Converts a PIL.Image (RGB) or numpy.ndarray (H x W x C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]
| 347 | |
| 348 | |
| 349 | class ToTorchFormatTensor(object): |
| 350 | """ Converts a PIL.Image (RGB) or numpy.ndarray (H x W x C) in the range [0, 255] |
| 351 | to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] """ |
| 352 | def __init__(self, div=True): |
| 353 | self.div = div |
| 354 | |
| 355 | def __call__(self, img_dict): |
| 356 | # handle numpy array |
| 357 | img_dict['gt_frames'] = torch.from_numpy(img_dict['gt_frames']).permute(2, 3, 0, 1).contiguous().float().div(255) |
| 358 | img_dict['flow_forward'] = torch.from_numpy(img_dict['flow_forward']).permute(2, 3, 0, 1).contiguous().float() |
| 359 | img_dict['flow_backward'] = torch.from_numpy(img_dict['flow_backward']).permute(2, 3, 0, 1).contiguous().float() |
| 360 | img_dict['flowmask_forward'] = torch.from_numpy(img_dict['flowmask_forward']).permute(2, 3, 0, 1).contiguous().float().div(255) |
| 361 | img_dict['flowmask_backward'] = torch.from_numpy(img_dict['flowmask_backward']).permute(2, 3, 0, 1).contiguous().float().div(255) |
| 362 | return img_dict |
| 363 | |
| 364 | class IdentityTransform(object): |
| 365 | def __call__(self, data): |
no outgoing calls
no test coverage detected