ImageNet Training in PyTorch¶
This implements training of popular model architectures, such as ResNet, AlexNet, and VGG on the ImageNet dataset.
This version has been modified to use DALI. It assumes that the dataset is raw JPEGs from the ImageNet dataset. If offers CPU and GPU based pipeline for DALI - use dali_cpu switch to enable CPU one. For heavy GPU networks (like RN50) CPU based one is faster, for some lighter where CPU is the bottleneck like RN18 GPU is. This version has been modified to use the DistributedDataParallel module in APEx instead of the one in upstream PyTorch. Please install APEx from here.
To run use the following commands
ln -s /path/to/train/jpeg/ train ln -s /path/to/validation/jpeg/ val torchrun --nproc_per_node=NUM_GPUS main.py -a resnet50 --dali_cpu --b 128 --loss-scale 128.0 --workers 4 --lr=0.4 --fp16-mode ./
APEx - optional (form PyTorch 1.6 it is part of the upstream so there is no need to install it separately), required for fp16 mode or distributed (multi-GPU) operation
Install PyTorch from source, main branch of PyTorch on github
pip install -r requirements.txt
Download the ImageNet dataset and move validation images to labeled subfolders
To do this, you can use the following script
To train a model, run docs/examples/use_cases/pytorch/resnet50/main.py with the desired model architecture and the path to the ImageNet dataset:
python main.py -a resnet18 [imagenet-folder with train and val folders]
The default learning rate schedule starts at 0.1 and decays by a factor of 10 every 30 epochs. This is appropriate for ResNet and models with batch normalization, but too high for AlexNet and VGG. Use 0.01 as the initial learning rate for AlexNet or VGG:
python main.py -a alexnet --lr 0.01 [imagenet-folder with train and val folders]
main.py [-h] [--arch ARCH] [-j N] [--epochs N] [--start-epoch N] [-b N] [--lr LR] [--momentum M] [--weight-decay W] [--print-freq N] [--resume PATH] [-e] [--pretrained] [--opt-level] DIR PyTorch ImageNet Training positional arguments: DIR path(s) to dataset (if one path is provided, it is assumed to have subdirectories named "train" and "val"; alternatively, train and val paths can be specified directly by providing both paths as arguments) optional arguments (for the full list please check `Apex ImageNet example <https://github.com/NVIDIA/apex/tree/master/examples/imagenet>`_) -h, --help show this help message and exit --arch ARCH, -a ARCH model architecture: alexnet | resnet | resnet101 | resnet152 | resnet18 | resnet34 | resnet50 | vgg | vgg11 | vgg11_bn | vgg13 | vgg13_bn | vgg16 | vgg16_bn | vgg19 | vgg19_bn (default: resnet18) -j N, --workers N number of data loading workers (default: 4) --epochs N number of total epochs to run --start-epoch N manual epoch number (useful on restarts) -b N, --batch-size N mini-batch size (default: 256) --lr LR, --learning-rate LR initial learning rate --momentum M momentum --weight-decay W, --wd W weight decay (default: 1e-4) --print-freq N, -p N print frequency (default: 10) --resume PATH path to latest checkpoint (default: none) -e, --evaluate evaluate model on validation set --pretrained use pre-trained model --dali_cpu use CPU based pipeline for DALI, for heavy GPU networks it may work better, for IO bottlenecked one like RN18 GPU default should be faster --disable_dali turns off DALI and switches to the native PyTorch data processing --fp16-mode enables mixed precision mode