228 lines
8.6 KiB
C++
228 lines
8.6 KiB
C++
/*
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* Copyright (c) 2018, Alliance for Open Media. All rights reserved.
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*
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* This source code is subject to the terms of the BSD 2 Clause License and
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* the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License
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* was not distributed with this source code in the LICENSE file, you can
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* obtain it at www.aomedia.org/license/software. If the Alliance for Open
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* Media Patent License 1.0 was not distributed with this source code in the
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* PATENTS file, you can obtain it at www.aomedia.org/license/patent.
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*/
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#include <tuple>
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#include "gtest/gtest.h"
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#include "aom/aom_integer.h"
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#include "aom_ports/aom_timer.h"
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#include "av1/encoder/ml.h"
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#include "config/aom_config.h"
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#include "config/aom_dsp_rtcd.h"
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#include "config/av1_rtcd.h"
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#include "test/util.h"
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#include "test/register_state_check.h"
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#include "test/acm_random.h"
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namespace {
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typedef void (*NnPredict_Func)(const float *const input_nodes,
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const NN_CONFIG *const nn_config,
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int reduce_prec, float *const output);
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typedef std::tuple<const NnPredict_Func> NnPredictTestParam;
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const float epsilon = 1e-3f; // Error threshold for functional equivalence
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class NnPredictTest : public ::testing::TestWithParam<NnPredictTestParam> {
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public:
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void SetUp() override {
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const int MAX_NODES2 = NN_MAX_NODES_PER_LAYER * NN_MAX_NODES_PER_LAYER;
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// Allocate two massive buffers on the heap for edge weights and node bias
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// Then set-up the double-dimension arrays pointing into the big buffers
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weights_buf = (float *)aom_malloc(MAX_NODES2 * (NN_MAX_HIDDEN_LAYERS + 1) *
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sizeof(*weights_buf));
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bias_buf =
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(float *)aom_malloc(NN_MAX_NODES_PER_LAYER *
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(NN_MAX_HIDDEN_LAYERS + 1) * sizeof(*bias_buf));
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ASSERT_NE(weights_buf, nullptr);
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ASSERT_NE(bias_buf, nullptr);
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for (int i = 0; i < NN_MAX_HIDDEN_LAYERS + 1; i++) {
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weights[i] = &weights_buf[i * MAX_NODES2];
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bias[i] = &bias_buf[i * NN_MAX_NODES_PER_LAYER];
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}
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target_func_ = GET_PARAM(0);
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}
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void TearDown() override {
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aom_free(weights_buf);
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aom_free(bias_buf);
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}
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void RunNnPredictTest(const NN_CONFIG *const shape);
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void RunNnPredictSpeedTest(const NN_CONFIG *const shape, const int run_times);
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void RunNnPredictTest_all(const NN_CONFIG *const shapes,
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const int num_shapes);
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void RunNnPredictSpeedTest_all(const NN_CONFIG *const shapes,
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const int num_shapes, const int run_times);
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private:
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NnPredict_Func target_func_;
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libaom_test::ACMRandom rng_;
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float *weights[NN_MAX_HIDDEN_LAYERS + 1] = {};
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float *bias[NN_MAX_HIDDEN_LAYERS + 1] = {};
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float *weights_buf = nullptr, *bias_buf = nullptr;
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};
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GTEST_ALLOW_UNINSTANTIATED_PARAMETERIZED_TEST(NnPredictTest);
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void NnPredictTest::RunNnPredictTest(const NN_CONFIG *const shape) {
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float inputs[NN_MAX_NODES_PER_LAYER] = { 0 };
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float outputs_test[NN_MAX_NODES_PER_LAYER] = { 0 };
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float outputs_ref[NN_MAX_NODES_PER_LAYER] = { 0 };
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NN_CONFIG nn_config;
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memcpy(&nn_config, shape, sizeof(nn_config));
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char shape_str[32] = { 0 };
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snprintf(shape_str, sizeof(shape_str), "%d", shape->num_inputs);
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for (int layer = 0; layer < shape->num_hidden_layers; layer++)
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snprintf(&shape_str[strlen(shape_str)],
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sizeof(shape_str) - strlen(shape_str), "x%d",
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shape->num_hidden_nodes[layer]);
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snprintf(&shape_str[strlen(shape_str)], sizeof(shape_str) - strlen(shape_str),
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"x%d", shape->num_outputs);
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for (int i = 0; i < NN_MAX_HIDDEN_LAYERS + 1; i++) {
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nn_config.weights[i] = weights[i];
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nn_config.bias[i] = bias[i];
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}
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for (int iter = 0; iter < 10000 && !HasFatalFailure(); ++iter) {
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for (int node = 0; node < shape->num_inputs; node++) {
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inputs[node] = ((float)rng_.Rand31() - (1 << 30)) / (1u << 31);
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}
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for (int layer = 0; layer < shape->num_hidden_layers; layer++) {
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for (int node = 0; node < NN_MAX_NODES_PER_LAYER; node++) {
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bias[layer][node] = ((float)rng_.Rand31() - (1 << 30)) / (1u << 31);
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}
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for (int node = 0; node < NN_MAX_NODES_PER_LAYER * NN_MAX_NODES_PER_LAYER;
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node++) {
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weights[layer][node] = ((float)rng_.Rand31() - (1 << 30)) / (1u << 31);
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}
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}
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// Now the outputs:
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int layer = shape->num_hidden_layers;
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for (int node = 0; node < NN_MAX_NODES_PER_LAYER; node++) {
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bias[layer][node] = ((float)rng_.Rand31() - (1 << 30)) / (1u << 31);
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}
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for (int node = 0; node < NN_MAX_NODES_PER_LAYER * NN_MAX_NODES_PER_LAYER;
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node++) {
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weights[layer][node] = ((float)rng_.Rand31() - (1 << 30)) / (1u << 31);
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}
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av1_nn_predict_c(inputs, &nn_config, 0, outputs_ref);
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target_func_(inputs, &nn_config, 0, outputs_test);
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for (int node = 0; node < shape->num_outputs; node++) {
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if (outputs_ref[node] < epsilon) {
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ASSERT_LE(outputs_test[node], epsilon)
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<< "Reference output was near-zero, test output was not ("
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<< shape_str << ")";
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} else {
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const float error = outputs_ref[node] - outputs_test[node];
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const float relative_error = fabsf(error / outputs_ref[node]);
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ASSERT_LE(relative_error, epsilon)
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<< "Excessive relative error between reference and test ("
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<< shape_str << ")";
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}
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}
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}
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}
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void NnPredictTest::RunNnPredictSpeedTest(const NN_CONFIG *const shape,
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const int run_times) {
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float inputs[NN_MAX_NODES_PER_LAYER] = { 0 };
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float outputs_test[NN_MAX_NODES_PER_LAYER] = { 0 };
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float outputs_ref[NN_MAX_NODES_PER_LAYER] = { 0 };
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NN_CONFIG nn_config;
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memcpy(&nn_config, shape, sizeof(nn_config));
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for (int i = 0; i < NN_MAX_HIDDEN_LAYERS; i++) {
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nn_config.weights[i] = weights[i];
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nn_config.bias[i] = bias[i];
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}
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// Don't bother actually changing the values for inputs/weights/bias: it
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// shouldn't make any difference for a speed test.
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aom_usec_timer timer;
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aom_usec_timer_start(&timer);
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for (int i = 0; i < run_times; ++i) {
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av1_nn_predict_c(inputs, &nn_config, 0, outputs_ref);
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}
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aom_usec_timer_mark(&timer);
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const double time1 = static_cast<double>(aom_usec_timer_elapsed(&timer));
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aom_usec_timer_start(&timer);
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for (int i = 0; i < run_times; ++i) {
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target_func_(inputs, &nn_config, 0, outputs_test);
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}
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aom_usec_timer_mark(&timer);
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const double time2 = static_cast<double>(aom_usec_timer_elapsed(&timer));
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printf("%d", shape->num_inputs);
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for (int layer = 0; layer < shape->num_hidden_layers; layer++)
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printf("x%d", shape->num_hidden_nodes[layer]);
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printf("x%d: ", shape->num_outputs);
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printf("%7.2f/%7.2fns (%3.2f)\n", time1, time2, time1 / time2);
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}
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// This is all the neural network shapes observed executed in a few different
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// runs of the encoder. It also conveniently covers all the kernels
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// implemented.
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static const NN_CONFIG kShapes[] = {
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{ 37, 1, 2, { 16, 24 }, {}, {} }, { 24, 24, 1, { 12 }, {}, {} },
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{ 10, 16, 1, { 64 }, {}, {} }, { 12, 1, 1, { 12 }, {}, {} },
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{ 12, 1, 1, { 24 }, {}, {} }, { 12, 1, 1, { 32 }, {}, {} },
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{ 18, 4, 1, { 24 }, {}, {} }, { 18, 4, 1, { 32 }, {}, {} },
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{ 4, 1, 1, { 16 }, {}, {} }, { 8, 1, 0, { 0 }, {}, {} },
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{ 8, 4, 1, { 16 }, {}, {} }, { 8, 1, 1, { 32 }, {}, {} },
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{ 9, 3, 1, { 32 }, {}, {} }, { 8, 4, 0, { 0 }, {}, {} },
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{ 8, 8, 0, { 0 }, {}, {} }, { 4, 4, 1, { 8 }, {}, {} },
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{ 4, 3, 0, { 64 }, {}, {} },
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};
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void NnPredictTest::RunNnPredictTest_all(const NN_CONFIG *const shapes,
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const int num_shapes) {
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for (int i = 0; i < num_shapes; i++) RunNnPredictTest(&shapes[i]);
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}
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void NnPredictTest::RunNnPredictSpeedTest_all(const NN_CONFIG *const shapes,
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const int num_shapes,
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const int run_times) {
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for (int i = 0; i < num_shapes; i++)
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NnPredictTest::RunNnPredictSpeedTest(&shapes[i], run_times);
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}
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TEST_P(NnPredictTest, RandomValues) {
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RunNnPredictTest_all(kShapes, sizeof(kShapes) / sizeof(kShapes[0]));
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}
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TEST_P(NnPredictTest, DISABLED_Speed) {
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RunNnPredictSpeedTest_all(kShapes, sizeof(kShapes) / sizeof(kShapes[0]),
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10000000);
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}
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#if !CONFIG_EXCLUDE_SIMD_MISMATCH
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#if HAVE_SSE3
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INSTANTIATE_TEST_SUITE_P(SSE3, NnPredictTest,
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::testing::Values(av1_nn_predict_sse3));
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#endif
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#if HAVE_AVX2
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INSTANTIATE_TEST_SUITE_P(AVX2, NnPredictTest,
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::testing::Values(av1_nn_predict_avx2));
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#endif
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#if HAVE_NEON
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INSTANTIATE_TEST_SUITE_P(NEON, NnPredictTest,
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::testing::Values(av1_nn_predict_neon));
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#endif
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#endif // !CONFIG_EXCLUDE_SIMD_MISMATCH
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} // namespace
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