From 477c24b673ca5bad0a3ed5997a73525ec3459b86 Mon Sep 17 00:00:00 2001 From: zhouyaqiang Date: Thu, 10 Dec 2020 20:36:04 +0800 Subject: [PATCH] Fix readme of inceptionv3 --- model_zoo/official/cv/inceptionv3/README.md | 90 ++++++++------------- 1 file changed, 32 insertions(+), 58 deletions(-) diff --git a/model_zoo/official/cv/inceptionv3/README.md b/model_zoo/official/cv/inceptionv3/README.md index ddb43fe20dd..f03b25aebaf 100644 --- a/model_zoo/official/cv/inceptionv3/README.md +++ b/model_zoo/official/cv/inceptionv3/README.md @@ -13,8 +13,8 @@ - [Evaluation](#evaluation) - [Model Description](#model-description) - [Performance](#performance) - - [Training Performance](#evaluation-performance) - - [Inference Performance](#evaluation-performance) + - [Evaluation Performance](#evaluation-performance) + - [Inference Performance](#inference-performance) - [Description of Random Situation](#description-of-random-situation) - [ModelZoo Homepage](#modelzoo-homepage) @@ -50,8 +50,8 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil # [Environment Requirements](#contents) -- Hardware（Ascend/GPU） -- Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. +- Hardware（Ascend） +- Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. - Framework - [MindSpore](https://www.mindspore.cn/install/en) - For more information, please check the resources below： @@ -68,11 +68,8 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil ├─README.md ├─scripts ├─run_standalone_train.sh # launch standalone training with ascend platform(1p) - ├─run_standalone_train_gpu.sh # launch standalone training with gpu platform(1p) ├─run_distribute_train.sh # launch distributed training with ascend platform(8p) - ├─run_distribute_train_gpu.sh # launch distributed training with gpu platform(8p) - ├─run_eval.sh # launch evaluating with ascend platform - └─run_eval_gpu.sh # launch evaluating with gpu platform + └─run_eval.sh # launch evaluating with ascend platform ├─src ├─config.py # parameter configuration ├─dataset.py # data preprocessing @@ -124,7 +121,7 @@ You can start training using python or shell scripts. The usage of shell scripts - Ascend: ```shell -# distribute training example(8p) +# distribute training(8p) sh scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH # standalone training sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH @@ -134,34 +131,19 @@ sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH > > This is processor cores binding operation regarding the `device_num` and total processor numbers. If you are not expect to do it, remove the operations `taskset` in `scripts/run_distribute_train.sh` -- GPU: - -```python -# distribute training example(8p) -sh scripts/run_distribute_train_gpu.sh DATA_DIR -# standalone training -sh scripts/run_standalone_train_gpu.sh DEVICE_ID DATA_DIR -``` - ### Launch ```python # training example python: - Ascend: python train.py --dataset_path /dataset/train --platform Ascend - GPU: python train.py --dataset_path /dataset/train --platform GPU + Ascend: python train.py --dataset_path DATA_PATH --platform Ascend shell: Ascend: # distribute training example(8p) sh scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH - # standalone training - sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH - GPU: - # distributed training example(8p) - sh scripts/run_distribute_train_gpu.sh /dataset/train # standalone training example - sh scripts/run_standalone_train_gpu.sh 0 /dataset/train + sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH ``` ### Result @@ -184,13 +166,7 @@ You can start training using python or shell scripts. The usage of shell scripts - Ascend: ```python - sh scripts/run_eval.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT -``` - -- GPU: - -```python - sh scripts/run_eval_gpu.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT + sh scripts/run_eval.sh DEVICE_ID DATA_PATH PATH_CHECKPOINT ``` ### Launch @@ -198,12 +174,10 @@ You can start training using python or shell scripts. The usage of shell scripts ```python # eval example python: - Ascend: python eval.py --dataset_path DATA_DIR --checkpoint PATH_CHECKPOINT --platform Ascend - GPU: python eval.py --dataset_path DATA_DIR --checkpoint PATH_CHECKPOINT --platform GPU + Ascend: python eval.py --dataset_path DATA_PATH --checkpoint PATH_CHECKPOINT --platform Ascend shell: - Ascend: sh scripts/run_eval.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT - GPU: sh scripts/run_eval_gpu.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT + Ascend: sh scripts/run_eval.sh DEVICE_ID DATA_PATH PATH_CHECKPOINT ``` > checkpoint can be produced in training process. @@ -220,28 +194,29 @@ metric: {'Loss': 1.778, 'Top1-Acc':0.788, 'Top5-Acc':0.942} ## [Performance](#contents) -### Training Performance +### Evaluation Performance -| Parameters | Ascend | GPU | -| -------------------------- | ---------------------------------------------- | ------------------------- | -| Model Version | InceptionV3 | InceptionV3 | -| Resource | Ascend 910, cpu:2.60GHz 192cores, memory:755G | NV SMI V100-16G(PCIE),cpu:2.10GHz 96cores, memory:250G | -| uploaded Date | 08/21/2020 | 08/21/2020 | -| MindSpore Version | 0.6.0-beta | 0.6.0-beta | -| Dataset | 1200k images | 1200k images | -| Batch_size | 128 | 128 | -| Training Parameters | src/config.py | src/config.py | -| Optimizer | RMSProp | RMSProp | -| Loss Function | SoftmaxCrossEntropy | SoftmaxCrossEntropy | -| Outputs | probability | probability | -| Loss | 1.98 | 1.98 | -| Accuracy (8p) | ACC1[78.8%] ACC5[94.2%] | ACC1[78.7%] ACC5[94.1%] | -| Total time (8p) | 11h | 72h | -| Params (M) | 103M | 103M | -| Checkpoint for Fine tuning | 313M | 312M | -| Scripts | [inceptionv3 script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/inceptionv3) | [inceptionv3 script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/inceptionv3) | +| Parameters | Ascend | +| -------------------------- | ---------------------------------------------- | +| Model Version | InceptionV3 | +| Resource | Ascend 910, cpu:2.60GHz 192cores, memory:755G | +| uploaded Date | 08/21/2020 | +| MindSpore Version | 0.6.0-beta | +| Dataset | 1200k images | +| Batch_size | 128 | +| Training Parameters | src/config.py | +| Optimizer | RMSProp | +| Loss Function | SoftmaxCrossEntropy | +| Outputs | probability | +| Loss | 1.98 | +| Total time (8p) | 11h | +| Params (M) | 103M | +| Checkpoint for Fine tuning | 313M | +| Model for inference | 92M (.onnx file) | +| Speed | 1pc:1050 img/s;8pc:8000 img/s | +| Scripts | [inceptionv3 script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/inceptionv3) | -#### Inference Performance +### Inference Performance | Parameters | Ascend | | ------------------- | --------------------------- | @@ -254,7 +229,6 @@ metric: {'Loss': 1.778, 'Top1-Acc':0.788, 'Top5-Acc':0.942} | Outputs | probability | | Accuracy | ACC1[78.8%] ACC5[94.2%] | | Total time | 2mins | -| Model for inference | 92M (.onnx file) | # [Description of Random Situation](#contents)