test/av1_nn_predict_test.cc - aom - Git at Google

 /*
  * Copyright (c) 2018, Alliance for Open Media. All rights reserved
  *
  * This source code is subject to the terms of the BSD 2 Clause License and
  * the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License
  * was not distributed with this source code in the LICENSE file, you can
  * obtain it at www.aomedia.org/license/software. If the Alliance for Open
  * Media Patent License 1.0 was not distributed with this source code in the
  * PATENTS file, you can obtain it at www.aomedia.org/license/patent.
  */

 #include <tuple>

 #include "third_party/googletest/src/googletest/include/gtest/gtest.h"

 #include "aom/aom_integer.h"
 #include "aom_ports/aom_timer.h"
 #include "av1/encoder/ml.h"
 #include "config/aom_config.h"
 #include "config/aom_dsp_rtcd.h"
 #include "config/av1_rtcd.h"
 #include "test/util.h"
 #include "test/register_state_check.h"
 #include "test/acm_random.h"

 namespace {
 typedef void (*NnPredict_Func)(const float *const input_nodes,
                                const NN_CONFIG *const nn_config,
                                int reduce_prec, float *const output);

 typedef std::tuple<const NnPredict_Func> NnPredictTestParam;

 const float epsilon = 1e-3f;  // Error threshold for functional equivalence

 class NnPredictTest : public ::testing::TestWithParam<NnPredictTestParam> {
  public:
   virtual void SetUp() {
     const int MAX_NODES2 = NN_MAX_NODES_PER_LAYER * NN_MAX_NODES_PER_LAYER;
     // Allocate two massive buffers on the heap for edge weights and node bias
     // Then set-up the double-dimension arrays pointing into the big buffers
     weights_buf = (float *)aom_malloc(MAX_NODES2 * (NN_MAX_HIDDEN_LAYERS + 1) *
                                       sizeof(*weights_buf));
     bias_buf =
         (float *)aom_malloc(NN_MAX_NODES_PER_LAYER *
                             (NN_MAX_HIDDEN_LAYERS + 1) * sizeof(*bias_buf));
     ASSERT_NE(weights_buf, nullptr);
     ASSERT_NE(bias_buf, nullptr);
     for (int i = 0; i < NN_MAX_HIDDEN_LAYERS + 1; i++) {
       weights[i] = &weights_buf[i * MAX_NODES2];
       bias[i] = &bias_buf[i * NN_MAX_NODES_PER_LAYER];
     }
     target_func_ = GET_PARAM(0);
   }
   virtual void TearDown() {
     aom_free(weights_buf);
     aom_free(bias_buf);
   }
   void RunNnPredictTest(const NN_CONFIG *const shape);
   void RunNnPredictSpeedTest(const NN_CONFIG *const shape, const int run_times);
   void RunNnPredictTest_all(const NN_CONFIG *const shapes,
                             const int num_shapes);
   void RunNnPredictSpeedTest_all(const NN_CONFIG *const shapes,
                                  const int num_shapes, const int run_times);

  private:
   NnPredict_Func target_func_;
   libaom_test::ACMRandom rng_;
   float *weights[NN_MAX_HIDDEN_LAYERS + 1] = { 0 };
   float *bias[NN_MAX_HIDDEN_LAYERS + 1] = { 0 };
   float *weights_buf = nullptr, *bias_buf = nullptr;
 };
 GTEST_ALLOW_UNINSTANTIATED_PARAMETERIZED_TEST(NnPredictTest);

 void NnPredictTest::RunNnPredictTest(const NN_CONFIG *const shape) {
   float inputs[NN_MAX_NODES_PER_LAYER] = { 0 };
   float outputs_test[NN_MAX_NODES_PER_LAYER] = { 0 };
   float outputs_ref[NN_MAX_NODES_PER_LAYER] = { 0 };

   NN_CONFIG nn_config;
   memcpy(&nn_config, shape, sizeof(nn_config));

   char shape_str[32] = { 0 };
   snprintf(shape_str, sizeof(shape_str), "%d", shape->num_inputs);
   for (int layer = 0; layer < shape->num_hidden_layers; layer++)
     snprintf(&shape_str[strlen(shape_str)],
              sizeof(shape_str) - strlen(shape_str), "x%d",
              shape->num_hidden_nodes[layer]);
   snprintf(&shape_str[strlen(shape_str)], sizeof(shape_str) - strlen(shape_str),
            "x%d", shape->num_outputs);

   for (int i = 0; i < NN_MAX_HIDDEN_LAYERS + 1; i++) {
     nn_config.weights[i] = weights[i];
     nn_config.bias[i] = bias[i];
   }

   for (int iter = 0; iter < 10000 && !HasFatalFailure(); ++iter) {
     for (int node = 0; node < shape->num_inputs; node++) {
       inputs[node] = ((float)rng_.Rand31() - (1 << 30)) / (1u << 31);
     }
     for (int layer = 0; layer < shape->num_hidden_layers; layer++) {
       for (int node = 0; node < NN_MAX_NODES_PER_LAYER; node++) {
         bias[layer][node] = ((float)rng_.Rand31() - (1 << 30)) / (1u << 31);
       }
       for (int node = 0; node < NN_MAX_NODES_PER_LAYER * NN_MAX_NODES_PER_LAYER;
            node++) {
         weights[layer][node] = ((float)rng_.Rand31() - (1 << 30)) / (1u << 31);
       }
     }
     // Now the outputs:
     int layer = shape->num_hidden_layers;
     for (int node = 0; node < NN_MAX_NODES_PER_LAYER; node++) {
       bias[layer][node] = ((float)rng_.Rand31() - (1 << 30)) / (1u << 31);
     }
     for (int node = 0; node < NN_MAX_NODES_PER_LAYER * NN_MAX_NODES_PER_LAYER;
          node++) {
       weights[layer][node] = ((float)rng_.Rand31() - (1 << 30)) / (1u << 31);
     }

     av1_nn_predict_c(inputs, &nn_config, 0, outputs_ref);
     target_func_(inputs, &nn_config, 0, outputs_test);

     for (int node = 0; node < shape->num_outputs; node++) {
       if (outputs_ref[node] < epsilon) {
         ASSERT_LE(outputs_test[node], epsilon)
             << "Reference output was near-zero, test output was not ("
             << shape_str << ")";
       } else {
         const float error = outputs_ref[node] - outputs_test[node];
         const float relative_error = fabsf(error / outputs_ref[node]);
         ASSERT_LE(relative_error, epsilon)
             << "Excessive relative error between reference and test ("
             << shape_str << ")";
       }
     }
   }
 }

 void NnPredictTest::RunNnPredictSpeedTest(const NN_CONFIG *const shape,
                                           const int run_times) {
   float inputs[NN_MAX_NODES_PER_LAYER] = { 0 };
   float outputs_test[NN_MAX_NODES_PER_LAYER] = { 0 };
   float outputs_ref[NN_MAX_NODES_PER_LAYER] = { 0 };

   NN_CONFIG nn_config;
   memcpy(&nn_config, shape, sizeof(nn_config));

   for (int i = 0; i < NN_MAX_HIDDEN_LAYERS; i++) {
     nn_config.weights[i] = weights[i];
     nn_config.bias[i] = bias[i];
   }
   // Don't bother actually changing the values for inputs/weights/bias: it
   // shouldn't make any difference for a speed test.

   aom_usec_timer timer;
   aom_usec_timer_start(&timer);
   for (int i = 0; i < run_times; ++i) {
     av1_nn_predict_c(inputs, &nn_config, 0, outputs_ref);
   }
   aom_usec_timer_mark(&timer);
   const double time1 = static_cast<double>(aom_usec_timer_elapsed(&timer));
   aom_usec_timer_start(&timer);
   for (int i = 0; i < run_times; ++i) {
     target_func_(inputs, &nn_config, 0, outputs_test);
   }
   aom_usec_timer_mark(&timer);
   const double time2 = static_cast<double>(aom_usec_timer_elapsed(&timer));

   printf("%d", shape->num_inputs);
   for (int layer = 0; layer < shape->num_hidden_layers; layer++)
     printf("x%d", shape->num_hidden_nodes[layer]);
   printf("x%d: ", shape->num_outputs);
   printf("%7.2f/%7.2fns (%3.2f)\n", time1, time2, time1 / time2);
 }

 // This is all the neural network shapes observed executed in a few different
 // runs of the encoder.  It also conveniently covers all the kernels
 // implemented.
 static const NN_CONFIG shapes[] = {
   { 10, 16, 1, { 64 }, { 0 }, { 0 } }, { 12, 1, 1, { 12 }, { 0 }, { 0 } },
   { 12, 1, 1, { 24 }, { 0 }, { 0 } },  { 12, 1, 1, { 32 }, { 0 }, { 0 } },
   { 18, 4, 1, { 24 }, { 0 }, { 0 } },  { 18, 4, 1, { 32 }, { 0 }, { 0 } },
   { 4, 1, 1, { 16 }, { 0 }, { 0 } },   { 8, 1, 1, { 16 }, { 0 }, { 0 } },
   { 8, 4, 1, { 16 }, { 0 }, { 0 } },   { 8, 1, 1, { 24 }, { 0 }, { 0 } },
   { 8, 1, 1, { 32 }, { 0 }, { 0 } },   { 8, 1, 1, { 64 }, { 0 }, { 0 } },
   { 9, 3, 1, { 32 }, { 0 }, { 0 } },   { 4, 4, 1, { 8 }, { 0 }, { 0 } },
 };

 void NnPredictTest::RunNnPredictTest_all(const NN_CONFIG *const shapes,
                                          const int num_shapes) {
   for (int i = 0; i < num_shapes; i++) RunNnPredictTest(&shapes[i]);
 }

 void NnPredictTest::RunNnPredictSpeedTest_all(const NN_CONFIG *const shapes,
                                               const int num_shapes,
                                               const int run_times) {
   for (int i = 0; i < num_shapes; i++)
     NnPredictTest::RunNnPredictSpeedTest(&shapes[i], run_times);
 }

 TEST_P(NnPredictTest, RandomValues) {
   RunNnPredictTest_all(shapes, sizeof(shapes) / sizeof(*shapes));
 }

 TEST_P(NnPredictTest, DISABLED_Speed) {
   RunNnPredictSpeedTest_all(shapes, sizeof(shapes) / sizeof(*shapes), 10000000);
 }

 #if HAVE_SSE3 && !CONFIG_EXCLUDE_SIMD_MISMATCH
 INSTANTIATE_TEST_SUITE_P(SSE3, NnPredictTest,
                          ::testing::Values(av1_nn_predict_sse3));
 #endif

 #if HAVE_NEON
 INSTANTIATE_TEST_SUITE_P(NEON, NnPredictTest,
                          ::testing::Values(av1_nn_predict_neon));
 #endif

 }  // namespace
	/*
	* Copyright (c) 2018, Alliance for Open Media. All rights reserved
	*
	* This source code is subject to the terms of the BSD 2 Clause License and
	* the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License
	* was not distributed with this source code in the LICENSE file, you can
	* obtain it at www.aomedia.org/license/software. If the Alliance for Open
	* Media Patent License 1.0 was not distributed with this source code in the
	* PATENTS file, you can obtain it at www.aomedia.org/license/patent.
	*/

	#include <tuple>

	#include "third_party/googletest/src/googletest/include/gtest/gtest.h"

	#include "aom/aom_integer.h"
	#include "aom_ports/aom_timer.h"
	#include "av1/encoder/ml.h"
	#include "config/aom_config.h"
	#include "config/aom_dsp_rtcd.h"
	#include "config/av1_rtcd.h"
	#include "test/util.h"
	#include "test/register_state_check.h"
	#include "test/acm_random.h"

	namespace {
	typedef void (NnPredict_Func)(const float const input_nodes,
	const NN_CONFIG *const nn_config,
	int reduce_prec, float *const output);

	typedef std::tuple<const NnPredict_Func> NnPredictTestParam;

	const float epsilon = 1e-3f; // Error threshold for functional equivalence

	class NnPredictTest : public ::testing::TestWithParam<NnPredictTestParam> {
	public:
	virtual void SetUp() {
	const int MAX_NODES2 = NN_MAX_NODES_PER_LAYER * NN_MAX_NODES_PER_LAYER;
	// Allocate two massive buffers on the heap for edge weights and node bias
	// Then set-up the double-dimension arrays pointing into the big buffers
	weights_buf = (float )aom_malloc(MAX_NODES2 (NN_MAX_HIDDEN_LAYERS + 1) *
	sizeof(*weights_buf));
	bias_buf =
	(float )aom_malloc(NN_MAX_NODES_PER_LAYER
	(NN_MAX_HIDDEN_LAYERS + 1) * sizeof(*bias_buf));
	ASSERT_NE(weights_buf, nullptr);
	ASSERT_NE(bias_buf, nullptr);
	for (int i = 0; i < NN_MAX_HIDDEN_LAYERS + 1; i++) {
	weights[i] = &weights_buf[i * MAX_NODES2];
	bias[i] = &bias_buf[i * NN_MAX_NODES_PER_LAYER];
	}
	target_func_ = GET_PARAM(0);
	}
	virtual void TearDown() {
	aom_free(weights_buf);
	aom_free(bias_buf);
	}
	void RunNnPredictTest(const NN_CONFIG *const shape);
	void RunNnPredictSpeedTest(const NN_CONFIG *const shape, const int run_times);
	void RunNnPredictTest_all(const NN_CONFIG *const shapes,
	const int num_shapes);
	void RunNnPredictSpeedTest_all(const NN_CONFIG *const shapes,
	const int num_shapes, const int run_times);

	private:
	NnPredict_Func target_func_;
	libaom_test::ACMRandom rng_;
	float *weights[NN_MAX_HIDDEN_LAYERS + 1] = { 0 };
	float *bias[NN_MAX_HIDDEN_LAYERS + 1] = { 0 };
	float weights_buf = nullptr, bias_buf = nullptr;
	};
	GTEST_ALLOW_UNINSTANTIATED_PARAMETERIZED_TEST(NnPredictTest);

	void NnPredictTest::RunNnPredictTest(const NN_CONFIG *const shape) {
	float inputs[NN_MAX_NODES_PER_LAYER] = { 0 };
	float outputs_test[NN_MAX_NODES_PER_LAYER] = { 0 };
	float outputs_ref[NN_MAX_NODES_PER_LAYER] = { 0 };

	NN_CONFIG nn_config;
	memcpy(&nn_config, shape, sizeof(nn_config));

	char shape_str[32] = { 0 };
	snprintf(shape_str, sizeof(shape_str), "%d", shape->num_inputs);
	for (int layer = 0; layer < shape->num_hidden_layers; layer++)
	snprintf(&shape_str[strlen(shape_str)],
	sizeof(shape_str) - strlen(shape_str), "x%d",
	shape->num_hidden_nodes[layer]);
	snprintf(&shape_str[strlen(shape_str)], sizeof(shape_str) - strlen(shape_str),
	"x%d", shape->num_outputs);

	for (int i = 0; i < NN_MAX_HIDDEN_LAYERS + 1; i++) {
	nn_config.weights[i] = weights[i];
	nn_config.bias[i] = bias[i];
	}

	for (int iter = 0; iter < 10000 && !HasFatalFailure(); ++iter) {
	for (int node = 0; node < shape->num_inputs; node++) {
	inputs[node] = ((float)rng_.Rand31() - (1 << 30)) / (1u << 31);
	}
	for (int layer = 0; layer < shape->num_hidden_layers; layer++) {
	for (int node = 0; node < NN_MAX_NODES_PER_LAYER; node++) {
	bias[layer][node] = ((float)rng_.Rand31() - (1 << 30)) / (1u << 31);
	}
	for (int node = 0; node < NN_MAX_NODES_PER_LAYER * NN_MAX_NODES_PER_LAYER;
	node++) {
	weights[layer][node] = ((float)rng_.Rand31() - (1 << 30)) / (1u << 31);
	}
	}
	// Now the outputs:
	int layer = shape->num_hidden_layers;
	for (int node = 0; node < NN_MAX_NODES_PER_LAYER; node++) {
	bias[layer][node] = ((float)rng_.Rand31() - (1 << 30)) / (1u << 31);
	}
	for (int node = 0; node < NN_MAX_NODES_PER_LAYER * NN_MAX_NODES_PER_LAYER;
	node++) {
	weights[layer][node] = ((float)rng_.Rand31() - (1 << 30)) / (1u << 31);
	}

	av1_nn_predict_c(inputs, &nn_config, 0, outputs_ref);
	target_func_(inputs, &nn_config, 0, outputs_test);

	for (int node = 0; node < shape->num_outputs; node++) {
	if (outputs_ref[node] < epsilon) {
	ASSERT_LE(outputs_test[node], epsilon)
	<< "Reference output was near-zero, test output was not ("
	<< shape_str << ")";
	} else {
	const float error = outputs_ref[node] - outputs_test[node];
	const float relative_error = fabsf(error / outputs_ref[node]);
	ASSERT_LE(relative_error, epsilon)
	<< "Excessive relative error between reference and test ("
	<< shape_str << ")";
	}
	}
	}
	}

	void NnPredictTest::RunNnPredictSpeedTest(const NN_CONFIG *const shape,
	const int run_times) {
	float inputs[NN_MAX_NODES_PER_LAYER] = { 0 };
	float outputs_test[NN_MAX_NODES_PER_LAYER] = { 0 };
	float outputs_ref[NN_MAX_NODES_PER_LAYER] = { 0 };

	NN_CONFIG nn_config;
	memcpy(&nn_config, shape, sizeof(nn_config));

	for (int i = 0; i < NN_MAX_HIDDEN_LAYERS; i++) {
	nn_config.weights[i] = weights[i];
	nn_config.bias[i] = bias[i];
	}
	// Don't bother actually changing the values for inputs/weights/bias: it
	// shouldn't make any difference for a speed test.

	aom_usec_timer timer;
	aom_usec_timer_start(&timer);
	for (int i = 0; i < run_times; ++i) {
	av1_nn_predict_c(inputs, &nn_config, 0, outputs_ref);
	}
	aom_usec_timer_mark(&timer);
	const double time1 = static_cast<double>(aom_usec_timer_elapsed(&timer));
	aom_usec_timer_start(&timer);
	for (int i = 0; i < run_times; ++i) {
	target_func_(inputs, &nn_config, 0, outputs_test);
	}
	aom_usec_timer_mark(&timer);
	const double time2 = static_cast<double>(aom_usec_timer_elapsed(&timer));

	printf("%d", shape->num_inputs);
	for (int layer = 0; layer < shape->num_hidden_layers; layer++)
	printf("x%d", shape->num_hidden_nodes[layer]);
	printf("x%d: ", shape->num_outputs);
	printf("%7.2f/%7.2fns (%3.2f)\n", time1, time2, time1 / time2);
	}

	// This is all the neural network shapes observed executed in a few different
	// runs of the encoder. It also conveniently covers all the kernels
	// implemented.
	static const NN_CONFIG shapes[] = {
	{ 10, 16, 1, { 64 }, { 0 }, { 0 } }, { 12, 1, 1, { 12 }, { 0 }, { 0 } },
	{ 12, 1, 1, { 24 }, { 0 }, { 0 } }, { 12, 1, 1, { 32 }, { 0 }, { 0 } },
	{ 18, 4, 1, { 24 }, { 0 }, { 0 } }, { 18, 4, 1, { 32 }, { 0 }, { 0 } },
	{ 4, 1, 1, { 16 }, { 0 }, { 0 } }, { 8, 1, 1, { 16 }, { 0 }, { 0 } },
	{ 8, 4, 1, { 16 }, { 0 }, { 0 } }, { 8, 1, 1, { 24 }, { 0 }, { 0 } },
	{ 8, 1, 1, { 32 }, { 0 }, { 0 } }, { 8, 1, 1, { 64 }, { 0 }, { 0 } },
	{ 9, 3, 1, { 32 }, { 0 }, { 0 } }, { 4, 4, 1, { 8 }, { 0 }, { 0 } },
	};

	void NnPredictTest::RunNnPredictTest_all(const NN_CONFIG *const shapes,
	const int num_shapes) {
	for (int i = 0; i < num_shapes; i++) RunNnPredictTest(&shapes[i]);
	}

	void NnPredictTest::RunNnPredictSpeedTest_all(const NN_CONFIG *const shapes,
	const int num_shapes,
	const int run_times) {
	for (int i = 0; i < num_shapes; i++)
	NnPredictTest::RunNnPredictSpeedTest(&shapes[i], run_times);
	}

	TEST_P(NnPredictTest, RandomValues) {
	RunNnPredictTest_all(shapes, sizeof(shapes) / sizeof(*shapes));
	}

	TEST_P(NnPredictTest, DISABLED_Speed) {
	RunNnPredictSpeedTest_all(shapes, sizeof(shapes) / sizeof(*shapes), 10000000);
	}

	#if HAVE_SSE3 && !CONFIG_EXCLUDE_SIMD_MISMATCH
	INSTANTIATE_TEST_SUITE_P(SSE3, NnPredictTest,
	::testing::Values(av1_nn_predict_sse3));
	#endif

	#if HAVE_NEON
	INSTANTIATE_TEST_SUITE_P(NEON, NnPredictTest,
	::testing::Values(av1_nn_predict_neon));
	#endif

	} // namespace