ENet 训练

This implements training of ENet on the Cityscapes dataset.

• Reference implementation：

url=https://github.com/Tramac/awesome-semantic-segmentation-pytorch

Requirements

• Install Packages
• 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
• The Cityscapes dataset can be downloaded from the link.
• Move the datasets to root directory and run the script unzip.sh.
  • bash ./unzip.sh

Training

To train a model, change the working directory to ./NPU,then run:

# 1p train perf
bash ./test/train_performance_1p.sh '[your_dataset_path]'

# 8p train perf
bash ./test/train_performance_8p.sh '[your_dataset_path]'

# 1p train full
bash ./test/train_full_1p.sh '[your_dataset_path]'

# 8p train full
bash ./test/train_full_8p.sh '[your_dataset_path]'

# finetuning
bash ./test/train_finetune_1p.sh '[your_dataset_path]'

After running,you can see the results in ./NPU/stargan_full_8p/samples or ./NPU/stargan_full_1p/samples

GAN training result

Type	FPS	Epochs	AMP_Type
NPU-1p	14.398	400	O2
NPU-8p	74.310	400	O2
GPU-1p	21.885	400	O2
GPU-8p	161.495	400	O2

Statement

For details about the public address of the code in this repository, you can get from the file public_address_statement.md