Conv2DTranspose ambigous output shape

Currently, Conv2DTranspose infers the shape of the output using deconv_length but because the output shape of a transposed convolution is ambigous it can infer an undesired shape.

Example:

conv = Conv2D(16, 3, strides=2, padding='same')
transpose_conv = Conv2DTranspose(1, 3, strides=2, padding='same')

input_a = Input(shape=(23, 23, 1))
x_conv_a = conv(input_a)
x_transpose_a = transpose_conv(x_conv_a)

print("(a) Input shape: {}".format(int_shape(input_a)))
print("(a) Shape after convolution: {}".format(int_shape(x_conv_a)))
print("(a) Shape after transposed convolution: {}".format(int_shape(x_transpose_a)))
print()

input_b = Input(shape=(24, 24, 1))
x_conv_b = conv(input_b)
x_transpose_b = transpose_conv(x_conv_b)

print("(b) Input shape: {}".format(int_shape(input_b)))
print("(b) Shape after convolution: {}".format(int_shape(x_conv_b)))
print("(b) Shape after transposed convolution: {}".format(int_shape(x_transpose_b)))

The output:

(a) Input shape: (None, 23, 23, 1)
(a) Shape after convolution: (None, 12, 12, 16)
(a) Shape after transposed convolution: (None, 24, 24, 1)

(b) Input shape: (None, 24, 24, 1)
(b) Shape after convolution: (None, 12, 12, 16)
(b) Shape after transposed convolution: (None, 24, 24, 1)

From an input shape (None, 12, 12, 16) a transposed convolution can output either (None, 24, 24, 1) or (None, 23, 23, 1). Conv2DTranspose always outputs (None, 24, 24, 1).

Shouldn’t the user have to supply the output_shape (like in Tensorflow) or an output padding (like in PyTorch) to resolve the ambiguity?

[davidtvs]

PR #10246 has been merged. The shape of the output can now be controlled through a new optional argument (output_padding).

[emiljoha]

@davidtvs After further testing, not specifying output_shape and letting it default to None causes a TypeError exception when the output_shape si cast to a touple.

self._output_shape = tuple(output_shape)

This is easily fixed by checking for that case and only casting to tuple if not None.