def test_scale_converter(self):
input_dim = (3,)
output_dim = (3,)
input = [('input', datatypes.Array(*input_dim))]
output = [('output', datatypes.Array(*output_dim))]
builder = NeuralNetworkBuilder(input, output)
scale = numpy.ndarray(shape=(1,))
scale[:] = 10
bias = numpy.ndarray(shape=(1,))
bias[:] = -100
builder.add_scale(name='ImageScaler', W=scale, b=bias, has_bias=True, input_name='input', output_name='output')
model_onnx = convert_coreml(builder.spec)
self.assertTrue(model_onnx is not None)
def test_scale_converter(self):
input_dim = (3, )
output_dim = (3, )
input = [('input', datatypes.Array(*input_dim))]
output = [('output', datatypes.Array(*output_dim))]
builder = NeuralNetworkBuilder(input, output)
scale = numpy.ndarray(shape=(1, ))
scale[:] = 10
bias = numpy.ndarray(shape=(1, ))
bias[:] = -100
builder.add_scale(name='ImageScaler',
W=scale,
b=bias,
has_bias=True,
input_name='input',
output_name='output')
context = ConvertContext()
node = ScaleLayerConverter.convert(
context, builder.spec.neuralNetwork.layers[0], ['input'],
['output'])
self.assertTrue(node is not None)
