[Enhance] Add `dist_train_arm.sh` for ARM device and update NPU results. (#1218)
* update npu results * add dist_train_arm.sh & updata docs * del contentpull/1249/head
wangjiangben-hw
GitHub
parent
0eb3b61fc5
commit
578c035d5c
|
|
@ -2,33 +2,63 @@
|
|||
|
||||
## Usage
|
||||
|
||||
### General 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
|
||||
bash ./tools/dist_train.sh configs/resnet/resnet50_8xb32_in1k.py 8 --device npu
|
||||
```
|
||||
|
||||
Also, you can use only one NPU to trian the model with the following command:
|
||||
Also, you can use only one NPU to train the model with the following command:
|
||||
|
||||
```shell
|
||||
python tools/train.py configs/cspnet/resnet50_8xb32_in1k.py --device npu
|
||||
python ./tools/train.py configs/resnet/resnet50_8xb32_in1k.py --device npu
|
||||
```
|
||||
|
||||
## Verified Models
|
||||
### High-performance Usage on ARM server
|
||||
|
||||
| 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](https://download.openmmlab.com/mmclassification/v0/device/npu/resnetv1d152_8xb32_in1k.log.json) |
|
||||
| [ResNet-50](../papers/resnet.md) | 76.38 | 93.22 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/resnet50_8xb32_in1k.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) |
|
||||
Since the scheduling ability of ARM CPUs when processing resource preemption is not as good as that of X86 CPUs during multi-card training, we provide a high-performance startup script to accelerate training with the following command:
|
||||
|
||||
```shell
|
||||
# The script under the 8 cards of a single machine is shown here
|
||||
bash tools/dist_train_arm.sh configs/resnet/resnet50_8xb32_in1k.py 8 --device npu --cfg-options data.workers_per_gpu=$(($(nproc)/8))
|
||||
```
|
||||
|
||||
For resnet50 8 NPUs training with batch_size(data.samples_per_gpu)=512, the performance data is shown below:
|
||||
|
||||
| CPU | Start Script | IterTime(s) |
|
||||
| :------------------ | :------------------------ | :--------------: |
|
||||
| ARM(Kunpeng920 \*4) | ./tools/dist_train.sh | ~0.9(0.85-1.0) |
|
||||
| ARM(Kunpeng920 \*4) | ./tools/dist_train_arm.sh | ~0.8(0.78s-0.85) |
|
||||
|
||||
## Models Results
|
||||
|
||||
| Model | Top-1 (%) | Top-5 (%) | Config | Download |
|
||||
| :---------------------------------------------------------: | :-------: | :-------: | :----------------------------------------------------------: | :-------------------------------------------------------------: |
|
||||
| [ResNet-50](../papers/resnet.md) | 76.38 | 93.22 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/resnet50_8xb32_in1k.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) |
|
||||
| [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) | 79.11 | 94.54 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/resnetv1d152_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) |
|
||||
| [MobileNetV2](../papers/mobilenet_v2.md) | 71.758 | 90.394 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/mobilenet-v2_8xb32_in1k.json) |
|
||||
| [MobileNetV3-Small](../papers/mobilenet_v3.md) | 67.522 | 87.316 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v3/mobilenet-v3-small_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/mobilenet-v3-small_8xb32_in1k.json) |
|
||||
| [\*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) |
|
||||
| [\*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) |
|
||||
| [\*\*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) |
|
||||
|
||||
**Notes:**
|
||||
|
||||
- If not specially marked, the results are almost same between results on the NPU and results on the GPU with FP32.
|
||||
- (\*) The training results of these models are lower than the results on the readme in the corresponding model, mainly
|
||||
because the results on the readme are directly the weight of the timm of the eval, and the results on this side are
|
||||
retrained according to the config with mmcls. The results of the config training on the GPU are consistent with the
|
||||
results of the NPU.
|
||||
- (\*\*) The accuracy of this model is slightly lower because config is a 4-card config, we use 8 cards to run, and users
|
||||
can adjust hyperparameters to get the best accuracy results.
|
||||
|
||||
**All above models are provided by Huawei Ascend group.**
|
||||
|
|
|
|||
|
|
@ -0,0 +1,32 @@
|
|||
#!/usr/bin/env bash
|
||||
|
||||
CONFIG=$1
|
||||
GPUS=$2
|
||||
NNODES=${NNODES:-1}
|
||||
NODE_RANK=${NODE_RANK:-0}
|
||||
PORT=${PORT:-29500}
|
||||
MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"}
|
||||
|
||||
export PYTHONPATH="$(dirname $0)/..":$PYTHONPATH
|
||||
|
||||
# launch core setting
|
||||
KERNEL_NUM=$(($(nproc)/GPUS))
|
||||
|
||||
# dist env setting
|
||||
export WORLD_SIZE=$((NNODES*GPUS))
|
||||
export MASTER_ADDR=$MASTER_ADDR
|
||||
export MASTER_PORT=$PORT
|
||||
LOCAL_RANK_START=$((NODE_RANK*GPUS))
|
||||
LOCAL_RANK_END=$((LOCAL_RANK_START+GPUS))
|
||||
|
||||
for((RANK_ID=LOCAL_RANK_START;RANK_ID<LOCAL_RANK_END;RANK_ID++))
|
||||
do
|
||||
export RANK=$RANK_ID
|
||||
PID_START=$((KERNEL_NUM*(RANK_ID%GPUS)))
|
||||
PID_END=$((PID_START+KERNEL_NUM-1))
|
||||
nohup taskset -c $PID_START-$PID_END \
|
||||
python $(dirname "$0")/train.py \
|
||||
$CONFIG \
|
||||
--launcher pytorch ${@:3} \
|
||||
&
|
||||
done
|
||||
Loading…
Reference in New Issue