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)