| # Copyright 2019 The TensorFlow Authors. All Rights Reserved. |
| # |
| # 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 |
| # |
| # http://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. |
| # ============================================================================== |
| """Test configs for static_rnn_with_control_flow_v2.""" |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| import tensorflow.compat.v1 as tf |
| from tensorflow.lite.testing.zip_test_utils import create_tensor_data |
| from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests |
| from tensorflow.lite.testing.zip_test_utils import register_make_test_function |
| from tensorflow.python.framework import test_util |
| from tensorflow.python.ops import rnn |
| |
| |
| @register_make_test_function("make_static_rnn_with_control_flow_v2_tests") |
| @test_util.enable_control_flow_v2 |
| def make_static_rnn_with_control_flow_v2_tests(options): |
| """Make a set of tests to do basic Lstm cell.""" |
| |
| test_parameters = [ |
| { |
| "dtype": [tf.float32], |
| "num_batches": [4], |
| "time_step_size": [4], |
| "input_vec_size": [3], |
| "num_cells": [4], |
| "use_sequence_length": [True, False], |
| }, |
| ] |
| |
| def build_graph(parameters): |
| """Build a simple graph with BasicLSTMCell.""" |
| |
| num_batches = parameters["num_batches"] |
| time_step_size = parameters["time_step_size"] |
| input_vec_size = parameters["input_vec_size"] |
| num_cells = parameters["num_cells"] |
| inputs_after_split = [] |
| for i in range(time_step_size): |
| one_timestamp_input = tf.placeholder( |
| dtype=parameters["dtype"], |
| name="split_{}".format(i), |
| shape=[num_batches, input_vec_size]) |
| inputs_after_split.append(one_timestamp_input) |
| lstm_cell = tf.nn.rnn_cell.BasicLSTMCell( |
| num_cells, activation=tf.nn.relu, state_is_tuple=True) |
| sequence_length = None |
| |
| if parameters["use_sequence_length"]: |
| # Using different sequence length in each bach, like [1, 2, 3, 3...]. |
| sequence_length = [ |
| min(i + 1, time_step_size) for i in range(num_batches) |
| ] |
| cell_outputs, _ = rnn.static_rnn( |
| lstm_cell, |
| inputs_after_split, |
| dtype=tf.float32, |
| sequence_length=sequence_length) |
| out = cell_outputs[-1] |
| return inputs_after_split, [out] |
| |
| def build_inputs(parameters, sess, inputs, outputs): |
| """Feed inputs, assign variables, and freeze graph.""" |
| |
| with tf.variable_scope("", reuse=True): |
| kernel = tf.get_variable("rnn/basic_lstm_cell/kernel") |
| bias = tf.get_variable("rnn/basic_lstm_cell/bias") |
| kernel_values = create_tensor_data(parameters["dtype"], |
| [kernel.shape[0], kernel.shape[1]], -1, |
| 1) |
| bias_values = create_tensor_data(parameters["dtype"], [bias.shape[0]], 0, |
| 1) |
| sess.run(tf.group(kernel.assign(kernel_values), bias.assign(bias_values))) |
| |
| num_batches = parameters["num_batches"] |
| time_step_size = parameters["time_step_size"] |
| input_vec_size = parameters["input_vec_size"] |
| input_values = [] |
| for _ in range(time_step_size): |
| tensor_data = create_tensor_data(parameters["dtype"], |
| [num_batches, input_vec_size], 0, 1) |
| input_values.append(tensor_data) |
| out = sess.run(outputs, feed_dict=dict(zip(inputs, input_values))) |
| return input_values, out |
| |
| make_zip_of_tests( |
| options, |
| test_parameters, |
| build_graph, |
| build_inputs, |
| use_frozen_graph=True) |