remove more deconv3d code

keras_contrib/backend/tensorflow_backend.py

@@ -15,16 +15,6 @@ from keras.backend.tensorflow_backend import _to_tensor
 py_all = all
 
 
-def _preprocess_deconv_output_shape(x, shape, data_format):
-    if data_format == 'channels_first':
-        shape = (shape[0],) + tuple(shape[2:]) + (shape[1],)
-
-    if shape[0] is None:
-        shape = (tf.shape(x)[0],) + tuple(shape[1:])
-        shape = tf.stack(list(shape))
-    return shape
-
-
 def conv2d(x, kernel, strides=(1, 1), padding='valid', data_format='channels_first',
            image_shape=None, filter_shape=None):
     '''2D convolution.

keras_contrib/backend/theano_backend.py

@@ -86,56 +86,6 @@ def conv2d(x, kernel, strides=(1, 1), padding='valid', data_format='channels_fir
     return conv_out
 
 
-def deconv3d(x, kernel, output_shape, strides=(1, 1, 1),
-             padding='valid',
-             data_format=None, filter_shape=None):
-    '''3D deconvolution (transposed convolution).
-
-    # Arguments
-        kernel: kernel tensor.
-        output_shape: desired dimensions of output.
-        strides: strides tuple.
-        padding: string, "same" or "valid".
-        data_format: "channels_last" or "channels_first".
-            Whether to use Theano or TensorFlow dimension ordering
-        in inputs/kernels/ouputs.
-    '''
-    flip_filters = False
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format: ' + str(data_format))
-
-    if data_format == 'channels_last':
-        output_shape = (output_shape[0], output_shape[4], output_shape[1],
-                        output_shape[2], output_shape[3])
-
-    x = _preprocess_conv3d_input(x, data_format)
-    kernel = _preprocess_conv3d_kernel(kernel, data_format)
-    kernel = kernel.dimshuffle((1, 0, 2, 3, 4))
-    th_padding = _preprocess_padding(padding)
-
-    if hasattr(kernel, '_keras_shape'):
-        kernel_shape = kernel._keras_shape
-    else:
-        # Will only work if `kernel` is a shared variable.
-        kernel_shape = kernel.eval().shape
-
-    filter_shape = _preprocess_conv3d_filter_shape(filter_shape, data_format)
-    filter_shape = tuple(filter_shape[i] for i in (1, 0, 2, 3, 4))
-
-    conv_out = T.nnet.abstract_conv.conv3d_grad_wrt_inputs(
-        x, kernel, output_shape,
-        filter_shape=filter_shape,
-        border_mode=th_padding,
-        subsample=strides,
-        filter_flip=not flip_filters)
-
-    conv_out = _postprocess_conv3d_output(conv_out, x, padding,
-                                          kernel_shape, strides, data_format)
-    return conv_out
-
-
 def extract_image_patches(X, ksizes, strides, padding='valid', data_format='channels_first'):
     '''
     Extract the patches from an image