Scale -> Resize + RandomSizedCrop -> RandomResizedCrop

sheacoding · Nov 29, 2017 · e0d33a6 · e0d33a6
1 parent cf74c81
commit e0d33a6
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Showing 4 changed files with 8 additions and 8 deletions.
diff --git a/dcgan/main.py b/dcgan/main.py
@@ -58,23 +58,23 @@
     # folder dataset
     dataset = dset.ImageFolder(root=opt.dataroot,
                                transform=transforms.Compose([
-                                   transforms.Scale(opt.imageSize),
+                                   transforms.Resize(opt.imageSize),
                                    transforms.CenterCrop(opt.imageSize),
                                    transforms.ToTensor(),
                                    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
                                ]))
 elif opt.dataset == 'lsun':
     dataset = dset.LSUN(db_path=opt.dataroot, classes=['bedroom_train'],
                         transform=transforms.Compose([
-                            transforms.Scale(opt.imageSize),
+                            transforms.Resize(opt.imageSize),
                             transforms.CenterCrop(opt.imageSize),
                             transforms.ToTensor(),
                             transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
                         ]))
 elif opt.dataset == 'cifar10':
     dataset = dset.CIFAR10(root=opt.dataroot, download=True,
                            transform=transforms.Compose([
-                               transforms.Scale(opt.imageSize),
+                               transforms.Resize(opt.imageSize),
                                transforms.ToTensor(),
                                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
                            ]))

diff --git a/fast_neural_style/neural_style/neural_style.py b/fast_neural_style/neural_style/neural_style.py
@@ -35,7 +35,7 @@ def train(args):
         torch.cuda.manual_seed(args.seed)
 
     transform = transforms.Compose([
-        transforms.Scale(args.image_size),
+        transforms.Resize(args.image_size),
         transforms.CenterCrop(args.image_size),
         transforms.ToTensor(),
         transforms.Lambda(lambda x: x.mul(255))

diff --git a/imagenet/main.py b/imagenet/main.py
@@ -119,7 +119,7 @@ def main():
     train_dataset = datasets.ImageFolder(
         traindir,
         transforms.Compose([
-            transforms.RandomSizedCrop(224),
+            transforms.RandomResizedCrop(224),
             transforms.RandomHorizontalFlip(),
             transforms.ToTensor(),
             normalize,
@@ -136,7 +136,7 @@ def main():
 
     val_loader = torch.utils.data.DataLoader(
         datasets.ImageFolder(valdir, transforms.Compose([
-            transforms.Scale(256),
+            transforms.Resize(256),
             transforms.CenterCrop(224),
             transforms.ToTensor(),
             normalize,

diff --git a/super_resolution/data.py b/super_resolution/data.py
@@ -2,7 +2,7 @@
 from os import makedirs, remove
 from six.moves import urllib
 import tarfile
-from torchvision.transforms import Compose, CenterCrop, ToTensor, Scale
+from torchvision.transforms import Compose, CenterCrop, ToTensor, Resize
 
 from dataset import DatasetFromFolder
 
@@ -38,7 +38,7 @@ def calculate_valid_crop_size(crop_size, upscale_factor):
 def input_transform(crop_size, upscale_factor):
     return Compose([
         CenterCrop(crop_size),
-        Scale(crop_size // upscale_factor),
+        Resize(crop_size // upscale_factor),
         ToTensor(),
     ])