Merge branch 'open-mmlab:master' into custom/face_occlusion
whooray
GitHub
commit
d5e79fa0ef
|
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@ -61,17 +61,15 @@ jobs:
|
|||
name: Install mmseg dependencies
|
||||
command: |
|
||||
python -m pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch<< parameters.torch >>/index.html
|
||||
python -m pip install mmdet
|
||||
python -m pip install -r requirements.txt
|
||||
- run:
|
||||
name: Build and install
|
||||
command: |
|
||||
python -m pip install -e .
|
||||
- run:
|
||||
name: Run unittests
|
||||
name: Run unittests but skip timm unittests
|
||||
command: |
|
||||
python -m pip install timm
|
||||
python -m coverage run --branch --source mmseg -m pytest tests/
|
||||
python -m coverage run --branch --source mmseg -m pytest tests/ --ignore tests/test_models/test_backbones/test_timm_backbone.py
|
||||
python -m coverage xml
|
||||
python -m coverage report -m
|
||||
|
||||
|
|
@ -102,7 +100,6 @@ jobs:
|
|||
# python -m pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch${{matrix.torch_version}}/index.html
|
||||
command: |
|
||||
python -m pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html
|
||||
python -m pip install mmdet
|
||||
python -m pip install -r requirements.txt
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||||
- run:
|
||||
name: Build and install
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||||
|
|
@ -110,10 +107,9 @@ jobs:
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|||
python setup.py check -m -s
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||||
TORCH_CUDA_ARCH_LIST=7.0 python -m pip install -e .
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||||
- run:
|
||||
name: Run unittests
|
||||
name: Run unittests but skip timm unittests
|
||||
command: |
|
||||
python -m pip install timm
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||||
python -m pytest tests/
|
||||
python -m pytest tests/ --ignore tests/test_models/test_backbones/test_timm_backbone.py
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||||
|
||||
workflows:
|
||||
unit_tests:
|
||||
|
|
|
|||
|
|
@ -70,13 +70,14 @@ jobs:
|
|||
coverage run --branch --source mmseg -m pytest tests/
|
||||
coverage xml
|
||||
coverage report -m
|
||||
if: ${{matrix.torch >= '1.5.0'}}
|
||||
# timm from v0.6.11 requires torch>=1.7
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||||
if: ${{matrix.torch >= '1.7.0'}}
|
||||
- name: Skip timm unittests and generate coverage report
|
||||
run: |
|
||||
coverage run --branch --source mmseg -m pytest tests/ --ignore tests/test_models/test_backbones/test_timm_backbone.py
|
||||
coverage xml
|
||||
coverage report -m
|
||||
if: ${{matrix.torch < '1.5.0'}}
|
||||
if: ${{matrix.torch < '1.7.0'}}
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||||
|
||||
build_cuda101:
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runs-on: ubuntu-18.04
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|
|
@ -144,13 +145,14 @@ jobs:
|
|||
coverage run --branch --source mmseg -m pytest tests/
|
||||
coverage xml
|
||||
coverage report -m
|
||||
if: ${{matrix.torch >= '1.5.0'}}
|
||||
# timm from v0.6.11 requires torch>=1.7
|
||||
if: ${{matrix.torch >= '1.7.0'}}
|
||||
- name: Skip timm unittests and generate coverage report
|
||||
run: |
|
||||
coverage run --branch --source mmseg -m pytest tests/ --ignore tests/test_models/test_backbones/test_timm_backbone.py
|
||||
coverage xml
|
||||
coverage report -m
|
||||
if: ${{matrix.torch < '1.5.0'}}
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||||
if: ${{matrix.torch < '1.7.0'}}
|
||||
- name: Upload coverage to Codecov
|
||||
uses: codecov/codecov-action@v1.0.10
|
||||
with:
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||||
|
|
@ -249,7 +251,7 @@ jobs:
|
|||
run: pip install -e .
|
||||
- name: Run unittests
|
||||
run: |
|
||||
python -m pip install timm
|
||||
python -m pip install 'timm<0.6.11'
|
||||
coverage run --branch --source mmseg -m pytest tests/
|
||||
- name: Generate coverage report
|
||||
run: |
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
repos:
|
||||
- repo: https://gitlab.com/pycqa/flake8.git
|
||||
rev: 3.8.3
|
||||
rev: 5.0.4
|
||||
hooks:
|
||||
- id: flake8
|
||||
- repo: https://github.com/PyCQA/isort
|
||||
|
|
@ -8,11 +8,11 @@ repos:
|
|||
hooks:
|
||||
- id: isort
|
||||
- repo: https://github.com/pre-commit/mirrors-yapf
|
||||
rev: v0.30.0
|
||||
rev: v0.32.0
|
||||
hooks:
|
||||
- id: yapf
|
||||
- repo: https://github.com/pre-commit/pre-commit-hooks
|
||||
rev: v3.1.0
|
||||
rev: v4.3.0
|
||||
hooks:
|
||||
- id: trailing-whitespace
|
||||
- id: check-yaml
|
||||
|
|
@ -34,7 +34,7 @@ repos:
|
|||
- mdformat_frontmatter
|
||||
- linkify-it-py
|
||||
- repo: https://github.com/codespell-project/codespell
|
||||
rev: v2.1.0
|
||||
rev: v2.2.1
|
||||
hooks:
|
||||
- id: codespell
|
||||
- repo: https://github.com/myint/docformatter
|
||||
|
|
|
|||
|
|
@ -77,10 +77,9 @@ The master branch works with **PyTorch 1.5+**.
|
|||
|
||||
### 💎 Stable version
|
||||
|
||||
v0.28.0 was released in 9/08/2022:
|
||||
v0.29.0 was released on 10/10/2022:
|
||||
|
||||
- Support Tversky Loss
|
||||
- Fix binary segmentation
|
||||
- Support PoolFormer (CVPR'2022)
|
||||
|
||||
Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
|
||||
|
||||
|
|
@ -130,6 +129,7 @@ Supported backbones:
|
|||
- [x] [BEiT (ICLR'2022)](configs/beit)
|
||||
- [x] [ConvNeXt (CVPR'2022)](configs/convnext)
|
||||
- [x] [MAE (CVPR'2022)](configs/mae)
|
||||
- [x] [PoolFormer (CVPR'2022)](configs/poolformer)
|
||||
|
||||
Supported methods:
|
||||
|
||||
|
|
|
|||
|
|
@ -74,10 +74,9 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
|
|||
|
||||
### 💎 稳定版本
|
||||
|
||||
最新版本 v0.28.0 在 2022.9.8 发布：
|
||||
最新版本 v0.29.0 在 2022.10.10 发布：
|
||||
|
||||
- 支持 Tversky Loss
|
||||
- 修复二值分割
|
||||
- 支持 PoolFormer (CVPR'2022)
|
||||
|
||||
如果想了解更多版本更新细节和历史信息，请阅读[更新日志](docs/en/changelog.md)。
|
||||
|
||||
|
|
@ -127,6 +126,7 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
|
|||
- [x] [BEiT (ICLR'2022)](configs/beit)
|
||||
- [x] [ConvNeXt (CVPR'2022)](configs/convnext)
|
||||
- [x] [MAE (CVPR'2022)](configs/mae)
|
||||
- [x] [PoolFormer (CVPR'2022)](configs/poolformer)
|
||||
|
||||
已支持的算法：
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,42 @@
|
|||
# model settings
|
||||
norm_cfg = dict(type='SyncBN', requires_grad=True)
|
||||
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s12_3rdparty_32xb128_in1k_20220414-f8d83051.pth' # noqa
|
||||
custom_imports = dict(imports='mmcls.models', allow_failed_imports=False)
|
||||
model = dict(
|
||||
type='EncoderDecoder',
|
||||
backbone=dict(
|
||||
type='mmcls.PoolFormer',
|
||||
arch='s12',
|
||||
init_cfg=dict(
|
||||
type='Pretrained', checkpoint=checkpoint_file, prefix='backbone.'),
|
||||
in_patch_size=7,
|
||||
in_stride=4,
|
||||
in_pad=2,
|
||||
down_patch_size=3,
|
||||
down_stride=2,
|
||||
down_pad=1,
|
||||
drop_rate=0.,
|
||||
drop_path_rate=0.,
|
||||
out_indices=(0, 2, 4, 6),
|
||||
frozen_stages=0,
|
||||
),
|
||||
neck=dict(
|
||||
type='FPN',
|
||||
in_channels=[256, 512, 1024, 2048],
|
||||
out_channels=256,
|
||||
num_outs=4),
|
||||
decode_head=dict(
|
||||
type='FPNHead',
|
||||
in_channels=[256, 256, 256, 256],
|
||||
in_index=[0, 1, 2, 3],
|
||||
feature_strides=[4, 8, 16, 32],
|
||||
channels=128,
|
||||
dropout_ratio=0.1,
|
||||
num_classes=19,
|
||||
norm_cfg=norm_cfg,
|
||||
align_corners=False,
|
||||
loss_decode=dict(
|
||||
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
|
||||
# model training and testing settings
|
||||
train_cfg=dict(),
|
||||
test_cfg=dict(mode='whole'))
|
||||
|
|
@ -0,0 +1,63 @@
|
|||
# PoolFormer
|
||||
|
||||
[MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418)
|
||||
|
||||
## Introduction
|
||||
|
||||
<!-- [BACKBONE] -->
|
||||
|
||||
<a href="https://github.com/sail-sg/poolformer/tree/main/segmentation">Official Repo</a>
|
||||
|
||||
<a href="https://github.com/open-mmlab/mmclassification/blob/v0.23.0/mmcls/models/backbones/poolformer.py#L198">Code Snippet</a>
|
||||
|
||||
## Abstract
|
||||
|
||||
<!-- [ABSTRACT] -->
|
||||
|
||||
Transformers have shown great potential in computer vision tasks. A common belief is their attention-based token mixer module contributes most to their competence. However, recent works show the attention-based module in transformers can be replaced by spatial MLPs and the resulted models still perform quite well. Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model's performance. To verify this, we deliberately replace the attention module in transformers with an embarrassingly simple spatial pooling operator to conduct only the most basic token mixing. Surprisingly, we observe that the derived model, termed as PoolFormer, achieves competitive performance on multiple computer vision tasks. For example, on ImageNet-1K, PoolFormer achieves 82.1% top-1 accuracy, surpassing well-tuned vision transformer/MLP-like baselines DeiT-B/ResMLP-B24 by 0.3%/1.1% accuracy with 35%/52% fewer parameters and 48%/60% fewer MACs. The effectiveness of PoolFormer verifies our hypothesis and urges us to initiate the concept of "MetaFormer", a general architecture abstracted from transformers without specifying the token mixer. Based on the extensive experiments, we argue that MetaFormer is the key player in achieving superior results for recent transformer and MLP-like models on vision tasks. This work calls for more future research dedicated to improving MetaFormer instead of focusing on the token mixer modules. Additionally, our proposed PoolFormer could serve as a starting baseline for future MetaFormer architecture design. Code is available at [this https URL](https://github.com/sail-sg/poolformer)
|
||||
|
||||
<!-- [IMAGE] -->
|
||||
|
||||
<div align=center>
|
||||
<img src="https://user-images.githubusercontent.com/15921929/144710761-1635f59a-abde-4946-984c-a2c3f22a19d2.png" width="70%"/>
|
||||
</div>
|
||||
|
||||
## Citation
|
||||
|
||||
```bibtex
|
||||
@inproceedings{yu2022metaformer,
|
||||
title={Metaformer is actually what you need for vision},
|
||||
author={Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng},
|
||||
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
|
||||
pages={10819--10829},
|
||||
year={2022}
|
||||
}
|
||||
```
|
||||
|
||||
### Usage
|
||||
|
||||
- PoolFormer backbone needs to install [MMClassification](https://github.com/open-mmlab/mmclassification) first, which has abundant backbones for downstream tasks.
|
||||
|
||||
```shell
|
||||
pip install mmcls>=0.23.0
|
||||
```
|
||||
|
||||
- The pretrained models could also be downloaded from [PoolFormer config of MMClassification](https://github.com/open-mmlab/mmclassification/tree/master/configs/poolformer).
|
||||
|
||||
## Results and models
|
||||
|
||||
### ADE20K
|
||||
|
||||
| Method | Backbone | Crop Size | pretrain | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | mIoU\* | mIoU\*(ms+flip) | config | download |
|
||||
| ------ | -------------- | --------- | ----------- | ---------- | ------- | -------- | -------------- | ----- | ------------: | ------ | --------------: | ---------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| FPN | PoolFormer-S12 | 512x512 | ImageNet-1K | 32 | 40000 | 4.17 | 23.48 | 36.0 | 36.42 | 37.07 | 38.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/poolformer/fpn_poolformer_s12_8x4_512x512_40k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s12_8x4_512x512_40k_ade20k/fpn_poolformer_s12_8x4_512x512_40k_ade20k_20220501_115154-b5aa2f49.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s12_8x4_512x512_40k_ade20k/fpn_poolformer_s12_8x4_512x512_40k_ade20k_20220501_115154.log.json) |
|
||||
| FPN | PoolFormer-S24 | 512x512 | ImageNet-1K | 32 | 40000 | 5.47 | 15.74 | 39.35 | 39.73 | 40.36 | 41.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/poolformer/fpn_poolformer_s24_8x4_512x512_40k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s24_8x4_512x512_40k_ade20k/fpn_poolformer_s24_8x4_512x512_40k_ade20k_20220503_222049-394a7cf7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s24_8x4_512x512_40k_ade20k/fpn_poolformer_s24_8x4_512x512_40k_ade20k_20220503_222049.log.json) |
|
||||
| FPN | PoolFormer-S36 | 512x512 | ImageNet-1K | 32 | 40000 | 6.77 | 11.34 | 40.64 | 40.99 | 41.81 | 42.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/poolformer/fpn_poolformer_s36_8x4_512x512_40k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s36_8x4_512x512_40k_ade20k/fpn_poolformer_s36_8x4_512x512_40k_ade20k_20220501_151122-b47e607d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s36_8x4_512x512_40k_ade20k/fpn_poolformer_s36_8x4_512x512_40k_ade20k_20220501_151122.log.json) |
|
||||
| FPN | PoolFormer-M36 | 512x512 | ImageNet-1K | 32 | 40000 | 8.59 | 8.97 | 40.91 | 41.28 | 42.35 | 43.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/poolformer/fpn_poolformer_m36_8x4_512x512_40k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m36_8x4_512x512_40k_ade20k/fpn_poolformer_m36_8x4_512x512_40k_ade20k_20220501_164230-3dc83921.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m36_8x4_512x512_40k_ade20k/fpn_poolformer_m36_8x4_512x512_40k_ade20k_20220501_164230.log.json) |
|
||||
| FPN | PoolFormer-M48 | 512x512 | ImageNet-1K | 32 | 40000 | 10.48 | 6.69 | 41.82 | 42.2 | 42.76 | 43.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/poolformer/fpn_poolformer_m48_8x4_512x512_40k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m48_8x4_512x512_40k_ade20k/fpn_poolformer_m48_8x4_512x512_40k_ade20k_20220504_003923-64168d3b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m48_8x4_512x512_40k_ade20k/fpn_poolformer_m48_8x4_512x512_40k_ade20k_20220504_003923.log.json) |
|
||||
|
||||
Note:
|
||||
|
||||
- We replace `AlignedResize` in original PoolFormer implementation to `Resize + ResizeToMultiple`.
|
||||
|
||||
- `mIoU` with * is collected when `Resize + ResizeToMultiple` is adopted in `test_pipeline`, so do `mIoU` in logs.
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
_base_ = './fpn_poolformer_s12_8x4_512x512_40k_ade20k.py'
|
||||
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-m36_3rdparty_32xb128_in1k_20220414-c55e0949.pth' # noqa
|
||||
|
||||
# model settings
|
||||
model = dict(
|
||||
backbone=dict(
|
||||
arch='m36',
|
||||
init_cfg=dict(
|
||||
type='Pretrained', checkpoint=checkpoint_file,
|
||||
prefix='backbone.')),
|
||||
neck=dict(in_channels=[96, 192, 384, 768]))
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
_base_ = './fpn_poolformer_s12_8x4_512x512_40k_ade20k.py'
|
||||
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-m48_3rdparty_32xb128_in1k_20220414-9378f3eb.pth' # noqa
|
||||
|
||||
# model settings
|
||||
model = dict(
|
||||
backbone=dict(
|
||||
arch='m48',
|
||||
init_cfg=dict(
|
||||
type='Pretrained', checkpoint=checkpoint_file,
|
||||
prefix='backbone.')),
|
||||
neck=dict(in_channels=[96, 192, 384, 768]))
|
||||
|
|
@ -0,0 +1,74 @@
|
|||
_base_ = [
|
||||
'../_base_/models/fpn_poolformer_s12.py', '../_base_/default_runtime.py',
|
||||
'../_base_/schedules/schedule_40k.py'
|
||||
]
|
||||
|
||||
# model settings
|
||||
model = dict(
|
||||
neck=dict(in_channels=[64, 128, 320, 512]),
|
||||
decode_head=dict(num_classes=150))
|
||||
|
||||
# optimizer
|
||||
optimizer = dict(_delete_=True, type='AdamW', lr=0.0002, weight_decay=0.0001)
|
||||
optimizer_config = dict()
|
||||
# learning policy
|
||||
lr_config = dict(policy='poly', power=0.9, min_lr=0.0, by_epoch=False)
|
||||
|
||||
# dataset settings
|
||||
dataset_type = 'ADE20KDataset'
|
||||
data_root = 'data/ade/ADEChallengeData2016'
|
||||
img_norm_cfg = dict(
|
||||
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
|
||||
crop_size = (512, 512)
|
||||
train_pipeline = [
|
||||
dict(type='LoadImageFromFile'),
|
||||
dict(type='LoadAnnotations', reduce_zero_label=True),
|
||||
dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
|
||||
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
|
||||
dict(type='RandomFlip', prob=0.5),
|
||||
dict(type='PhotoMetricDistortion'),
|
||||
dict(type='Normalize', **img_norm_cfg),
|
||||
dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
|
||||
dict(type='DefaultFormatBundle'),
|
||||
dict(type='Collect', keys=['img', 'gt_semantic_seg']),
|
||||
]
|
||||
test_pipeline = [
|
||||
dict(type='LoadImageFromFile'),
|
||||
dict(
|
||||
type='MultiScaleFlipAug',
|
||||
img_scale=(2048, 512),
|
||||
# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
|
||||
flip=False,
|
||||
transforms=[
|
||||
dict(type='Resize', keep_ratio=True),
|
||||
dict(type='ResizeToMultiple', size_divisor=32),
|
||||
dict(type='RandomFlip'),
|
||||
dict(type='Normalize', **img_norm_cfg),
|
||||
dict(type='ImageToTensor', keys=['img']),
|
||||
dict(type='Collect', keys=['img']),
|
||||
])
|
||||
]
|
||||
data = dict(
|
||||
samples_per_gpu=4,
|
||||
workers_per_gpu=4,
|
||||
train=dict(
|
||||
type='RepeatDataset',
|
||||
times=50,
|
||||
dataset=dict(
|
||||
type=dataset_type,
|
||||
data_root=data_root,
|
||||
img_dir='images/training',
|
||||
ann_dir='annotations/training',
|
||||
pipeline=train_pipeline)),
|
||||
val=dict(
|
||||
type=dataset_type,
|
||||
data_root=data_root,
|
||||
img_dir='images/validation',
|
||||
ann_dir='annotations/validation',
|
||||
pipeline=test_pipeline),
|
||||
test=dict(
|
||||
type=dataset_type,
|
||||
data_root=data_root,
|
||||
img_dir='images/validation',
|
||||
ann_dir='annotations/validation',
|
||||
pipeline=test_pipeline))
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
_base_ = './fpn_poolformer_s12_8x4_512x512_40k_ade20k.py'
|
||||
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s24_3rdparty_32xb128_in1k_20220414-d7055904.pth' # noqa
|
||||
# model settings
|
||||
model = dict(
|
||||
backbone=dict(
|
||||
arch='s24',
|
||||
init_cfg=dict(
|
||||
type='Pretrained', checkpoint=checkpoint_file,
|
||||
prefix='backbone.')))
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
_base_ = './fpn_poolformer_s12_8x4_512x512_40k_ade20k.py'
|
||||
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s36_3rdparty_32xb128_in1k_20220414-d78ff3e8.pth' # noqa
|
||||
|
||||
# model settings
|
||||
model = dict(
|
||||
backbone=dict(
|
||||
arch='s36',
|
||||
init_cfg=dict(
|
||||
type='Pretrained', checkpoint=checkpoint_file,
|
||||
prefix='backbone.')))
|
||||
|
|
@ -0,0 +1,111 @@
|
|||
Models:
|
||||
- Name: fpn_poolformer_s12_8x4_512x512_40k_ade20k
|
||||
In Collection: FPN
|
||||
Metadata:
|
||||
backbone: PoolFormer-S12
|
||||
crop size: (512,512)
|
||||
lr schd: 40000
|
||||
inference time (ms/im):
|
||||
- value: 42.59
|
||||
hardware: V100
|
||||
backend: PyTorch
|
||||
batch size: 1
|
||||
mode: FP32
|
||||
resolution: (512,512)
|
||||
Training Memory (GB): 4.17
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: ADE20K
|
||||
Metrics:
|
||||
mIoU: 36.0
|
||||
mIoU(ms+flip): 36.42
|
||||
Config: configs/poolformer/fpn_poolformer_s12_8x4_512x512_40k_ade20k.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s12_8x4_512x512_40k_ade20k/fpn_poolformer_s12_8x4_512x512_40k_ade20k_20220501_115154-b5aa2f49.pth
|
||||
- Name: fpn_poolformer_s24_8x4_512x512_40k_ade20k
|
||||
In Collection: FPN
|
||||
Metadata:
|
||||
backbone: PoolFormer-S24
|
||||
crop size: (512,512)
|
||||
lr schd: 40000
|
||||
inference time (ms/im):
|
||||
- value: 63.53
|
||||
hardware: V100
|
||||
backend: PyTorch
|
||||
batch size: 1
|
||||
mode: FP32
|
||||
resolution: (512,512)
|
||||
Training Memory (GB): 5.47
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: ADE20K
|
||||
Metrics:
|
||||
mIoU: 39.35
|
||||
mIoU(ms+flip): 39.73
|
||||
Config: configs/poolformer/fpn_poolformer_s24_8x4_512x512_40k_ade20k.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s24_8x4_512x512_40k_ade20k/fpn_poolformer_s24_8x4_512x512_40k_ade20k_20220503_222049-394a7cf7.pth
|
||||
- Name: fpn_poolformer_s36_8x4_512x512_40k_ade20k
|
||||
In Collection: FPN
|
||||
Metadata:
|
||||
backbone: PoolFormer-S36
|
||||
crop size: (512,512)
|
||||
lr schd: 40000
|
||||
inference time (ms/im):
|
||||
- value: 88.18
|
||||
hardware: V100
|
||||
backend: PyTorch
|
||||
batch size: 1
|
||||
mode: FP32
|
||||
resolution: (512,512)
|
||||
Training Memory (GB): 6.77
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: ADE20K
|
||||
Metrics:
|
||||
mIoU: 40.64
|
||||
mIoU(ms+flip): 40.99
|
||||
Config: configs/poolformer/fpn_poolformer_s36_8x4_512x512_40k_ade20k.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s36_8x4_512x512_40k_ade20k/fpn_poolformer_s36_8x4_512x512_40k_ade20k_20220501_151122-b47e607d.pth
|
||||
- Name: fpn_poolformer_m36_8x4_512x512_40k_ade20k
|
||||
In Collection: FPN
|
||||
Metadata:
|
||||
backbone: PoolFormer-M36
|
||||
crop size: (512,512)
|
||||
lr schd: 40000
|
||||
inference time (ms/im):
|
||||
- value: 111.48
|
||||
hardware: V100
|
||||
backend: PyTorch
|
||||
batch size: 1
|
||||
mode: FP32
|
||||
resolution: (512,512)
|
||||
Training Memory (GB): 8.59
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: ADE20K
|
||||
Metrics:
|
||||
mIoU: 40.91
|
||||
mIoU(ms+flip): 41.28
|
||||
Config: configs/poolformer/fpn_poolformer_m36_8x4_512x512_40k_ade20k.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m36_8x4_512x512_40k_ade20k/fpn_poolformer_m36_8x4_512x512_40k_ade20k_20220501_164230-3dc83921.pth
|
||||
- Name: fpn_poolformer_m48_8x4_512x512_40k_ade20k
|
||||
In Collection: FPN
|
||||
Metadata:
|
||||
backbone: PoolFormer-M48
|
||||
crop size: (512,512)
|
||||
lr schd: 40000
|
||||
inference time (ms/im):
|
||||
- value: 149.48
|
||||
hardware: V100
|
||||
backend: PyTorch
|
||||
batch size: 1
|
||||
mode: FP32
|
||||
resolution: (512,512)
|
||||
Training Memory (GB): 10.48
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: ADE20K
|
||||
Metrics:
|
||||
mIoU: 41.82
|
||||
mIoU(ms+flip): 42.2
|
||||
Config: configs/poolformer/fpn_poolformer_m48_8x4_512x512_40k_ade20k.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m48_8x4_512x512_40k_ade20k/fpn_poolformer_m48_8x4_512x512_40k_ade20k_20220504_003923-64168d3b.pth
|
||||
|
|
@ -4,10 +4,14 @@ ARG CUDNN="8"
|
|||
FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
|
||||
|
||||
ARG MMCV="1.5.0"
|
||||
ARG MMSEG="0.28.0"
|
||||
ARG MMSEG="0.29.0"
|
||||
|
||||
ENV PYTHONUNBUFFERED TRUE
|
||||
|
||||
# NVIDIA APT KEYS
|
||||
RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
|
||||
RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
|
||||
|
||||
RUN apt-get update && \
|
||||
DEBIAN_FRONTEND=noninteractive apt-get install --no-install-recommends -y \
|
||||
ca-certificates \
|
||||
|
|
@ -21,7 +25,7 @@ ENV PATH="/opt/conda/bin:$PATH"
|
|||
RUN export FORCE_CUDA=1
|
||||
|
||||
# TORCHSEVER
|
||||
RUN pip install torchserve torch-model-archiver
|
||||
RUN pip install torchserve torch-model-archiver nvgpu
|
||||
|
||||
# MMLAB
|
||||
ARG PYTORCH
|
||||
|
|
|
|||
|
|
@ -1,5 +1,32 @@
|
|||
## Changelog
|
||||
|
||||
### V0.29.0 (10/10/2022)
|
||||
|
||||
**New Features**
|
||||
|
||||
- Support PoolFormer (CVPR'2022) ([#1537](https://github.com/open-mmlab/mmsegmentation/pull/1537))
|
||||
|
||||
**Enhancement**
|
||||
|
||||
- Improve structure and readability for FCNHead ([#2142](https://github.com/open-mmlab/mmsegmentation/pull/2142))
|
||||
- Support IterableDataset in distributed training ([#2151](https://github.com/open-mmlab/mmsegmentation/pull/2151))
|
||||
- Upgrade .dev scripts ([#2020](https://github.com/open-mmlab/mmsegmentation/pull/2020))
|
||||
- Upgrade pre-commit hooks ([#2155](https://github.com/open-mmlab/mmsegmentation/pull/2155))
|
||||
|
||||
**Bug Fixes**
|
||||
|
||||
- Fix mmseg.api.inference inference_segmentor ([#1849](https://github.com/open-mmlab/mmsegmentation/pull/1849))
|
||||
- fix bug about label_map in evaluation part ([#2075](https://github.com/open-mmlab/mmsegmentation/pull/2075))
|
||||
- Add missing dependencies to torchserve docker file ([#2133](https://github.com/open-mmlab/mmsegmentation/pull/2133))
|
||||
- Fix ddp unittest ([#2060](https://github.com/open-mmlab/mmsegmentation/pull/2060))
|
||||
|
||||
**Contributors**
|
||||
|
||||
- @jinwonkim93 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1849
|
||||
- @rlatjcj made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2075
|
||||
- @ShirleyWangCVR made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2151
|
||||
- @mangelroman made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2133
|
||||
|
||||
### V0.28.0 (9/8/2022)
|
||||
|
||||
**New Features**
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ The compatible MMSegmentation and MMCV versions are as below. Please install the
|
|||
| MMSegmentation version | MMCV version | MMClassification version |
|
||||
| :--------------------: | :-------------------------: | :----------------------: |
|
||||
| master | mmcv-full>=1.5.0, \<1.7.0 | mmcls>=0.20.1, \<=1.0.0 |
|
||||
| 0.29.0 | mmcv-full>=1.5.0, \<1.7.0 | mmcls>=0.20.1, \<=1.0.0 |
|
||||
| 0.28.0 | mmcv-full>=1.5.0, \<1.7.0 | mmcls>=0.20.1, \<=1.0.0 |
|
||||
| 0.27.0 | mmcv-full>=1.5.0, \<1.7.0 | mmcls>=0.20.1, \<=1.0.0 |
|
||||
| 0.26.0 | mmcv-full>=1.5.0, \<=1.6.0 | mmcls>=0.20.1, \<=1.0.0 |
|
||||
|
|
@ -53,7 +54,7 @@ Briefly, it is a deep supervision trick to improve the accuracy. In the training
|
|||
|
||||
## Why is the log file not created
|
||||
|
||||
In the train script, we call `get_root_logger`at Line 167, and `get_root_logger` in mmseg calls `get_logger` in mmcv, mmcv will return the same logger which has beed initialized in 'mmsegmentation/tools/train.py' with the parameter `log_file`. There is only one logger (initialized with `log_file`) during training.
|
||||
In the train script, we call `get_root_logger`at Line 167, and `get_root_logger` in mmseg calls `get_logger` in mmcv, mmcv will return the same logger which has been initialized in 'mmsegmentation/tools/train.py' with the parameter `log_file`. There is only one logger (initialized with `log_file`) during training.
|
||||
Ref: [https://github.com/open-mmlab/mmcv/blob/21bada32560c7ed7b15b017dc763d862789e29a8/mmcv/utils/logging.py#L9-L16](https://github.com/open-mmlab/mmcv/blob/21bada32560c7ed7b15b017dc763d862789e29a8/mmcv/utils/logging.py#L9-L16)
|
||||
|
||||
If you find the log file not been created, you might check if `mmcv.utils.get_logger` is called elsewhere.
|
||||
|
|
|
|||
|
|
@ -33,7 +33,7 @@ data = dict(
|
|||
- `train`, `val` and `test`: The [`config`](https://github.com/open-mmlab/mmcv/blob/master/docs/en/understand_mmcv/config.md)s to build dataset instances for model training, validation and testing by
|
||||
using [`build and registry`](https://github.com/open-mmlab/mmcv/blob/master/docs/en/understand_mmcv/registry.md) mechanism.
|
||||
|
||||
- `samples_per_gpu`: How many samples per batch and per gpu to load during model training, and the `batch_size` of training is equal to `samples_per_gpu` times gpu number, e.g. when using 8 gpus for distributed data parallel trainig and `samples_per_gpu=4`, the `batch_size` is `8*4=32`.
|
||||
- `samples_per_gpu`: How many samples per batch and per gpu to load during model training, and the `batch_size` of training is equal to `samples_per_gpu` times gpu number, e.g. when using 8 gpus for distributed data parallel training and `samples_per_gpu=4`, the `batch_size` is `8*4=32`.
|
||||
If you would like to define `batch_size` for testing and validation, please use `test_dataloaser` and
|
||||
`val_dataloader` with mmseg >=0.24.1.
|
||||
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@
|
|||
| MMSegmentation version | MMCV version | MMClassification version |
|
||||
| :--------------------: | :-------------------------: | :----------------------: |
|
||||
| master | mmcv-full>=1.5.0, \<1.7.0 | mmcls>=0.20.1, \<=1.0.0 |
|
||||
| 0.29.0 | mmcv-full>=1.5.0, \<1.7.0 | mmcls>=0.20.1, \<=1.0.0 |
|
||||
| 0.28.0 | mmcv-full>=1.5.0, \<1.7.0 | mmcls>=0.20.1, \<=1.0.0 |
|
||||
| 0.27.0 | mmcv-full>=1.5.0, \<1.7.0 | mmcls>=0.20.1, \<=1.0.0 |
|
||||
| 0.26.0 | mmcv-full>=1.5.0, \<=1.6.0 | mmcls>=0.20.1, \<=1.0.0 |
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ import torch
|
|||
from mmcv.parallel import collate
|
||||
from mmcv.runner import get_dist_info
|
||||
from mmcv.utils import Registry, build_from_cfg, digit_version
|
||||
from torch.utils.data import DataLoader
|
||||
from torch.utils.data import DataLoader, IterableDataset
|
||||
|
||||
from .samplers import DistributedSampler
|
||||
|
||||
|
|
@ -129,12 +129,17 @@ def build_dataloader(dataset,
|
|||
DataLoader: A PyTorch dataloader.
|
||||
"""
|
||||
rank, world_size = get_dist_info()
|
||||
if dist:
|
||||
if dist and not isinstance(dataset, IterableDataset):
|
||||
sampler = DistributedSampler(
|
||||
dataset, world_size, rank, shuffle=shuffle, seed=seed)
|
||||
shuffle = False
|
||||
batch_size = samples_per_gpu
|
||||
num_workers = workers_per_gpu
|
||||
elif dist:
|
||||
sampler = None
|
||||
shuffle = False
|
||||
batch_size = samples_per_gpu
|
||||
num_workers = workers_per_gpu
|
||||
else:
|
||||
sampler = None
|
||||
batch_size = num_gpus * samples_per_gpu
|
||||
|
|
|
|||
|
|
@ -337,7 +337,7 @@ class VisionTransformer(BaseModule):
|
|||
constant_init(m, val=1.0, bias=0.)
|
||||
|
||||
def _pos_embeding(self, patched_img, hw_shape, pos_embed):
|
||||
"""Positiong embeding method.
|
||||
"""Positioning embeding method.
|
||||
|
||||
Resize the pos_embed, if the input image size doesn't match
|
||||
the training size.
|
||||
|
|
|
|||
|
|
@ -37,20 +37,11 @@ class FCNHead(BaseDecodeHead):
|
|||
|
||||
conv_padding = (kernel_size // 2) * dilation
|
||||
convs = []
|
||||
convs.append(
|
||||
ConvModule(
|
||||
self.in_channels,
|
||||
self.channels,
|
||||
kernel_size=kernel_size,
|
||||
padding=conv_padding,
|
||||
dilation=dilation,
|
||||
conv_cfg=self.conv_cfg,
|
||||
norm_cfg=self.norm_cfg,
|
||||
act_cfg=self.act_cfg))
|
||||
for i in range(num_convs - 1):
|
||||
for i in range(num_convs):
|
||||
_in_channels = self.in_channels if i == 0 else self.channels
|
||||
convs.append(
|
||||
ConvModule(
|
||||
self.channels,
|
||||
_in_channels,
|
||||
self.channels,
|
||||
kernel_size=kernel_size,
|
||||
padding=conv_padding,
|
||||
|
|
@ -58,7 +49,8 @@ class FCNHead(BaseDecodeHead):
|
|||
conv_cfg=self.conv_cfg,
|
||||
norm_cfg=self.norm_cfg,
|
||||
act_cfg=self.act_cfg))
|
||||
if num_convs == 0:
|
||||
|
||||
if len(convs) == 0:
|
||||
self.convs = nn.Identity()
|
||||
else:
|
||||
self.convs = nn.Sequential(*convs)
|
||||
|
|
|
|||
|
|
@ -78,7 +78,7 @@ def sigmoid_focal_loss(pred,
|
|||
valid_mask=None,
|
||||
reduction='mean',
|
||||
avg_factor=None):
|
||||
r"""A warpper of cuda version `Focal Loss
|
||||
r"""A wrapper of cuda version `Focal Loss
|
||||
<https://arxiv.org/abs/1708.02002>`_.
|
||||
Args:
|
||||
pred (torch.Tensor): The prediction with shape (N, C), C is the number
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
# Copyright (c) Open-MMLab. All rights reserved.
|
||||
|
||||
__version__ = '0.28.0'
|
||||
__version__ = '0.29.0'
|
||||
|
||||
|
||||
def parse_version_info(version_str):
|
||||
|
|
|
|||
|
|
@ -30,6 +30,7 @@ Import:
|
|||
- configs/nonlocal_net/nonlocal_net.yml
|
||||
- configs/ocrnet/ocrnet.yml
|
||||
- configs/point_rend/point_rend.yml
|
||||
- configs/poolformer/poolformer.yml
|
||||
- configs/psanet/psanet.yml
|
||||
- configs/pspnet/pspnet.yml
|
||||
- configs/resnest/resnest.yml
|
||||
|
|
|
|||
Loading…
Reference in New Issue