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Commit e6ff2c55 authored by Zheng Zhang's avatar Zheng Zhang Committed by Kai Chen
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Code for "an empirical study of spatial attention mechanisms in deep networks" (#730) 

* update configs and related code

* remove debug info

* fix style issues

* fix unnecessary changes

* move generalized_attention.py from ops/dcn to models/plugins

* change the arguments name of generalized attention, add related description and update resnet backbone

* revert the change of weight_init.py

* format touched files by yapf

* change default data_root

* update code for passing flake8 test

* delete unnecessary __init__.py

* Change the argument order of resnext.__init__()

* remove constant_init

* simplifying the init of generalized_attention module

* remove unused import

* format the code

* change the name of config folder, add more configs, add README.md

* rename num_head to num_heads

* minor formatting
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configs/empirical_attention/README.md 0 → 100644
View file @ e6ff2c55
# An Empirical Study of Spatial Attention Mechanisms in Deep Networks
## Introduction
```
@article{zhu2019empirical,
title={An Empirical Study of Spatial Attention Mechanisms in Deep Networks},
author={Zhu, Xizhou and Cheng, Dazhi and Zhang, Zheng and Lin, Stephen and Dai, Jifeng},
journal={arXiv preprint arXiv:1904.05873},
year={2019}
}
```
## Results and Models
| Backbone | Attention Component | DCN | Lr schd | box AP | Download |
|:---------:|:-------------------:|:----:|:-------:|:------:|:--------:|
| R-50 | 1111 | N | 1x | 38.6 | - |
| R-50 | 0010 | N | 1x | 38.2 | - |
| R-50 | 1111 | Y | 1x | 41.0 | - |
| R-50 | 0010 | Y | 1x | 40.8 | - |
configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x.py 0 → 100644
View file @ e6ff2c55
# model settings
model = dict(
type='FasterRCNN',
pretrained='modelzoo://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
gen_attention=dict(
spatial_range=-1, num_heads=8, attention_type='0010', kv_stride=2),
stage_with_gen_attention=[[], [], [0, 1, 2, 3, 4, 5], [0, 1, 2]],
),
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_scales=[8],
anchor_ratios=[0.5, 1.0, 2.0],
anchor_strides=[4, 8, 16, 32, 64],
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)),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='SharedFCBBoxHead',
num_fcs=2,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=81,
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='SmoothL1Loss', beta=1.0, 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,
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_across_levels=False,
nms_pre=2000,
nms_post=2000,
max_num=2000,
nms_thr=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,
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_across_levels=False,
nms_pre=1000,
nms_post=1000,
max_num=1000,
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(
score_thr=0.05, nms=dict(type='nms', iou_thr=0.5), max_per_img=100)
# soft-nms is also supported for rcnn testing
# e.g., nms=dict(type='soft_nms', iou_thr=0.5, min_score=0.05)
)
# 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)
data = dict(
imgs_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
flip_ratio=0.5,
with_mask=False,
with_crowd=True,
with_label=True),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
flip_ratio=0,
with_mask=False,
with_crowd=True,
with_label=True),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
flip_ratio=0,
with_mask=False,
with_label=False,
test_mode=True))
# optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=1.0 / 3,
step=[8, 11])
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
# runtime settings
total_epochs = 12
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/faster_rcnn_r50_fpn_attention_0010_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x.py 0 → 100644
View file @ e6ff2c55
# model settings
model = dict(
type='FasterRCNN',
pretrained='modelzoo://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
gen_attention=dict(
spatial_range=-1, num_heads=8, attention_type='0010', kv_stride=2),
stage_with_gen_attention=[[], [], [0, 1, 2, 3, 4, 5], [0, 1, 2]],
dcn=dict(
modulated=False, deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
),
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_scales=[8],
anchor_ratios=[0.5, 1.0, 2.0],
anchor_strides=[4, 8, 16, 32, 64],
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)),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='SharedFCBBoxHead',
num_fcs=2,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=81,
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='SmoothL1Loss', beta=1.0, 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,
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_across_levels=False,
nms_pre=2000,
nms_post=2000,
max_num=2000,
nms_thr=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,
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_across_levels=False,
nms_pre=1000,
nms_post=1000,
max_num=1000,
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(
score_thr=0.05, nms=dict(type='nms', iou_thr=0.5), max_per_img=100)
# soft-nms is also supported for rcnn testing
# e.g., nms=dict(type='soft_nms', iou_thr=0.5, min_score=0.05)
)
# 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)
data = dict(
imgs_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
flip_ratio=0.5,
with_mask=False,
with_crowd=True,
with_label=True),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
flip_ratio=0,
with_mask=False,
with_crowd=True,
with_label=True),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
flip_ratio=0,
with_mask=False,
with_label=False,
test_mode=True))
# optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=1.0 / 3,
step=[8, 11])
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
# runtime settings
total_epochs = 12
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/faster_rcnn_r50_fpn_attention_0010_dcn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x.py 0 → 100644
View file @ e6ff2c55
# model settings
model = dict(
type='FasterRCNN',
pretrained='modelzoo://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
gen_attention=dict(
spatial_range=-1, num_heads=8, attention_type='1111', kv_stride=2),
stage_with_gen_attention=[[], [], [0, 1, 2, 3, 4, 5], [0, 1, 2]],
),
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_scales=[8],
anchor_ratios=[0.5, 1.0, 2.0],
anchor_strides=[4, 8, 16, 32, 64],
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)),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='SharedFCBBoxHead',
num_fcs=2,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=81,
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='SmoothL1Loss', beta=1.0, 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,
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_across_levels=False,
nms_pre=2000,
nms_post=2000,
max_num=2000,
nms_thr=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,
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_across_levels=False,
nms_pre=1000,
nms_post=1000,
max_num=1000,
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(
score_thr=0.05, nms=dict(type='nms', iou_thr=0.5), max_per_img=100)
# soft-nms is also supported for rcnn testing
# e.g., nms=dict(type='soft_nms', iou_thr=0.5, min_score=0.05)
)
# 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)
data = dict(
imgs_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
flip_ratio=0.5,
with_mask=False,
with_crowd=True,
with_label=True),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
flip_ratio=0,
with_mask=False,
with_crowd=True,
with_label=True),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
flip_ratio=0,
with_mask=False,
with_label=False,
test_mode=True))
# optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=1.0 / 3,
step=[8, 11])
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
# runtime settings
total_epochs = 12
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/faster_rcnn_r50_fpn_attention_1111_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x.py 0 → 100644
View file @ e6ff2c55
# model settings
model = dict(
type='FasterRCNN',
pretrained='modelzoo://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
gen_attention=dict(
spatial_range=-1, num_heads=8, attention_type='1111', kv_stride=2),
stage_with_gen_attention=[[], [], [0, 1, 2, 3, 4, 5], [0, 1, 2]],
dcn=dict(
modulated=False, deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
),
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_scales=[8],
anchor_ratios=[0.5, 1.0, 2.0],
anchor_strides=[4, 8, 16, 32, 64],
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)),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='SharedFCBBoxHead',
num_fcs=2,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=81,
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='SmoothL1Loss', beta=1.0, 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,
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_across_levels=False,
nms_pre=2000,
nms_post=2000,
max_num=2000,
nms_thr=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,
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_across_levels=False,
nms_pre=1000,
nms_post=1000,
max_num=1000,
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(
score_thr=0.05, nms=dict(type='nms', iou_thr=0.5), max_per_img=100)
# soft-nms is also supported for rcnn testing
# e.g., nms=dict(type='soft_nms', iou_thr=0.5, min_score=0.05)
)
# 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)
data = dict(
imgs_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
flip_ratio=0.5,
with_mask=False,
with_crowd=True,
with_label=True),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
flip_ratio=0,
with_mask=False,
with_crowd=True,
with_label=True),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
flip_ratio=0,
with_mask=False,
with_label=False,
test_mode=True))
# optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=1.0 / 3,
step=[8, 11])
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
# runtime settings
total_epochs = 12
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/faster_rcnn_r50_fpn_attention_1111_dcn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
mmdet/models/backbones/resnet.py
View file @ e6ff2c55
......@@ -8,6 +8,8 @@ from mmcv.cnn import constant_init, kaiming_init
from mmcv.runner import load_checkpoint
from mmdet.ops import DeformConv, ModulatedDeformConv, ContextBlock
from mmdet.models.plugins import GeneralizedAttention
from ..registry import BACKBONES
from ..utils import build_conv_layer, build_norm_layer
......@@ -26,9 +28,11 @@ class BasicBlock(nn.Module):
conv_cfg=None,
norm_cfg=dict(type='BN'),
dcn=None,
gcb=None):
gcb=None,
gen_attention=None):
super(BasicBlock, self).__init__()
assert dcn is None, "Not implemented yet."
assert gen_attention is None, "Not implemented yet."
assert gcb is None, "Not implemented yet."
self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
......@@ -95,7 +99,8 @@ class Bottleneck(nn.Module):
conv_cfg=None,
norm_cfg=dict(type='BN'),
dcn=None,
gcb=None):
gcb=None,
gen_attention=None):
"""Bottleneck block for ResNet.
If style is "pytorch", the stride-two layer is the 3x3 conv layer,
if it is "caffe", the stride-two layer is the first 1x1 conv layer.
......@@ -104,6 +109,8 @@ class Bottleneck(nn.Module):
assert style in ['pytorch', 'caffe']
assert dcn is None or isinstance(dcn, dict)
assert gcb is None or isinstance(gcb, dict)
assert gen_attention is None or isinstance(gen_attention, dict)
self.inplanes = inplanes
self.planes = planes
self.stride = stride
......@@ -116,6 +123,9 @@ class Bottleneck(nn.Module):
self.with_dcn = dcn is not None
self.gcb = gcb
self.with_gcb = gcb is not None
self.gen_attention = gen_attention
self.with_gen_attention = gen_attention is not None
if self.style == 'pytorch':
self.conv1_stride = 1
self.conv2_stride = stride
......@@ -187,12 +197,15 @@ class Bottleneck(nn.Module):
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
if self.with_gcb:
gcb_inplanes = planes * self.expansion
self.context_block = ContextBlock(
inplanes=gcb_inplanes,
**gcb
)
self.context_block = ContextBlock(inplanes=gcb_inplanes, **gcb)
# gen_attention
if self.with_gen_attention:
self.gen_attention_block = GeneralizedAttention(
planes, **gen_attention)
@property
def norm1(self):
......@@ -228,6 +241,9 @@ class Bottleneck(nn.Module):
out = self.norm2(out)
out = self.relu(out)
if self.with_gen_attention:
out = self.gen_attention_block(out)
out = self.conv3(out)
out = self.norm3(out)
......@@ -262,7 +278,9 @@ def make_res_layer(block,
conv_cfg=None,
norm_cfg=dict(type='BN'),
dcn=None,
gcb=None):
gcb=None,
gen_attention=None,
gen_attention_blocks=[]):
downsample = None
if stride != 1 or inplanes != planes * block.expansion:
downsample = nn.Sequential(
......@@ -289,7 +307,9 @@ def make_res_layer(block,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
dcn=dcn,
gcb=gcb))
gcb=gcb,
gen_attention=gen_attention if
(0 in gen_attention_blocks) else None))
inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(
......@@ -303,7 +323,9 @@ def make_res_layer(block,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
dcn=dcn,
gcb=gcb))
gcb=gcb,
gen_attention=gen_attention if
(i in gen_attention_blocks) else None))
return nn.Sequential(*layers)
......@@ -356,6 +378,8 @@ class ResNet(nn.Module):
stage_with_dcn=(False, False, False, False),
gcb=None,
stage_with_gcb=(False, False, False, False),
gen_attention=None,
stage_with_gen_attention=((), (), (), ()),
with_cp=False,
zero_init_residual=True):
super(ResNet, self).__init__()
......@@ -379,6 +403,7 @@ class ResNet(nn.Module):
self.stage_with_dcn = stage_with_dcn
if dcn is not None:
assert len(stage_with_dcn) == num_stages
self.gen_attention = gen_attention
self.gcb = gcb
self.stage_with_gcb = stage_with_gcb
if gcb is not None:
......@@ -409,7 +434,9 @@ class ResNet(nn.Module):
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
dcn=dcn,
gcb=gcb)
gcb=gcb,
gen_attention=gen_attention,
gen_attention_blocks=stage_with_gen_attention[i])
self.inplanes = planes * self.block.expansion
layer_name = 'layer{}'.format(i + 1)
self.add_module(layer_name, res_layer)
......
mmdet/models/backbones/resnext.py
View file @ e6ff2c55
......@@ -11,7 +11,7 @@ from ..utils import build_conv_layer, build_norm_layer
class Bottleneck(_Bottleneck):
def __init__(self, *args, groups=1, base_width=4, **kwargs):
def __init__(self, groups=1, base_width=4, *args, **kwargs):
"""Bottleneck block for ResNeXt.
If style is "pytorch", the stride-two layer is the 3x3 conv layer,
if it is "caffe", the stride-two layer is the first 1x1 conv layer.
......
mmdet/models/plugins/__init__.py
View file @ e6ff2c55
from .non_local import NonLocal2D
from .generalized_attention import GeneralizedAttention
__all__ = ['NonLocal2D']
__all__ = ['NonLocal2D', 'GeneralizedAttention']
mmdet/models/plugins/generalized_attention.py 0 → 100644
View file @ e6ff2c55
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import numpy as np
from mmcv.cnn import kaiming_init
class GeneralizedAttention(nn.Module):
"""GeneralizedAttention module.
See 'An Empirical Study of Spatial Attention Mechanisms in Deep Networks'
(https://arxiv.org/abs/1711.07971) for details.
Args:
in_dim (int): Channels of the input feature map.
spatial_range (int): The spatial range.
-1 indicates no spatial range constraint.
num_heads (int): The head number of empirical_attention module.
position_embedding_dim (int): The position embedding dimension.
position_magnitude (int): A multiplier acting on coord difference.
kv_stride (int): The feature stride acting on key/value feature map.
q_stride (int): The feature stride acting on query feature map.
attention_type (str): A binary indicator string for indicating which
items in generalized empirical_attention module are used.
'1000' indicates 'query and key content' (appr - appr) item,
'0100' indicates 'query content and relative position'
(appr - position) item,
'0010' indicates 'key content only' (bias - appr) item,
'0001' indicates 'relative position only' (bias - position) item.
"""
def __init__(self,
in_dim,
spatial_range=-1,
num_heads=9,
position_embedding_dim=-1,
position_magnitude=1,
kv_stride=2,
q_stride=1,
attention_type='1111'):
super(GeneralizedAttention, self).__init__()
# hard range means local range for non-local operation
self.position_embedding_dim = (
position_embedding_dim if position_embedding_dim > 0 else in_dim)
self.position_magnitude = position_magnitude
self.num_heads = num_heads
self.channel_in = in_dim
self.spatial_range = spatial_range
self.kv_stride = kv_stride
self.q_stride = q_stride
self.attention_type = [bool(int(_)) for _ in attention_type]
self.qk_embed_dim = in_dim // num_heads
out_c = self.qk_embed_dim * num_heads
if self.attention_type[0] or self.attention_type[1]:
self.query_conv = nn.Conv2d(
in_channels=in_dim,
out_channels=out_c,
kernel_size=1,
bias=False)
self.query_conv.kaiming_init = True
if self.attention_type[0] or self.attention_type[2]:
self.key_conv = nn.Conv2d(
in_channels=in_dim,
out_channels=out_c,
kernel_size=1,
bias=False)
self.key_conv.kaiming_init = True
self.v_dim = in_dim // num_heads
self.value_conv = nn.Conv2d(
in_channels=in_dim,
out_channels=self.v_dim * num_heads,
kernel_size=1,
bias=False)
self.value_conv.kaiming_init = True
if self.attention_type[1] or self.attention_type[3]:
self.appr_geom_fc_x = nn.Linear(
self.position_embedding_dim // 2, out_c, bias=False)
self.appr_geom_fc_x.kaiming_init = True
self.appr_geom_fc_y = nn.Linear(
self.position_embedding_dim // 2, out_c, bias=False)
self.appr_geom_fc_y.kaiming_init = True
if self.attention_type[2]:
stdv = 1.0 / math.sqrt(self.qk_embed_dim * 2)
appr_bias_value = -2 * stdv * torch.rand(out_c) + stdv
self.appr_bias = nn.Parameter(appr_bias_value)
if self.attention_type[3]:
stdv = 1.0 / math.sqrt(self.qk_embed_dim * 2)
geom_bias_value = -2 * stdv * torch.rand(out_c) + stdv
self.geom_bias = nn.Parameter(geom_bias_value)
self.proj_conv = nn.Conv2d(
in_channels=self.v_dim * num_heads,
out_channels=in_dim,
kernel_size=1,
bias=True)
self.proj_conv.kaiming_init = True
self.gamma = nn.Parameter(torch.zeros(1))
if self.spatial_range >= 0:
# only works when non local is after 3*3 conv
if in_dim == 256:
max_len = 84
elif in_dim == 512:
max_len = 42
max_len_kv = int((max_len - 1.0) / self.kv_stride + 1)
local_constraint_map = np.ones(
(max_len, max_len, max_len_kv, max_len_kv), dtype=np.int)
for iy in range(max_len):
for ix in range(max_len):
local_constraint_map[iy, ix,
max((iy - self.spatial_range) //
self.kv_stride, 0):min(
(iy + self.spatial_range +
1) // self.kv_stride +
1, max_len),
max((ix - self.spatial_range) //
self.kv_stride, 0):min(
(ix + self.spatial_range +
1) // self.kv_stride +
1, max_len)] = 0
self.local_constraint_map = nn.Parameter(
torch.from_numpy(local_constraint_map).byte(),
requires_grad=False)
if self.q_stride > 1:
self.q_downsample = nn.AvgPool2d(
kernel_size=1, stride=self.q_stride)
else:
self.q_downsample = None
if self.kv_stride > 1:
self.kv_downsample = nn.AvgPool2d(
kernel_size=1, stride=self.kv_stride)
else:
self.kv_downsample = None
self.init_weights()
def get_position_embedding(self,
h,
w,
h_kv,
w_kv,
q_stride,
kv_stride,
device,
feat_dim,
wave_length=1000):
h_idxs = torch.linspace(0, h - 1, h).cuda(device)
h_idxs = h_idxs.view((h, 1)) * q_stride
w_idxs = torch.linspace(0, w - 1, w).cuda(device)
w_idxs = w_idxs.view((w, 1)) * q_stride
h_kv_idxs = torch.linspace(0, h_kv - 1, h_kv).cuda(device)
h_kv_idxs = h_kv_idxs.view((h_kv, 1)) * kv_stride
w_kv_idxs = torch.linspace(0, w_kv - 1, w_kv).cuda(device)
w_kv_idxs = w_kv_idxs.view((w_kv, 1)) * kv_stride
# (h, h_kv, 1)
h_diff = h_idxs.unsqueeze(1) - h_kv_idxs.unsqueeze(0)
h_diff *= self.position_magnitude
# (w, w_kv, 1)
w_diff = w_idxs.unsqueeze(1) - w_kv_idxs.unsqueeze(0)
w_diff *= self.position_magnitude
feat_range = torch.arange(0, feat_dim / 4).cuda(device)
dim_mat = torch.Tensor([wave_length]).cuda(device)
dim_mat = dim_mat**((4. / feat_dim) * feat_range)
dim_mat = dim_mat.view((1, 1, -1))
embedding_x = torch.cat(
((w_diff / dim_mat).sin(), (w_diff / dim_mat).cos()), dim=2)
embedding_y = torch.cat(
((h_diff / dim_mat).sin(), (h_diff / dim_mat).cos()), dim=2)
return embedding_x, embedding_y
def forward(self, x_input):
num_heads = self.num_heads
# use empirical_attention
if self.q_downsample is not None:
x_q = self.q_downsample(x_input)
else:
x_q = x_input
n, _, h, w = x_q.shape
if self.kv_downsample is not None:
x_kv = self.kv_downsample(x_input)
else:
x_kv = x_input
_, _, h_kv, w_kv = x_kv.shape
if self.attention_type[0] or self.attention_type[1]:
proj_query = self.query_conv(x_q).view(
(n, num_heads, self.qk_embed_dim, h * w))
proj_query = proj_query.permute(0, 1, 3, 2)
if self.attention_type[0] or self.attention_type[2]:
proj_key = self.key_conv(x_kv).view(
(n, num_heads, self.qk_embed_dim, h_kv * w_kv))
if self.attention_type[1] or self.attention_type[3]:
position_embed_x, position_embed_y = self.get_position_embedding(
h, w, h_kv, w_kv, self.q_stride, self.kv_stride,
x_input.device, self.position_embedding_dim)
# (n, num_heads, w, w_kv, dim)
position_feat_x = self.appr_geom_fc_x(position_embed_x).\
view(1, w, w_kv, num_heads, self.qk_embed_dim).\
permute(0, 3, 1, 2, 4).\
repeat(n, 1, 1, 1, 1)
# (n, num_heads, h, h_kv, dim)
position_feat_y = self.appr_geom_fc_y(position_embed_y).\
view(1, h, h_kv, num_heads, self.qk_embed_dim).\
permute(0, 3, 1, 2, 4).\
repeat(n, 1, 1, 1, 1)
position_feat_x /= math.sqrt(2)
position_feat_y /= math.sqrt(2)
# accelerate for saliency only
if (np.sum(self.attention_type) == 1) and self.attention_type[2]:
appr_bias = self.appr_bias.\
view(1, num_heads, 1, self.qk_embed_dim).\
repeat(n, 1, 1, 1)
energy = torch.matmul(appr_bias, proj_key).\
view(n, num_heads, 1, h_kv * w_kv)
h = 1
w = 1
else:
# (n, num_heads, h*w, h_kv*w_kv), query before key, 540mb for
if not self.attention_type[0]:
energy = torch.zeros(
n,
num_heads,
h,
w,
h_kv,
w_kv,
dtype=x_input.dtype,
device=x_input.device)
# attention_type[0]: appr - appr
# attention_type[1]: appr - position
# attention_type[2]: bias - appr
# attention_type[3]: bias - position
if self.attention_type[0] or self.attention_type[2]:
if self.attention_type[0] and self.attention_type[2]:
appr_bias = self.appr_bias.\
view(1, num_heads, 1, self.qk_embed_dim)
energy = torch.matmul(proj_query + appr_bias, proj_key).\
view(n, num_heads, h, w, h_kv, w_kv)
elif self.attention_type[0]:
energy = torch.matmul(proj_query, proj_key).\
view(n, num_heads, h, w, h_kv, w_kv)
elif self.attention_type[2]:
appr_bias = self.appr_bias.\
view(1, num_heads, 1, self.qk_embed_dim).\
repeat(n, 1, 1, 1)
energy += torch.matmul(appr_bias, proj_key).\
view(n, num_heads, 1, 1, h_kv, w_kv)
if self.attention_type[1] or self.attention_type[3]:
if self.attention_type[1] and self.attention_type[3]:
geom_bias = self.geom_bias.\
view(1, num_heads, 1, self.qk_embed_dim)
proj_query_reshape = (proj_query + geom_bias).\
view(n, num_heads, h, w, self.qk_embed_dim)
energy_x = torch.matmul(
proj_query_reshape.permute(0, 1, 3, 2, 4),
position_feat_x.permute(0, 1, 2, 4, 3))
energy_x = energy_x.\
permute(0, 1, 3, 2, 4).unsqueeze(4)
energy_y = torch.matmul(
proj_query_reshape,
position_feat_y.permute(0, 1, 2, 4, 3))
energy_y = energy_y.unsqueeze(5)
energy += energy_x + energy_y
elif self.attention_type[1]:
proj_query_reshape = proj_query.\
view(n, num_heads, h, w, self.qk_embed_dim)
proj_query_reshape = proj_query_reshape.\
permute(0, 1, 3, 2, 4)
position_feat_x_reshape = position_feat_x.\
permute(0, 1, 2, 4, 3)
position_feat_y_reshape = position_feat_y.\
permute(0, 1, 2, 4, 3)
energy_x = torch.matmul(proj_query_reshape,
position_feat_x_reshape)
energy_x = energy_x.permute(0, 1, 3, 2, 4).unsqueeze(4)
energy_y = torch.matmul(proj_query_reshape,
position_feat_y_reshape)
energy_y = energy_y.unsqueeze(5)
energy += energy_x + energy_y
elif self.attention_type[3]:
geom_bias = self.geom_bias.\
view(1, num_heads, self.qk_embed_dim, 1).\
repeat(n, 1, 1, 1)
position_feat_x_reshape = position_feat_x.\
view(n, num_heads, w*w_kv, self.qk_embed_dim)
position_feat_y_reshape = position_feat_y.\
view(n, num_heads, h * h_kv, self.qk_embed_dim)
energy_x = torch.matmul(position_feat_x_reshape, geom_bias)
energy_x = energy_x.view(n, num_heads, 1, w, 1, w_kv)
energy_y = torch.matmul(position_feat_y_reshape, geom_bias)
energy_y = energy_y.view(n, num_heads, h, 1, h_kv, 1)
energy += energy_x + energy_y
energy = energy.view(n, num_heads, h * w, h_kv * w_kv)
if self.spatial_range >= 0:
cur_local_constraint_map = \
self.local_constraint_map[:h, :w, :h_kv, :w_kv].\
contiguous().\
view(1, 1, h*w, h_kv*w_kv)
energy = energy.masked_fill_(cur_local_constraint_map,
float('-inf'))
attention = F.softmax(energy, 3)
proj_value = self.value_conv(x_kv)
proj_value_reshape = proj_value.\
view((n, num_heads, self.v_dim, h_kv * w_kv)).\
permute(0, 1, 3, 2)
out = torch.matmul(attention, proj_value_reshape).\
permute(0, 1, 3, 2).\
contiguous().\
view(n, self.v_dim * self.num_heads, h, w)
out = self.proj_conv(out)
out = self.gamma * out + x_input
return out
def init_weights(self):
for m in self.modules():
if hasattr(m, 'kaiming_init') and m.kaiming_init:
kaiming_init(
m,
mode='fan_in',
nonlinearity='leaky_relu',
bias=0,
distribution='uniform',
a=1)
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