def setUp(self): # type: () -> None
self.img_arr = np.uint8(npr.rand(224, 224, 3) * 255) # type: ignore
self.img = Image.fromarray(np.uint8(self.img_arr)) # type: ignore
self.img_arr = np.float32(self.img_arr) # type: ignore
self.onnx_model = _onnx_create_single_node_model(
"Relu", [(3, 224, 224)], [(3, 224, 224)])
self.input_names = [i.name for i in self.onnx_model.graph.input]
self.output_names = [o.name for o in self.onnx_model.graph.output]
def test_create_node(self): # type: () -> None
model = _onnx_create_single_node_model("Elu", [(1, 3, 224, 224)],
[(1, 3, 224, 224)],
alpha=0.5)
graph = model.graph
node = graph.node[0]
node_ = Node.from_onnx(node)
self.assertTrue(len(node_.inputs) == 1)
self.assertTrue(len(node_.outputs) == 1)
self.assertTrue(len(node_.attrs) == 1)
self.assertTrue(node_.attrs["alpha"] == 0.5)
def skip_test_lstm(self): # type: () -> None
x = 4
h = 2
seq_length = 3
W = from_array(_random_array((4 * h, x)), name="gate_weights")
R = from_array(_random_array((4 * h, h)), name="recursion_weights")
B = from_array(_random_array((8 * h, )), name="biases")
seq_lens_input = from_array(np.array([seq_length]).astype(np.int32),
name='seq_lens_input')
initial_h = from_array(np.zeros((1, 1, h)).astype(np.float32),
name='initial_h')
initial_c = from_array(np.zeros((1, 1, h)).astype(np.float32),
name='initial_c')
input_shape = (seq_length, 1, x)
output_shape_all = (seq_length, 1, h)
output_shape_last = (1, 1, h)
onnx_model = _onnx_create_single_node_model(
"LSTM", [input_shape], [output_shape_all, output_shape_last],
initializer=[W, R, B, seq_lens_input, initial_h, initial_c],
hidden_size=h)
X = np.random.rand(*input_shape).astype("float32") #type: ignore
prepared_backend = caffe2.python.onnx.backend.prepare(onnx_model)
out = prepared_backend.run({'input0': X})
caffe2_out_all = out['output0']
caffe2_out_last = out['output1']
coreml_model = convert(onnx_model)
inputdict = {}
inputdict['input0'] = X
inputdict['initial_h'] = np.zeros((h), dtype=np.float32)
inputdict['initial_c'] = np.zeros((h), dtype=np.float32)
coreml_out_dict = coreml_model.predict(inputdict, useCPUOnly=True)
coreml_out_all = coreml_out_dict['output0']
coreml_out_last = coreml_out_dict['output1']
_assert_outputs(caffe2_out_all.flatten(),
coreml_out_all.flatten(),
decimal=5)
_assert_outputs(caffe2_out_last.flatten(),
coreml_out_last.flatten(),
decimal=5)
