Initial version of classification reference scripts

references/classification/train.py

@@ -0,0 +1,216 @@
+import datetime
+import os
+import time
+
+import torch
+import torch.utils.data
+from torch import nn
+import torchvision
+from torchvision import transforms
+
+import utils
+
+
+def train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, print_freq):
+    model.train()
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value}'))
+    header = 'Epoch: [{}]'.format(epoch)
+    for image, target in metric_logger.log_every(data_loader, print_freq, header):
+        image, target = image.to(device), target.to(device)
+        output = model(image)
+        loss = criterion(output, target)
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        acc1, acc5 = utils.accuracy(output, target, topk=(1, 5))
+        batch_size = image.shape[0]
+        metric_logger.update(loss=loss.item(), lr=optimizer.param_groups[0]["lr"])
+        metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
+        metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
+
+
+def evaluate(model, criterion, data_loader, device):
+    model.eval()
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    header = 'Test:'
+    with torch.no_grad():
+        for image, target in metric_logger.log_every(data_loader, 100, header):
+            image = image.to(device, non_blocking=True)
+            target = target.to(device, non_blocking=True)
+            output = model(image)
+            loss = criterion(output, target)
+
+            acc1, acc5 = utils.accuracy(output, target, topk=(1, 5))
+            # FIXME need to take into account that the datasets
+            # could have been padded in distributed setup
+            batch_size = image.shape[0]
+            metric_logger.update(loss=loss.item())
+            metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
+            metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
+    # gather the stats from all processes
+    metric_logger.synchronize_between_processes()
+
+    print(' * Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f}'
+          .format(top1=metric_logger.acc1, top5=metric_logger.acc5))
+    return metric_logger.acc1.global_avg
+
+
+def main(args):
+    args.gpu = args.local_rank
+
+    if args.distributed:
+        args.rank = int(os.environ["RANK"])
+        torch.cuda.set_device(args.gpu)
+        args.dist_backend = 'nccl'
+        dist_url = 'env://'
+        print('| distributed init (rank {}): {}'.format(
+            args.rank, dist_url), flush=True)
+        torch.distributed.init_process_group(backend=args.dist_backend, init_method=dist_url)
+        utils.setup_for_distributed(args.rank == 0)
+
+    device = torch.device(args.device)
+
+    torch.backends.cudnn.benchmark = True
+
+    # Data loading code
+    print("Loading data")
+    traindir = os.path.join(args.data_path, 'train')
+    valdir = os.path.join(args.data_path, 'val')
+    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                                     std=[0.229, 0.224, 0.225])
+
+    print("Loading training data")
+    st = time.time()
+    scale = (0.08, 1.0)
+    if args.model == 'mobilenet_v2':
+        scale = (0.2, 1.0)
+    dataset = torchvision.datasets.ImageFolder(
+        traindir,
+        transforms.Compose([
+            transforms.RandomResizedCrop(224, scale=scale),
+            transforms.RandomHorizontalFlip(),
+            transforms.ToTensor(),
+            normalize,
+        ]))
+    print("Took", time.time() - st)
+
+    print("Loading validation data")
+    dataset_test = torchvision.datasets.ImageFolder(
+        valdir,
+        transforms.Compose([
+            transforms.Resize(256),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            normalize,
+        ]))
+
+    print("Creating data loaders")
+    if args.distributed:
+        train_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
+        test_sampler = torch.utils.data.distributed.DistributedSampler(dataset_test)
+    else:
+        train_sampler = torch.utils.data.RandomSampler(dataset)
+        test_sampler = torch.utils.data.SequentialSampler(dataset_test)
+
+    data_loader = torch.utils.data.DataLoader(
+        dataset, batch_size=args.batch_size,
+        sampler=train_sampler, num_workers=args.workers, pin_memory=True)
+
+    data_loader_test = torch.utils.data.DataLoader(
+        dataset_test, batch_size=args.batch_size,
+        sampler=test_sampler, num_workers=args.workers, pin_memory=True)
+
+    print("Creating model")
+    model = torchvision.models.__dict__[args.model]()
+    model.to(device)
+    if args.distributed:
+        model = torch.nn.utils.convert_sync_batchnorm(model)
+
+    model_without_ddp = model
+    if args.distributed:
+        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
+        model_without_ddp = model.module
+
+    criterion = nn.CrossEntropyLoss()
+
+    optimizer = torch.optim.SGD(
+        model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
+
+    # if using mobilenet, step_size=2 and gamma=0.94
+    lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.lr_step_size, gamma=args.lr_gamma)
+
+    if args.resume:
+        checkpoint = torch.load(args.resume, map_location='cpu')
+        model_without_ddp.load_state_dict(checkpoint['model'])
+        optimizer.load_state_dict(checkpoint['optimizer'])
+        lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
+
+    if args.test_only:
+        evaluate(model, criterion, data_loader_test, device=device)
+        return
+
+    print("Start training")
+    start_time = time.time()
+    for epoch in range(args.epochs):
+        if args.distributed:
+            train_sampler.set_epoch(epoch)
+        lr_scheduler.step()
+        train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, args.print_freq)
+        evaluate(model, criterion, data_loader_test, device=device)
+        if args.output_dir:
+            utils.save_on_master({
+                'model': model_without_ddp.state_dict(),
+                'optimizer': optimizer.state_dict(),
+                'lr_scheduler': lr_scheduler.state_dict(),
+                'args': args},
+                os.path.join(args.output_dir, 'model_{}.pth'.format(epoch)))
+
+    total_time = time.time() - start_time
+    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+    print('Training time {}'.format(total_time_str))
+
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser(description='PyTorch Classification Training')
+
+    parser.add_argument('--data-path', default='/datasets01/imagenet_full_size/061417/', help='dataset')
+    parser.add_argument('--model', default='resnet18', help='model')
+    parser.add_argument('--device', default='cuda', help='device')
+    parser.add_argument('-b', '--batch-size', default=32, type=int)
+    parser.add_argument('--epochs', default=90, type=int, metavar='N',
+                        help='number of total epochs to run')
+    parser.add_argument('-j', '--workers', default=16, type=int, metavar='N',
+                        help='number of data loading workers (default: 16)')
+    parser.add_argument('--lr', default=0.1, type=float, help='initial learning rate')
+    parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
+                        help='momentum')
+    parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
+                        metavar='W', help='weight decay (default: 1e-4)',
+                        dest='weight_decay')
+    parser.add_argument('--lr-step-size', default=30, type=int, help='decrease lr every step-size epochs')
+    parser.add_argument('--lr-gamma', default=0.1, type=float, help='decrease lr by a factor of lr-gamma')
+    parser.add_argument('--print-freq', default=10, type=int, help='print frequency')
+    parser.add_argument('--output-dir', default='.', help='path where to save')
+    parser.add_argument('--resume', default='', help='resume from checkpoint')
+    parser.add_argument(
+        "--test-only",
+        dest="test_only",
+        help="Only test the model",
+        action="store_true",
+    )
+    parser.add_argument('--local_rank', default=0, type=int, help='print frequency')
+
+    args = parser.parse_args()
+    print(args)
+
+    if args.output_dir:
+        utils.mkdir(args.output_dir)
+
+    num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
+    args.distributed = num_gpus > 1
+
+    main(args)