def basic_checks(input_images,
input_flows,
images_order='HWC',
flows_order='HWC',
fp16=False,
device='cpu',
tolerance=1e-8):
tt = ToTensor(images_order, flows_order, fp16, device)
images, flows = tt(input_images, input_flows)
assert isinstance(images, torch.Tensor)
assert isinstance(flows, torch.Tensor)
assert images.shape == torch.Size(
[NUM_IMAGES, 3, IMAGE_HEIGHT, IMAGE_WIDTH])
assert flows.shape == torch.Size(
[NUM_IMAGES - 1, 2, IMAGE_HEIGHT, IMAGE_WIDTH])
input_images = np.stack(input_images, axis=0)
input_flows = np.stack(input_flows, axis=0)
if images_order == 'CHW':
input_images = input_images.transpose(0, 2, 3, 1)
if flows_order == 'CHW':
input_flows = input_flows.transpose(0, 2, 3, 1)
images = images.detach().cpu().numpy().transpose(0, 2, 3, 1)
flows = flows.detach().cpu().numpy().transpose(0, 2, 3, 1)
assert np.allclose(images, input_images, atol=tolerance)
assert np.allclose(flows, input_flows, atol=tolerance)
def get_tensor_data(images_list, flows_list):
tt = ToTensor()
images, flows = tt(images_list, flows_list)
return images, flows