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