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 575
| 32.882353
| 72
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py
|
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='0010',
kv_stride=2),
stages=(False, False, True, True),
position='after_conv2')
]))
| 403
| 27.857143
| 56
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/yolox/yolox_m_8x8_300e_coco.py
|
_base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=0.67, widen_factor=0.75),
neck=dict(in_channels=[192, 384, 768], out_channels=192, num_csp_blocks=2),
bbox_head=dict(in_channels=192, feat_channels=192),
)
| 266
| 28.666667
| 79
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/yolox/yolox_s_8x8_300e_coco.py
|
_base_ = ['../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py']
# model settings
model = dict(
type='YOLOX',
backbone=dict(type='CSPDarknet', deepen_factor=0.33, widen_factor=0.5),
neck=dict(
type='YOLOXPAFPN',
in_channels=[128, 256, 512],
out_channels=128,
num_csp_blocks=1),
bbox_head=dict(
type='YOLOXHead', num_classes=80, in_channels=128, feat_channels=128),
train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)),
# In order to align the source code, the threshold of the val phase is
# 0.01, and the threshold of the test phase is 0.001.
test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))
# dataset settings
data_root = 'data/coco/'
dataset_type = 'CocoDataset'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
img_scale = (640, 640)
train_pipeline = [
dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
dict(
type='RandomAffine',
scaling_ratio_range=(0.1, 2),
border=(-img_scale[0] // 2, -img_scale[1] // 2)),
dict(
type='MixUp',
img_scale=img_scale,
ratio_range=(0.8, 1.6),
pad_val=114.0),
dict(
type='PhotoMetricDistortion',
brightness_delta=32,
contrast_range=(0.5, 1.5),
saturation_range=(0.5, 1.5),
hue_delta=18),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Resize', keep_ratio=True),
dict(type='Pad', pad_to_square=True, pad_val=114.0),
dict(type='Normalize', **img_norm_cfg),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
train_dataset = dict(
type='MultiImageMixDataset',
dataset=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
pipeline=[
dict(type='LoadImageFromFile', to_float32=True),
dict(type='LoadAnnotations', with_bbox=True)
],
filter_empty_gt=False,
),
pipeline=train_pipeline,
dynamic_scale=img_scale)
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=img_scale,
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Pad', size=img_scale, pad_val=114.0),
dict(type='Normalize', **img_norm_cfg),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=8,
workers_per_gpu=2,
train=train_dataset,
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline))
# optimizer
# default 8 gpu
optimizer = dict(
type='SGD',
lr=0.01,
momentum=0.9,
weight_decay=5e-4,
nesterov=True,
paramwise_cfg=dict(norm_decay_mult=0., bias_decay_mult=0.))
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
_delete_=True,
policy='YOLOX',
warmup='exp',
by_epoch=False,
warmup_by_epoch=True,
warmup_ratio=1,
warmup_iters=5, # 5 epoch
num_last_epochs=15,
min_lr_ratio=0.05)
runner = dict(type='EpochBasedRunner', max_epochs=300)
resume_from = None
interval = 10
custom_hooks = [
dict(type='YOLOXModeSwitchHook', num_last_epochs=15, priority=48),
dict(
type='SyncRandomSizeHook',
ratio_range=(14, 26),
img_scale=img_scale,
interval=interval,
priority=48),
dict(
type='SyncNormHook',
num_last_epochs=15,
interval=interval,
priority=48),
dict(type='ExpMomentumEMAHook', resume_from=resume_from, priority=49)
]
checkpoint_config = dict(interval=interval)
evaluation = dict(interval=interval, metric='bbox')
log_config = dict(interval=50)
| 4,236
| 28.423611
| 79
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/yolox/yolox_l_8x8_300e_coco.py
|
_base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=1.0, widen_factor=1.0),
neck=dict(
in_channels=[256, 512, 1024], out_channels=256, num_csp_blocks=3),
bbox_head=dict(in_channels=256, feat_channels=256))
| 272
| 29.333333
| 74
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/yolox/yolox_x_8x8_300e_coco.py
|
_base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=1.33, widen_factor=1.25),
neck=dict(
in_channels=[320, 640, 1280], out_channels=320, num_csp_blocks=4),
bbox_head=dict(in_channels=320, feat_channels=320))
| 274
| 29.555556
| 74
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/yolox/yolox_nano_8x8_300e_coco.py
|
_base_ = './yolox_tiny_8x8_300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=0.33, widen_factor=0.25, use_depthwise=True),
neck=dict(
in_channels=[64, 128, 256],
out_channels=64,
num_csp_blocks=1,
use_depthwise=True),
bbox_head=dict(in_channels=64, feat_channels=64, use_depthwise=True))
| 356
| 28.75
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/yolox/yolox_tiny_8x8_300e_coco.py
|
_base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=0.33, widen_factor=0.375),
neck=dict(in_channels=[96, 192, 384], out_channels=96),
bbox_head=dict(in_channels=96, feat_channels=96))
# dataset settings
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
img_scale = (640, 640)
train_pipeline = [
dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
dict(
type='RandomAffine',
scaling_ratio_range=(0.5, 1.5),
border=(-img_scale[0] // 2, -img_scale[1] // 2)),
dict(
type='PhotoMetricDistortion',
brightness_delta=32,
contrast_range=(0.5, 1.5),
saturation_range=(0.5, 1.5),
hue_delta=18),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Resize', keep_ratio=True),
dict(type='Pad', pad_to_square=True, pad_val=114.0),
dict(type='Normalize', **img_norm_cfg),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(416, 416),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Pad', size=(416, 416), pad_val=114.0),
dict(type='Normalize', **img_norm_cfg),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
]
train_dataset = dict(pipeline=train_pipeline)
data = dict(
train=train_dataset,
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
resume_from = None
interval = 10
# Execute in the order of insertion when the priority is the same.
# The smaller the value, the higher the priority
custom_hooks = [
dict(type='YOLOXModeSwitchHook', num_last_epochs=15, priority=48),
dict(
type='SyncRandomSizeHook',
ratio_range=(10, 20),
img_scale=img_scale,
interval=interval,
priority=48),
dict(
type='SyncNormHook',
num_last_epochs=15,
interval=interval,
priority=48),
dict(type='ExpMomentumEMAHook', resume_from=resume_from, priority=49)
]
checkpoint_config = dict(interval=interval)
evaluation = dict(interval=interval, metric='bbox')
| 2,389
| 28.875
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py
|
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
type='MaskRCNN',
backbone=dict(
_delete_=True,
type='SwinTransformer',
embed_dims=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.2,
patch_norm=True,
out_indices=(0, 1, 2, 3),
with_cp=False,
convert_weights=True,
init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
neck=dict(in_channels=[96, 192, 384, 768]))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# augmentation strategy originates from DETR / Sparse RCNN
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='AutoAugment',
policies=[[
dict(
type='Resize',
img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
(608, 1333), (640, 1333), (672, 1333), (704, 1333),
(736, 1333), (768, 1333), (800, 1333)],
multiscale_mode='value',
keep_ratio=True)
],
[
dict(
type='Resize',
img_scale=[(400, 1333), (500, 1333), (600, 1333)],
multiscale_mode='value',
keep_ratio=True),
dict(
type='RandomCrop',
crop_type='absolute_range',
crop_size=(384, 600),
allow_negative_crop=True),
dict(
type='Resize',
img_scale=[(480, 1333), (512, 1333), (544, 1333),
(576, 1333), (608, 1333), (640, 1333),
(672, 1333), (704, 1333), (736, 1333),
(768, 1333), (800, 1333)],
multiscale_mode='value',
override=True,
keep_ratio=True)
]]),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
data = dict(train=dict(pipeline=train_pipeline))
optimizer = dict(
_delete_=True,
type='AdamW',
lr=0.0001,
betas=(0.9, 0.999),
weight_decay=0.05,
paramwise_cfg=dict(
custom_keys={
'absolute_pos_embed': dict(decay_mult=0.),
'relative_position_bias_table': dict(decay_mult=0.),
'norm': dict(decay_mult=0.)
}))
lr_config = dict(warmup_iters=1000, step=[27, 33])
runner = dict(max_epochs=36)
| 3,305
| 34.934783
| 123
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/swin/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco.py
|
_base_ = './mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py'
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth' # noqa
model = dict(
backbone=dict(
depths=[2, 2, 18, 2],
init_cfg=dict(type='Pretrained', checkpoint=pretrained)))
| 318
| 44.571429
| 124
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_1x_coco.py
|
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
type='MaskRCNN',
backbone=dict(
_delete_=True,
type='SwinTransformer',
embed_dims=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.2,
patch_norm=True,
out_indices=(0, 1, 2, 3),
with_cp=False,
convert_weights=True,
init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
neck=dict(in_channels=[96, 192, 384, 768]))
optimizer = dict(
_delete_=True,
type='AdamW',
lr=0.0001,
betas=(0.9, 0.999),
weight_decay=0.05,
paramwise_cfg=dict(
custom_keys={
'absolute_pos_embed': dict(decay_mult=0.),
'relative_position_bias_table': dict(decay_mult=0.),
'norm': dict(decay_mult=0.)
}))
lr_config = dict(warmup_iters=1000, step=[8, 11])
runner = dict(max_epochs=12)
| 1,301
| 29.27907
| 123
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py
|
_base_ = './mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py'
# you need to set mode='dynamic' if you are using pytorch<=1.5.0
fp16 = dict(loss_scale=dict(init_scale=512))
| 169
| 41.5
| 64
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/vfnet/vfnet_r2_101_fpn_mstrain_2x_coco.py
|
_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
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_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 464
| 26.352941
| 62
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py
|
_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py'
model = dict(
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=True),
norm_eval=True,
style='pytorch',
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 546
| 33.1875
| 74
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/vfnet/vfnet_r50_fpn_1x_coco.py
|
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='VFNet',
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_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_output', # use P5
num_outs=5,
relu_before_extra_convs=True),
bbox_head=dict(
type='VFNetHead',
num_classes=80,
in_channels=256,
stacked_convs=3,
feat_channels=256,
strides=[8, 16, 32, 64, 128],
center_sampling=False,
dcn_on_last_conv=False,
use_atss=True,
use_vfl=True,
loss_cls=dict(
type='VarifocalLoss',
use_sigmoid=True,
alpha=0.75,
gamma=2.0,
iou_weighted=True,
loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=1.5),
loss_bbox_refine=dict(type='GIoULoss', loss_weight=2.0)),
# training and testing settings
train_cfg=dict(
assigner=dict(type='ATSSAssigner', topk=9),
allowed_border=-1,
pos_weight=-1,
debug=False),
test_cfg=dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.6),
max_per_img=100))
# data setting
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
# optimizer
optimizer = dict(
lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.))
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.1,
step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
| 3,240
| 29.009259
| 79
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/vfnet/vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py
|
_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
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',
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 602
| 30.736842
| 74
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py
|
_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 201
| 27.857143
| 61
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py
|
_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_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),
norm_eval=True,
style='pytorch',
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 585
| 31.555556
| 76
|
py
|
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