| 2165 | """ |
| 2166 | |
| 2167 | def __init__(self, padding=(1, 1), data_format=None, **kwargs): |
| 2168 | super(ZeroPadding2D, self).__init__(**kwargs) |
| 2169 | self.data_format = conv_utils.normalize_data_format(data_format) |
| 2170 | if isinstance(padding, int): |
| 2171 | self.padding = ((padding, padding), (padding, padding)) |
| 2172 | elif hasattr(padding, '__len__'): |
| 2173 | if len(padding) != 2: |
| 2174 | raise ValueError('`padding` should have two elements. ' |
| 2175 | 'Found: ' + str(padding)) |
| 2176 | height_padding = conv_utils.normalize_tuple(padding[0], 2, |
| 2177 | '1st entry of padding') |
| 2178 | width_padding = conv_utils.normalize_tuple(padding[1], 2, |
| 2179 | '2nd entry of padding') |
| 2180 | self.padding = (height_padding, width_padding) |
| 2181 | else: |
| 2182 | raise ValueError('`padding` should be either an int, ' |
| 2183 | 'a tuple of 2 ints ' |
| 2184 | '(symmetric_height_pad, symmetric_width_pad), ' |
| 2185 | 'or a tuple of 2 tuples of 2 ints ' |
| 2186 | '((top_pad, bottom_pad), (left_pad, right_pad)). ' |
| 2187 | 'Found: ' + str(padding)) |
| 2188 | self.input_spec = InputSpec(ndim=4) |
| 2189 | |
| 2190 | def compute_output_shape(self, input_shape): |
| 2191 | input_shape = tensor_shape.TensorShape(input_shape).as_list() |