161 lines
5.1 KiB
Python
161 lines
5.1 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 fully_connected."""
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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 numpy as np
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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 make_zip_of_tests
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from tensorflow.lite.testing.zip_test_utils import register_make_test_function
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@register_make_test_function()
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def make_fully_connected_tests(options):
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"""Make a set of tests to do fully_connected."""
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test_parameters = [{
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"shape1": [[3, 3]],
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"shape2": [[3, 3]],
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"transpose_a": [True, False],
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"transpose_b": [True, False],
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"constant_filter": [True, False],
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"fully_quantize": [False],
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"quant_16x8": [False]
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}, {
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"shape1": [[4, 4], [1, 4], [4]],
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"shape2": [[4, 4], [4, 1], [4]],
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"transpose_a": [False],
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"transpose_b": [False],
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"constant_filter": [True, False],
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"fully_quantize": [False],
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"quant_16x8": [False]
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}, {
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"shape1": [[40, 37]],
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"shape2": [[37, 40]],
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"transpose_a": [False],
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"transpose_b": [False],
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"constant_filter": [True, False],
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"fully_quantize": [False],
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"quant_16x8": [False]
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}, {
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"shape1": [[40, 37]],
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"shape2": [[40, 37]],
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"transpose_a": [False],
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"transpose_b": [True],
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"constant_filter": [True, False],
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"fully_quantize": [False],
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"quant_16x8": [False]
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}, {
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"shape1": [[5, 3]],
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"shape2": [[5, 3]],
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"transpose_a": [True],
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"transpose_b": [False],
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"constant_filter": [True, False],
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"fully_quantize": [False],
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"quant_16x8": [False]
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}, {
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"shape1": [[1, 3]],
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"shape2": [[3, 3]],
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"transpose_a": [False],
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"transpose_b": [False],
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"constant_filter": [True],
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"fully_quantize": [True],
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"quant_16x8": [False]
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}, {
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"shape1": [[1, 4], [4]],
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"shape2": [[4, 4], [4, 1], [4]],
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"transpose_a": [False],
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"transpose_b": [False],
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"constant_filter": [True],
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"fully_quantize": [True],
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"quant_16x8": [False]
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}, {
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"shape1": [[1, 37], [2, 37]],
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"shape2": [[37, 40]],
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"transpose_a": [False],
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"transpose_b": [False],
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"constant_filter": [True],
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"fully_quantize": [True],
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"quant_16x8": [False]
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}, {
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"shape1": [[1, 3], [2, 3]],
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"shape2": [[3, 5], [3, 1]],
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"transpose_a": [False],
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"transpose_b": [False],
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"constant_filter": [True],
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"fully_quantize": [True],
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"quant_16x8": [False]
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}, {
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"shape1": [[2, 3]],
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"shape2": [[3, 5]],
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"transpose_a": [False],
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"transpose_b": [False],
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"constant_filter": [True],
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"fully_quantize": [True],
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"quant_16x8": [True]
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}]
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def build_graph(parameters):
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"""Build a matmul graph given `parameters`."""
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input_tensor1 = tf.compat.v1.placeholder(
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dtype=tf.float32, name="input1", shape=parameters["shape1"])
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# Get input_tensor2 either as a placeholder or constants. Also get a list of
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# the input tensors that are represented as placeholders.
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if parameters["constant_filter"]:
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input_tensor2 = create_tensor_data(
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np.float32, parameters["shape2"], min_value=-1, max_value=1)
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input_tensors = [input_tensor1]
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else:
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input_tensor2 = tf.compat.v1.placeholder(
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dtype=tf.float32, name="input2", shape=parameters["shape2"])
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input_tensors = [input_tensor1, input_tensor2]
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out = tf.matmul(
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input_tensor1,
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input_tensor2,
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transpose_a=parameters["transpose_a"],
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transpose_b=parameters["transpose_b"])
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return input_tensors, [out]
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def build_inputs(parameters, sess, inputs, outputs):
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# pylint: disable=g-doc-return-or-yield, g-doc-args
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"""Build list of input values.
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It either contains 1 tensor (input_values1) or
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2 tensors (input_values1, input_values2) based on whether the second input
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is a constant or variable input.
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"""
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values = [
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create_tensor_data(
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np.float32, shape=parameters["shape1"], min_value=-1, max_value=1)
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]
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if not parameters["constant_filter"]:
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values.append(
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create_tensor_data(
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np.float32, parameters["shape2"], min_value=-1, max_value=1))
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return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))
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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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expected_tf_failures=14)
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