Squeezenet1_1
This implements training of Squeezenet1_1 on the ImageNet dataset, mainly modified from pytorch/examples.
Squeezenet1_1 Detail
As of the current date, Ascend-Pytorch is still inefficient for contiguous operations. Therefore, Squeezenet1_1 is re-implemented using semantics such as custom OP. For details, see models/Squeezenet.py .
Requirements
- Install PyTorch (pytorch.org)
pip install -r requirements.txtNote: pillow recommends installing a newer version. If the corresponding torchvision version cannot be installed directly, you can use the source code to install the corresponding version. The source code reference link: Suggestion the pillow is 9.1.0 and the torchvision is 0.6.0- Download the ImageNet dataset from http://www.image-net.org/
- Then, and move validation images to labeled subfolders, using the following shell script
Training
To train a model, run main.py or main_8p.py with the desired model architecture and the path to the ImageNet dataset:
# O2 training 1p
bash scripts/run_1p.sh
# O2 training 8p
bash scripts/run_8p.sh
Squeezenet1_1 training result
| Acc@1 | FPS | Npu_nums | Epochs | AMP_Type |
|---|---|---|---|---|
| - | 384 | 1 | 240 | O2 |
| 58.54 | 1963 | 8 | 240 | O2 |
公网地址说明
代码涉及公网地址参考 public_address_statement.md