ad559aaa0a
Increase relative error tolerance PiperOrigin-RevId: 308708260 Change-Id: I02762ff631e2d62334b4fe641d988dba2bd18813
125 lines
4.3 KiB
C++
125 lines
4.3 KiB
C++
/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include <cstdint>
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#include <functional>
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#include <memory>
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#include <random>
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#include <gtest/gtest.h>
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#include "tensorflow/lite/delegates/xnnpack/unary_elementwise_tester.h"
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#include "tensorflow/lite/delegates/xnnpack/xnnpack_delegate.h"
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namespace tflite {
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namespace xnnpack {
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TEST(Logistic, 4D) {
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std::unique_ptr<TfLiteDelegate, decltype(&TfLiteXNNPackDelegateDelete)>
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xnnpack_delegate(TfLiteXNNPackDelegateCreate(nullptr),
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TfLiteXNNPackDelegateDelete);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto shape_rng =
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std::bind(std::uniform_int_distribution<int32_t>(2, 5), std::ref(rng));
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const auto batch = shape_rng();
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const auto height = shape_rng();
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const auto width = shape_rng();
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const auto channels = shape_rng();
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UnaryElementwiseTester()
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.Shape({batch, height, width, channels})
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.RelativeTolerance(1.0e+4f)
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.Test(BuiltinOperator_LOGISTIC, xnnpack_delegate.get());
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}
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TEST(Logistic, 3D) {
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std::unique_ptr<TfLiteDelegate, decltype(&TfLiteXNNPackDelegateDelete)>
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xnnpack_delegate(TfLiteXNNPackDelegateCreate(nullptr),
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TfLiteXNNPackDelegateDelete);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto shape_rng =
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std::bind(std::uniform_int_distribution<int32_t>(2, 5), std::ref(rng));
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const auto batch = shape_rng();
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const auto width = shape_rng();
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const auto channels = shape_rng();
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UnaryElementwiseTester()
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.Shape({batch, width, channels})
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.RelativeTolerance(1.0e+4f)
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.Test(BuiltinOperator_LOGISTIC, xnnpack_delegate.get());
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}
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TEST(Logistic, 2D) {
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std::unique_ptr<TfLiteDelegate, decltype(&TfLiteXNNPackDelegateDelete)>
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xnnpack_delegate(TfLiteXNNPackDelegateCreate(nullptr),
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TfLiteXNNPackDelegateDelete);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto shape_rng =
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std::bind(std::uniform_int_distribution<int32_t>(2, 5), std::ref(rng));
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const auto batch = shape_rng();
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const auto channels = shape_rng();
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UnaryElementwiseTester()
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.Shape({batch, channels})
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.RelativeTolerance(1.0e+4f)
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.Test(BuiltinOperator_LOGISTIC, xnnpack_delegate.get());
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}
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TEST(Logistic, 1D) {
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std::unique_ptr<TfLiteDelegate, decltype(&TfLiteXNNPackDelegateDelete)>
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xnnpack_delegate(TfLiteXNNPackDelegateCreate(nullptr),
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TfLiteXNNPackDelegateDelete);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto shape_rng =
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std::bind(std::uniform_int_distribution<int32_t>(2, 5), std::ref(rng));
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const auto batch = shape_rng();
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UnaryElementwiseTester().Shape({batch}).RelativeTolerance(1.0e+4f).Test(
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BuiltinOperator_LOGISTIC, xnnpack_delegate.get());
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}
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TEST(Logistic, MultiThreading) {
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TfLiteXNNPackDelegateOptions delegate_options =
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TfLiteXNNPackDelegateOptionsDefault();
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delegate_options.num_threads = 2;
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std::unique_ptr<TfLiteDelegate, decltype(&TfLiteXNNPackDelegateDelete)>
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xnnpack_delegate(TfLiteXNNPackDelegateCreate(&delegate_options),
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TfLiteXNNPackDelegateDelete);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto shape_rng =
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std::bind(std::uniform_int_distribution<int32_t>(2, 5), std::ref(rng));
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const auto batch = shape_rng();
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const auto height = shape_rng();
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const auto width = shape_rng();
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const auto channels = shape_rng();
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UnaryElementwiseTester()
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.Shape({batch, height, width, channels})
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.RelativeTolerance(1.0e+4f)
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.Test(BuiltinOperator_LOGISTIC, xnnpack_delegate.get());
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}
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} // namespace xnnpack
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} // namespace tflite
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