Skip to content

GitLab

"PyTorch/NLP/new-Transformer/scripts/constraints/validate.py" did not exist on "c056df7823f6c8d5ad4234870596871f9ece9df1"
Commit 85529f35 authored by unknown's avatar unknown
Browse files

添加openmmlab测试用例

parent b21b0c01
Hide whitespace changes
Inline Side-by-side
Showing with 708 additions and 0 deletions
openmmlab_test/mmdetection-speed_xinpian/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py 0 → 100644
View file @ 85529f35
_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
openmmlab_test/mmdetection-speed_xinpian/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py 0 → 100644
View file @ 85529f35
_base_ = '../faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
openmmlab_test/mmdetection-speed_xinpian/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py 0 → 100644
View file @ 85529f35
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
openmmlab_test/mmdetection-speed_xinpian/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py 0 → 100644
View file @ 85529f35
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
_delete_=True,
type='DeformRoIPoolPack',
output_size=7,
output_channels=256),
out_channels=256,
featmap_strides=[4, 8, 16, 32])))
openmmlab_test/mmdetection-speed_xinpian/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py 0 → 100644
View file @ 85529f35
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
openmmlab_test/mmdetection-speed_xinpian/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py 0 → 100644
View file @ 85529f35
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=4, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
openmmlab_test/mmdetection-speed_xinpian/configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py 0 → 100644
View file @ 85529f35
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
_delete_=True,
type='ModulatedDeformRoIPoolPack',
output_size=7,
output_channels=256),
out_channels=256,
featmap_strides=[4, 8, 16, 32])))
openmmlab_test/mmdetection-speed_xinpian/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py 0 → 100644
View file @ 85529f35
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
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,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
openmmlab_test/mmdetection-speed_xinpian/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py 0 → 100644
View file @ 85529f35
_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
openmmlab_test/mmdetection-speed_xinpian/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py 0 → 100644
View file @ 85529f35
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
openmmlab_test/mmdetection-speed_xinpian/configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py 0 → 100644
View file @ 85529f35
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
openmmlab_test/mmdetection-speed_xinpian/configs/dcn/metafile.yml 0 → 100644
View file @ 85529f35
Collections:
- Name: Deformable Convolutional Networks
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x NVIDIA V100 GPUs
Architecture:
- Deformable Convolution
Paper: https://arxiv.org/abs/1811.11168
README: configs/dcn/README.md
Models:
- Name: faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco
In Collection: Deformable Convolutional Networks
Config: configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
Metadata:
Training Memory (GB): 4.0
inference time (s/im): 0.05618
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 41.3
Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-d68aed1e.pth
- Name: faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco
In Collection: Deformable Convolutional Networks
Config: configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
Metadata:
Training Memory (GB): 4.1
inference time (s/im): 0.05682
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 41.4
Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200130-d099253b.pth
- Name: faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco
In Collection: Deformable Convolutional Networks
Config: configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py
Metadata:
Training Memory (GB): 4.2
inference time (s/im): 0.05747
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 41.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco_20200130-01262257.pth
- Name: faster_rcnn_r50_fpn_dpool_1x_coco
In Collection: Deformable Convolutional Networks
Config: configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py
Metadata:
Training Memory (GB): 5.0
inference time (s/im): 0.05814
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 38.9
Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dpool_1x_coco/faster_rcnn_r50_fpn_dpool_1x_coco_20200307-90d3c01d.pth
- Name: faster_rcnn_r50_fpn_mdpool_1x_coco
In Collection: Deformable Convolutional Networks
Config: configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py
Metadata:
Training Memory (GB): 5.8
inference time (s/im): 0.06024
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 38.7
Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco/faster_rcnn_r50_fpn_mdpool_1x_coco_20200307-c0df27ff.pth
- Name: faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco
In Collection: Deformable Convolutional Networks
Config: configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
Metadata:
Training Memory (GB): 6.0
inference time (s/im): 0.08
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 42.7
Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-1377f13d.pth
- Name: faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco
In Collection: Deformable Convolutional Networks
Config: configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py
Metadata:
Training Memory (GB): 7.3
inference time (s/im): 0.1
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 44.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco_20200203-4f85c69c.pth
- Name: mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco
In Collection: Deformable Convolutional Networks
Config: configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
Metadata:
Training Memory (GB): 4.5
inference time (s/im): 0.06494
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 41.8
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 37.4
Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200203-4d9ad43b.pth
- Name: mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco
In Collection: Deformable Convolutional Networks
Config: configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
Metadata:
Training Memory (GB): 4.5
inference time (s/im): 0.06623
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 41.5
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 37.1
Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200203-ad97591f.pth
- Name: mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco
In Collection: Deformable Convolutional Networks
Config: configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
Metadata:
Training Memory (GB): 6.5
inference time (s/im): 0.08547
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 43.5
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 38.9
Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200216-a71f5bce.pth
- Name: cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco
In Collection: Deformable Convolutional Networks
Config: configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
Metadata:
Training Memory (GB): 4.5
inference time (s/im): 0.06849
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 43.8
Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-2f1fca44.pth
- Name: cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco
In Collection: Deformable Convolutional Networks
Config: configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
Metadata:
Training Memory (GB): 6.4
inference time (s/im): 0.09091
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 45.0
Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-3b2f0594.pth
- Name: cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco
In Collection: Deformable Convolutional Networks
Config: configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
Metadata:
Training Memory (GB): 6.0
inference time (s/im): 0.1
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 44.4
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 38.6
Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200202-42e767a2.pth
- Name: cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco
In Collection: Deformable Convolutional Networks
Config: configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
Metadata:
Training Memory (GB): 8.0
inference time (s/im): 0.11628
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 45.8
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 39.7
Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200204-df0c5f10.pth
- Name: cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco
In Collection: Deformable Convolutional Networks
Config: configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py
Metadata:
Training Memory (GB): 9.2
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 47.3
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 41.1
Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco-e75f90c8.pth
openmmlab_test/mmdetection-speed_xinpian/configs/deepfashion/README.md 0 → 100644
View file @ 85529f35
# DeepFashion
<!-- [DATASET] -->
[MMFashion](https://github.com/open-mmlab/mmfashion) develops "fashion parsing and segmentation" module
based on the dataset
[DeepFashion-Inshop](https://drive.google.com/drive/folders/0B7EVK8r0v71pVDZFQXRsMDZCX1E?usp=sharing).
Its annotation follows COCO style.
To use it, you need to first download the data. Note that we only use "img_highres" in this task.
The file tree should be like this:
```sh
mmdetection
├── mmdet
├── tools
├── configs
├── data
│ ├── DeepFashion
│ │ ├── In-shop
│ │ ├── Anno
│ │ │   ├── segmentation
│ │ │   | ├── DeepFashion_segmentation_train.json
│ │ │   | ├── DeepFashion_segmentation_query.json
│ │ │   | ├── DeepFashion_segmentation_gallery.json
│ │ │   ├── list_bbox_inshop.txt
│ │ │   ├── list_description_inshop.json
│ │ │   ├── list_item_inshop.txt
│ │ │   └── list_landmarks_inshop.txt
│ │ ├── Eval
│ │ │ └── list_eval_partition.txt
│ │ ├── Img
│ │ │ ├── img
│ │ │ │ ├──XXX.jpg
│ │ │ ├── img_highres
│ │ │ └── ├──XXX.jpg
```
After that you can train the Mask RCNN r50 on DeepFashion-In-shop dataset by launching training with the `mask_rcnn_r50_fpn_1x.py` config
or creating your own config file.
```
@inproceedings{liuLQWTcvpr16DeepFashion,
author = {Liu, Ziwei and Luo, Ping and Qiu, Shi and Wang, Xiaogang and Tang, Xiaoou},
title = {DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations},
booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}
```
## Model Zoo
| Backbone | Model type | Dataset | bbox detection Average Precision | segmentation Average Precision | Config | Download (Google) |
| :---------: | :----------: | :-----------------: | :--------------------------------: | :----------------------------: | :---------:| :-------------------------: |
| ResNet50 | Mask RCNN | DeepFashion-In-shop | 0.599 | 0.584 |[config](https://github.com/open-mmlab/mmdetection/blob/master/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py)| [model](https://drive.google.com/open?id=1q6zF7J6Gb-FFgM87oIORIt6uBozaXp5r) &#124; [log](https://drive.google.com/file/d/1qTK4Dr4FFLa9fkdI6UVko408gkrfTRLP/view?usp=sharing) |
openmmlab_test/mmdetection-speed_xinpian/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py 0 → 100644
View file @ 85529f35
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/deepfashion.py', '../_base_/schedules/schedule_1x.py',
'../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(num_classes=15), mask_head=dict(num_classes=15)))
# runtime settings
runner = dict(type='EpochBasedRunner', max_epochs=15)
openmmlab_test/mmdetection-speed_xinpian/configs/deformable_detr/README.md 0 → 100644
View file @ 85529f35
# Deformable DETR
## Introduction
<!-- [ALGORITHM] -->
We provide the config files for Deformable DETR: [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159).
```
@inproceedings{
zhu2021deformable,
title={Deformable DETR: Deformable Transformers for End-to-End Object Detection},
author={Xizhou Zhu and Weijie Su and Lewei Lu and Bin Li and Xiaogang Wang and Jifeng Dai},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=gZ9hCDWe6ke}
}
```
## Results and Models
| Backbone | Model | Lr schd | box AP | Config | Download |
|:------:|:--------:|:--------------:|:------:|:------:|:--------:|
| R-50 | Deformable DETR |50e | 44.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_r50_16x2_50e_coco/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_r50_16x2_50e_coco/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.log.json) |
| R-50 | + iterative bounding box refinement |50e | 46.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco/deformable_detr_refine_r50_16x2_50e_coco_20210419_220503-5f5dff21.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco/deformable_detr_refine_r50_16x2_50e_coco_20210419_220503-5f5dff21.log.json) |
| R-50 | ++ two-stage Deformable DETR |50e | 46.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco/deformable_detr_twostage_refine_r50_16x2_50e_coco_20210419_220613-9d28ab72.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco/deformable_detr_twostage_refine_r50_16x2_50e_coco_20210419_220613-9d28ab72.log.json) |
# NOTE
1. All models are trained with batch size 32.
2. The performance is unstable. `Deformable DETR` and `iterative bounding box refinement` may fluctuate about 0.3 mAP. `two-stage Deformable DETR` may fluctuate about 0.2 mAP.
openmmlab_test/mmdetection-speed_xinpian/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py 0 → 100644
View file @ 85529f35
_base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='DeformableDETR',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='pytorch'),
neck=dict(
type='ChannelMapper',
in_channels=[512, 1024, 2048],
kernel_size=1,
out_channels=256,
act_cfg=None,
norm_cfg=dict(type='GN', num_groups=32),
num_outs=4),
bbox_head=dict(
type='DeformableDETRHead',
num_query=300,
num_classes=80,
in_channels=2048,
sync_cls_avg_factor=True,
as_two_stage=False,
transformer=dict(
type='DeformableDetrTransformer',
encoder=dict(
type='DetrTransformerEncoder',
num_layers=6,
transformerlayers=dict(
type='BaseTransformerLayer',
attn_cfgs=dict(
type='MultiScaleDeformableAttention', embed_dims=256),
feedforward_channels=1024,
ffn_dropout=0.1,
operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
decoder=dict(
type='DeformableDetrTransformerDecoder',
num_layers=6,
return_intermediate=True,
transformerlayers=dict(
type='DetrTransformerDecoderLayer',
attn_cfgs=[
dict(
type='MultiheadAttention',
embed_dims=256,
num_heads=8,
dropout=0.1),
dict(
type='MultiScaleDeformableAttention',
embed_dims=256)
],
feedforward_channels=1024,
ffn_dropout=0.1,
operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
'ffn', 'norm')))),
positional_encoding=dict(
type='SinePositionalEncoding',
num_feats=128,
normalize=True,
offset=-0.5),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=2.0),
loss_bbox=dict(type='L1Loss', loss_weight=5.0),
loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='HungarianAssigner',
cls_cost=dict(type='FocalLossCost', weight=2.0),
reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))),
test_cfg=dict(max_per_img=100))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different
# from the default setting in mmdet.
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='AutoAugment',
policies=[
[
dict(
type='Resize',
img_scale=[(480, 1333), (512, 1333), (544, 1333),
(576, 1333), (608, 1333), (640, 1333),
(672, 1333), (704, 1333), (736, 1333),
(768, 1333), (800, 1333)],
multiscale_mode='value',
keep_ratio=True)
],
[
dict(
type='Resize',
# The radio of all image in train dataset < 7
# follow the original impl
img_scale=[(400, 4200), (500, 4200), (600, 4200)],
multiscale_mode='value',
keep_ratio=True),
dict(
type='RandomCrop',
crop_type='absolute_range',
crop_size=(384, 600),
allow_negative_crop=True),
dict(
type='Resize',
img_scale=[(480, 1333), (512, 1333), (544, 1333),
(576, 1333), (608, 1333), (640, 1333),
(672, 1333), (704, 1333), (736, 1333),
(768, 1333), (800, 1333)],
multiscale_mode='value',
override=True,
keep_ratio=True)
]
]),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=1),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
# test_pipeline, NOTE the Pad's size_divisor is different from the default
# setting (size_divisor=32). While there is little effect on the performance
# whether we use the default setting or use size_divisor=1.
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=1),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(filter_empty_gt=False, pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
# optimizer
optimizer = dict(
type='AdamW',
lr=2e-4,
weight_decay=0.0001,
paramwise_cfg=dict(
custom_keys={
'backbone': dict(lr_mult=0.1),
'sampling_offsets': dict(lr_mult=0.1),
'reference_points': dict(lr_mult=0.1)
}))
optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
# learning policy
lr_config = dict(policy='step', step=[40])
runner = dict(type='EpochBasedRunner', max_epochs=50)
openmmlab_test/mmdetection-speed_xinpian/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py 0 → 100644
View file @ 85529f35
_base_ = 'deformable_detr_r50_16x2_50e_coco.py'
model = dict(bbox_head=dict(with_box_refine=True))
openmmlab_test/mmdetection-speed_xinpian/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py 0 → 100644
View file @ 85529f35
_base_ = 'deformable_detr_refine_r50_16x2_50e_coco.py'
model = dict(bbox_head=dict(as_two_stage=True))
openmmlab_test/mmdetection-speed_xinpian/configs/deformable_detr/metafile.yml 0 → 100644
View file @ 85529f35
Collections:
- Name: Deformable DETR
Metadata:
Training Data: COCO
Training Techniques:
- AdamW
- Multi Scale Train
- Gradient Clip
Training Resources: 8x NVIDIA V100 GPUs
Architecture:
- ResNet
- Transformer
Paper: https://openreview.net/forum?id=gZ9hCDWe6ke
README: configs/deformable_detr/README.md
Models:
- Name: deformable_detr_r50_16x2_50e_coco
In Collection: Deformable DETR
Config: configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py
Metadata:
Epochs: 50
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 44.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_r50_16x2_50e_coco/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.pth
- Name: deformable_detr_refine_r50_16x2_50e_coco
In Collection: Deformable DETR
Config: configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py
Metadata:
Epochs: 50
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 46.1
Weights: https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco/deformable_detr_refine_r50_16x2_50e_coco_20210419_220503-5f5dff21.pth
- Name: deformable_detr_twostage_refine_r50_16x2_50e_coco
In Collection: Deformable DETR
Config: configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py
Metadata:
Epochs: 50
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 46.8
Weights: https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco/deformable_detr_twostage_refine_r50_16x2_50e_coco_20210419_220613-9d28ab72.pth
openmmlab_test/mmdetection-speed_xinpian/configs/detectors/README.md 0 → 100644
View file @ 85529f35
# DetectoRS
## Introduction
<!-- [ALGORITHM] -->
We provide the config files for [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://arxiv.org/pdf/2006.02334.pdf).
```BibTeX
@article{qiao2020detectors,
title={DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution},
author={Qiao, Siyuan and Chen, Liang-Chieh and Yuille, Alan},
journal={arXiv preprint arXiv:2006.02334},
year={2020}
}
```
## Dataset
DetectoRS requires COCO and [COCO-stuff](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip) dataset for training. You need to download and extract it in the COCO dataset path.
The directory should be like this.
```none
mmdetection
├── mmdet
├── tools
├── configs
├── data
│ ├── coco
│ │ ├── annotations
│ │ ├── train2017
│ │ ├── val2017
│ │ ├── test2017
| | ├── stuffthingmaps
```
## Results and Models
DetectoRS includes two major components:
- Recursive Feature Pyramid (RFP).
- Switchable Atrous Convolution (SAC).
They can be used independently.
Combining them together results in DetectoRS.
The results on COCO 2017 val are shown in the below table.
| Method | Detector | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
|:------:|:--------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:|
| RFP | Cascade + ResNet-50 | 1x | 7.5 | - | 44.8 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_rfp_1x_coco/cascade_rcnn_r50_rfp_1x_coco-8cf51bfd.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_rfp_1x_coco/cascade_rcnn_r50_rfp_1x_coco_20200624_104126.log.json) |
| SAC | Cascade + ResNet-50 | 1x | 5.6 | - | 45.0| | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/cascade_rcnn_r50_sac_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_sac_1x_coco/cascade_rcnn_r50_sac_1x_coco-24bfda62.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_sac_1x_coco/cascade_rcnn_r50_sac_1x_coco_20200624_104402.log.json) |
| DetectoRS | Cascade + ResNet-50 | 1x | 9.9 | - | 47.4 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_cascade_rcnn_r50_1x_coco/detectors_cascade_rcnn_r50_1x_coco-32a10ba0.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_cascade_rcnn_r50_1x_coco/detectors_cascade_rcnn_r50_1x_coco_20200706_001203.log.json) |
| RFP | HTC + ResNet-50 | 1x | 11.2 | - | 46.6 | 40.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/htc_r50_rfp_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_rfp_1x_coco/htc_r50_rfp_1x_coco-8ff87c51.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_rfp_1x_coco/htc_r50_rfp_1x_coco_20200624_103053.log.json) |
| SAC | HTC + ResNet-50 | 1x | 9.3 | - | 46.4 | 40.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/htc_r50_sac_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_sac_1x_coco/htc_r50_sac_1x_coco-bfa60c54.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_sac_1x_coco/htc_r50_sac_1x_coco_20200624_103111.log.json) |
| DetectoRS | HTC + ResNet-50 | 1x | 13.6 | - | 49.1 | 42.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_htc_r50_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r50_1x_coco/detectors_htc_r50_1x_coco-329b1453.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r50_1x_coco/detectors_htc_r50_1x_coco_20200624_103659.log.json) |
| DetectoRS | HTC + ResNet-101 | 20e | 19.6 | | 50.5 | 43.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_htc_r101_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r101_20e_coco/detectors_htc_r101_20e_coco_20210419_203638-348d533b.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r101_20e_coco/detectors_htc_r101_20e_coco_20210419_203638.log.json) |
*Note*: This is a re-implementation based on MMDetection-V2.
The original implementation is based on MMDetection-V1.
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