@ -120,7 +120,7 @@ The following optimizers add the target interface: Adam, FTRL, LazyAdam, Proxim
</tr>
</table>
###### `export` Modify the input parameters and export's file name ([!7385](https://gitee.com/mind_spore/dashboard/projects/mindspore/mindspore/pulls/7385?tab=diffs)， [!9057](https://gitee.com/mindspore/mindspore/pulls/9057/files))
###### `export` Modify the input parameters and export's file name ([!7385](https://gitee.com/mindspore/mindspore/pulls/7385)， [!9057](https://gitee.com/mindspore/mindspore/pulls/9057/files))
Export the MindSpore prediction model to a file in the specified format.
@ -227,7 +227,7 @@ However, from a user's perspective, tensor.size and tensor.ndim (methods -> prop
</tr>
</table>
###### `EmbeddingLookup` add a config in the interface: sparse ([!8202](https://gitee.com/mind_spore/dashboard/projects/mindspore/mindspore/pulls/8202?tab=diffs))
###### `EmbeddingLookup` add a config in the interface: sparse ([!8202](https://gitee.com/mindspore/mindspore/pulls/8202))
sparse (bool): Using sparse mode. When 'target' is set to 'CPU', 'sparse' has to be true. Default: True.
@ -878,7 +878,7 @@ Contributions of any kind are welcome!
- Fix bug of list cannot be used as input in pynative mode([!1765](https://gitee.com/mindspore/mindspore/pulls/1765))
- Fix bug of kernel select ([!2103](https://gitee.com/mindspore/mindspore/pulls/2103))
- Fix bug of pattern matching for batchnorm fusion in the case of auto mix precision.([!1851](https://gitee.com/mindspore/mindspore/pulls/1851))
- Fix bug of generate hccl's kernel info.([!2393](https://gitee.com/mindspore/mindspore/mindspore/pulls/2393))
- Fix bug of generate hccl's kernel info.([!2393](https://gitee.com/mindspore/mindspore/pulls/2393))
- GPU platform
- Fix bug of summary feature invalid([!2173](https://gitee.com/mindspore/mindspore/pulls/2173))
@ -67,7 +67,7 @@ All the models in this repository are trained and validated on ImageNet-1K. The
## [Mixed Precision](#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’.
The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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)
@ -81,8 +81,8 @@ To run the python scripts in the repository, you need to prepare the environment
- Easydict
- MXNet 1.6.0 if running the script `param_convert.py`
- For more information, please check the resources below：
[论文](https://arxiv.org/pdf/1905.02244)：Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang et al."Searching for MobileNetV2."In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324.2019.
[论文](https://arxiv.org/pdf/1905.02244)：Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang et al."Searching for mobilenetv3."In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324.2019.
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’.
The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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)
@ -68,8 +68,8 @@ To run the python scripts in the repository, you need to prepare the environment
- opencv-python 4.3.0.36
- pycocotools 2.0
- For more information, please check the resources below：
@ -624,7 +624,7 @@ Get the log and output files under the path `./train_mass_*/`, and the model fil
## Inference
If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/network_migration.html).
If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/migrate_3rd_scripts.html).
For inference, config the options in `config.json` firstly:
- Assign the `test_dataset` under `dataset_config` node to the dataset path.
@ -32,7 +32,7 @@ FCN-4 is a convolutional neural network architecture, its name FCN-4 comes from
### Mixed Precision
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.
The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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)
@ -42,8 +42,8 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
lvmingfu
4 years ago