def test_bias_converter(self):
input_dim = (2, 1, 1)
output_dim = (2, 1, 1)
input = [('input', datatypes.Array(*input_dim))]
output = [('output', datatypes.Array(*output_dim))]
builder = NeuralNetworkBuilder(input, output)
bias = numpy.ndarray(shape=(2,))
bias[:] = [1, 2]
builder.add_bias(name='Bias', b=bias, input_name='input', output_name='output', shape_bias=[2])
model_onnx = convert_coreml(builder.spec)
self.assertTrue(model_onnx is not None)
def test_bias_converter(self):
input_dim = (2, 1, 1)
output_dim = (2, 1, 1)
input = [('input', datatypes.Array(*input_dim))]
output = [('output', datatypes.Array(*output_dim))]
builder = NeuralNetworkBuilder(input, output)
bias = numpy.ndarray(shape=(2, ))
bias[:] = [1, 2]
builder.add_bias(name='Bias',
b=bias,
input_name='input',
output_name='output',
shape_bias=[2])
context = ConvertContext()
node = BiasLayerConverter.convert(context,
builder.spec.neuralNetwork.layers[0],
['input'], ['output'])
self.assertTrue(node is not None)
output_features = [('probs', datatypes.Array(3))]
weights = np.zeros((3, 3)) + 3
bias = np.ones(3)
builder = NeuralNetworkBuilder(input_features, output_features)
builder.add_inner_product(name='ip_layer',
W=weights,
b=None,
input_channels=3,
output_channels=3,
has_bias=False,
input_name='image',
output_name='med')
#builder.add_inner_product(name='ip_layer_2', W=weights, b=None, input_channels=3, output_channels=3, has_bias=False, input_name='med', output_name='probs')
#builder.add_elementwise(name='element', input_names=['med', 'med'], output_name='probs', mode='ADD')
builder.add_bias(name='bias',
b=bias,
input_name='med',
output_name='probs',
shape_bias=(3, ))
#builder.add_activation(name='act_layer', non_linearity='SIGMOID', input_name='med', output_name='probs')
# compile the spec
mlmodel = ct.models.MLModel(builder.spec)
# trigger the ANE!
out = mlmodel.predict({"image": np.array([1337, 0, 0], dtype=np.float32)})
print(out)
mlmodel.save('test.mlmodel')
