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dist_cascade_mask_rcnn_x101-fpn_x50_coco.py
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dist_cascade_mask_rcnn_x101-fpn_x50_coco.py
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_base_ = [
'../../_base_/datasets/mmdet/coco_detection.py',
'../../_base_/schedules/mmdet/schedule_2x.py',
'../../_base_/mmdet_runtime.py'
]
# model settings
student = dict(
type='mmdet.FasterRCNN',
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(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', 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=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='Shared2FCBBoxHead',
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=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
# model training and testing settings
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
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(
nms_pre=2000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100)
# soft-nms is also supported for rcnn testing
# e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05)
))
teacher = dict(
type='mmdet.CascadeRCNN',
init_cfg=dict(type='Pretrained', checkpoint='https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033-bdb5126a.pth'),
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'),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
roi_head=dict(
type='CascadeRoIHead',
num_stages=1,
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(
# NOTE: use `KDShared2FCBBoxHead` to discard loss computation
type='KDShared2FCBBoxHead',
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='Shared2FCBBoxHead',
# 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='Shared2FCBBoxHead',
# 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='FCNMaskHead',
# num_convs=4,
# in_channels=256,
# conv_out_channels=256,
# num_classes=80,
# loss_mask=dict(
# type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
),
# model training and testing settings
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
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=0,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=[
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False),
# dict(
# assigner=dict(
# type='MaxIoUAssigner',
# pos_iou_thr=0.6,
# neg_iou_thr=0.6,
# min_pos_iou=0.6,
# match_low_quality=False,
# ignore_iof_thr=-1),
# sampler=dict(
# type='RandomSampler',
# num=512,
# pos_fraction=0.25,
# neg_pos_ub=-1,
# add_gt_as_proposals=True),
# pos_weight=-1,
# debug=False),
# dict(
# assigner=dict(
# type='MaxIoUAssigner',
# pos_iou_thr=0.7,
# neg_iou_thr=0.7,
# min_pos_iou=0.7,
# match_low_quality=False,
# ignore_iof_thr=-1),
# sampler=dict(
# type='RandomSampler',
# num=512,
# pos_fraction=0.25,
# neg_pos_ub=-1,
# add_gt_as_proposals=True),
# pos_weight=-1,
# debug=False)
]),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100,
)))
# algorithm setting
algorithm = dict(
type='GeneralDistill',
architecture=dict(
type='MMDetArchitecture',
model=student,
),
distiller=dict(
type='SingleTeacherDistiller',
teacher=teacher,
teacher_trainable=False,
components=[
dict(
student_module='roi_head.bbox_head.fc_cls',
teacher_module='roi_head.bbox_head.0.fc_cls',
modules_with_student_inputs=[
dict(
student_module='roi_head.bbox_roi_extractor',
teacher_module='roi_head.bbox_roi_extractor.0',
same_indices=[1],)
],
losses=[
dict(
type='DIST',
name='loss_dist_roi_cls_head',
beta=1,
gamma=1,
loss_weight=1,
)
]),
]),
)
find_unused_parameters = True