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))