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.txt Note: 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