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Unverified Commit 6fe5ccde authored by Kai Chen's avatar Kai Chen Committed by GitHub
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Add support for weight standardization (#521) 

* add support for weight standardization

* add ws support for htc heads

* add a config file for 20-23-24e lr schedule

* update readme of weight standardization
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README.md
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......@@ -96,6 +96,7 @@ Results and models are available in the [Model zoo](MODEL_ZOO.md).
Other features
- [x] DCNv2
- [x] Group Normalization
- [x] Weight Standardization
- [x] OHEM
- [x] Soft-NMS
......
configs/gn+ws/README.md 0 → 100644
View file @ 6fe5ccde
# Weight Standardization
## Introduction
```
@article{weightstandardization,
author = {Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Yuille},
title = {Weight Standardization},
journal = {arXiv preprint arXiv:1903.10520},
year = {2019},
}
```
## Results and Models
Faster R-CNN
| Backbone | Style | Normalization | Lr schd | box AP | mask AP | Download |
|:---------:|:-------:|:-------------:|:-------:|:------:|:-------:|:--------:|
| R-50-FPN | pytorch | GN | 1x | 37.8 | - | - |
| R-50-FPN | pytorch | GN+WS | 1x | 38.9 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ws/faster_rcnn_r50_fpn_gn_ws_1x_20190418-935d00b6.pth) |
| R-101-FPN | pytorch | GN | 1x | 39.8 | - | - |
| R-101-FPN | pytorch | GN+WS | 1x | 41.4 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ws/faster_rcnn_r101_fpn_gn_ws_1x_20190419-728705ec.pth) |
| X-50-32x4d-FPN | pytorch | GN | 1x | 36.5 | - | - |
| X-50-32x4d-FPN | pytorch | GN+WS | 1x | 39.9 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ws/faster_rcnn_x50_32x4d_fpn_gn_ws_1x_20190419-4e61072b.pth) |
| X-101-32x4d-FPN | pytorch | GN | 1x | 33.2 | - | - |
| X-101-32x4d-FPN | pytorch | GN+WS | 1x | 41.8 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ws/faster_rcnn_x101_32x4d_fpn_gn_ws_1x_20190419-c78e5583.pth) |
Mask R-CNN
| Backbone | Style | Normalization | Lr schd | box AP | mask AP | Download |
|:---------:|:-------:|:-------------:|:-------:|:------:|:-------:|:--------:|
| R-50-FPN | pytorch | GN | 2x | 39.9 | 36.0 | - |
| R-50-FPN | pytorch | GN+WS | 2x | 40.3 | 36.2 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ws/mask_rcnn_r50_fpn_gn_ws_2x_20190419-9ec97bbb.pth) |
| R-101-FPN | pytorch | GN | 2x | 41.6 | 37.3 | - |
| R-101-FPN | pytorch | GN+WS | 2x | 42.0 | 37.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ws/mask_rcnn_r101_fpn_gn_ws_2x_20190419-bc7399a6.pth) |
| X-50-32x4d-FPN | pytorch | GN | 2x | 39.2 | 35.5 | - |
| X-50-32x4d-FPN | pytorch | GN+WS | 2x | 40.7 | 36.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ws/mask_rcnn_x50_32x4d_fpn_gn_ws_2x_20190419-2110205e.pth) |
| X-101-32x4d-FPN | pytorch | GN | 2x | 36.4 | 33.1 | - |
| X-101-32x4d-FPN | pytorch | GN+WS | 2x | 42.1 | 37.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ws/mask_rcnn_x101_32x4d_fpn_gn_ws_2x_20190419-7777b15f.pth) |
| R-50-FPN | pytorch | GN | 20-23-24e | 40.6 | 36.6 | - |
| R-50-FPN | pytorch | GN+WS | 20-23-24e | 41.1 | 37.0 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ws/mask_rcnn_r50_fpn_gn_ws_20_23_24e_20190425-1d9e499e.pth) |
| R-101-FPN | pytorch | GN | 20-23-24e | 42.3 | 38.1 | - |
| R-101-FPN | pytorch | GN+WS | 20-23-24e | 43.0 | 38.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ws/mask_rcnn_r101_fpn_gn_ws_20_23_24e_20190425-66cb3792.pth) |
| X-50-32x4d-FPN | pytorch | GN | 20-23-24e | 39.6 | 35.9 | - |
| X-50-32x4d-FPN | pytorch | GN+WS | 20-23-24e | 41.9 | 37.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ws/mask_rcnn_x50_32x4d_fpn_gn_ws_20_23_24e_20190425-d01e2200.pth) |
| X-101-32x4d-FPN | pytorch | GN | 20-23-24e | 36.6 | 33.4 | - |
| X-101-32x4d-FPN | pytorch | GN+WS | 20-23-24e | 43.4 | 38.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ws/mask_rcnn_x101_32x4d_fpn_gn_ws_20_23_24e_20190425-1ff3e5b2.pth) |
Note:
- GN+WS requires about 5% more memory than GN, and it is only 5% slower than GN.
- In the paper, a 20-23-24e lr schedule is used instead of 2x.
- The X-50-GN and X-101-GN pretrained models are also shared by the authors.
\ No newline at end of file
configs/gn+ws/faster_rcnn_r50_fpn_gn_ws_1x.py 0 → 100644
View file @ 6fe5ccde
# model settings
conv_cfg = dict(type='ConvWS')
normalize = dict(type='GN', num_groups=32, frozen=False)
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://jhu/resnet50_gn_ws',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
conv_cfg=conv_cfg,
normalize=normalize),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
conv_cfg=conv_cfg,
normalize=normalize),
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],
use_sigmoid_cls=True),
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='ConvFCBBoxHead',
num_shared_convs=4,
num_shared_fcs=1,
in_channels=256,
conv_out_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,
conv_cfg=conv_cfg,
normalize=normalize))
# 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,
smoothl1_beta=1 / 9.0,
debug=False),
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=2000,
nms_post=2000,
max_num=2000,
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))
# 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_gn_ws_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/gn+ws/mask_rcnn_r50_fpn_gn_ws_20_23_24e.py 0 → 100644
View file @ 6fe5ccde
# model settings
conv_cfg = dict(type='ConvWS')
normalize = dict(type='GN', num_groups=32, frozen=False)
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://jhu/resnet50_gn_ws',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
conv_cfg=conv_cfg,
normalize=normalize),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
conv_cfg=conv_cfg,
normalize=normalize),
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],
use_sigmoid_cls=True),
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='ConvFCBBoxHead',
num_shared_convs=4,
num_shared_fcs=1,
in_channels=256,
conv_out_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,
conv_cfg=conv_cfg,
normalize=normalize),
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
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=81,
conv_cfg=conv_cfg,
normalize=normalize))
# 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,
smoothl1_beta=1 / 9.0,
debug=False),
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),
mask_size=28,
pos_weight=-1,
debug=False))
test_cfg = dict(
rpn=dict(
nms_across_levels=False,
nms_pre=2000,
nms_post=2000,
max_num=2000,
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,
mask_thr_binary=0.5))
# 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=True,
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=True,
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=[20, 23])
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 = 24
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/mask_rcnn_r50_fpn_gn_ws_20_23_24e'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/gn+ws/mask_rcnn_r50_fpn_gn_ws_2x.py 0 → 100644
View file @ 6fe5ccde
# model settings
conv_cfg = dict(type='ConvWS')
normalize = dict(type='GN', num_groups=32, frozen=False)
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://jhu/resnet50_gn_ws',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
conv_cfg=conv_cfg,
normalize=normalize),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
conv_cfg=conv_cfg,
normalize=normalize),
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],
use_sigmoid_cls=True),
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='ConvFCBBoxHead',
num_shared_convs=4,
num_shared_fcs=1,
in_channels=256,
conv_out_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,
conv_cfg=conv_cfg,
normalize=normalize),
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
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=81,
conv_cfg=conv_cfg,
normalize=normalize))
# 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,
smoothl1_beta=1 / 9.0,
debug=False),
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),
mask_size=28,
pos_weight=-1,
debug=False))
test_cfg = dict(
rpn=dict(
nms_across_levels=False,
nms_pre=2000,
nms_post=2000,
max_num=2000,
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,
mask_thr_binary=0.5))
# 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=True,
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=True,
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=[16, 22])
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 = 24
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/mask_rcnn_r50_fpn_gn_ws_2x'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws_2x.py 0 → 100644
View file @ 6fe5ccde
# model settings
conv_cfg = dict(type='ConvWS')
normalize = dict(type='GN', num_groups=32, frozen=False)
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://jhu/resnext101_32x4d_gn_ws',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
conv_cfg=conv_cfg,
normalize=normalize),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
conv_cfg=conv_cfg,
normalize=normalize),
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],
use_sigmoid_cls=True),
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='ConvFCBBoxHead',
num_shared_convs=4,
num_shared_fcs=1,
in_channels=256,
conv_out_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,
conv_cfg=conv_cfg,
normalize=normalize),
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
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=81,
conv_cfg=conv_cfg,
normalize=normalize))
# 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,
smoothl1_beta=1 / 9.0,
debug=False),
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),
mask_size=28,
pos_weight=-1,
debug=False))
test_cfg = dict(
rpn=dict(
nms_across_levels=False,
nms_pre=2000,
nms_post=2000,
max_num=2000,
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,
mask_thr_binary=0.5))
# 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=True,
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=True,
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=[16, 22])
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 = 24
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/mask_rcnn_x101_32x4d_fpn_gn_ws_2x'
load_from = None
resume_from = None
workflow = [('train', 1)]
mmdet/models/backbones/resnet.py
View file @ 6fe5ccde
......@@ -8,19 +8,7 @@ from mmcv.runner import load_checkpoint
from mmdet.ops import DeformConv, ModulatedDeformConv
from ..registry import BACKBONES
from ..utils import build_norm_layer
def conv3x3(in_planes, out_planes, stride=1, dilation=1):
"3x3 convolution with padding"
return nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=dilation,
dilation=dilation,
bias=False)
from ..utils import build_conv_layer, build_norm_layer
class BasicBlock(nn.Module):
......@@ -34,6 +22,7 @@ class BasicBlock(nn.Module):
downsample=None,
style='pytorch',
with_cp=False,
conv_cfg=None,
normalize=dict(type='BN'),
dcn=None):
super(BasicBlock, self).__init__()
......@@ -42,9 +31,25 @@ class BasicBlock(nn.Module):
self.norm1_name, norm1 = build_norm_layer(normalize, planes, postfix=1)
self.norm2_name, norm2 = build_norm_layer(normalize, planes, postfix=2)
self.conv1 = conv3x3(inplanes, planes, stride, dilation)
self.conv1 = build_conv_layer(
conv_cfg,
inplanes,
planes,
3,
stride=stride,
padding=dilation,
dilation=dilation,
bias=False)
self.add_module(self.norm1_name, norm1)
self.conv2 = conv3x3(planes, planes)
self.conv2 = build_conv_layer(
conv_cfg,
planes,
planes,
3,
stride=stride,
padding=dilation,
dilation=dilation,
bias=False)
self.add_module(self.norm2_name, norm2)
self.relu = nn.ReLU(inplace=True)
......@@ -91,6 +96,7 @@ class Bottleneck(nn.Module):
downsample=None,
style='pytorch',
with_cp=False,
conv_cfg=None,
normalize=dict(type='BN'),
dcn=None):
"""Bottleneck block for ResNet.
......@@ -102,6 +108,7 @@ class Bottleneck(nn.Module):
assert dcn is None or isinstance(dcn, dict)
self.inplanes = inplanes
self.planes = planes
self.conv_cfg = conv_cfg
self.normalize = normalize
self.dcn = dcn
self.with_dcn = dcn is not None
......@@ -117,7 +124,8 @@ class Bottleneck(nn.Module):
self.norm3_name, norm3 = build_norm_layer(
normalize, planes * self.expansion, postfix=3)
self.conv1 = nn.Conv2d(
self.conv1 = build_conv_layer(
conv_cfg,
inplanes,
planes,
kernel_size=1,
......@@ -130,7 +138,8 @@ class Bottleneck(nn.Module):
fallback_on_stride = dcn.get('fallback_on_stride', False)
self.with_modulated_dcn = dcn.get('modulated', False)
if not self.with_dcn or fallback_on_stride:
self.conv2 = nn.Conv2d(
self.conv2 = build_conv_layer(
conv_cfg,
planes,
planes,
kernel_size=3,
......@@ -139,6 +148,7 @@ class Bottleneck(nn.Module):
dilation=dilation,
bias=False)
else:
assert conv_cfg is None, 'conv_cfg must be None for DCN'
deformable_groups = dcn.get('deformable_groups', 1)
if not self.with_modulated_dcn:
conv_op = DeformConv
......@@ -163,8 +173,12 @@ class Bottleneck(nn.Module):
deformable_groups=deformable_groups,
bias=False)
self.add_module(self.norm2_name, norm2)
self.conv3 = nn.Conv2d(
planes, planes * self.expansion, kernel_size=1, bias=False)
self.conv3 = build_conv_layer(
conv_cfg,
planes,
planes * self.expansion,
kernel_size=1,
bias=False)
self.add_module(self.norm3_name, norm3)
self.relu = nn.ReLU(inplace=True)
......@@ -236,12 +250,14 @@ def make_res_layer(block,
dilation=1,
style='pytorch',
with_cp=False,
conv_cfg=None,
normalize=dict(type='BN'),
dcn=None):
downsample = None
if stride != 1 or inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(
build_conv_layer(
conv_cfg,
inplanes,
planes * block.expansion,
kernel_size=1,
......@@ -260,6 +276,7 @@ def make_res_layer(block,
downsample,
style=style,
with_cp=with_cp,
conv_cfg=conv_cfg,
normalize=normalize,
dcn=dcn))
inplanes = planes * block.expansion
......@@ -272,6 +289,7 @@ def make_res_layer(block,
dilation,
style=style,
with_cp=with_cp,
conv_cfg=conv_cfg,
normalize=normalize,
dcn=dcn))
......@@ -319,6 +337,7 @@ class ResNet(nn.Module):
out_indices=(0, 1, 2, 3),
style='pytorch',
frozen_stages=-1,
conv_cfg=None,
normalize=dict(type='BN', frozen=False),
norm_eval=True,
dcn=None,
......@@ -338,6 +357,7 @@ class ResNet(nn.Module):
assert max(out_indices) < num_stages
self.style = style
self.frozen_stages = frozen_stages
self.conv_cfg = conv_cfg
self.normalize = normalize
self.with_cp = with_cp
self.norm_eval = norm_eval
......@@ -367,6 +387,7 @@ class ResNet(nn.Module):
dilation=dilation,
style=self.style,
with_cp=with_cp,
conv_cfg=conv_cfg,
normalize=normalize,
dcn=dcn)
self.inplanes = planes * self.block.expansion
......@@ -384,8 +405,14 @@ class ResNet(nn.Module):
return getattr(self, self.norm1_name)
def _make_stem_layer(self):
self.conv1 = nn.Conv2d(
3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.conv1 = build_conv_layer(
self.conv_cfg,
3,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False)
self.norm1_name, norm1 = build_norm_layer(
self.normalize, 64, postfix=1)
self.add_module(self.norm1_name, norm1)
......
mmdet/models/backbones/resnext.py
View file @ 6fe5ccde
......@@ -6,7 +6,7 @@ from mmdet.ops import DeformConv, ModulatedDeformConv
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNet
from ..registry import BACKBONES
from ..utils import build_norm_layer
from ..utils import build_conv_layer, build_norm_layer
class Bottleneck(_Bottleneck):
......@@ -30,7 +30,8 @@ class Bottleneck(_Bottleneck):
self.norm3_name, norm3 = build_norm_layer(
self.normalize, self.planes * self.expansion, postfix=3)
self.conv1 = nn.Conv2d(
self.conv1 = build_conv_layer(
self.conv_cfg,
self.inplanes,
width,
kernel_size=1,
......@@ -43,7 +44,8 @@ class Bottleneck(_Bottleneck):
fallback_on_stride = self.dcn.get('fallback_on_stride', False)
self.with_modulated_dcn = self.dcn.get('modulated', False)
if not self.with_dcn or fallback_on_stride:
self.conv2 = nn.Conv2d(
self.conv2 = build_conv_layer(
self.conv_cfg,
width,
width,
kernel_size=3,
......@@ -53,6 +55,7 @@ class Bottleneck(_Bottleneck):
groups=groups,
bias=False)
else:
assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
groups = self.dcn.get('groups', 1)
deformable_groups = self.dcn.get('deformable_groups', 1)
if not self.with_modulated_dcn:
......@@ -79,8 +82,12 @@ class Bottleneck(_Bottleneck):
deformable_groups=deformable_groups,
bias=False)
self.add_module(self.norm2_name, norm2)
self.conv3 = nn.Conv2d(
width, self.planes * self.expansion, kernel_size=1, bias=False)
self.conv3 = build_conv_layer(
self.conv_cfg,
width,
self.planes * self.expansion,
kernel_size=1,
bias=False)
self.add_module(self.norm3_name, norm3)
......@@ -94,12 +101,14 @@ def make_res_layer(block,
base_width=4,
style='pytorch',
with_cp=False,
conv_cfg=None,
normalize=dict(type='BN'),
dcn=None):
downsample = None
if stride != 1 or inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(
build_conv_layer(
conv_cfg,
inplanes,
planes * block.expansion,
kernel_size=1,
......@@ -120,6 +129,7 @@ def make_res_layer(block,
base_width=base_width,
style=style,
with_cp=with_cp,
conv_cfg=conv_cfg,
normalize=normalize,
dcn=dcn))
inplanes = planes * block.expansion
......@@ -134,6 +144,7 @@ def make_res_layer(block,
base_width=base_width,
style=style,
with_cp=with_cp,
conv_cfg=conv_cfg,
normalize=normalize,
dcn=dcn))
......@@ -196,6 +207,7 @@ class ResNeXt(ResNet):
base_width=self.base_width,
style=self.style,
with_cp=self.with_cp,
conv_cfg=self.conv_cfg,
normalize=self.normalize,
dcn=dcn)
self.inplanes = planes * self.block.expansion
......
mmdet/models/bbox_heads/convfc_bbox_head.py
View file @ 6fe5ccde
......@@ -24,6 +24,7 @@ class ConvFCBBoxHead(BBoxHead):
num_reg_fcs=0,
conv_out_channels=256,
fc_out_channels=1024,
conv_cfg=None,
normalize=None,
*args,
**kwargs):
......@@ -44,6 +45,7 @@ class ConvFCBBoxHead(BBoxHead):
self.num_reg_fcs = num_reg_fcs
self.conv_out_channels = conv_out_channels
self.fc_out_channels = fc_out_channels
self.conv_cfg = conv_cfg
self.normalize = normalize
self.with_bias = normalize is None
......@@ -101,6 +103,7 @@ class ConvFCBBoxHead(BBoxHead):
self.conv_out_channels,
3,
padding=1,
conv_cfg=self.conv_cfg,
normalize=self.normalize,
bias=self.with_bias))
last_layer_dim = self.conv_out_channels
......
mmdet/models/mask_heads/fcn_mask_head.py
View file @ 6fe5ccde
......@@ -22,6 +22,7 @@ class FCNMaskHead(nn.Module):
upsample_ratio=2,
num_classes=81,
class_agnostic=False,
conv_cfg=None,
normalize=None):
super(FCNMaskHead, self).__init__()
if upsample_method not in [None, 'deconv', 'nearest', 'bilinear']:
......@@ -37,6 +38,7 @@ class FCNMaskHead(nn.Module):
self.upsample_ratio = upsample_ratio
self.num_classes = num_classes
self.class_agnostic = class_agnostic
self.conv_cfg = conv_cfg
self.normalize = normalize
self.with_bias = normalize is None
......@@ -51,6 +53,7 @@ class FCNMaskHead(nn.Module):
self.conv_out_channels,
self.conv_kernel_size,
padding=padding,
conv_cfg=conv_cfg,
normalize=normalize,
bias=self.with_bias))
upsample_in_channels = (self.conv_out_channels
......
mmdet/models/mask_heads/fused_semantic_head.py
View file @ 6fe5ccde
......@@ -30,6 +30,7 @@ class FusedSemanticHead(nn.Module):
num_classes=183,
ignore_label=255,
loss_weight=0.2,
conv_cfg=None,
normalize=None):
super(FusedSemanticHead, self).__init__()
self.num_ins = num_ins
......@@ -40,6 +41,7 @@ class FusedSemanticHead(nn.Module):
self.num_classes = num_classes
self.ignore_label = ignore_label
self.loss_weight = loss_weight
self.conv_cfg = conv_cfg
self.normalize = normalize
self.with_bias = normalize is None
......@@ -50,6 +52,7 @@ class FusedSemanticHead(nn.Module):
self.in_channels,
self.in_channels,
1,
conv_cfg=self.conv_cfg,
normalize=self.normalize,
bias=self.with_bias,
inplace=False))
......@@ -63,12 +66,14 @@ class FusedSemanticHead(nn.Module):
conv_out_channels,
3,
padding=1,
conv_cfg=self.conv_cfg,
normalize=self.normalize,
bias=self.with_bias))
self.conv_embedding = ConvModule(
conv_out_channels,
conv_out_channels,
1,
conv_cfg=self.conv_cfg,
normalize=self.normalize,
bias=self.with_bias)
self.conv_logits = nn.Conv2d(conv_out_channels, self.num_classes, 1)
......
mmdet/models/mask_heads/htc_mask_head.py
View file @ 6fe5ccde
......@@ -12,6 +12,7 @@ class HTCMaskHead(FCNMaskHead):
self.conv_out_channels,
self.conv_out_channels,
1,
conv_cfg=self.conv_cfg,
normalize=self.normalize,
bias=self.with_bias)
......
mmdet/models/necks/fpn.py
View file @ 6fe5ccde
......@@ -17,6 +17,7 @@ class FPN(nn.Module):
end_level=-1,
add_extra_convs=False,
extra_convs_on_inputs=True,
conv_cfg=None,
normalize=None,
activation=None):
super(FPN, self).__init__()
......@@ -49,6 +50,7 @@ class FPN(nn.Module):
in_channels[i],
out_channels,
1,
conv_cfg=conv_cfg,
normalize=normalize,
bias=self.with_bias,
activation=self.activation,
......@@ -58,6 +60,7 @@ class FPN(nn.Module):
out_channels,
3,
padding=1,
conv_cfg=conv_cfg,
normalize=normalize,
bias=self.with_bias,
activation=self.activation,
......
mmdet/models/utils/__init__.py
View file @ 6fe5ccde
from .conv_module import ConvModule
from .conv_ws import conv_ws_2d, ConvWS2d
from .conv_module import build_conv_layer, ConvModule
from .norm import build_norm_layer
from .weight_init import (xavier_init, normal_init, uniform_init, kaiming_init,
bias_init_with_prob)
__all__ = [
'ConvModule', 'build_norm_layer', 'xavier_init', 'normal_init',
'uniform_init', 'kaiming_init', 'bias_init_with_prob'
'conv_ws_2d', 'ConvWS2d', 'build_conv_layer', 'ConvModule',
'build_norm_layer', 'xavier_init', 'normal_init', 'uniform_init',
'kaiming_init', 'bias_init_with_prob'
]
mmdet/models/utils/conv_module.py
View file @ 6fe5ccde
......@@ -3,8 +3,43 @@ import warnings
import torch.nn as nn
from mmcv.cnn import kaiming_init, constant_init
from .conv_ws import ConvWS2d
from .norm import build_norm_layer
conv_cfg = {
'Conv': nn.Conv2d,
'ConvWS': ConvWS2d,
# TODO: octave conv
}
def build_conv_layer(cfg, *args, **kwargs):
""" Build convolution layer
Args:
cfg (None or dict): cfg should contain:
type (str): identify conv layer type.
layer args: args needed to instantiate a conv layer.
Returns:
layer (nn.Module): created conv layer
"""
if cfg is None:
cfg_ = dict(type='Conv')
else:
assert isinstance(cfg, dict) and 'type' in cfg
cfg_ = cfg.copy()
layer_type = cfg_.pop('type')
if layer_type not in conv_cfg:
raise KeyError('Unrecognized norm type {}'.format(layer_type))
else:
conv_layer = conv_cfg[layer_type]
layer = conv_layer(*args, **kwargs, **cfg_)
return layer
class ConvModule(nn.Module):
......@@ -17,11 +52,14 @@ class ConvModule(nn.Module):
dilation=1,
groups=1,
bias=True,
conv_cfg=None,
normalize=None,
activation='relu',
inplace=True,
activate_last=True):
super(ConvModule, self).__init__()
assert conv_cfg is None or isinstance(conv_cfg, dict)
assert normalize is None or isinstance(normalize, dict)
self.with_norm = normalize is not None
self.with_activatation = activation is not None
self.with_bias = bias
......@@ -31,7 +69,8 @@ class ConvModule(nn.Module):
if self.with_norm and self.with_bias:
warnings.warn('ConvModule has norm and bias at the same time')
self.conv = nn.Conv2d(
self.conv = build_conv_layer(
conv_cfg,
in_channels,
out_channels,
kernel_size,
......
mmdet/models/utils/conv_ws.py 0 → 100644
View file @ 6fe5ccde
import torch.nn as nn
import torch.nn.functional as F
def conv_ws_2d(input,
weight,
bias=None,
stride=1,
padding=0,
dilation=1,
groups=1,
eps=1e-5):
c_in = weight.size(0)
weight_flat = weight.view(c_in, -1)
mean = weight_flat.mean(dim=1, keepdim=True).view(c_in, 1, 1, 1)
std = weight_flat.std(dim=1, keepdim=True).view(c_in, 1, 1, 1)
weight = (weight - mean) / (std + eps)
return F.conv2d(input, weight, bias, stride, padding, dilation, groups)
class ConvWS2d(nn.Conv2d):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
eps=1e-5):
super(ConvWS2d, self).__init__(
in_channels,
out_channels,
kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias)
self.eps = eps
def forward(self, x):
return conv_ws_2d(x, self.weight, self.bias, self.stride, self.padding,
self.dilation, self.groups, self.eps)
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