minor change

train_tongue_diagnosis.py

@@ -16,8 +16,8 @@ parser = argparse.ArgumentParser(description='PyTorch DenseNet Training')
 parser.add_argument('--model_name', default='resnet-50',
                     help='models: densenet121, resnet-50, resnet-101, inception_v, vgg19_bn')
 parser.add_argument('--gpuid', default='0', type=str, help='which gpu to use')
-parser.add_argument('--no_cuda', action='store_true', default=False, help='disables CUDA training')
-parser.add_argument('-b', '--batch_size', default=32, type=int, help='mini-batch size (default: 32)')
+parser.add_argument('--no_cuda', action='store_true', default=True, help='disables CUDA training')
+parser.add_argument('-b', '--batch_size', default=2, type=int, help='mini-batch size (default: 32)')
 parser.add_argument('--lr', type=float, default=0.1, metavar='LR', help='learning rate')
 parser.add_argument('--momentum', type=float, default=0.9, metavar='M', help='SGD momentum')
 parser.add_argument('--weight_decay', '--wd', default=2e-4, type=float, metavar='W')
@@ -52,7 +52,7 @@ normalizer = transforms.Normalize(mean=[x / 255.0 for x in [125.3, 123.0, 113.9]
 transform_train = transforms.Compose([
     transforms.ToTensor(),
     transforms.ToPILImage(),
-    transforms.RandomResizedCrop((224, 224)),
+    transforms.RandomResizedCrop((224, 168)),
     # transforms.RandomCrop((224, 224), padding=14),
     transforms.RandomHorizontalFlip(),
     transforms.ToTensor(),