def test_permute_converter(self):
input_dim = (4, 1, 2, 3)
output_dim = (4, 3, 1, 2)
inputs = [('input', datatypes.Array(*input_dim))]
outputs = [('output', datatypes.Array(*output_dim))]
builder = NeuralNetworkBuilder(inputs, outputs)
builder.add_permute(name='Permute', input_name='input', output_name='output', dim=(0, 2, 3, 1))
model_onnx = convert_coreml(builder.spec)
self.assertTrue(model_onnx is not None)
def test_load_constant_converter(self):
value = numpy.ndarray(shape=(1, 1, 2))
value[:] = [[[-95, 95]]]
shape = value.shape
input_dim = (1, 2, 3, 4)
inputs = [('input', datatypes.Array(*input_dim))]
outputs = [('const', datatypes.Array(*shape)), ('output', datatypes.Array(*input_dim))]
builder = NeuralNetworkBuilder(inputs, outputs)
builder.add_load_constant(name='LoadConstant', output_name='const', constant_value=value, shape=shape)
builder.add_permute(name='Permute', input_name='input', output_name='output', dim=(0, 1, 2, 3))
model_onnx = convert_coreml(builder.spec)
self.assertTrue(model_onnx is not None)
def test_permute_converter(self):
input_dim = (4, 1, 2, 3)
output_dim = (4, 3, 1, 2)
inputs = [('input', datatypes.Array(*input_dim))]
outputs = [('output', datatypes.Array(*output_dim))]
builder = NeuralNetworkBuilder(inputs, outputs)
builder.add_permute(name='Permute',
input_name='input',
output_name='output',
dim=(0, 2, 3, 1))
context = ConvertContext()
node = PermuteLayerConverter.convert(
context, builder.spec.neuralNetwork.layers[0], ['input'],
['output'])
self.assertTrue(node is not None)
