# transforms
transform_input_image = transforms.Compose([
transforms.Resize((256, 256)),
transforms.ColorJitter(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
transform_target_image = transforms.Compose([
transforms.Resize((256, 256)),
# transforms.ToTensor()
])
train_set = DataSet.ImageSegmentationDataset(root_train_txt, root_segm,
root_images,
transform_input_image,
transform_target_image)
train_loader = DataLoader(train_set, batch_size=8, shuffle=True)
test_set = DataSet.ImageSegmentationDataset(root_test_txt, root_segm,
root_images, transform_input_image,
transform_target_image)
test_loader = DataLoader(test_set, batch_size=8, shuffle=True)
#net = Network.UNet(in_channel=3, out_channel=21).cuda()
net = torch.load(
'/home/freeaccess/PycharmProjects/VOCPascal/Last folder/val_loss_try_second60'
)
'''def one_hot(batch_idx, target, class_count):
