def test_reshape_converter(self):
input_dim = (1, 1, 2)
output_dim = (1, 2, 1)
inputs = [('input', datatypes.Array(*input_dim))]
outputs = [('output', datatypes.Array(*output_dim))]
builder = NeuralNetworkBuilder(inputs, outputs)
builder.add_reshape(name='Reshape', input_name='input', output_name='output', target_shape=output_dim, mode=1)
model_onnx = convert_coreml(builder.spec)
self.assertTrue(model_onnx is not None)
def test_reshape_converter(self):
input_dim = (1, 1, 2)
output_dim = (1, 2, 1)
inputs = [('input', datatypes.Array(*input_dim))]
outputs = [('output', datatypes.Array(*output_dim))]
builder = NeuralNetworkBuilder(inputs, outputs)
builder.add_reshape(name='Reshape',
input_name='input',
output_name='output',
target_shape=output_dim,
mode=1)
context = ConvertContext()
node = ReshapeLayerConverter.convert(
context, builder.spec.neuralNetwork.layers[0], ['input'],
['output'])
self.assertTrue(node is not None)
def verify_reshape(input_dim, target_shape, mode):
dtype = 'float32'
a_np = np.random.uniform(-100.0, 100.0, size=input_dim).astype(dtype)
ref_val = np.reshape(a_np, target_shape)
inputs = [('input', datatypes.Array(*input_dim))]
output = [('output', datatypes.Array(*ref_val.shape))]
builder = NeuralNetworkBuilder(inputs, output)
builder.add_reshape(name="reshape",
input_name='input',
output_name='output',
target_shape=target_shape,
mode=mode)
model = cm.models.MLModel(builder.spec)
for target, ctx in tvm.testing.enabled_targets():
out = run_tvm_graph(model, target, ctx, [a_np],
['input'], ref_val.shape, dtype)
tvm.testing.assert_allclose(out, ref_val, rtol=1e-5)
