def test_GRU(self):
GRU = keras.layers.GRU
inputs1 = keras.Input(shape=(3, 1))
cls = GRU(2, return_state=False, return_sequences=False)
oname = cls(inputs1)
model = keras.Model(inputs=inputs1, outputs=[oname])
onnx_model = keras2onnx.convert_keras(model, model.name)
data = np.array([0.1, 0.2, 0.3]).astype(np.float32).reshape((1, 3, 1))
expected = model.predict(data)
self.assertTrue(self.run_onnx_runtime(onnx_model.graph.name, onnx_model, data, expected))
# GRU with initial state
for return_sequences in [True, False]:
cls = GRU(2, return_state=False, return_sequences=return_sequences)
initial_state_input = keras.Input(shape=(2, ))
oname = cls(inputs1, initial_state=initial_state_input)
model = keras.Model(inputs=[inputs1, initial_state_input], outputs=[oname])
onnx_model = keras2onnx.convert_keras(model, model.name)
data = np.array([0.1, 0.2, 0.3]).astype(np.float32).reshape((1, 3, 1))
init_state = np.array([0.4, 0.5]).astype(np.float32).reshape((1, 2))
init_state_onnx = np.array([0.4, 0.5]).astype(np.float32).reshape((1, 1, 2))
expected = model.predict([data, init_state])
self.assertTrue(self.run_onnx_runtime(onnx_model.graph.name, onnx_model, [data, init_state_onnx], expected))
def _misc_conv_helper(self, layer, ishape):
input = keras.Input(ishape)
out = layer(input)
model = keras.models.Model(input, out)
onnx_model = keras2onnx.convert_keras(model, model.name)
data = np.random.uniform(-0.5, 0.5, size=(1,) + ishape).astype(np.float32)
expected = model.predict(data)
self.assertTrue(self.run_onnx_runtime(onnx_model.graph.name, onnx_model, data, expected))
def test_LSTM(self):
LSTM = keras.layers.LSTM
inputs1 = keras.Input(shape=(3, 5))
for use_bias in [True, False]:
cls = LSTM(units=2, return_state=True, return_sequences=True, use_bias=use_bias)
lstm1, state_h, state_c = cls(inputs1)
model = keras.Model(inputs=inputs1, outputs=[lstm1, state_h, state_c])
data = np.random.rand(3, 5).astype(np.float32).reshape((1, 3, 5))
onnx_model = keras2onnx.convert_keras(model, model.name)
expected = model.predict(data)
self.assertTrue(self.run_onnx_runtime(onnx_model.graph.name, onnx_model, data, expected))
def test_LSTM_with_bias(self):
LSTM = keras.layers.LSTM
inputs1 = keras.Input(shape=(1, 1))
cls = LSTM(units=1, return_state=True, return_sequences=True)
lstm1, state_h, state_c = cls(inputs1)
model = keras.Model(inputs=inputs1, outputs=[lstm1, state_h, state_c])
# Set weights: kernel, recurrent_kernel and bias
model.set_weights([[[1, 2, 3, 4]], [[5, 6, 7, 8]], [1, 2, 3, 4]])
data = np.random.rand(1, 1).astype(np.float32).reshape((1, 1, 1))
onnx_model = keras2onnx.convert_keras(model, model.name)
expected = model.predict(data)
self.assertTrue(self.run_onnx_runtime(onnx_model.graph.name, onnx_model, data, expected))
def test_simpleRNN(self):
inputs1 = keras.Input(shape=(3, 1))
cls = keras.layers.SimpleRNN(2, return_state=False, return_sequences=True)
oname = cls(inputs1) # , initial_state=t0)
model = keras.Model(inputs=inputs1, outputs=[oname])
onnx_model = keras2onnx.convert_keras(model, model.name)
data = np.array([0.1, 0.2, 0.3]).astype(np.float32).reshape((1, 3, 1))
expected = model.predict(data)
self.assertTrue(self.run_onnx_runtime(onnx_model.graph.name, onnx_model, data, expected))
# with initial state
inputs2 = keras.Input(shape=(1, 2))
state = keras.Input(shape=(5,))
hidden_1 = keras.layers.SimpleRNN(5, activation='relu', return_sequences=True)(inputs2, initial_state=[state])
output = keras.layers.Dense(2, activation='sigmoid')(hidden_1)
keras_model = keras.Model(inputs=[inputs2, state], outputs=output)
onnx_model = keras2onnx.convert_keras(keras_model, keras_model.name, debug_mode=True)
N, H, W, C = 3, 1, 2, 5
x = np.random.rand(N, H, W).astype(np.float32, copy=False)
s = np.random.rand(N, C).astype(np.float32, copy=False)
expected = keras_model.predict([x, s])
self.assertTrue(self.run_onnx_runtime(onnx_model.graph.name, onnx_model, [x, s], expected))
# with initial state and output state
input = keras.Input(shape=(1, 2))
state_in = keras.Input(shape=(10,))
hidden_1, state_out = keras.layers.SimpleRNN(10, activation='relu', return_sequences=True, return_state=True)(input,
initial_state=[state_in])
output = keras.layers.Dense(2, activation='linear')(hidden_1)
keras_model = keras.Model(inputs=[input, state_in], outputs=[output, state_out])
onnx_model = keras2onnx.convert_keras(keras_model, keras_model.name)
N, H, W, C = 3, 1, 2, 10
x = np.random.rand(N, H, W).astype(np.float32, copy=False)
s = np.random.rand(N, C).astype(np.float32, copy=False)
expected = keras_model.predict([x, s])
self.assertTrue(self.run_onnx_runtime(onnx_model.graph.name, onnx_model, [x, s], expected))
def test_LSTM_reshape(self):
input_dim = 7
sequence_len = 3
inputs1 = keras.Input(shape=(sequence_len, input_dim))
cls = keras.layers.LSTM(units=5, return_state=False, return_sequences=True)
lstm1 = cls(inputs1)
output = keras.layers.Reshape((sequence_len, 5))(lstm1)
model = keras.Model(inputs=inputs1, outputs=output)
model.compile(optimizer='sgd', loss='mse')
onnx_model = keras2onnx.convert_keras(model, 'test')
data = np.random.rand(input_dim, sequence_len).astype(np.float32).reshape((1, sequence_len, input_dim))
expected = model.predict(data)
self.assertTrue(self.run_onnx_runtime('tf_lstm', onnx_model, data, expected))