Merge branch 'master' into patch-3

implementations/esrgan/datasets.py

@@ -13,6 +13,13 @@
 std = np.array([0.229, 0.224, 0.225])
 
 
+def denormalize(tensors):
+    """ Denormalizes image tensors using mean and std """
+    for c in range(3):
+        tensors[:, c].mul_(std[c]).add_(mean[c])
+    return torch.clamp(tensors, 0, 255)
+
+
 class ImageDataset(Dataset):
     def __init__(self, root, hr_shape):
         hr_height, hr_width = hr_shape

implementations/esrgan/esrgan.py

@@ -72,9 +72,8 @@
 
 if opt.epoch != 0:
     # Load pretrained models
-    print("loading pretrain model")
-    generator.load_state_dict(torch.load("saved_models/generator_%d.pth"%opt.epoch))
-    discriminator.load_state_dict(torch.load("saved_models/discriminator_%d.pth"%opt.epoch))
+    generator.load_state_dict(torch.load("saved_models/generator_%d.pth" % opt.epoch))
+    discriminator.load_state_dict(torch.load("saved_models/discriminator_%d.pth" % opt.epoch))
 
 # Optimizers
 optimizer_G = torch.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))

implementations/esrgan/test_on_image.py

@@ -4,6 +4,7 @@
 from torch.autograd import Variable
 import argparse
 import os
+from torchvision import transforms
 from torchvision.utils import save_image
 from PIL import Image