p60975123
/
mindspore
forked from mindspore-Ecosystem/mindspore
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

!9795 Fix readme of inceptionv3 

From: @zhouyaqiang0
Reviewed-by: @oacjiewen
Signed-off-by:
This commit is contained in:
mindspore-ci-bot 2020-12-14 14:55:17 +08:00 committed by Gitee
parent aeeed2b8f6 477c24b673
commit 740ea71fc1
1 changed files with 32 additions and 58 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

90
model_zoo/official/cv/inceptionv3/README.md
View File

@ -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)