ResNet Training in PaddlePaddle#

This is a demo showcasing ResNet50 training on ImageNet. The code is based on NVIDIA Deep Learning Examples

Data augmentation#

This model uses the following data augmentation:

  • For training:

    • Normalization

    • Random resized crop to 224x224

      • Scale from 8% to 100%

      • Aspect ratio from 3/4 to 4/3

    • Random horizontal flip

  • For inference:

    • Normalization

    • Scale to 256x256

    • Center crop to 224x224

Usage#

Install the necessary packages from requirements.txt before use.

The startup script is docs/examples/use_cases/paddle/resnet50/train.py.

 # For single GPU training with AMP
FLAGS_apply_pass_to_program=1 python -m paddle.distributed.launch \
  --gpus=0 train.py \
  --epochs 90 \
  --amp \
  --scale-loss 128.0 \
  --use-dynamic-loss-scaling \
  --data-layout NHWC

# For 8 GPUs training with AMP
FLAGS_apply_pass_to_program=1 python -m paddle.distributed.launch \
  --gpus=0,1,2,3,4,5,6,7 train.py \
  --epochs 90 \
  --amp \
  --scale-loss 128.0 \
  --use-dynamic-loss-scaling \
  --data-layout NHWC

# For all available options
python train.py --help
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