As of the current date, Ascend-Pytorch is still inefficient for contiguous operations. Therefore, ShufflenetV2 is re-implemented using semantics such as custom OP. For details, see models/shufflenetv2_wock_op_woct.py .

Requirements

Install PyTorch (pytorch.org)
pip install -r requirements.txt
Download the ImageNet dataset from http://www.image-net.org/
- Then, and move validation images to labeled subfolders, using the following shell script

Training 1p

To train a model, run main.py with the desired model architecture and the path to the ImageNet dataset:

# FP32 training
bash 1p.sh

# O2 training
bash 1p_amp.sh

# FP32 training for 20 epoch experiment
bash 1p_20e.sh

# FP32 training for 20 epoch experiment
bash 1p_20e_amp.sh

Training multi-cards

# O2 training 2p
# Only Support device-list setting in [[0,1], [2,3], [4,5], [6,7]]
bash 2p_amp_med.sh

# O2 training 4p
# Only Support device-list setting in [[0,1,2,3], [4,5,6,7]]
bash 4p_amp_med.sh

# O2 training 8p
bash 8p_amp_med.sh

ShufflenetV2 training result

Acc@1	FPS	Npu_nums	Epochs	Type
61.5	1200	1	20	O2
68.5	2200	1	240	O2
66.3	14000	1	240	O2