adding marat's bench script

test/preprocess-bench.py

+import argparse
+import os
+from timeit import default_timer as timer
+from tqdm import tqdm
+import torch
+import torch.utils.data
+import torchvision.transforms as transforms
+import torchvision.datasets as datasets
+
+
+parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
+parser.add_argument('--data', metavar='PATH', required=True,
+                    help='path to dataset')
+parser.add_argument('--nThreads', '-j', default=2, type=int, metavar='N',
+                    help='number of data loading threads (default: 2)')
+parser.add_argument('--batchSize', '-b', default=256, type=int, metavar='N',
+                    help='mini-batch size (1 = pure stochastic) Default: 256')
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+
+
+    # Data loading code
+    transform = transforms.Compose([
+        transforms.RandomSizedCrop(224),
+        transforms.RandomHorizontalFlip(),
+        transforms.ToTensor(),
+        transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ],
+                             std = [ 0.229, 0.224, 0.225 ]),
+    ])
+
+    traindir = os.path.join(args.data, 'train')
+    valdir = os.path.join(args.data, 'val')
+    train = datasets.ImageFolder(traindir, transform)
+    val = datasets.ImageFolder(valdir, transform)
+    train_loader = torch.utils.data.DataLoader(
+        train, batch_size=args.batchSize, shuffle=True, num_workers=args.nThreads)
+    train_iter = iter(train_loader)
+
+    start_time = timer()
+    batch_count = 100 * args.nThreads
+    for i in tqdm(xrange(batch_count)):
+        batch = next(train_iter)
+    end_time = timer()
+    print("Performance: {dataset:.0f} minutes/dataset, {batch:.2f} secs/batch, {image:.2f} ms/image".format(
+        dataset=(end_time - start_time) * len(train_loader) / (batch_count * args.batchSize) / 60.0,
+        batch=(end_time - start_time) / float(batch_count),
+        image=(end_time - start_time) / (batch_count * args.batchSize) * 1.0e+3))
+