def test_deconvolution_tuple_padding(self):
network = layers.join(
layers.Input((10, 10, 3)),
layers.Convolution((3, 3, 7), padding=(9, 3)),
layers.Deconvolution((3, 3, 4), padding=(9, 3)),
)
shapes = network.output_shapes_per_layer
shapes = {l: shape_to_tuple(s) for l, s in shapes.items()}
self.assertSequenceEqual(
shapes, {
network.layers[0]: (None, 10, 10, 3),
network.layers[1]: (None, 26, 14, 7),
network.layers[2]: (None, 10, 10, 4),
})
input_value = asfloat(np.random.random((1, 10, 10, 3)))
actual_output = self.eval(network.output(input_value))
self.assertEqual(actual_output.shape, (1, 10, 10, 4))
def test_deconv_unknown_input_width_and_height(self):
network = layers.join(
layers.Input((None, None, 3)),
layers.Convolution((3, 3, 7)),
layers.Deconvolution((3, 3, 4)),
)
shapes = network.output_shapes_per_layer
shapes = {l: shape_to_tuple(s) for l, s in shapes.items()}
self.assertDictEqual(
shapes, {
network.layers[0]: (None, None, None, 3),
network.layers[1]: (None, None, None, 7),
network.layers[2]: (None, None, None, 4),
})
input_value = asfloat(np.random.random((1, 10, 10, 3)))
actual_output = self.eval(network.output(input_value))
self.assertEqual(actual_output.shape, (1, 10, 10, 4))
input_value = asfloat(np.random.random((1, 7, 7, 3)))
actual_output = self.eval(network.output(input_value))
self.assertEqual(actual_output.shape, (1, 7, 7, 4))
def assertShapesEqual(self, shape1, shape2, *args, **kwargs):
shape1 = shape_to_tuple(shape1)
shape2 = shape_to_tuple(shape2)
self.assertEqual(shape1, shape2, *args, **kwargs)