1# Copyright 2019 The TensorFlow Authors. All Rights Reserved. 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================== 15"""Test configs for static_rnn_with_control_flow_v2.""" 16from __future__ import absolute_import 17from __future__ import division 18from __future__ import print_function 19 20import tensorflow.compat.v1 as tf 21from tensorflow.lite.testing.zip_test_utils import create_tensor_data 22from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests 23from tensorflow.lite.testing.zip_test_utils import register_make_test_function 24from tensorflow.python.framework import test_util 25from tensorflow.python.ops import rnn 26 27 28@register_make_test_function("make_static_rnn_with_control_flow_v2_tests") 29@test_util.enable_control_flow_v2 30def make_static_rnn_with_control_flow_v2_tests(options): 31 """Make a set of tests to do basic Lstm cell.""" 32 33 test_parameters = [ 34 { 35 "dtype": [tf.float32], 36 "num_batches": [4], 37 "time_step_size": [4], 38 "input_vec_size": [3], 39 "num_cells": [4], 40 "use_sequence_length": [True, False], 41 }, 42 ] 43 44 def build_graph(parameters): 45 """Build a simple graph with BasicLSTMCell.""" 46 47 num_batches = parameters["num_batches"] 48 time_step_size = parameters["time_step_size"] 49 input_vec_size = parameters["input_vec_size"] 50 num_cells = parameters["num_cells"] 51 inputs_after_split = [] 52 for i in range(time_step_size): 53 one_timestamp_input = tf.placeholder( 54 dtype=parameters["dtype"], 55 name="split_{}".format(i), 56 shape=[num_batches, input_vec_size]) 57 inputs_after_split.append(one_timestamp_input) 58 lstm_cell = tf.nn.rnn_cell.BasicLSTMCell( 59 num_cells, activation=tf.nn.relu, state_is_tuple=True) 60 sequence_length = None 61 62 if parameters["use_sequence_length"]: 63 # Using different sequence length in each bach, like [1, 2, 3, 3...]. 64 sequence_length = [ 65 min(i + 1, time_step_size) for i in range(num_batches) 66 ] 67 cell_outputs, _ = rnn.static_rnn( 68 lstm_cell, 69 inputs_after_split, 70 dtype=tf.float32, 71 sequence_length=sequence_length) 72 out = cell_outputs[-1] 73 return inputs_after_split, [out] 74 75 def build_inputs(parameters, sess, inputs, outputs): 76 """Feed inputs, assign variables, and freeze graph.""" 77 78 with tf.variable_scope("", reuse=True): 79 kernel = tf.get_variable("rnn/basic_lstm_cell/kernel") 80 bias = tf.get_variable("rnn/basic_lstm_cell/bias") 81 kernel_values = create_tensor_data(parameters["dtype"], 82 [kernel.shape[0], kernel.shape[1]], -1, 83 1) 84 bias_values = create_tensor_data(parameters["dtype"], [bias.shape[0]], 0, 85 1) 86 sess.run(tf.group(kernel.assign(kernel_values), bias.assign(bias_values))) 87 88 num_batches = parameters["num_batches"] 89 time_step_size = parameters["time_step_size"] 90 input_vec_size = parameters["input_vec_size"] 91 input_values = [] 92 for _ in range(time_step_size): 93 tensor_data = create_tensor_data(parameters["dtype"], 94 [num_batches, input_vec_size], 0, 1) 95 input_values.append(tensor_data) 96 out = sess.run(outputs, feed_dict=dict(zip(inputs, input_values))) 97 return input_values, out 98 99 make_zip_of_tests( 100 options, 101 test_parameters, 102 build_graph, 103 build_inputs, 104 use_frozen_graph=True) 105