| # Lint as: python3 |
| # Copyright 2019 Google LLC |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # https://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| |
| import numpy as np |
| from pyiree.tf.support import tf_test_utils |
| from pyiree.tf.support import tf_utils |
| import tensorflow.compat.v2 as tf |
| |
| NUM_UNITS = 10 |
| NUM_TIMESTEPS = 24 |
| NUM_BATCH = 7 |
| INPUT_SHAPE = [None, None, NUM_UNITS] |
| |
| |
| def lstm_module(): |
| tf_utils.set_random_seed() |
| inputs = tf.keras.layers.Input(batch_size=None, shape=INPUT_SHAPE[1:]) |
| outputs = tf.keras.layers.LSTM(units=NUM_UNITS, return_sequences=True)(inputs) |
| model = tf.keras.Model(inputs, outputs) |
| module = tf.Module() |
| module.m = model |
| module.predict = tf.function( |
| input_signature=[tf.TensorSpec(INPUT_SHAPE, tf.float32)])( |
| model.call) |
| return module |
| |
| |
| @tf_test_utils.compile_module(lstm_module, exported_names=["predict"]) |
| class LstmTest(tf_test_utils.SavedModelTestCase): |
| |
| def test_lstm(self): |
| m = self.get_module() |
| m.predict( |
| tf.constant( |
| np.arange(NUM_BATCH * NUM_TIMESTEPS * NUM_UNITS, |
| dtype=np.float32).reshape( |
| [NUM_BATCH, NUM_TIMESTEPS, NUM_UNITS]), |
| shape=[NUM_BATCH, NUM_TIMESTEPS, |
| NUM_UNITS])).print().assert_all_close() |
| |
| |
| if __name__ == "__main__": |
| if hasattr(tf, "enable_v2_behavior"): |
| tf.enable_v2_behavior() |
| tf.test.main() |
