107 lines
4.2 KiB
Python
107 lines
4.2 KiB
Python
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Test configs for lstm."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow.compat.v1 as tf
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from tensorflow.lite.testing.zip_test_utils import create_tensor_data
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from tensorflow.lite.testing.zip_test_utils import ExtraTocoOptions
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from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests
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from tensorflow.lite.testing.zip_test_utils import register_make_test_function
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from tensorflow.python.ops import rnn
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@register_make_test_function()
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def make_lstm_tests(options):
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"""Make a set of tests to do basic Lstm cell."""
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test_parameters = [
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{
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"dtype": [tf.float32],
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"num_batchs": [1],
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"time_step_size": [1],
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"input_vec_size": [3],
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"num_cells": [4],
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"split_tflite_lstm_inputs": [False],
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},
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]
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def build_graph(parameters):
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"""Build a simple graph with BasicLSTMCell."""
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num_batchs = parameters["num_batchs"]
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time_step_size = parameters["time_step_size"]
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input_vec_size = parameters["input_vec_size"]
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num_cells = parameters["num_cells"]
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inputs_after_split = []
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for i in range(time_step_size):
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one_timestamp_input = tf.compat.v1.placeholder(
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dtype=parameters["dtype"],
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name="split_{}".format(i),
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shape=[num_batchs, input_vec_size])
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inputs_after_split.append(one_timestamp_input)
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# Currently lstm identifier has a few limitations: only supports
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# forget_bias == 0, inner state activation == tanh.
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# TODO(zhixianyan): Add another test with forget_bias == 1.
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# TODO(zhixianyan): Add another test with relu as activation.
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lstm_cell = tf.compat.v1.nn.rnn_cell.BasicLSTMCell(
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num_cells, forget_bias=0.0, state_is_tuple=True)
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cell_outputs, _ = rnn.static_rnn(
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lstm_cell, inputs_after_split, dtype=tf.float32)
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out = cell_outputs[-1]
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return inputs_after_split, [out]
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def build_inputs(parameters, sess, inputs, outputs):
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"""Feed inputs, assign variables, and freeze graph."""
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with tf.compat.v1.variable_scope("", reuse=True):
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kernel = tf.get_variable("rnn/basic_lstm_cell/kernel")
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bias = tf.get_variable("rnn/basic_lstm_cell/bias")
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kernel_values = create_tensor_data(parameters["dtype"],
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[kernel.shape[0], kernel.shape[1]], -1,
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1)
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bias_values = create_tensor_data(parameters["dtype"], [bias.shape[0]], 0,
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1)
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sess.run(tf.group(kernel.assign(kernel_values), bias.assign(bias_values)))
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num_batchs = parameters["num_batchs"]
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time_step_size = parameters["time_step_size"]
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input_vec_size = parameters["input_vec_size"]
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input_values = []
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for _ in range(time_step_size):
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tensor_data = create_tensor_data(parameters["dtype"],
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[num_batchs, input_vec_size], 0, 1)
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input_values.append(tensor_data)
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out = sess.run(outputs, feed_dict=dict(zip(inputs, input_values)))
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return input_values, out
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# TODO(zhixianyan): Automatically generate rnn_states for lstm cell.
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extra_toco_options = ExtraTocoOptions()
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extra_toco_options.rnn_states = (
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"{state_array:rnn/BasicLSTMCellZeroState/zeros,"
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"back_edge_source_array:rnn/basic_lstm_cell/Add_1,size:4},"
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"{state_array:rnn/BasicLSTMCellZeroState/zeros_1,"
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"back_edge_source_array:rnn/basic_lstm_cell/Mul_2,size:4}")
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make_zip_of_tests(
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options,
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test_parameters,
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build_graph,
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build_inputs,
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extra_toco_options,
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use_frozen_graph=True)
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