diff --git a/model_zoo/official/cv/deeplabv3/README.md b/model_zoo/official/cv/deeplabv3/README.md index b105df7e5a7b14d46c406eee512a8c5253477e4b..464c51e7a2d588971d4ad47d1a9f0835d857668a 100644 --- a/model_zoo/official/cv/deeplabv3/README.md +++ b/model_zoo/official/cv/deeplabv3/README.md @@ -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) | diff --git a/model_zoo/official/cv/inceptionv3/README.md b/model_zoo/official/cv/inceptionv3/README.md index 2f56f7a9c01439c3adb229b0f6cf37b69b2b6668..3e4313ef658db718a63826060c1ad9a34a7df4a0 100644 --- a/model_zoo/official/cv/inceptionv3/README.md +++ b/model_zoo/official/cv/inceptionv3/README.md @@ -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 |