ImageNet training in PyTorch

This implements training of popular model architectures, such as ResNet, AlexNet, and VGG on the ImageNet dataset.

Requirements

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

Training

To train a model, run 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]

Use Dummy Data

ImageNet dataset is large and time-consuming to download. To get started quickly, run main.py using dummy data by "--dummy". It's also useful for training speed benchmark. Note that the loss or accuracy is useless in this case.

python main.py -a resnet18 --dummy

Multi-processing Distributed Data Parallel Training

You should always use the NCCL backend for multi-processing distributed training since it currently provides the best distributed training performance.

Single node, multiple GPUs:

python main.py -a resnet50 --dist-url 'tcp://127.0.0.1:FREEPORT' --dist-backend 'nccl' --multiprocessing-distributed --world-size 1 --rank 0 [imagenet-folder with train and val folders]

Multiple nodes:

Node 0:

python main.py -a resnet50 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' --dist-backend 'nccl' --multiprocessing-distributed --world-size 2 --rank 0 [imagenet-folder with train and val folders]

Node 1:

python main.py -a resnet50 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' --dist-backend 'nccl' --multiprocessing-distributed --world-size 2 --rank 1 [imagenet-folder with train and val folders]

Usage

usage: main.py [-h] [-a ARCH] [-j N] [--epochs N] [--start-epoch N] [-b N] [--lr LR] [--momentum M] [--wd W] [-p N] [--resume PATH] [-e] [--pretrained] [--world-size WORLD_SIZE] [--rank RANK]
               [--dist-url DIST_URL] [--dist-backend DIST_BACKEND] [--seed SEED] [--gpu GPU] [--multiprocessing-distributed] [--dummy]
               [DIR]

PyTorch ImageNet Training

positional arguments:
  DIR                   path to dataset (default: imagenet)

optional arguments:
  -h, --help            show this help message and exit
  -a ARCH, --arch ARCH  model architecture: alexnet | convnext_base | convnext_large | convnext_small | convnext_tiny | densenet121 | densenet161 | densenet169 | densenet201 | efficientnet_b0 |
                        efficientnet_b1 | efficientnet_b2 | efficientnet_b3 | efficientnet_b4 | efficientnet_b5 | efficientnet_b6 | efficientnet_b7 | googlenet | inception_v3 | mnasnet0_5 | mnasnet0_75 |
                        mnasnet1_0 | mnasnet1_3 | mobilenet_v2 | mobilenet_v3_large | mobilenet_v3_small | regnet_x_16gf | regnet_x_1_6gf | regnet_x_32gf | regnet_x_3_2gf | regnet_x_400mf | regnet_x_800mf |
                        regnet_x_8gf | regnet_y_128gf | regnet_y_16gf | regnet_y_1_6gf | regnet_y_32gf | regnet_y_3_2gf | regnet_y_400mf | regnet_y_800mf | regnet_y_8gf | resnet101 | resnet152 | resnet18 |
                        resnet34 | resnet50 | resnext101_32x8d | resnext50_32x4d | shufflenet_v2_x0_5 | shufflenet_v2_x1_0 | shufflenet_v2_x1_5 | shufflenet_v2_x2_0 | squeezenet1_0 | squeezenet1_1 | vgg11 |
                        vgg11_bn | vgg13 | vgg13_bn | vgg16 | vgg16_bn | vgg19 | vgg19_bn | vit_b_16 | vit_b_32 | vit_l_16 | vit_l_32 | wide_resnet101_2 | wide_resnet50_2 (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), this is the total batch size of all GPUs on the current node when using Data Parallel or Distributed Data Parallel
  --lr LR, --learning-rate LR
                        initial learning rate
  --momentum M          momentum
  --wd W, --weight-decay W
                        weight decay (default: 1e-4)
  -p N, --print-freq 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
  --world-size WORLD_SIZE
                        number of nodes for distributed training
  --rank RANK           node rank for distributed training
  --dist-url DIST_URL   url used to set up distributed training
  --dist-backend DIST_BACKEND
                        distributed backend
  --seed SEED           seed for initializing training.
  --gpu GPU             GPU id to use.
  --multiprocessing-distributed
                        Use multi-processing distributed training to launch N processes per node, which has N GPUs. This is the fastest way to use PyTorch for either single node or multi node data parallel
                        training
  --dummy               use fake data to benchmark