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7 changes: 7 additions & 0 deletions README.md
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Expand Up @@ -33,6 +33,8 @@
[🆕 Update News](https://mmclassification.readthedocs.io/en/latest/changelog.html) |
[🤔 Reporting Issues](https://github.com/open-mmlab/mmclassification/issues/new/choose)

:point_right: **MMClassification 1.0 branch is in trial, welcome every to [try it](https://github.com/open-mmlab/mmclassification/tree/1.x) and [discuss with us](https://github.com/open-mmlab/mmclassification/discussions)!** :point_left:

</div>

## Introduction
Expand Down Expand Up @@ -62,6 +64,11 @@ The MMClassification 1.0 has released! It's still unstable and in release candid
to [the 1.x branch](https://github.com/open-mmlab/mmclassification/tree/1.x) and discuss it with us in
[the discussion](https://github.com/open-mmlab/mmclassification/discussions).

v0.24.1 was released in 31/10/2022.
Highlights of the new version:

- Support HUAWEI Ascend device.

v0.24.0 was released in 30/9/2022.
Highlights of the new version:

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10 changes: 8 additions & 2 deletions README_zh-CN.md
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Expand Up @@ -33,6 +33,10 @@
[🆕 更新日志](https://mmclassification.readthedocs.io/en/latest/changelog.html) |
[🤔 报告问题](https://github.com/open-mmlab/mmclassification/issues/new/choose)

:point_right: **MMClassification 1.0 版本即将正式发布,欢迎大家 [试用](https://github.com/open-mmlab/mmclassification/tree/1.x)[参与讨论](https://github.com/open-mmlab/mmclassification/discussions)** :point_left:

</div>

</div>

## Introduction
Expand All @@ -59,15 +63,17 @@ MMClassification 是一款基于 PyTorch 的开源图像分类工具箱,是 [O

MMClassification 1.0 已经发布!目前仍在公测中,如果希望试用,请切换到 [1.x 分支](https://github.com/open-mmlab/mmclassification/tree/1.x),并在[讨论版](https://github.com/open-mmlab/mmclassification/discussions) 参加开发讨论!

2022/10/31 发布了 v0.24.1 版本

- 支持了华为昇腾 NPU 设备。

2022/9/30 发布了 v0.24.0 版本

- 支持了 **HorNet****EfficientFormerm****SwinTransformer V2****MViT** 等主干网络。
- 支持了 Support Standford Cars 数据集。

2022/5/1 发布了 v0.23.0 版本

新版本亮点:

- 支持了 **DenseNet****VAN****PoolFormer** 三个网络,并提供了预训练模型。
- 支持在 IPU 上进行训练。
- 更新了 API 文档的样式,更方便查阅,[欢迎查阅](https://mmclassification.readthedocs.io/en/master/api/models.html)
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4 changes: 2 additions & 2 deletions docker/serve/Dockerfile
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Expand Up @@ -3,8 +3,8 @@ ARG CUDA="10.2"
ARG CUDNN="7"
FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel

ARG MMCV="1.6.2"
ARG MMCLS="0.24.0"
ARG MMCV="1.7.0"
ARG MMCLS="0.24.1"

ENV PYTHONUNBUFFERED TRUE

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11 changes: 9 additions & 2 deletions docs/en/_static/css/readthedocs.css
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Expand Up @@ -9,12 +9,19 @@ pre {
white-space: pre;
}

article.pytorch-article .section :not(dt) > code {
article.pytorch-article section code {
padding: .2em .4em;
background-color: #f3f4f7;
border-radius: 5px;
}

table.colwidths-auto td {
/* Disable the change in tables */
article.pytorch-article section table code {
padding: unset;
background-color: unset;
border-radius: unset;
}

table.autosummary td {
width: 50%
}
10 changes: 10 additions & 0 deletions docs/en/changelog.md
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@@ -1,5 +1,15 @@
# Changelog

## v0.24.1(31/10/2022)

### New Features

- Support mmcls with NPU backend. ([#1072](https://github.com/open-mmlab/mmclassification/pull/1072))

### Bug Fixes

- Fix performance issue in convnext DDP train. ([#1098](https://github.com/open-mmlab/mmclassification/pull/1098))

## v0.24.0(30/9/2022)

### Highlights
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1 change: 0 additions & 1 deletion docs/en/conf.py
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Expand Up @@ -48,7 +48,6 @@ def get_version():
'sphinx.ext.intersphinx',
'sphinx.ext.napoleon',
'sphinx.ext.viewcode',
'sphinx_markdown_tables',
'myst_parser',
'sphinx_copybutton',
]
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34 changes: 34 additions & 0 deletions docs/en/device/npu.md
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@@ -0,0 +1,34 @@
# NPU (HUAWEI Ascend)

## Usage

Please install MMCV with NPU device support according to {external+mmcv:doc}`the tutorial <get_started/build>`.

Here we use 8 NPUs on your computer to train the model with the following command:

```shell
bash tools/dist_train.sh configs/cspnet/resnet50_8xb32_in1k.py 8 --device npu
```

Also, you can use only one NPU to trian the model with the following command:

```shell
python tools/train.py configs/cspnet/resnet50_8xb32_in1k.py --device npu
```

## Verified Models

| Model | Top-1 (%) | Top-5 (%) | Config | Download |
| :--------------------------------------------------------: | :-------: | :-------: | :-----------------------------------------------------------: | :-------------------------------------------------------------: |
| [CSPResNeXt50](../papers/cspnet.md) | 77.10 | 93.55 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnext50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/cspresnext50_8xb32_in1k.log.json) |
| [DenseNet121](../papers/densenet.md) | 72.62 | 91.04 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet121_4xb256_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/densenet121_4xb256_in1k.log.json) |
| [EfficientNet-B4(AA + AdvProp)](../papers/efficientnet.md) | 75.55 | 92.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/efficientnet-b4_8xb32-01norm_in1k.log.json) |
| [HRNet-W18](../papers/hrnet.md) | 77.01 | 93.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/hrnet-w18_4xb32_in1k.log.json) |
| [ResNetV1D-152](../papers/resnet.md) | 77.11 | 94.54 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_8xb32_in1k.py) | [model](<>) \| [log](<>) |
| [ResNet-50](../papers/resnet.md) | 76.40 | - | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32_in1k.py) | [model](<>) \| [log](<>) |
| [ResNetXt-32x4d-50](../papers/resnext.md) | 77.55 | 93.75 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/resnext50-32x4d_8xb32_in1k.log.json) |
| [SE-ResNet-50](../papers/seresnet.md) | 77.64 | 93.76 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/seresnet50_8xb32_in1k.log.json) |
| [VGG-11](../papers/vgg.md) | 68.92 | 88.83 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/vgg11_8xb32_in1k.log.json) |
| [ShuffleNetV2 1.0x](../papers/shufflenet_v2.md) | 69.53 | 88.82 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/shufflenet-v2-1x_16xb64_in1k.json) |

**All above models are provided by Huawei Ascend group.**
4 changes: 2 additions & 2 deletions docs/en/faq.md
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Expand Up @@ -17,8 +17,8 @@ and make sure you fill in all required information in the template.

| MMClassification version | MMCV version |
| :----------------------: | :--------------------: |
| dev | mmcv>=1.6.0, \<1.7.0 |
| 0.24.0 (master) | mmcv>=1.4.2, \<1.7.0 |
| dev | mmcv>=1.7.0, \<1.9.0 |
| 0.24.1 (master) | mmcv>=1.4.2, \<1.9.0 |
| 0.23.2 | mmcv>=1.4.2, \<1.7.0 |
| 0.22.1 | mmcv>=1.4.2, \<1.6.0 |
| 0.21.0 | mmcv>=1.4.2, \<=1.5.0 |
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7 changes: 7 additions & 0 deletions docs/en/index.rst
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Expand Up @@ -78,6 +78,13 @@ You can switch between Chinese and English documentation in the lower-left corne
compatibility.md
faq.md


.. toctree::
:maxdepth: 1
:caption: Device Support

device/npu.md

.. toctree::
:caption: Language Switch

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11 changes: 9 additions & 2 deletions docs/zh_CN/_static/css/readthedocs.css
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Expand Up @@ -9,12 +9,19 @@ pre {
white-space: pre;
}

article.pytorch-article .section :not(dt) > code {
article.pytorch-article section code {
padding: .2em .4em;
background-color: #f3f4f7;
border-radius: 5px;
}

table.colwidths-auto td {
/* Disable the change in tables */
article.pytorch-article section table code {
padding: unset;
background-color: unset;
border-radius: unset;
}

table.autosummary td {
width: 50%
}
3 changes: 1 addition & 2 deletions docs/zh_CN/conf.py
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Expand Up @@ -48,7 +48,6 @@ def get_version():
'sphinx.ext.intersphinx',
'sphinx.ext.napoleon',
'sphinx.ext.viewcode',
'sphinx_markdown_tables',
'myst_parser',
'sphinx_copybutton',
]
Expand Down Expand Up @@ -214,7 +213,7 @@ def get_version():
'python': ('https://docs.python.org/3', None),
'numpy': ('https://numpy.org/doc/stable', None),
'torch': ('https://pytorch.org/docs/stable/', None),
'mmcv': ('https://mmcv.readthedocs.io/en/master/', None),
'mmcv': ('https://mmcv.readthedocs.io/zh_CN/latest/', None),
}


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34 changes: 34 additions & 0 deletions docs/zh_CN/device/npu.md
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@@ -0,0 +1,34 @@
# NPU (华为昇腾)

## 使用方法

首先,请参考 {external+mmcv:doc}`教程 <get_started/build>` 安装带有 NPU 支持的 MMCV。

使用如下命令,可以利用 8 个 NPU 在机器上训练模型(以 ResNet 为例):

```shell
bash tools/dist_train.sh configs/cspnet/resnet50_8xb32_in1k.py 8 --device npu
```

或者,使用如下命令,在一个 NPU 上训练模型(以 ResNet 为例):

```shell
python tools/train.py configs/cspnet/resnet50_8xb32_in1k.py --device npu
```

## 经过验证的模型

| 模型 | Top-1 (%) | Top-5 (%) | 配置文件 | 相关下载 |
| :--------------------------------------------------------: | :-------: | :-------: | :------------------------------------------------------------: | :------------------------------------------------------------: |
| [CSPResNeXt50](../papers/cspnet.md) | 77.10 | 93.55 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnext50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/cspresnext50_8xb32_in1k.log.json) |
| [DenseNet121](../papers/densenet.md) | 72.62 | 91.04 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet121_4xb256_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/densenet121_4xb256_in1k.log.json) |
| [EfficientNet-B4(AA + AdvProp)](../papers/efficientnet.md) | 75.55 | 92.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/efficientnet-b4_8xb32-01norm_in1k.log.json) |
| [HRNet-W18](../papers/hrnet.md) | 77.01 | 93.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/hrnet-w18_4xb32_in1k.log.json) |
| [ResNetV1D-152](../papers/resnet.md) | 77.11 | 94.54 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_8xb32_in1k.py) | [model](<>) \| [log](<>) |
| [ResNet-50](../papers/resnet.md) | 76.40 | - | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32_in1k.py) | [model](<>) \| [log](<>) |
| [ResNetXt-32x4d-50](../papers/resnext.md) | 77.55 | 93.75 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/resnext50-32x4d_8xb32_in1k.log.json) |
| [SE-ResNet-50](../papers/seresnet.md) | 77.64 | 93.76 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/seresnet50_8xb32_in1k.log.json) |
| [VGG-11](../papers/vgg.md) | 68.92 | 88.83 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/vgg11_8xb32_in1k.log.json) |
| [ShuffleNetV2 1.0x](../papers/shufflenet_v2.md) | 69.53 | 88.82 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py) | [model](<>) \| [log](<>) |

**以上所有模型权重及训练日志均由华为昇腾团队提供**
4 changes: 2 additions & 2 deletions docs/zh_CN/faq.md
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Expand Up @@ -15,8 +15,8 @@

| MMClassification version | MMCV version |
| :----------------------: | :--------------------: |
| dev | mmcv>=1.6.0, \<1.7.0 |
| 0.24.0 (master) | mmcv>=1.4.2, \<1.7.0 |
| dev | mmcv>=1.7.0, \<1.9.0 |
| 0.24.1 (master) | mmcv>=1.4.2, \<1.9.0 |
| 0.23.2 | mmcv>=1.4.2, \<1.7.0 |
| 0.22.1 | mmcv>=1.4.2, \<1.6.0 |
| 0.21.0 | mmcv>=1.4.2, \<=1.5.0 |
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7 changes: 7 additions & 0 deletions docs/zh_CN/index.rst
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Expand Up @@ -78,6 +78,13 @@ You can switch between Chinese and English documentation in the lower-left corne
faq.md


.. toctree::
:maxdepth: 1
:caption: 设备支持

device/npu.md


.. toctree::
:caption: 语言切换

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2 changes: 1 addition & 1 deletion mmcls/__init__.py
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Expand Up @@ -48,7 +48,7 @@ def digit_version(version_str: str, length: int = 4):


mmcv_minimum_version = '1.4.2'
mmcv_maximum_version = '1.7.0'
mmcv_maximum_version = '1.9.0'
mmcv_version = digit_version(mmcv.__version__)


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5 changes: 4 additions & 1 deletion mmcls/apis/train.py
Original file line number Diff line number Diff line change
Expand Up @@ -131,7 +131,6 @@ def train_model(model,
model = wrap_distributed_model(
model,
cfg.device,
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False,
find_unused_parameters=find_unused_parameters)
else:
Expand Down Expand Up @@ -173,6 +172,10 @@ def train_model(model,

# fp16 setting
fp16_cfg = cfg.get('fp16', None)

if fp16_cfg is None and device == 'npu':
fp16_cfg = {'loss_scale': 'dynamic'}

if fp16_cfg is not None:
if device == 'ipu':
from mmcv.device.ipu import IPUFp16OptimizerHook
Expand Down
3 changes: 2 additions & 1 deletion mmcls/datasets/samplers/distributed_sampler.py
Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@

from mmcls.core.utils import sync_random_seed
from mmcls.datasets import SAMPLERS
from mmcls.utils import auto_select_device


@SAMPLERS.register_module()
Expand All @@ -30,7 +31,7 @@ def __init__(self,
# in the same order based on the same seed. Then different ranks
# could use different indices to select non-overlapped data from the
# same data list.
self.seed = sync_random_seed(seed)
self.seed = sync_random_seed(seed, device=auto_select_device())

def __iter__(self):
# deterministically shuffle based on epoch
Expand Down
4 changes: 2 additions & 2 deletions mmcls/models/backbones/convnext.py
Original file line number Diff line number Diff line change
Expand Up @@ -36,8 +36,8 @@ def forward(self, x):
assert x.dim() == 4, 'LayerNorm2d only supports inputs with shape ' \
f'(N, C, H, W), but got tensor with shape {x.shape}'
return F.layer_norm(
x.permute(0, 2, 3, 1), self.normalized_shape, self.weight,
self.bias, self.eps).permute(0, 3, 1, 2)
x.permute(0, 2, 3, 1).contiguous(), self.normalized_shape,
self.weight, self.bias, self.eps).permute(0, 3, 1, 2).contiguous()


class ConvNeXtBlock(BaseModule):
Expand Down
16 changes: 13 additions & 3 deletions mmcls/utils/distribution.py
Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,10 @@ def wrap_non_distributed_model(model, device='cuda', dim=0, *args, **kwargs):
Returns:
model(nn.Module): the model to be parallelized.
"""
if device == 'cuda':
if device == 'npu':
from mmcv.device.npu import NPUDataParallel
model = NPUDataParallel(model.npu(), dim=dim, *args, **kwargs)
elif device == 'cuda':
from mmcv.parallel import MMDataParallel
model = MMDataParallel(model.cuda(), dim=dim, *args, **kwargs)
elif device == 'cpu':
Expand Down Expand Up @@ -49,9 +52,16 @@ def wrap_distributed_model(model, device='cuda', *args, **kwargs):
.. [1] https://pytorch.org/docs/stable/generated/torch.nn.parallel.
DistributedDataParallel.html
"""
if device == 'cuda':
if device == 'npu':
from mmcv.device.npu import NPUDistributedDataParallel
from torch.npu import current_device
model = NPUDistributedDataParallel(
model.npu(), *args, device_ids=[current_device()], **kwargs)
elif device == 'cuda':
from mmcv.parallel import MMDistributedDataParallel
model = MMDistributedDataParallel(model.cuda(), *args, **kwargs)
from torch.cuda import current_device
model = MMDistributedDataParallel(
model.cuda(), *args, device_ids=[current_device()], **kwargs)
else:
raise RuntimeError(f'Unavailable device "{device}"')

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2 changes: 1 addition & 1 deletion mmcls/version.py
Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
# Copyright (c) OpenMMLab. All rights reserved

__version__ = '0.24.0'
__version__ = '0.24.1'


def parse_version_info(version_str):
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2 changes: 1 addition & 1 deletion requirements/mminstall.txt
Original file line number Diff line number Diff line change
@@ -1 +1 @@
mmcv-full>=1.4.2,<1.7.0
mmcv-full>=1.4.2,<1.9.0

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