From 40ea9ca23a1cbe65dbe800dc065acfe44f5626f8 Mon Sep 17 00:00:00 2001
From: Chongruo Wu <chongruo@gmail.com>
Date: Thu, 12 Sep 2019 00:05:40 +0800
Subject: [PATCH] [Modulated DeformableConv] fix a bug for deformable_group>1
(#1359)
* fix a bug for Modulated Deformable Convolution
* resolve cases for CI test
* resolve cases for CI test
* resolve cases for CI test
* create a new script
* change dir name in config
* update model url
---
configs/dcn/README.md | 2 +
...ter_rcnn_mdconv_c3-c5_group4_r50_fpn_1x.py | 177 ++++++++++++++++++
mmdet/models/backbones/resnet.py | 11 +-
3 files changed, 185 insertions(+), 5 deletions(-)
create mode 100644 configs/dcn/faster_rcnn_mdconv_c3-c5_group4_r50_fpn_1x.py
diff --git a/configs/dcn/README.md b/configs/dcn/README.md
index d95ee40..12f003f 100644
--- a/configs/dcn/README.md
+++ b/configs/dcn/README.md
@@ -24,6 +24,7 @@
|:---------:|:------------:|:-------:|:-------------:|:------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:-------:|:--------:|
| 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 | 40.2 | - | [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 (dg=4) | Faster | pytorch | mdconv(c3-c5) | - | 1x | 3.9 | 0.350 | 11.36 | 40.4 | - | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/dcn/faster_rcnn_mdconv_c3-c5_group4_r50_fpn_1x_20190911-5591a7e4.pth) |
| R-50-FPN | Faster | pytorch | - | dpool | 1x | 4.6 | 0.714 | 8.7 | 37.8 | - | [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 | 38.0 | - | [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) |
@@ -40,4 +41,5 @@
- `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 dcn ops are modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch, which should be more memory efficient and slightly faster.
+- (*) For R-50-FPN (dg=4), dg is short for deformable_group. This model is trained and tested on Amazon EC2 p3dn.24xlarge instance.
- **Memory, Train/Inf time is outdated.**
\ No newline at end of file
diff --git a/configs/dcn/faster_rcnn_mdconv_c3-c5_group4_r50_fpn_1x.py b/configs/dcn/faster_rcnn_mdconv_c3-c5_group4_r50_fpn_1x.py
new file mode 100644
index 0000000..4e91bb0
--- /dev/null
+++ b/configs/dcn/faster_rcnn_mdconv_c3-c5_group4_r50_fpn_1x.py
@@ -0,0 +1,177 @@
+# model settings
+model = dict(
+ type='FasterRCNN',
+ pretrained='torchvision://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=4, fallback_on_stride=False),
+ stage_with_dcn=(False, True, True, True)),
+ neck=dict(
+ type='FPN',
+ in_channels=[256, 512, 1024, 2048],
+ out_channels=256,
+ num_outs=5),
+ rpn_head=dict(
+ type='RPNHead',
+ in_channels=256,
+ feat_channels=256,
+ anchor_scales=[8],
+ anchor_ratios=[0.5, 1.0, 2.0],
+ anchor_strides=[4, 8, 16, 32, 64],
+ target_means=[.0, .0, .0, .0],
+ target_stds=[1.0, 1.0, 1.0, 1.0],
+ loss_cls=dict(
+ type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+ loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
+ bbox_roi_extractor=dict(
+ type='SingleRoIExtractor',
+ roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
+ out_channels=256,
+ featmap_strides=[4, 8, 16, 32]),
+ bbox_head=dict(
+ type='SharedFCBBoxHead',
+ num_fcs=2,
+ in_channels=256,
+ fc_out_channels=1024,
+ roi_feat_size=7,
+ num_classes=81,
+ target_means=[0., 0., 0., 0.],
+ target_stds=[0.1, 0.1, 0.2, 0.2],
+ reg_class_agnostic=False,
+ loss_cls=dict(
+ type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+ loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)))
+# model training and testing settings
+train_cfg = dict(
+ rpn=dict(
+ assigner=dict(
+ type='MaxIoUAssigner',
+ pos_iou_thr=0.7,
+ neg_iou_thr=0.3,
+ min_pos_iou=0.3,
+ ignore_iof_thr=-1),
+ sampler=dict(
+ type='RandomSampler',
+ num=256,
+ pos_fraction=0.5,
+ neg_pos_ub=-1,
+ add_gt_as_proposals=False),
+ allowed_border=0,
+ pos_weight=-1,
+ debug=False),
+ rpn_proposal=dict(
+ nms_across_levels=False,
+ nms_pre=2000,
+ nms_post=2000,
+ max_num=2000,
+ nms_thr=0.7,
+ min_bbox_size=0),
+ rcnn=dict(
+ assigner=dict(
+ type='MaxIoUAssigner',
+ pos_iou_thr=0.5,
+ neg_iou_thr=0.5,
+ min_pos_iou=0.5,
+ ignore_iof_thr=-1),
+ sampler=dict(
+ type='RandomSampler',
+ num=512,
+ pos_fraction=0.25,
+ neg_pos_ub=-1,
+ add_gt_as_proposals=True),
+ pos_weight=-1,
+ debug=False))
+test_cfg = dict(
+ rpn=dict(
+ nms_across_levels=False,
+ nms_pre=1000,
+ nms_post=1000,
+ max_num=1000,
+ nms_thr=0.7,
+ min_bbox_size=0),
+ rcnn=dict(
+ score_thr=0.05, nms=dict(type='nms', iou_thr=0.5), max_per_img=100)
+ # soft-nms is also supported for rcnn testing
+ # e.g., nms=dict(type='soft_nms', iou_thr=0.5, min_score=0.05)
+)
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+ mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+ dict(type='LoadImageFromFile'),
+ dict(type='LoadAnnotations', with_bbox=True),
+ dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+ dict(type='RandomFlip', flip_ratio=0.5),
+ dict(type='Normalize', **img_norm_cfg),
+ dict(type='Pad', size_divisor=32),
+ dict(type='DefaultFormatBundle'),
+ dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+ dict(type='LoadImageFromFile'),
+ dict(
+ type='MultiScaleFlipAug',
+ img_scale=(1333, 800),
+ flip=False,
+ transforms=[
+ dict(type='Resize', keep_ratio=True),
+ dict(type='RandomFlip'),
+ dict(type='Normalize', **img_norm_cfg),
+ dict(type='Pad', size_divisor=32),
+ dict(type='ImageToTensor', keys=['img']),
+ dict(type='Collect', keys=['img']),
+ ])
+]
+data = dict(
+ 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/',
+ pipeline=train_pipeline),
+ val=dict(
+ type=dataset_type,
+ ann_file=data_root + 'annotations/instances_val2017.json',
+ img_prefix=data_root + 'val2017/',
+ pipeline=test_pipeline),
+ test=dict(
+ type=dataset_type,
+ ann_file=data_root + 'annotations/instances_val2017.json',
+ img_prefix=data_root + 'val2017/',
+ pipeline=test_pipeline))
+# 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_group4_r50_fpn_1x'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]
diff --git a/mmdet/models/backbones/resnet.py b/mmdet/models/backbones/resnet.py
index eaead29..d87b736 100644
--- a/mmdet/models/backbones/resnet.py
+++ b/mmdet/models/backbones/resnet.py
@@ -161,7 +161,7 @@ class Bottleneck(nn.Module):
bias=False)
else:
assert conv_cfg is None, 'conv_cfg must be None for DCN'
- deformable_groups = dcn.get('deformable_groups', 1)
+ self.deformable_groups = dcn.get('deformable_groups', 1)
if not self.with_modulated_dcn:
conv_op = DeformConv
offset_channels = 18
@@ -170,7 +170,7 @@ class Bottleneck(nn.Module):
offset_channels = 27
self.conv2_offset = nn.Conv2d(
planes,
- deformable_groups * offset_channels,
+ self.deformable_groups * offset_channels,
kernel_size=3,
stride=self.conv2_stride,
padding=dilation,
@@ -182,7 +182,7 @@ class Bottleneck(nn.Module):
stride=self.conv2_stride,
padding=dilation,
dilation=dilation,
- deformable_groups=deformable_groups,
+ deformable_groups=self.deformable_groups,
bias=False)
self.add_module(self.norm2_name, norm2)
self.conv3 = build_conv_layer(
@@ -230,8 +230,9 @@ class Bottleneck(nn.Module):
out = self.conv2(out)
elif self.with_modulated_dcn:
offset_mask = self.conv2_offset(out)
- offset = offset_mask[:, :18, :, :]
- mask = offset_mask[:, -9:, :, :].sigmoid()
+ offset = offset_mask[:, :18 * self.deformable_groups, :, :]
+ mask = offset_mask[:, -9 * self.deformable_groups:, :, :]
+ mask = mask.sigmoid()
out = self.conv2(out, offset, mask)
else:
offset = self.conv2_offset(out)
--
GitLab