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| from tensorflow.keras.layers import Layer, InputSpec | |
| import keras.utils.conv_utils as conv_utils | |
| import tensorflow as tf | |
| import tensorflow.keras.backend as K | |
| def normalize_data_format(value): | |
| if value is None: | |
| value = K.image_data_format() | |
| data_format = value.lower() | |
| if data_format not in {'channels_first', 'channels_last'}: | |
| raise ValueError('The `data_format` argument must be one of ' | |
| '"channels_first", "channels_last". Received: ' + | |
| str(value)) | |
| return data_format | |
| class BilinearUpSampling2D(Layer): | |
| def __init__(self, size=(2, 2), data_format=None, **kwargs): | |
| super(BilinearUpSampling2D, self).__init__(**kwargs) | |
| self.data_format = normalize_data_format(data_format) | |
| self.size = conv_utils.normalize_tuple(size, 2, 'size') | |
| self.input_spec = InputSpec(ndim=4) | |
| def compute_output_shape(self, input_shape): | |
| if self.data_format == 'channels_first': | |
| height = self.size[0] * input_shape[2] if input_shape[2] is not None else None | |
| width = self.size[1] * input_shape[3] if input_shape[3] is not None else None | |
| return (input_shape[0], | |
| input_shape[1], | |
| height, | |
| width) | |
| elif self.data_format == 'channels_last': | |
| height = self.size[0] * input_shape[1] if input_shape[1] is not None else None | |
| width = self.size[1] * input_shape[2] if input_shape[2] is not None else None | |
| return (input_shape[0], | |
| height, | |
| width, | |
| input_shape[3]) | |
| def call(self, inputs): | |
| input_shape = K.shape(inputs) | |
| if self.data_format == 'channels_first': | |
| height = self.size[0] * input_shape[2] if input_shape[2] is not None else None | |
| width = self.size[1] * input_shape[3] if input_shape[3] is not None else None | |
| elif self.data_format == 'channels_last': | |
| height = self.size[0] * input_shape[1] if input_shape[1] is not None else None | |
| width = self.size[1] * input_shape[2] if input_shape[2] is not None else None | |
| return tf.image.resize(inputs, [height, width], method=tf.image.ResizeMethod.BILINEAR) | |
| def get_config(self): | |
| config = {'size': self.size, 'data_format': self.data_format} | |
| base_config = super(BilinearUpSampling2D, self).get_config() | |
| return dict(list(base_config.items()) + list(config.items())) | |