def test_pixel_shuffle(self):
scale_factor = 2
input_shape = (1, 8, 2, 2)
output_shape = (input_shape[0], int(
input_shape[1] / (scale_factor**2)), input_shape[2] * scale_factor,
input_shape[3] * scale_factor)
inputs = [('input0', input_shape)]
outputs = [('output0', output_shape)]
node_0 = helper.make_node("Reshape",
inputs=[inputs[0][0]],
outputs=['node0'],
shape=[
output_shape[0], output_shape[1],
scale_factor, scale_factor,
input_shape[2], input_shape[3]
])
node_1 = helper.make_node("Transpose",
inputs=['node0'],
outputs=['node1'],
perm=[0, 1, 4, 2, 5, 3])
node_2 = helper.make_node("Reshape",
inputs=['node1'],
outputs=[outputs[0][0]],
shape=list(output_shape))
model = _onnx_create_model([node_0, node_1, node_2], inputs, outputs)
_test_onnx_model(model, decimal=7)
def test_pixel_shuffle(self): # type: () -> None
scale_factor = 2
input_shape = (1, 8, 2, 2)
output_shape = (
input_shape[0],
int(input_shape[1] / (scale_factor ** 2)),
input_shape[2] * scale_factor,
input_shape[3] * scale_factor
)
inputs = [('input0', input_shape)]
outputs = [('output0', output_shape, TensorProto.FLOAT)]
shape1 = [
output_shape[0],
output_shape[1],
scale_factor,
scale_factor,
input_shape[2],
input_shape[3]
]
shape1 = numpy_helper.from_array(np.asarray(shape1), name="shape1")
shape2 = numpy_helper.from_array(np.asarray(list(output_shape)), name="shape2")
node_0 = helper.make_node(
"Reshape",
inputs=[inputs[0][0], 'shape1'],
outputs=['node0'],
)
node_1 = helper.make_node(
"Transpose",
inputs=['node0'],
outputs=['node1'],
perm=[0, 1, 4, 2, 5, 3]
)
node_2 = helper.make_node(
"Reshape",
inputs=['node1','shape2'],
outputs=[outputs[0][0]],
)
model = _onnx_create_model(
[node_0, node_1, node_2], inputs, outputs, initializer=[shape1, shape2]
)
_test_onnx_model(model, decimal=7)