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Merge branch 'dev'

pull/1249/head v0.25.0
mzr1996 2022-12-06 18:25:47 +08:00
parent 838735820c c737e65164
commit 2495400a98
17 changed files with 157 additions and 81 deletions
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4
.pre-commit-config.yaml
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@ -29,9 +29,9 @@ repos:
rev: 0.7.9
hooks:
- id: mdformat
args: ["--number", "--table-width", "200"]
args: ["--number", "--table-width", "200", '--disable-escape', 'backslash', '--disable-escape', 'link-enclosure']
additional_dependencies:
- mdformat-openmmlab
- "mdformat-openmmlab>=0.0.4"
- mdformat_frontmatter
- linkify-it-py
- repo: https://github.com/codespell-project/codespell

13
README.md
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@ -64,6 +64,12 @@ 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).
v0.25.0 was released in 06/12/2022.
Highlights of the new version:
- Support MLU backend.
- Add `dist_train_arm.sh` for ARM device.
v0.24.1 was released in 31/10/2022.
Highlights of the new version:
@ -75,13 +81,6 @@ Highlights of the new version:
- Support **HorNet**, **EfficientFormerm**, **SwinTransformer V2** and **MViT** backbones.
- Support Standford Cars dataset.
v0.23.0 was released in 1/5/2022.
Highlights of the new version:
- Support **DenseNet**, **VAN** and **PoolFormer**, and provide pre-trained models.
- Support training on IPU.
- New style API docs, welcome [view it](https://mmclassification.readthedocs.io/en/master/api/models.html).
Please refer to [changelog.md](docs/en/changelog.md) for more details and other release history.
## Installation

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README_zh-CN.md
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@ -63,6 +63,11 @@ 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！
2022/12/06 发布了 v0.25.0 版本
- 支持 MLU 设备
- 添加了用于 ARM 设备训练的 `dist_train_arm.sh`
2022/10/31 发布了 v0.24.1 版本
- 支持了华为昇腾 NPU 设备。

2
configs/t2t_vit/README.md
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@ -6,7 +6,7 @@
## Abstract
Transformers, which are popular for language modeling, have been explored for solving vision tasks recently, \\eg, the Vision Transformer (ViT) for image classification. The ViT model splits each image into a sequence of tokens with fixed length and then applies multiple Transformer layers to model their global relation for classification. However, ViT achieves inferior performance to CNNs when trained from scratch on a midsize dataset like ImageNet. We find it is because: 1) the simple tokenization of input images fails to model the important local structure such as edges and lines among neighboring pixels, leading to low training sample efficiency; 2) the redundant attention backbone design of ViT leads to limited feature richness for fixed computation budgets and limited training samples. To overcome such limitations, we propose a new Tokens-To-Token Vision Transformer (T2T-ViT), which incorporates 1) a layer-wise Tokens-to-Token (T2T) transformation to progressively structurize the image to tokens by recursively aggregating neighboring Tokens into one Token (Tokens-to-Token), such that local structure represented by surrounding tokens can be modeled and tokens length can be reduced; 2) an efficient backbone with a deep-narrow structure for vision transformer motivated by CNN architecture design after empirical study. Notably, T2T-ViT reduces the parameter count and MACs of vanilla ViT by half, while achieving more than 3.0% improvement when trained from scratch on ImageNet. It also outperforms ResNets and achieves comparable performance with MobileNets by directly training on ImageNet. For example, T2T-ViT with comparable size to ResNet50 (21.5M parameters) can achieve 83.3% top1 accuracy in image resolution 384×384 on ImageNet.
Transformers, which are popular for language modeling, have been explored for solving vision tasks recently, e.g., the Vision Transformer (ViT) for image classification. The ViT model splits each image into a sequence of tokens with fixed length and then applies multiple Transformer layers to model their global relation for classification. However, ViT achieves inferior performance to CNNs when trained from scratch on a midsize dataset like ImageNet. We find it is because: 1) the simple tokenization of input images fails to model the important local structure such as edges and lines among neighboring pixels, leading to low training sample efficiency; 2) the redundant attention backbone design of ViT leads to limited feature richness for fixed computation budgets and limited training samples. To overcome such limitations, we propose a new Tokens-To-Token Vision Transformer (T2T-ViT), which incorporates 1) a layer-wise Tokens-to-Token (T2T) transformation to progressively structurize the image to tokens by recursively aggregating neighboring Tokens into one Token (Tokens-to-Token), such that local structure represented by surrounding tokens can be modeled and tokens length can be reduced; 2) an efficient backbone with a deep-narrow structure for vision transformer motivated by CNN architecture design after empirical study. Notably, T2T-ViT reduces the parameter count and MACs of vanilla ViT by half, while achieving more than 3.0% improvement when trained from scratch on ImageNet. It also outperforms ResNets and achieves comparable performance with MobileNets by directly training on ImageNet. For example, T2T-ViT with comparable size to ResNet50 (21.5M parameters) can achieve 83.3% top1 accuracy in image resolution 384×384 on ImageNet.
<div align=center>
<img src="https://user-images.githubusercontent.com/26739999/142578381-e9040610-05d9-457c-8bf5-01c2fa94add2.png" width="60%"/>

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docker/serve/Dockerfile
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@ -4,7 +4,7 @@ ARG CUDNN="7"
FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
ARG MMCV="1.7.0"
ARG MMCLS="0.24.1"
ARG MMCLS="0.25.0"
ENV PYTHONUNBUFFERED TRUE

104
docs/en/changelog.md
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@ -1,5 +1,33 @@
# Changelog
## v0.25.0(06/12/2022)
### Highlights
- Support MLU backend.
### New Features
- Support MLU backend. ([#1159](https://github.com/open-mmlab/mmclassification/pull/1159))
- Support Activation Checkpointing for ConvNeXt. ([#1152](https://github.com/open-mmlab/mmclassification/pull/1152))
### Improvements
- Add `dist_train_arm.sh` for ARM device and update NPU results. ([#1218](https://github.com/open-mmlab/mmclassification/pull/1218))
### Bug Fixes
- Fix a bug caused `MMClsWandbHook` stuck. ([#1242](https://github.com/open-mmlab/mmclassification/pull/1242))
- Fix the redundant `device_ids` in `tools/test.py`. ([#1215](https://github.com/open-mmlab/mmclassification/pull/1215))
### Docs Update
- Add version banner and version warning in master docs. ([#1216](https://github.com/open-mmlab/mmclassification/pull/1216))
- Update NPU support doc. ([#1198](https://github.com/open-mmlab/mmclassification/pull/1198))
- Fixed typo in `pytorch2torchscript.md`. ([#1173](https://github.com/open-mmlab/mmclassification/pull/1173))
- Fix typo in `miscellaneous.md`. ([#1137](https://github.com/open-mmlab/mmclassification/pull/1137))
- further detail for the doc for `ClassBalancedDataset`. ([#901](https://github.com/open-mmlab/mmclassification/pull/901))
## v0.24.1(31/10/2022)
### New Features
@ -28,14 +56,14 @@
### Improvements
- \[Improve\] replace loop of progressbar in api/test. ([#878](https://github.com/open-mmlab/mmclassification/pull/878))
- \[Enhance\] RepVGG for YOLOX-PAI. ([#1025](https://github.com/open-mmlab/mmclassification/pull/1025))
- \[Enhancement\] Update VAN. ([#1017](https://github.com/open-mmlab/mmclassification/pull/1017))
- \[Refactor\] Re-write `get_sinusoid_encoding` from third-party implementation. ([#965](https://github.com/open-mmlab/mmclassification/pull/965))
- \[Improve\] Upgrade onnxsim to v0.4.0. ([#915](https://github.com/open-mmlab/mmclassification/pull/915))
- \[Improve\] Fixed typo in `RepVGG`. ([#985](https://github.com/open-mmlab/mmclassification/pull/985))
- \[Improve\] Using `train_step` instead of `forward` in PreciseBNHook ([#964](https://github.com/open-mmlab/mmclassification/pull/964))
- \[Improve\] Use `forward_dummy` to calculate FLOPS. ([#953](https://github.com/open-mmlab/mmclassification/pull/953))
- [Improve] replace loop of progressbar in api/test. ([#878](https://github.com/open-mmlab/mmclassification/pull/878))
- [Enhance] RepVGG for YOLOX-PAI. ([#1025](https://github.com/open-mmlab/mmclassification/pull/1025))
- [Enhancement] Update VAN. ([#1017](https://github.com/open-mmlab/mmclassification/pull/1017))
- [Refactor] Re-write `get_sinusoid_encoding` from third-party implementation. ([#965](https://github.com/open-mmlab/mmclassification/pull/965))
- [Improve] Upgrade onnxsim to v0.4.0. ([#915](https://github.com/open-mmlab/mmclassification/pull/915))
- [Improve] Fixed typo in `RepVGG`. ([#985](https://github.com/open-mmlab/mmclassification/pull/985))
- [Improve] Using `train_step` instead of `forward` in PreciseBNHook ([#964](https://github.com/open-mmlab/mmclassification/pull/964))
- [Improve] Use `forward_dummy` to calculate FLOPS. ([#953](https://github.com/open-mmlab/mmclassification/pull/953))
### Bug Fixes
@ -102,13 +130,13 @@
### New Features
- \[Feature\] Support resize relative position embedding in `SwinTransformer`. ([#749](https://github.com/open-mmlab/mmclassification/pull/749))
- \[Feature\] Add PoolFormer backbone and checkpoints. ([#746](https://github.com/open-mmlab/mmclassification/pull/746))
- [Feature] Support resize relative position embedding in `SwinTransformer`. ([#749](https://github.com/open-mmlab/mmclassification/pull/749))
- [Feature] Add PoolFormer backbone and checkpoints. ([#746](https://github.com/open-mmlab/mmclassification/pull/746))
### Improvements
- \[Enhance\] Improve CPE performance by reduce memory copy. ([#762](https://github.com/open-mmlab/mmclassification/pull/762))
- \[Enhance\] Add extra dataloader settings in configs. ([#752](https://github.com/open-mmlab/mmclassification/pull/752))
- [Enhance] Improve CPE performance by reduce memory copy. ([#762](https://github.com/open-mmlab/mmclassification/pull/762))
- [Enhance] Add extra dataloader settings in configs. ([#752](https://github.com/open-mmlab/mmclassification/pull/752))
## v0.22.0(30/3/2022)
@ -120,29 +148,29 @@
### New Features
- \[Feature\] Add CSPNet and backbone and checkpoints ([#735](https://github.com/open-mmlab/mmclassification/pull/735))
- \[Feature\] Add `CustomDataset`. ([#738](https://github.com/open-mmlab/mmclassification/pull/738))
- \[Feature\] Add diff seeds to diff ranks. ([#744](https://github.com/open-mmlab/mmclassification/pull/744))
- \[Feature\] Support ConvMixer. ([#716](https://github.com/open-mmlab/mmclassification/pull/716))
- \[Feature\] Our `dist_train` & `dist_test` tools support distributed training on multiple machines. ([#734](https://github.com/open-mmlab/mmclassification/pull/734))
- \[Feature\] Add RepMLP backbone and checkpoints. ([#709](https://github.com/open-mmlab/mmclassification/pull/709))
- \[Feature\] Support CUB dataset. ([#703](https://github.com/open-mmlab/mmclassification/pull/703))
- \[Feature\] Support ResizeMix. ([#676](https://github.com/open-mmlab/mmclassification/pull/676))
- [Feature] Add CSPNet and backbone and checkpoints ([#735](https://github.com/open-mmlab/mmclassification/pull/735))
- [Feature] Add `CustomDataset`. ([#738](https://github.com/open-mmlab/mmclassification/pull/738))
- [Feature] Add diff seeds to diff ranks. ([#744](https://github.com/open-mmlab/mmclassification/pull/744))
- [Feature] Support ConvMixer. ([#716](https://github.com/open-mmlab/mmclassification/pull/716))
- [Feature] Our `dist_train` & `dist_test` tools support distributed training on multiple machines. ([#734](https://github.com/open-mmlab/mmclassification/pull/734))
- [Feature] Add RepMLP backbone and checkpoints. ([#709](https://github.com/open-mmlab/mmclassification/pull/709))
- [Feature] Support CUB dataset. ([#703](https://github.com/open-mmlab/mmclassification/pull/703))
- [Feature] Support ResizeMix. ([#676](https://github.com/open-mmlab/mmclassification/pull/676))
### Improvements
- \[Enhance\] Use `--a-b` instead of `--a_b` in arguments. ([#754](https://github.com/open-mmlab/mmclassification/pull/754))
- \[Enhance\] Add `get_cat_ids` and `get_gt_labels` to KFoldDataset. ([#721](https://github.com/open-mmlab/mmclassification/pull/721))
- \[Enhance\] Set torch seed in `worker_init_fn`. ([#733](https://github.com/open-mmlab/mmclassification/pull/733))
- [Enhance] Use `--a-b` instead of `--a_b` in arguments. ([#754](https://github.com/open-mmlab/mmclassification/pull/754))
- [Enhance] Add `get_cat_ids` and `get_gt_labels` to KFoldDataset. ([#721](https://github.com/open-mmlab/mmclassification/pull/721))
- [Enhance] Set torch seed in `worker_init_fn`. ([#733](https://github.com/open-mmlab/mmclassification/pull/733))
### Bug Fixes
- \[Fix\] Fix the discontiguous output feature map of ConvNeXt. ([#743](https://github.com/open-mmlab/mmclassification/pull/743))
- [Fix] Fix the discontiguous output feature map of ConvNeXt. ([#743](https://github.com/open-mmlab/mmclassification/pull/743))
### Docs Update
- \[Docs\] Add brief installation steps in README for copy&paste. ([#755](https://github.com/open-mmlab/mmclassification/pull/755))
- \[Docs\] fix logo url link from mmocr to mmcls. ([#732](https://github.com/open-mmlab/mmclassification/pull/732))
- [Docs] Add brief installation steps in README for copy&paste. ([#755](https://github.com/open-mmlab/mmclassification/pull/755))
- [Docs] fix logo url link from mmocr to mmcls. ([#732](https://github.com/open-mmlab/mmclassification/pull/732))
## v0.21.0(04/03/2022)
@ -245,18 +273,18 @@
### Improvements
- \[Reproduction\] Reproduce RegNetX training accuracy. ([#587](https://github.com/open-mmlab/mmclassification/pull/587))
- \[Reproduction\] Reproduce training results of T2T-ViT. ([#610](https://github.com/open-mmlab/mmclassification/pull/610))
- \[Enhance\] Provide high-acc training settings of ResNet. ([#572](https://github.com/open-mmlab/mmclassification/pull/572))
- \[Enhance\] Set a random seed when the user does not set a seed. ([#554](https://github.com/open-mmlab/mmclassification/pull/554))
- \[Enhance\] Added `NumClassCheckHook` and unit tests. ([#559](https://github.com/open-mmlab/mmclassification/pull/559))
- \[Enhance\] Enhance feature extraction function. ([#593](https://github.com/open-mmlab/mmclassification/pull/593))
- \[Enhance\] Improve efficiency of precision, recall, f1_score and support. ([#595](https://github.com/open-mmlab/mmclassification/pull/595))
- \[Enhance\] Improve accuracy calculation performance. ([#592](https://github.com/open-mmlab/mmclassification/pull/592))
- \[Refactor\] Refactor `analysis_log.py`. ([#529](https://github.com/open-mmlab/mmclassification/pull/529))
- \[Refactor\] Use new API of matplotlib to handle blocking input in visualization. ([#568](https://github.com/open-mmlab/mmclassification/pull/568))
- \[CI\] Cancel previous runs that are not completed. ([#583](https://github.com/open-mmlab/mmclassification/pull/583))
- \[CI\] Skip build CI if only configs or docs modification. ([#575](https://github.com/open-mmlab/mmclassification/pull/575))
- [Reproduction] Reproduce RegNetX training accuracy. ([#587](https://github.com/open-mmlab/mmclassification/pull/587))
- [Reproduction] Reproduce training results of T2T-ViT. ([#610](https://github.com/open-mmlab/mmclassification/pull/610))
- [Enhance] Provide high-acc training settings of ResNet. ([#572](https://github.com/open-mmlab/mmclassification/pull/572))
- [Enhance] Set a random seed when the user does not set a seed. ([#554](https://github.com/open-mmlab/mmclassification/pull/554))
- [Enhance] Added `NumClassCheckHook` and unit tests. ([#559](https://github.com/open-mmlab/mmclassification/pull/559))
- [Enhance] Enhance feature extraction function. ([#593](https://github.com/open-mmlab/mmclassification/pull/593))
- [Enhance] Improve efficiency of precision, recall, f1_score and support. ([#595](https://github.com/open-mmlab/mmclassification/pull/595))
- [Enhance] Improve accuracy calculation performance. ([#592](https://github.com/open-mmlab/mmclassification/pull/592))
- [Refactor] Refactor `analysis_log.py`. ([#529](https://github.com/open-mmlab/mmclassification/pull/529))
- [Refactor] Use new API of matplotlib to handle blocking input in visualization. ([#568](https://github.com/open-mmlab/mmclassification/pull/568))
- [CI] Cancel previous runs that are not completed. ([#583](https://github.com/open-mmlab/mmclassification/pull/583))
- [CI] Skip build CI if only configs or docs modification. ([#575](https://github.com/open-mmlab/mmclassification/pull/575))
### Bug Fixes

3
docs/en/faq.md
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@ -18,7 +18,8 @@ and make sure you fill in all required information in the template.
| MMClassification version | MMCV version |
| :----------------------: | :--------------------: |
| dev | mmcv>=1.7.0, \<1.9.0 |
| 0.24.1 (master) | mmcv>=1.4.2, \<1.9.0 |
| 0.25.0 (master) | mmcv>=1.4.2, \<1.9.0 |
| 0.24.1 | 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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docs/zh_CN/faq.md
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@ -16,7 +16,8 @@
| MMClassification version | MMCV version |
| :----------------------: | :--------------------: |
| dev | mmcv>=1.7.0, \<1.9.0 |
| 0.24.1 (master) | mmcv>=1.4.2, \<1.9.0 |
| 0.25.0 (master) | mmcv>=1.4.2, \<1.9.0 |
| 0.24.1 | 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 |

9
mmcls/apis/train.py
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@ -10,11 +10,11 @@ from mmcv.runner import (DistSamplerSeedHook, Fp16OptimizerHook,
from mmcls.core import DistEvalHook, DistOptimizerHook, EvalHook
from mmcls.datasets import build_dataloader, build_dataset
from mmcls.utils import (get_root_logger, wrap_distributed_model,
wrap_non_distributed_model)
from mmcls.utils import (auto_select_device, get_root_logger,
wrap_distributed_model, wrap_non_distributed_model)
def init_random_seed(seed=None, device='cuda'):
def init_random_seed(seed=None, device=None):
"""Initialize random seed.
If the seed is not set, the seed will be automatically randomized,
@ -30,7 +30,8 @@ def init_random_seed(seed=None, device='cuda'):
"""
if seed is not None:
return seed
if device is None:
device = auto_select_device()
# Make sure all ranks share the same random seed to prevent
# some potential bugs. Please refer to
# https://github.com/open-mmlab/mmdetection/issues/6339

7
mmcls/core/hook/wandblogger_hook.py
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@ -3,7 +3,7 @@ import os.path as osp
import numpy as np
from mmcv.runner import HOOKS, BaseRunner
from mmcv.runner.dist_utils import master_only
from mmcv.runner.dist_utils import get_dist_info, master_only
from mmcv.runner.hooks.checkpoint import CheckpointHook
from mmcv.runner.hooks.evaluation import DistEvalHook, EvalHook
from mmcv.runner.hooks.logger.wandb import WandbLoggerHook
@ -190,7 +190,6 @@ class MMClsWandbHook(WandbLoggerHook):
# Log the evaluation table
self._log_eval_table(runner.epoch + 1)
@master_only
def after_train_iter(self, runner):
if self.get_mode(runner) == 'train':
# An ugly patch. The iter-based eval hook will call the
@ -201,6 +200,10 @@ class MMClsWandbHook(WandbLoggerHook):
else:
super(MMClsWandbHook, self).after_train_iter(runner)
rank, _ = get_dist_info()
if rank != 0:
return
if self.by_epoch:
return

6
mmcls/core/utils/dist_utils.py
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@ -8,6 +8,8 @@ from mmcv.runner import OptimizerHook, get_dist_info
from torch._utils import (_flatten_dense_tensors, _take_tensors,
_unflatten_dense_tensors)
from mmcls.utils import auto_select_device
def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1):
if bucket_size_mb > 0:
@ -59,7 +61,7 @@ class DistOptimizerHook(OptimizerHook):
runner.optimizer.step()
def sync_random_seed(seed=None, device='cuda'):
def sync_random_seed(seed=None, device=None):
"""Make sure different ranks share the same seed.
All workers must call this function, otherwise it will deadlock.
@ -81,6 +83,8 @@ def sync_random_seed(seed=None, device='cuda'):
Returns:
int: Seed to be used.
"""
if device is None:
device = auto_select_device()
if seed is None:
seed = np.random.randint(2**31)
assert isinstance(seed, int)

3
mmcls/datasets/samplers/distributed_sampler.py
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@ -4,7 +4,6 @@ from torch.utils.data import DistributedSampler as _DistributedSampler
from mmcls.core.utils import sync_random_seed
from mmcls.datasets import SAMPLERS
from mmcls.utils import auto_select_device
@SAMPLERS.register_module()
@ -31,7 +30,7 @@ class DistributedSampler(_DistributedSampler):
# 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, device=auto_select_device())
self.seed = sync_random_seed(seed)
def __iter__(self):
# deterministically shuffle based on epoch

48
mmcls/models/backbones/convnext.py
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@ -6,6 +6,7 @@ from typing import Sequence
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn.bricks import (NORM_LAYERS, DropPath, build_activation_layer,
build_norm_layer)
from mmcv.runner import BaseModule
@ -77,8 +78,11 @@ class ConvNeXtBlock(BaseModule):
mlp_ratio=4.,
linear_pw_conv=True,
drop_path_rate=0.,
layer_scale_init_value=1e-6):
layer_scale_init_value=1e-6,
with_cp=False):
super().__init__()
self.with_cp = with_cp
self.depthwise_conv = nn.Conv2d(
in_channels,
in_channels,
@ -108,24 +112,33 @@ class ConvNeXtBlock(BaseModule):
drop_path_rate) if drop_path_rate > 0. else nn.Identity()
def forward(self, x):
shortcut = x
x = self.depthwise_conv(x)
x = self.norm(x)
if self.linear_pw_conv:
x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
def _inner_forward(x):
shortcut = x
x = self.depthwise_conv(x)
x = self.norm(x)
x = self.pointwise_conv1(x)
x = self.act(x)
x = self.pointwise_conv2(x)
if self.linear_pw_conv:
x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
if self.linear_pw_conv:
x = x.permute(0, 3, 1, 2) # permute back
x = self.pointwise_conv1(x)
x = self.act(x)
x = self.pointwise_conv2(x)
if self.gamma is not None:
x = x.mul(self.gamma.view(1, -1, 1, 1))
if self.linear_pw_conv:
x = x.permute(0, 3, 1, 2) # permute back
if self.gamma is not None:
x = x.mul(self.gamma.view(1, -1, 1, 1))
x = shortcut + self.drop_path(x)
return x
if self.with_cp and x.requires_grad:
x = cp.checkpoint(_inner_forward, x)
else:
x = _inner_forward(x)
x = shortcut + self.drop_path(x)
return x
@ -169,6 +182,8 @@ class ConvNeXt(BaseBackbone):
gap_before_final_norm (bool): Whether to globally average the feature
map before the final norm layer. In the official repo, it's only
used in classification task. Defaults to True.
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed. Defaults to False.
init_cfg (dict, optional): Initialization config dict
""" # noqa: E501
arch_settings = {
@ -206,6 +221,7 @@ class ConvNeXt(BaseBackbone):
out_indices=-1,
frozen_stages=0,
gap_before_final_norm=True,
with_cp=False,
init_cfg=None):
super().__init__(init_cfg=init_cfg)
@ -288,8 +304,8 @@ class ConvNeXt(BaseBackbone):
norm_cfg=norm_cfg,
act_cfg=act_cfg,
linear_pw_conv=linear_pw_conv,
layer_scale_init_value=layer_scale_init_value)
for j in range(depth)
layer_scale_init_value=layer_scale_init_value,
with_cp=with_cp) for j in range(depth)
])
block_idx += depth

12
mmcls/utils/distribution.py
View File

@ -19,6 +19,9 @@ def wrap_non_distributed_model(model, device='cuda', dim=0, *args, **kwargs):
if device == 'npu':
from mmcv.device.npu import NPUDataParallel
model = NPUDataParallel(model.npu(), dim=dim, *args, **kwargs)
elif device == 'mlu':
from mmcv.device.mlu import MLUDataParallel
model = MLUDataParallel(model.mlu(), dim=dim, *args, **kwargs)
elif device == 'cuda':
from mmcv.parallel import MMDataParallel
model = MMDataParallel(model.cuda(), dim=dim, *args, **kwargs)
@ -57,6 +60,15 @@ def wrap_distributed_model(model, device='cuda', *args, **kwargs):
from torch.npu import current_device
model = NPUDistributedDataParallel(
model.npu(), *args, device_ids=[current_device()], **kwargs)
elif device == 'mlu':
import os
from mmcv.device.mlu import MLUDistributedDataParallel
model = MLUDistributedDataParallel(
model.mlu(),
*args,
device_ids=[int(os.environ['LOCAL_RANK'])],
**kwargs)
elif device == 'cuda':
from mmcv.parallel import MMDistributedDataParallel
from torch.cuda import current_device

2
mmcls/version.py
View File

@ -1,6 +1,6 @@
# Copyright (c) OpenMMLab. All rights reserved
__version__ = '0.24.1'
__version__ = '0.25.0'
def parse_version_info(version_str):

10
tests/test_models/test_backbones/test_convnext.py
View File

@ -84,3 +84,13 @@ def test_convnext():
for i in range(2, 4):
assert model.downsample_layers[i].training
assert model.stages[i].training
# Test Activation Checkpointing
model = ConvNeXt(arch='tiny', out_indices=-1, with_cp=True)
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 1
assert feat[0].shape == torch.Size([1, 768])

5
tools/test.py
View File

@ -195,10 +195,7 @@ def main():
**show_kwargs)
else:
model = wrap_distributed_model(
model,
device=cfg.device,
device_ids=[int(os.environ['LOCAL_RANK'])],
broadcast_buffers=False)
model, device=cfg.device, broadcast_buffers=False)
outputs = multi_gpu_test(model, data_loader, args.tmpdir,
args.gpu_collect)