m53297601
/
mindspore
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

!5503 Modify readme of deeplabv3 and inceptionv3

Browse Source 
Merge pull request !5503 from zhouyaqiang0/master
pull/5503/MERGE
mindspore-ci-bot 5 years ago committed by Gitee
parent 3ffe439073 d33ea18796
commit b2b238bbf0
2 changed files with 32 additions and 22 deletions
Show Stats Download Patch File Download Diff File

37
model_zoo/official/cv/deeplabv3/README.md
Unescape View File

@ -42,6 +42,7 @@ Dataset used: [VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.htm
## [Mixed Precision(Ascend)](#contents)
The [mixed precision](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware.
For FP16 operators, if the input data type is FP32, the backend of MindSpore will automatically handle it with reduced precision. Users could check the reduced-precision operators by enabling INFO log and then searching ‘reduce precision’.
# [Environment Requirements](#contents)
@ -119,9 +120,9 @@ Major parameters in train.py and config.py are:
You can start training using python or shell scripts. The usage of shell scripts as follows:
sh scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH (CKPT_PATH)
```
sh scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH (CKPT_PATH)
```
> Notes:
RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training_ascend.html) , and the device_ip can be got as https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools.
@ -140,7 +141,7 @@ sh scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH (CKPT_PATH)
### Result
Training result will be stored in the example path. Checkpoints will be stored at `. /LOG0/chec_deeplabv3-*` by default, and training log will be redirected to `./log.txt` like followings.
Training result(8p) will be stored in the example path. Checkpoints will be stored at `. /train_parallel0/` by default, and training log will be redirected to `./train_parallel0/log.txt` like followings.
```
epoch: 1 step: 732, loss is 0.11594
@ -154,8 +155,9 @@ Epoch time: 160917.911, per step time: 36.631
You can start training using python or shell scripts. The usage of shell scripts as follows:
sh scripts/run_eval.sh DEVICE_ID DATA_PATH PRETRAINED_CKPT_PATH
```
sh scripts/run_eval.sh DEVICE_ID DATA_PATH PRETRAINED_CKPT_PATH
```
### Launch
```
@ -184,14 +186,15 @@ mIoU = 0.65049
| Parameters | DeeplabV3 |
| -------------------------- | ---------------------------------------------------------- |
| Model Version | |
| Model Version | V1 |
| Resource | Ascend 910, cpu:2.60GHz 56cores, memory:314G |
| uploaded Date | 08/24/2020 |
| MindSpore Version | 0.6.0-beta |
| Training Parameters | src/config.py |
| Uploaded Date | 08/24/2020(month/day/year) |
| MindSpore Version | 0.6.0-beta |
| Dataset | voc2012/train |
| Batch_size | 2 |
| Optimizer | Momentum |
| Loss Function | SoftmaxCrossEntropy |
| outputs | probability |
| Outputs | probability |
| Loss | 0.98 |
| Accuracy | mIoU:65% |
| Total time | 5mins |
@ -201,15 +204,15 @@ mIoU = 0.65049
#### Inference Performance
| Parameters | DeeplabV3 |
| ------------------- | --------------------------- |
| Model Version | |
| Resource | Ascend 910 |
| Parameters | DeeplabV3 |
| -------------------------- | ---------------------------------------------------------- |
| Model Version | V1 |
| Resource | Ascend 910, cpu:2.60GHz 56cores, memory:314G |
| Uploaded Date | 08/24/2020 (month/day/year) |
| MindSpore Version | 0.6.0-beta |
| Dataset | voc2012/val |
| batch_size | 2 |
| outputs | probability |
| Batch_size | 2 |
| Outputs | probability |
| Accuracy | mIoU:65% |
| Total time | 10mins |
| Model for inference | 97M (.GEIR file) |

17
model_zoo/official/cv/inceptionv3/README.md
Unescape View File

@ -46,6 +46,7 @@ Dataset used can refer to paper.
## [Mixed Precision(Ascend)](#contents)
The [mixed precision](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware.
For FP16 operators, if the input data type is FP32, the backend of MindSpore will automatically handle it with reduced precision. Users could check the reduced-precision operators by enabling INFO log and then searching ‘reduce precision’.
# [Environment Requirements](#contents)
@ -131,7 +132,7 @@ sh run_distribute_train.sh RANK_TABLE_FILE DATA_PATH
sh run_standalone_train.sh DEVICE_ID DATA_PATH
```
> Notes:
RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training_ascend.html) , and the device_ip can be got as https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools.
RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training_ascend.html) , and the device_ip can be got as [Link]https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools.
- GPU:
```
@ -178,8 +179,14 @@ Epoch time: 160917.911, per step time: 128.631
You can start training using python or shell scripts. The usage of shell scripts as follows:
- Ascend: sh run_eval.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT
- GPU: sh run_eval_gpu.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT
- Ascend:
```
sh run_eval.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT
```
- GPU:
```
sh run_eval_gpu.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT
```
### Launch
@ -212,7 +219,7 @@ metric: {'Loss': 1.778, 'Top1-Acc':0.788, 'Top5-Acc':0.942}
| Parameters | InceptionV3 | |
| -------------------------- | ---------------------------------------------- | ------------------------- |
| Model Version | | |
| Model Version | V1 | V1 |
| Resource | Ascend 910, cpu:2.60GHz 56cores, memory:314G | 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 |
@ -232,7 +239,7 @@ metric: {'Loss': 1.778, 'Top1-Acc':0.788, 'Top5-Acc':0.942}
| Parameters | InceptionV3 |
| ------------------- | --------------------------- |
| Model Version | |
| Model Version | V1 |
| Resource | Ascend 910 |
| Uploaded Date | 08/22/2020 (month/day/year) |
| MindSpore Version | 0.6.0-beta |

Write Preview
Loading…
Cancel
Save