| 2269 | """ |
| 2270 | |
| 2271 | def __init__(self, padding=(1, 1, 1), data_format=None, **kwargs): |
| 2272 | super(ZeroPadding3D, self).__init__(**kwargs) |
| 2273 | self.data_format = conv_utils.normalize_data_format(data_format) |
| 2274 | if isinstance(padding, int): |
| 2275 | self.padding = ((padding, padding), (padding, padding), (padding, |
| 2276 | padding)) |
| 2277 | elif hasattr(padding, '__len__'): |
| 2278 | if len(padding) != 3: |
| 2279 | raise ValueError('`padding` should have 3 elements. ' |
| 2280 | 'Found: ' + str(padding)) |
| 2281 | dim1_padding = conv_utils.normalize_tuple(padding[0], 2, |
| 2282 | '1st entry of padding') |
| 2283 | dim2_padding = conv_utils.normalize_tuple(padding[1], 2, |
| 2284 | '2nd entry of padding') |
| 2285 | dim3_padding = conv_utils.normalize_tuple(padding[2], 2, |
| 2286 | '3rd entry of padding') |
| 2287 | self.padding = (dim1_padding, dim2_padding, dim3_padding) |
| 2288 | else: |
| 2289 | raise ValueError( |
| 2290 | '`padding` should be either an int, ' |
| 2291 | 'a tuple of 3 ints ' |
| 2292 | '(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad), ' |
| 2293 | 'or a tuple of 3 tuples of 2 ints ' |
| 2294 | '((left_dim1_pad, right_dim1_pad),' |
| 2295 | ' (left_dim2_pad, right_dim2_pad),' |
| 2296 | ' (left_dim3_pad, right_dim2_pad)). ' |
| 2297 | 'Found: ' + str(padding)) |
| 2298 | self.input_spec = InputSpec(ndim=5) |
| 2299 | |
| 2300 | def compute_output_shape(self, input_shape): |
| 2301 | input_shape = tensor_shape.TensorShape(input_shape).as_list() |