tensorflow/tensorflow

# 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."""
import tensorflow 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.compat.v1.placeholder(
          dtype=parameters["dtype"],
          name="split_{}".format(i),
          shape=[num_batches, input_vec_size])
      inputs_after_split.append(one_timestamp_input)
    lstm_cell = tf.compat.v1.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.compat.v1.variable_scope("", reuse=True):
      kernel = tf.compat.v1.get_variable("rnn/basic_lstm_cell/kernel")
      bias = tf.compat.v1.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)