def get_loaders(batch_size,device): data_root = 'ceng483-s19-hw3-dataset' train_set = hw3utils.HW3ImageFolder(root=os.path.join(data_root,'train'),device=device) train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=0) val_set = hw3utils.HW3ImageFolder(root=os.path.join(data_root,'val'),device=device) val_loader = torch.utils.data.DataLoader(val_set, batch_size=batch_size, shuffle=False, num_workers=0) # Note: you may later add test_loader to here. test_set = hw3utils.HW3ImageFolder(root=os.path.join(data_root,'test'),device=device) test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=0) return train_loader, val_loader, test_loader
def test_loader(batch_size, device): data_root = 'ceng483-s19-hw3-dataset' test_set = hw3utils.HW3ImageFolder(root=os.path.join(data_root, 'test'), device=device) test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=0) return test_loader
f.write("Average Loss = " + str(avg_loss) + "\n") f.write("Average ACC = " + str(avg_acc) + "\n") f.write("+--------------------+") if i % 2 == 1: batch_val += 1 if i % 6 == 5: batch_val = 0 lr_val += 1''' print('Finished Training') print("Estimations.npy Creating") #net.load_state_dict(os.path.join(LOG_DIR,'checkpoint.pt')) data_root = 'ceng483-s19-hw3-dataset' test_set = hw3utils.HW3ImageFolder(root=os.path.join(data_root, 'test'), device=device) test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=0) estimations2 = [] for iteri, data in enumerate(test_loader, 0): inputs, targets = data # inputs: low-resolution images, targets: high-resolution images. preds = net(inputs) for i in range(len(preds)): est = (((preds[i].permute(1, 2, 0).cpu().detach().numpy()) / 2) + 0.5) * 255 estimations2.append(est) estimations2 = np.array(estimations)