Skip to content

GitLab

"docs/source/vscode:/vscode.git/clone" did not exist on "fb056494e50be6ff729166734eff96f9f26d9e96"
Unverified Commit 896122c8 authored by Kai Chen's avatar Kai Chen Committed by GitHub
Browse files

Merge pull request #308 from hellock/pytorch-1.0 

sync pytorch-1.0 with master branch
parents 69f40c98 7f317a0a
Hide whitespace changes
Inline Side-by-side
Showing with 1643 additions and 57 deletions
MODEL_ZOO.md
View file @ 896122c8
...@@ -184,6 +184,29 @@ We released RPN, Faster R-CNN and Mask R-CNN models in the first version. More m ...@@ -184,6 +184,29 @@ We released RPN, Faster R-CNN and Mask R-CNN models in the first version. More m
- The `3x` schedule is epoch [28, 34, 36]. - The `3x` schedule is epoch [28, 34, 36].
- The memory is measured with `torch.cuda.max_memory_allocated()` instead of `torch.cuda.max_memory_cached()`. We will update the memory usage of other models in the future. - The memory is measured with `torch.cuda.max_memory_allocated()` instead of `torch.cuda.max_memory_cached()`. We will update the memory usage of other models in the future.
### Deformable Convolution v2
| Backbone | Model | Style | Conv | Pool | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download |
|:---------:|:------------:|:-------:|:-------------:|:------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:-------:|:--------:|
| R-50-FPN | Faster | pytorch | dconv(c3-c5) | - | 1x | 3.9 | 0.594 | 10.2 | 40.0 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/faster_rcnn_dconv_c3-c5_r50_fpn_1x_20190125-e41688c9.pth) |
| R-50-FPN | Faster | pytorch | mdconv(c3-c5) | - | 1x | 3.7 | 0.598 | 10.0 | 40.3 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/faster_rcnn_mdconv_c3-c5_r50_fpn_1x_20190125-1b768045.pth) |
| R-50-FPN | Faster | pytorch | - | dpool | 1x | 4.6 | 0.714 | 8.7 | 37.9 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/faster_rcnn_dpool_r50_fpn_1x_20190125-f4fc1d70.pth) |
| R-50-FPN | Faster | pytorch | - | mdpool | 1x | 5.2 | 0.769 | 8.2 | 38.1 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/faster_rcnn_mdpool_r50_fpn_1x_20190125-473d0f3d.pth) |
| R-101-FPN | Faster | pytorch | dconv(c3-c5) | - | 1x | 5.8 | 0.811 | 8.0 | 42.1 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/faster_rcnn_dconv_c3-c5_r101_fpn_1x_20190125-a7e31b65.pth) |
| X-101-32x4d-FPN | Faster | pytorch | dconv(c3-c5) | - | 1x | 7.1 | 1.126 | 6.6 | 43.5 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/faster_rcnn_dconv_c3-c5_x101_32x4d_fpn_1x_20190201-6d46376f.pth) |
| R-50-FPN | Mask | pytorch | dconv(c3-c5) | - | 1x | 4.5 | 0.712 | 7.7 | 41.1 | 37.2 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/mask_rcnn_dconv_c3-c5_r50_fpn_1x_20190125-4f94ff79.pth) |
| R-50-FPN | Mask | pytorch | mdconv(c3-c5) | - | 1x | 4.5 | 0.712 | 7.7 | 41.4 | 37.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/mask_rcnn_mdconv_c3-c5_r50_fpn_1x_20190125-c5601dc3.pth) |
| R-101-FPN | Mask | pytorch | dconv(c3-c5) | - | 1x | 6.4 | 0.939 | 6.5 | 43.2 | 38.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/mask_rcnn_dconv_c3-c5_r101_fpn_1x_20190125-decb6db5.pth) |
| R-50-FPN | Cascade | pytorch | dconv(c3-c5) | - | 1x | 4.4 | 0.660 | 7.6 | 44.1 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x_20190125-dfa53166.pth) |
| R-101-FPN | Cascade | pytorch | dconv(c3-c5) | - | 1x | 6.3 | 0.881 | 6.8 | 45.1 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/cascade_rcnn_dconv_c3-c5_r101_fpn_1x_20190125-aaa877cc.pth) |
| R-50-FPN | Cascade Mask | pytorch | dconv(c3-c5) | - | 1x | 6.6 | 0.942 | 5.7 | 44.5 | 38.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x_20190125-09d8a443.pth) |
| R-101-FPN | Cascade Mask | pytorch | dconv(c3-c5) | - | 1x | 8.5 | 1.156 | 5.1 | 45.8 | 39.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/cascade_mask_rcnn_dconv_c3-c5_r101_fpn_1x_20190125-0d62c190.pth) |
**Notes:**
- `dconv` and `mdconv` denote (modulated) deformable convolution, `c3-c5` means adding dconv in resnet stage 3 to 5. `dpool` and `mdpool` denote (modulated) deformable roi pooling.
- The memory is measured with `torch.cuda.max_memory_allocated()`. The batch size is 16 (2 images per GPU).
- The dcn ops are modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch, which should be more memory efficient and slightly faster.
## Comparison with Detectron ## Comparison with Detectron
......
README.md
View file @ 896122c8
...@@ -78,6 +78,7 @@ Results and models are available in the [Model zoo](MODEL_ZOO.md). ...@@ -78,6 +78,7 @@ Results and models are available in the [Model zoo](MODEL_ZOO.md).
| RetinaNet | ✓ | ✓ | ☐ | ✗ | | RetinaNet | ✓ | ✓ | ☐ | ✗ |
Other features Other features
- [x] DCNv2
- [x] Group Normalization - [x] Group Normalization
- [x] OHEM - [x] OHEM
- [x] Soft-NMS - [x] Soft-NMS
......
compile.sh
View file @ 896122c8
...@@ -20,3 +20,10 @@ echo "Building nms op..." ...@@ -20,3 +20,10 @@ echo "Building nms op..."
cd ../nms cd ../nms
make clean make clean
make PYTHON=${PYTHON} make PYTHON=${PYTHON}
echo "Building dcn..."
cd ../dcn
if [ -d "build" ]; then
rm -r build
fi
$PYTHON setup.py build_ext --inplace
configs/dcn/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x.py 0 → 100644
View file @ 896122c8
# model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='modelzoo://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
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],
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='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=True),
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=[0.05, 0.05, 0.1, 0.1],
reg_class_agnostic=True),
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=[0.033, 0.033, 0.067, 0.067],
reg_class_agnostic=True)
],
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))
# 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),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.6,
neg_iou_thr=0.6,
min_pos_iou=0.6,
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),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.7,
min_pos_iou=0.7,
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)
],
stage_loss_weights=[1, 0.5, 0.25])
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),
keep_all_stages=False)
# 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=True,
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/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x.py 0 → 100644
View file @ 896122c8
# model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='modelzoo://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
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],
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='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=True),
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=[0.05, 0.05, 0.1, 0.1],
reg_class_agnostic=True),
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=[0.033, 0.033, 0.067, 0.067],
reg_class_agnostic=True)
])
# 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),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.6,
neg_iou_thr=0.6,
min_pos_iou=0.6,
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,
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)
],
stage_loss_weights=[1, 0.5, 0.25])
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),
keep_all_stages=False)
# 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/cascade_rcnn_dconv_c3-c5_r50_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/dcn/faster_rcnn_dconv_c3-c5_r50_fpn_1x.py 0 → 100644
View file @ 896122c8
# 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',
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],
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='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))
# 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)
# 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_dconv_c3-c5_r50_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/dcn/faster_rcnn_dconv_c3-c5_x101_32x4d_fpn_1x.py 0 → 100644
View file @ 896122c8
# model settings
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://resnext101_32x4d',
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',
dcn=dict(
modulated=False,
groups=32,
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],
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='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))
# 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)
# 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_dconv_c3-c5_x101_32x4d_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/dcn/faster_rcnn_dpool_r50_fpn_1x.py 0 → 100644
View file @ 896122c8
# 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'),
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],
use_sigmoid_cls=True),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
type='DeformRoIPoolingPack',
out_size=7,
out_channels=256,
no_trans=False,
group_size=1,
trans_std=0.1),
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))
# 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)
# 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_dpool_r50_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/dcn/faster_rcnn_mdconv_c3-c5_r50_fpn_1x.py 0 → 100644
View file @ 896122c8
# 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',
dcn=dict(
modulated=True,
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],
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='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))
# 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)
# 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_mdconv_c3-c5_r50_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/dcn/faster_rcnn_mdpool_r50_fpn_1x.py 0 → 100644
View file @ 896122c8
# 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'),
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],
use_sigmoid_cls=True),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
type='ModulatedDeformRoIPoolingPack',
out_size=7,
out_channels=256,
no_trans=False,
group_size=1,
trans_std=0.1),
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))
# 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)
# 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_mdpool_r50_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/dcn/mask_rcnn_dconv_c3-c5_r50_fpn_1x.py 0 → 100644
View file @ 896122c8
# model settings
model = dict(
type='MaskRCNN',
pretrained='modelzoo://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
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],
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='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),
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))
# 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=[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/mask_rcnn_dconv_c3-c5_r50_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
mmdet/core/bbox/assigners/max_iou_assigner.py
View file @ 896122c8
...@@ -88,8 +88,8 @@ class MaxIoUAssigner(BaseAssigner): ...@@ -88,8 +88,8 @@ class MaxIoUAssigner(BaseAssigner):
"""Assign w.r.t. the overlaps of bboxes with gts. """Assign w.r.t. the overlaps of bboxes with gts.
Args: Args:
overlaps (Tensor): Overlaps between n bboxes and k gt_bboxes, overlaps (Tensor): Overlaps between k gt_bboxes and n bboxes,
shape(n, k). shape(k, n).
gt_labels (Tensor, optional): Labels of k gt_bboxes, shape (k, ). gt_labels (Tensor, optional): Labels of k gt_bboxes, shape (k, ).
Returns: Returns:
......
mmdet/core/bbox/geometry.py
View file @ 896122c8
...@@ -16,7 +16,7 @@ def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False): ...@@ -16,7 +16,7 @@ def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False):
foreground). foreground).
Returns: Returns:
ious(Tensor): shape (n, k) if is_aligned == False else shape (n, 1) ious(Tensor): shape (m, n) if is_aligned == False else shape (m, 1)
""" """
assert mode in ['iou', 'iof'] assert mode in ['iou', 'iof']
......
mmdet/core/evaluation/mean_ap.py
View file @ 896122c8
import mmcv
import numpy as np import numpy as np
from terminaltables import AsciiTable from terminaltables import AsciiTable
...@@ -234,8 +235,9 @@ def eval_map(det_results, ...@@ -234,8 +235,9 @@ def eval_map(det_results,
gt_ignore (list): gt ignore indicators of each image, a list of K array gt_ignore (list): gt ignore indicators of each image, a list of K array
scale_ranges (list, optional): [(min1, max1), (min2, max2), ...] scale_ranges (list, optional): [(min1, max1), (min2, max2), ...]
iou_thr (float): IoU threshold iou_thr (float): IoU threshold
dataset (None or str): dataset name, there are minor differences in dataset (None or str or list): dataset name or dataset classes, there
metrics for different datsets, e.g. "voc07", "imagenet_det", etc. are minor differences in metrics for different datsets, e.g.
"voc07", "imagenet_det", etc.
print_summary (bool): whether to print the mAP summary print_summary (bool): whether to print the mAP summary
Returns: Returns:
...@@ -333,7 +335,7 @@ def print_map_summary(mean_ap, results, dataset=None): ...@@ -333,7 +335,7 @@ def print_map_summary(mean_ap, results, dataset=None):
Args: Args:
mean_ap(float): calculated from `eval_map` mean_ap(float): calculated from `eval_map`
results(list): calculated from `eval_map` results(list): calculated from `eval_map`
dataset(None or str or list): dataset name. dataset(None or str or list): dataset name or dataset classes.
""" """
num_scales = len(results[0]['ap']) if isinstance(results[0]['ap'], num_scales = len(results[0]['ap']) if isinstance(results[0]['ap'],
np.ndarray) else 1 np.ndarray) else 1
...@@ -353,8 +355,10 @@ def print_map_summary(mean_ap, results, dataset=None): ...@@ -353,8 +355,10 @@ def print_map_summary(mean_ap, results, dataset=None):
if dataset is None: if dataset is None:
label_names = [str(i) for i in range(1, num_classes + 1)] label_names = [str(i) for i in range(1, num_classes + 1)]
else: elif mmcv.is_str(dataset):
label_names = get_classes(dataset) label_names = get_classes(dataset)
else:
label_names = dataset
if not isinstance(mean_ap, list): if not isinstance(mean_ap, list):
mean_ap = [mean_ap] mean_ap = [mean_ap]
......
mmdet/datasets/coco.py
View file @ 896122c8
...@@ -40,7 +40,7 @@ class CocoDataset(CustomDataset): ...@@ -40,7 +40,7 @@ class CocoDataset(CustomDataset):
img_id = self.img_infos[idx]['id'] img_id = self.img_infos[idx]['id']
ann_ids = self.coco.getAnnIds(imgIds=[img_id]) ann_ids = self.coco.getAnnIds(imgIds=[img_id])
ann_info = self.coco.loadAnns(ann_ids) ann_info = self.coco.loadAnns(ann_ids)
return self._parse_ann_info(ann_info) return self._parse_ann_info(ann_info, self.with_mask)
def _filter_imgs(self, min_size=32): def _filter_imgs(self, min_size=32):
"""Filter images too small or without ground truths.""" """Filter images too small or without ground truths."""
......
mmdet/models/backbones/resnet.py
View file @ 896122c8
...@@ -5,9 +5,10 @@ import torch.utils.checkpoint as cp ...@@ -5,9 +5,10 @@ import torch.utils.checkpoint as cp
from mmcv.cnn import constant_init, kaiming_init from mmcv.cnn import constant_init, kaiming_init
from mmcv.runner import load_checkpoint from mmcv.runner import load_checkpoint
from ..utils import build_norm_layer
from mmdet.ops import DeformConv, ModulatedDeformConv
from ..registry import BACKBONES from ..registry import BACKBONES
from ..utils import build_norm_layer
def conv3x3(in_planes, out_planes, stride=1, dilation=1): def conv3x3(in_planes, out_planes, stride=1, dilation=1):
...@@ -88,16 +89,20 @@ class Bottleneck(nn.Module): ...@@ -88,16 +89,20 @@ class Bottleneck(nn.Module):
downsample=None, downsample=None,
style='pytorch', style='pytorch',
with_cp=False, with_cp=False,
normalize=dict(type='BN')): normalize=dict(type='BN'),
dcn=None):
"""Bottleneck block for ResNet. """Bottleneck block for ResNet.
If style is "pytorch", the stride-two layer is the 3x3 conv layer, 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. if it is "caffe", the stride-two layer is the first 1x1 conv layer.
""" """
super(Bottleneck, self).__init__() super(Bottleneck, self).__init__()
assert style in ['pytorch', 'caffe'] assert style in ['pytorch', 'caffe']
assert dcn is None or isinstance(dcn, dict)
self.inplanes = inplanes self.inplanes = inplanes
self.planes = planes self.planes = planes
self.normalize = normalize self.normalize = normalize
self.dcn = dcn
self.with_dcn = dcn is not None
if style == 'pytorch': if style == 'pytorch':
self.conv1_stride = 1 self.conv1_stride = 1
self.conv2_stride = stride self.conv2_stride = stride
...@@ -107,9 +112,8 @@ class Bottleneck(nn.Module): ...@@ -107,9 +112,8 @@ class Bottleneck(nn.Module):
self.norm1_name, norm1 = build_norm_layer(normalize, planes, postfix=1) self.norm1_name, norm1 = build_norm_layer(normalize, planes, postfix=1)
self.norm2_name, norm2 = build_norm_layer(normalize, planes, postfix=2) self.norm2_name, norm2 = build_norm_layer(normalize, planes, postfix=2)
self.norm3_name, norm3 = build_norm_layer(normalize, self.norm3_name, norm3 = build_norm_layer(
planes * self.expansion, normalize, planes * self.expansion, postfix=3)
postfix=3)
self.conv1 = nn.Conv2d( self.conv1 = nn.Conv2d(
inplanes, inplanes,
...@@ -118,14 +122,44 @@ class Bottleneck(nn.Module): ...@@ -118,14 +122,44 @@ class Bottleneck(nn.Module):
stride=self.conv1_stride, stride=self.conv1_stride,
bias=False) bias=False)
self.add_module(self.norm1_name, norm1) self.add_module(self.norm1_name, norm1)
self.conv2 = nn.Conv2d( fallback_on_stride = False
planes, self.with_modulated_dcn = False
planes, if self.with_dcn:
kernel_size=3, fallback_on_stride = dcn.get('fallback_on_stride', False)
stride=self.conv2_stride, self.with_modulated_dcn = dcn.get('modulated', False)
padding=dilation, if not self.with_dcn or fallback_on_stride:
dilation=dilation, self.conv2 = nn.Conv2d(
bias=False) planes,
planes,
kernel_size=3,
stride=self.conv2_stride,
padding=dilation,
dilation=dilation,
bias=False)
else:
deformable_groups = dcn.get('deformable_groups', 1)
if not self.with_modulated_dcn:
conv_op = DeformConv
offset_channels = 18
else:
conv_op = ModulatedDeformConv
offset_channels = 27
self.conv2_offset = nn.Conv2d(
planes,
deformable_groups * offset_channels,
kernel_size=3,
stride=self.conv2_stride,
padding=dilation,
dilation=dilation)
self.conv2 = conv_op(
planes,
planes,
kernel_size=3,
stride=self.conv2_stride,
padding=dilation,
dilation=dilation,
deformable_groups=deformable_groups,
bias=False)
self.add_module(self.norm2_name, norm2) self.add_module(self.norm2_name, norm2)
self.conv3 = nn.Conv2d( self.conv3 = nn.Conv2d(
planes, planes * self.expansion, kernel_size=1, bias=False) planes, planes * self.expansion, kernel_size=1, bias=False)
...@@ -159,7 +193,16 @@ class Bottleneck(nn.Module): ...@@ -159,7 +193,16 @@ class Bottleneck(nn.Module):
out = self.norm1(out) out = self.norm1(out)
out = self.relu(out) out = self.relu(out)
out = self.conv2(out) if not self.with_dcn:
out = self.conv2(out)
elif self.with_modulated_dcn:
offset_mask = self.conv2_offset(out)
offset = offset_mask[:, :18, :, :]
mask = offset_mask[:, -9:, :, :].sigmoid()
out = self.conv2(out, offset, mask)
else:
offset = self.conv2_offset(out)
out = self.conv2(out, offset)
out = self.norm2(out) out = self.norm2(out)
out = self.relu(out) out = self.relu(out)
...@@ -191,7 +234,8 @@ def make_res_layer(block, ...@@ -191,7 +234,8 @@ def make_res_layer(block,
dilation=1, dilation=1,
style='pytorch', style='pytorch',
with_cp=False, with_cp=False,
normalize=dict(type='BN')): normalize=dict(type='BN'),
dcn=None):
downsample = None downsample = None
if stride != 1 or inplanes != planes * block.expansion: if stride != 1 or inplanes != planes * block.expansion:
downsample = nn.Sequential( downsample = nn.Sequential(
...@@ -214,12 +258,20 @@ def make_res_layer(block, ...@@ -214,12 +258,20 @@ def make_res_layer(block,
downsample, downsample,
style=style, style=style,
with_cp=with_cp, with_cp=with_cp,
normalize=normalize)) normalize=normalize,
dcn=dcn))
inplanes = planes * block.expansion inplanes = planes * block.expansion
for i in range(1, blocks): for i in range(1, blocks):
layers.append( layers.append(
block(inplanes, planes, 1, dilation, style=style, block(
with_cp=with_cp, normalize=normalize)) inplanes,
planes,
1,
dilation,
style=style,
with_cp=with_cp,
normalize=normalize,
dcn=dcn))
return nn.Sequential(*layers) return nn.Sequential(*layers)
...@@ -265,10 +317,10 @@ class ResNet(nn.Module): ...@@ -265,10 +317,10 @@ class ResNet(nn.Module):
out_indices=(0, 1, 2, 3), out_indices=(0, 1, 2, 3),
style='pytorch', style='pytorch',
frozen_stages=-1, frozen_stages=-1,
normalize=dict( normalize=dict(type='BN', frozen=False),
type='BN',
frozen=False),
norm_eval=True, norm_eval=True,
dcn=None,
stage_with_dcn=(False, False, False, False),
with_cp=False, with_cp=False,
zero_init_residual=True): zero_init_residual=True):
super(ResNet, self).__init__() super(ResNet, self).__init__()
...@@ -279,7 +331,8 @@ class ResNet(nn.Module): ...@@ -279,7 +331,8 @@ class ResNet(nn.Module):
assert num_stages >= 1 and num_stages <= 4 assert num_stages >= 1 and num_stages <= 4
self.strides = strides self.strides = strides
self.dilations = dilations self.dilations = dilations
assert len(strides) == len(dilations) == num_stages assert len(strides) == len(dilations) == len(
stage_with_dcn) == num_stages
self.out_indices = out_indices self.out_indices = out_indices
assert max(out_indices) < num_stages assert max(out_indices) < num_stages
self.style = style self.style = style
...@@ -287,6 +340,8 @@ class ResNet(nn.Module): ...@@ -287,6 +340,8 @@ class ResNet(nn.Module):
self.normalize = normalize self.normalize = normalize
self.with_cp = with_cp self.with_cp = with_cp
self.norm_eval = norm_eval self.norm_eval = norm_eval
self.dcn = dcn
self.stage_with_dcn = stage_with_dcn
self.zero_init_residual = zero_init_residual self.zero_init_residual = zero_init_residual
self.block, stage_blocks = self.arch_settings[depth] self.block, stage_blocks = self.arch_settings[depth]
self.stage_blocks = stage_blocks[:num_stages] self.stage_blocks = stage_blocks[:num_stages]
...@@ -298,6 +353,7 @@ class ResNet(nn.Module): ...@@ -298,6 +353,7 @@ class ResNet(nn.Module):
for i, num_blocks in enumerate(self.stage_blocks): for i, num_blocks in enumerate(self.stage_blocks):
stride = strides[i] stride = strides[i]
dilation = dilations[i] dilation = dilations[i]
dcn = self.dcn if self.stage_with_dcn[i] else None
planes = 64 * 2**i planes = 64 * 2**i
res_layer = make_res_layer( res_layer = make_res_layer(
self.block, self.block,
...@@ -308,7 +364,8 @@ class ResNet(nn.Module): ...@@ -308,7 +364,8 @@ class ResNet(nn.Module):
dilation=dilation, dilation=dilation,
style=self.style, style=self.style,
with_cp=with_cp, with_cp=with_cp,
normalize=normalize) normalize=normalize,
dcn=dcn)
self.inplanes = planes * self.block.expansion self.inplanes = planes * self.block.expansion
layer_name = 'layer{}'.format(i + 1) layer_name = 'layer{}'.format(i + 1)
self.add_module(layer_name, res_layer) self.add_module(layer_name, res_layer)
...@@ -326,8 +383,8 @@ class ResNet(nn.Module): ...@@ -326,8 +383,8 @@ class ResNet(nn.Module):
def _make_stem_layer(self): def _make_stem_layer(self):
self.conv1 = nn.Conv2d( self.conv1 = nn.Conv2d(
3, 64, kernel_size=7, stride=2, padding=3, bias=False) 3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.norm1_name, norm1 = build_norm_layer(self.normalize, self.norm1_name, norm1 = build_norm_layer(
64, postfix=1) self.normalize, 64, postfix=1)
self.add_module(self.norm1_name, norm1) self.add_module(self.norm1_name, norm1)
self.relu = nn.ReLU(inplace=True) self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
...@@ -354,6 +411,12 @@ class ResNet(nn.Module): ...@@ -354,6 +411,12 @@ class ResNet(nn.Module):
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
constant_init(m, 1) constant_init(m, 1)
if self.dcn is not None:
for m in self.modules():
if isinstance(m, Bottleneck) and hasattr(
m, 'conv2_offset'):
constant_init(m.conv2_offset, 0)
if self.zero_init_residual: if self.zero_init_residual:
for m in self.modules(): for m in self.modules():
if isinstance(m, Bottleneck): if isinstance(m, Bottleneck):
......
mmdet/models/backbones/resnext.py
View file @ 896122c8
...@@ -2,8 +2,9 @@ import math ...@@ -2,8 +2,9 @@ import math
import torch.nn as nn import torch.nn as nn
from .resnet import ResNet from mmdet.ops import DeformConv, ModulatedDeformConv
from .resnet import Bottleneck as _Bottleneck from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNet
from ..registry import BACKBONES from ..registry import BACKBONES
from ..utils import build_norm_layer from ..utils import build_norm_layer
...@@ -22,15 +23,12 @@ class Bottleneck(_Bottleneck): ...@@ -22,15 +23,12 @@ class Bottleneck(_Bottleneck):
else: else:
width = math.floor(self.planes * (base_width / 64)) * groups width = math.floor(self.planes * (base_width / 64)) * groups
self.norm1_name, norm1 = build_norm_layer(self.normalize, self.norm1_name, norm1 = build_norm_layer(
width, self.normalize, width, postfix=1)
postfix=1) self.norm2_name, norm2 = build_norm_layer(
self.norm2_name, norm2 = build_norm_layer(self.normalize, self.normalize, width, postfix=2)
width, self.norm3_name, norm3 = build_norm_layer(
postfix=2) self.normalize, self.planes * self.expansion, postfix=3)
self.norm3_name, norm3 = build_norm_layer(self.normalize,
self.planes * self.expansion,
postfix=3)
self.conv1 = nn.Conv2d( self.conv1 = nn.Conv2d(
self.inplanes, self.inplanes,
...@@ -39,15 +37,47 @@ class Bottleneck(_Bottleneck): ...@@ -39,15 +37,47 @@ class Bottleneck(_Bottleneck):
stride=self.conv1_stride, stride=self.conv1_stride,
bias=False) bias=False)
self.add_module(self.norm1_name, norm1) self.add_module(self.norm1_name, norm1)
self.conv2 = nn.Conv2d( fallback_on_stride = False
width, self.with_modulated_dcn = False
width, if self.with_dcn:
kernel_size=3, fallback_on_stride = self.dcn.get('fallback_on_stride', False)
stride=self.conv2_stride, self.with_modulated_dcn = self.dcn.get('modulated', False)
padding=self.dilation, if not self.with_dcn or fallback_on_stride:
dilation=self.dilation, self.conv2 = nn.Conv2d(
groups=groups, width,
bias=False) width,
kernel_size=3,
stride=self.conv2_stride,
padding=self.dilation,
dilation=self.dilation,
groups=groups,
bias=False)
else:
groups = self.dcn.get('groups', 1)
deformable_groups = self.dcn.get('deformable_groups', 1)
if not self.with_modulated_dcn:
conv_op = DeformConv
offset_channels = 18
else:
conv_op = ModulatedDeformConv
offset_channels = 27
self.conv2_offset = nn.Conv2d(
width,
deformable_groups * offset_channels,
kernel_size=3,
stride=self.conv2_stride,
padding=self.dilation,
dilation=self.dilation)
self.conv2 = conv_op(
width,
width,
kernel_size=3,
stride=self.conv2_stride,
padding=self.dilation,
dilation=self.dilation,
groups=groups,
deformable_groups=deformable_groups,
bias=False)
self.add_module(self.norm2_name, norm2) self.add_module(self.norm2_name, norm2)
self.conv3 = nn.Conv2d( self.conv3 = nn.Conv2d(
width, self.planes * self.expansion, kernel_size=1, bias=False) width, self.planes * self.expansion, kernel_size=1, bias=False)
...@@ -64,7 +94,8 @@ def make_res_layer(block, ...@@ -64,7 +94,8 @@ def make_res_layer(block,
base_width=4, base_width=4,
style='pytorch', style='pytorch',
with_cp=False, with_cp=False,
normalize=dict(type='BN')): normalize=dict(type='BN'),
dcn=None):
downsample = None downsample = None
if stride != 1 or inplanes != planes * block.expansion: if stride != 1 or inplanes != planes * block.expansion:
downsample = nn.Sequential( downsample = nn.Sequential(
...@@ -89,7 +120,8 @@ def make_res_layer(block, ...@@ -89,7 +120,8 @@ def make_res_layer(block,
base_width=base_width, base_width=base_width,
style=style, style=style,
with_cp=with_cp, with_cp=with_cp,
normalize=normalize)) normalize=normalize,
dcn=dcn))
inplanes = planes * block.expansion inplanes = planes * block.expansion
for i in range(1, blocks): for i in range(1, blocks):
layers.append( layers.append(
...@@ -102,7 +134,8 @@ def make_res_layer(block, ...@@ -102,7 +134,8 @@ def make_res_layer(block,
base_width=base_width, base_width=base_width,
style=style, style=style,
with_cp=with_cp, with_cp=with_cp,
normalize=normalize)) normalize=normalize,
dcn=dcn))
return nn.Sequential(*layers) return nn.Sequential(*layers)
...@@ -150,6 +183,7 @@ class ResNeXt(ResNet): ...@@ -150,6 +183,7 @@ class ResNeXt(ResNet):
for i, num_blocks in enumerate(self.stage_blocks): for i, num_blocks in enumerate(self.stage_blocks):
stride = self.strides[i] stride = self.strides[i]
dilation = self.dilations[i] dilation = self.dilations[i]
dcn = self.dcn if self.stage_with_dcn[i] else None
planes = 64 * 2**i planes = 64 * 2**i
res_layer = make_res_layer( res_layer = make_res_layer(
self.block, self.block,
...@@ -162,7 +196,8 @@ class ResNeXt(ResNet): ...@@ -162,7 +196,8 @@ class ResNeXt(ResNet):
base_width=self.base_width, base_width=self.base_width,
style=self.style, style=self.style,
with_cp=self.with_cp, with_cp=self.with_cp,
normalize=self.normalize) normalize=self.normalize,
dcn=dcn)
self.inplanes = planes * self.block.expansion self.inplanes = planes * self.block.expansion
layer_name = 'layer{}'.format(i + 1) layer_name = 'layer{}'.format(i + 1)
self.add_module(layer_name, res_layer) self.add_module(layer_name, res_layer)
......
mmdet/ops/__init__.py
View file @ 896122c8
from .dcn import (DeformConv, DeformRoIPooling, DeformRoIPoolingPack,
ModulatedDeformRoIPoolingPack, ModulatedDeformConv,
ModulatedDeformConvPack, deform_conv, modulated_deform_conv,
deform_roi_pooling)
from .nms import nms, soft_nms from .nms import nms, soft_nms
from .roi_align import RoIAlign, roi_align from .roi_align import RoIAlign, roi_align
from .roi_pool import RoIPool, roi_pool from .roi_pool import RoIPool, roi_pool
__all__ = ['nms', 'soft_nms', 'RoIAlign', 'roi_align', 'RoIPool', 'roi_pool'] __all__ = [
'nms', 'soft_nms', 'RoIAlign', 'roi_align', 'RoIPool', 'roi_pool',
'DeformConv', 'DeformRoIPooling', 'DeformRoIPoolingPack',
'ModulatedDeformRoIPoolingPack', 'ModulatedDeformConv',
'ModulatedDeformConvPack', 'deform_conv', 'modulated_deform_conv',
'deform_roi_pooling'
]
mmdet/ops/dcn/__init__.py 0 → 100644
View file @ 896122c8
from .functions.deform_conv import deform_conv, modulated_deform_conv
from .functions.deform_pool import deform_roi_pooling
from .modules.deform_conv import (DeformConv, ModulatedDeformConv,
ModulatedDeformConvPack)
from .modules.deform_pool import (DeformRoIPooling, DeformRoIPoolingPack,
ModulatedDeformRoIPoolingPack)
__all__ = [
'DeformConv', 'DeformRoIPooling', 'DeformRoIPoolingPack',
'ModulatedDeformRoIPoolingPack', 'ModulatedDeformConv',
'ModulatedDeformConvPack', 'deform_conv',
'modulated_deform_conv', 'deform_roi_pooling'
]
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment