Add a resizing layer to a keras sequential model

Question

How can I add a resizing layer to

model = Sequential()

using

model.add(...)

To resize an image from sha

Answer 1

A modification of @KeithWM 's answer, adding output_scale, e.g. output_scale=2 means the output is 2 times the input shape :)

class ResizeImages(Layer):
    """Resize Images to a specified size
    https://stackoverflow.com/questions/41903928/add-a-resizing-layer-to-a-keras-sequential-model

    # Arguments
        output_dim: Size of output layer width and height
        output_scale: scale compared with input
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, height, width, channels)` while `channels_first`
            corresponds to inputs with shape
            `(batch, channels, height, width)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".

    # Input shape
        - If `data_format='channels_last'`:
            4D tensor with shape:
            `(batch_size, rows, cols, channels)`
        - If `data_format='channels_first'`:
            4D tensor with shape:
            `(batch_size, channels, rows, cols)`

    # Output shape
        - If `data_format='channels_last'`:
            4D tensor with shape:
            `(batch_size, pooled_rows, pooled_cols, channels)`
        - If `data_format='channels_first'`:
            4D tensor with shape:
            `(batch_size, channels, pooled_rows, pooled_cols)`
    """

    def __init__(self, output_dim=(1, 1), output_scale=None, data_format=None, **kwargs):
        super(ResizeImages, self).__init__(**kwargs)
        data_format = normalize_data_format(data_format)  # does not have
        self.naive_output_dim = conv_utils.normalize_tuple(output_dim,
                                                           2, 'output_dim')
        self.naive_output_scale = output_scale
        self.data_format = normalize_data_format(data_format)
        self.input_spec = InputSpec(ndim=4)

    def build(self, input_shape):
        self.input_spec = [InputSpec(shape=input_shape)]
        if self.naive_output_scale is not None:
            if self.data_format == 'channels_first':
                self.output_dim = (self.naive_output_scale * input_shape[2],
                                   self.naive_output_scale * input_shape[3])
            elif self.data_format == 'channels_last':
                self.output_dim = (self.naive_output_scale * input_shape[1],
                                   self.naive_output_scale * input_shape[2])
        else:
            self.output_dim = self.naive_output_dim

    def compute_output_shape(self, input_shape):
        if self.data_format == 'channels_first':
            return (input_shape[0], input_shape[1], self.output_dim[0], self.output_dim[1])
        elif self.data_format == 'channels_last':
            return (input_shape[0], self.output_dim[0], self.output_dim[1], input_shape[3])

    def _resize_fun(self, inputs, data_format):
        try:
            assert keras.backend.backend() == 'tensorflow'
            assert self.data_format == 'channels_last'
        except AssertionError:
            print("Only tensorflow backend is supported for the resize layer and accordingly 'channels_last' ordering")
        output = tf.image.resize_images(inputs, self.output_dim)
        return output

    def call(self, inputs):
        output = self._resize_fun(inputs=inputs, data_format=self.data_format)
        return output

    def get_config(self):
        config = {'output_dim': self.output_dim,
                  'padding': self.padding,
                  'data_format': self.data_format}
        base_config = super(ResizeImages, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))