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/* * Copyright (c) 2019, 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 <assert.h> #include <math.h> #include <stdio.h> #include "gtest/gtest.h" #include "config/av1_rtcd.h" #include "aom_ports/aom_timer.h" #include "av1/encoder/cnn.h" #include "av1/encoder/partition_cnn_weights.h" #include "test/acm_random.h" #include "test/function_equivalence_test.h" #include "test/util.h" #define SQR(x) ((x) * (x)) // Best possible pixelwise guaranteed precision given each float has at most // 3 specified decimals. #define PIXELWISE_FLOAT_TOL 1E-2 #define MSE_FLOAT_TOL 1E-6 #define MSE_INT_TOL 0 // CNN convolve pixelwise error threshold for functional equivalence. #define CNN_CONVOLVE_PIXELWISE_FLOAT_TOL 1E-3f namespace { class CNNTest : public ::testing::Test { protected: static void RunCNNTest(int image_width, int image_height, const float *input, const float *expected, const CNN_CONFIG *cnn_config, int in_stride, CNN_THREAD_DATA *thread_data, double tolerance) { int out_width, out_height, out_channels; av1_find_cnn_output_size(image_width, image_height, cnn_config, &out_width, &out_height, &out_channels); const int out_size = out_width * out_height; const int out_stride = out_width; float *output_ = (float *)aom_malloc(sizeof(*output_) * out_size * out_channels); ASSERT_NE(output_, nullptr); float *output[CNN_MAX_CHANNELS] = { nullptr }; for (int channel = 0; channel < out_channels; ++channel) { output[channel] = output_ + (channel * out_size); } const int num_outputs = 1; const int output_chs[1] = { out_channels }; const int output_strides[1] = { out_stride }; CNN_MULTI_OUT output_struct = { num_outputs, output_chs, output_strides, output }; RunMultiOutCNNTest(&input, image_width, image_height, in_stride, cnn_config, thread_data, &output_struct, &expected, tolerance); aom_free(output_); } static void RunMultiOutCNNTest(const float **input, int image_width, int image_height, int in_stride, const CNN_CONFIG *cnn_config, CNN_THREAD_DATA *thread_data, CNN_MULTI_OUT *output, const float **expected, double tolerance) { const int num_outputs = output->num_outputs; const int *output_chs = output->output_channels; int *out_widths = (int *)aom_calloc(num_outputs, sizeof(*out_widths)); int *out_heights = (int *)aom_calloc(num_outputs, sizeof(*out_heights)); int *not_used = (int *)aom_calloc(num_outputs, sizeof(*not_used)); ASSERT_NE(out_widths, nullptr); ASSERT_NE(out_heights, nullptr); ASSERT_NE(not_used, nullptr); av1_find_cnn_output_size(image_width, image_height, cnn_config, out_widths, out_heights, not_used); ASSERT_TRUE(av1_cnn_predict(input, image_width, image_height, in_stride, cnn_config, thread_data, output)); int channel_offset = 0; for (int output_idx = 0; output_idx < num_outputs; output_idx++) { const float *expected_out = expected[output_idx]; const int curr_output_chs = output_chs[output_idx]; const int out_size = out_widths[output_idx] * out_heights[output_idx]; double mse = 0; int expected_ite = 0; for (int channel = 0; channel < curr_output_chs; ++channel) { const float *buf_out = output->output_buffer[channel_offset]; for (int i = 0; i < out_size; ++i) { EXPECT_NEAR(expected_out[expected_ite], buf_out[i], PIXELWISE_FLOAT_TOL) << " output " << output_idx << " channel " << channel << " pixel " << expected_ite % out_size << ": " << expected_out[expected_ite] << "/" << buf_out[i] << std::endl; mse += SQR(expected_out[expected_ite] - buf_out[i]); expected_ite++; } channel_offset++; } mse /= (out_size * curr_output_chs); EXPECT_LE(mse, tolerance) << " output " << output_idx << std::endl; } aom_free(out_widths); aom_free(out_heights); aom_free(not_used); } static void AssignLayerWeightsBiases(CNN_CONFIG *cnn_config, float *weights, float *bias) { size_t weight_offset = 0; size_t bias_offset = 0; for (int layer = 0; layer < cnn_config->num_layers; ++layer) { CNN_LAYER_CONFIG *layer_config = &cnn_config->layer_config[layer]; layer_config->weights = weights + weight_offset; layer_config->bias = bias + bias_offset; weight_offset += layer_config->filter_width * layer_config->filter_height * layer_config->in_channels * layer_config->out_channels; bias_offset += layer_config->out_channels; ASSERT_NE(layer_config->weights, nullptr); ASSERT_NE(layer_config->bias, nullptr); } } }; } // namespace TEST_F(CNNTest, TestMultilayerConvolution) { int image_height = 16; int image_width = 16; int filter_height = 5; int filter_width = 4; float input[] = { -3, 1, -3, 2, -2, -2, 2, -2, 1, -2, -3, 1, 2, 2, 2, -2, 0, 1, -1, -3, -1, -1, 1, 0, -3, 1, 0, -1, 1, 0, 0, -3, -3, -3, 0, 2, 1, -1, 2, 0, 1, -3, -1, 2, 2, 1, -2, 0, -1, 0, -2, -2, -1, 1, 0, 0, 0, -2, -2, -2, 1, 1, -2, 1, 1, -2, -2, 1, -2, -1, -2, -3, 2, -3, -1, 1, 0, -2, -2, -2, 1, -2, -2, -1, -1, 2, 2, 2, -1, 1, -3, -3, 0, 2, 0, 2, 1, -3, -3, 1, 2, 2, 1, -2, -3, 0, -3, 0, -3, -2, 0, 1, 1, 0, -3, 2, -1, 2, 1, 0, 1, -2, 1, -1, -1, 2, 0, -2, -3, 1, 1, -2, -1, -3, -3, -1, 0, -3, -2, 0, 0, 1, 0, -3, -2, -1, 1, 0, 2, 1, 0, -3, -2, -3, -3, -1, 0, -2, 2, -1, -3, 0, -1, -1, 2, 0, -3, -2, -1, 0, 0, 1, -2, 1, 2, 1, 2, 2, -3, 2, -1, 0, 0, -1, 0, 2, 2, -1, 2, -2, 1, 1, -3, -3, 1, -1, -1, -2, 2, -2, -2, 2, -1, -3, 2, -3, 1, -1, -1, -3, 1, -1, 1, 0, -3, -3, 1, -3, -3, 0, 2, 2, -2, -1, 2, 0, 2, 1, -1, -3, 0, 0, -1, -1, 1, 0, 2, 0, -3, 2, 1, 0, 1, -3, 2, -3, -3, -1, -3, -3, 2, 0, 2, -2, 1, -1, }; float weights[] = { -2, 2, -2, 2, -1, -3, 2, 2, 0, 0, -3, -1, -2, -3, 1, -1, 0, 0, 0, 2, -2, 2, -2, -3, 1, 1, 1, -3, -1, 0, 1, 2, -2, 0, -1, -3, -1, -2, 2, -3, -3, 1, -2, -3, 0, 2, 1, -3, -3, -1, -3, -2, -1, -3, -1, -3, -2, -1, -3, -1, -2, -2, -3, 2, 0, -3, 0, -3, -3, 1, -3, -1, 0, -1, 1, 1, -1, 1, -2, 0, 2, 0, -3, 1, -1, -1, 2, 0, 1, -3, -3, 1, 2, -3, -3, 1, -3, 2, 0, -3, 1, 2, 2, -2, -1, -2, 1, 1, 0, -2, -2, 1, 2, -1, -3, 1, -2, 2, -3, -2, -3, 2, 1, 0, -2, 0, 1, -3, 2, -2, -2, 0, 2, -3, 2, 0, 0, 1, -2, 1, 1, -2, -1, -2, 1, -2, 0, -2, -2, 0, -1, -1, -3, -3, -3, 1, -3, -2, 2, -1, 2, 0, 2, -2, 2, -2, 1, -3, -3, -1, 0, 2, 2, 1, -1, -3, -1, -3, 2, 1, -2, 0, -3, -1, -3, -1, 2, 1, 0, 2, -1, 1, 0, 1, 2, -1, -2, 2, 1, -3, -1, -3, 0, 1, -2, 0, -2, -3, 0, -2, 2, 2, 0, 0, 2, -3, 2, -3, -2, 1, 2, -3, -3, -1, -3, 0, -3, -3, -2, -2, -2, 0, 0, 1, 0, 0, -1, 0, 0, -3, 0, -3, -1, -2, 1, -2, -1, 2, -2, 0, 0, 1, 0, -2, -1, 0, -3, 1, 0, -1, -3, 1, -1, 1, -1, -3, 1, 0, 1, 1, -1, 2, 2, 0, 0, 1, -3, 2, -2, -2, -3, -2, -1, -2, 2, 0, 2, -2, -3, -1, -3, 2, 2, -1, 2, 2, -1, 0, -3, 1, }; float bias[] = { 1, -1, 0, 1, 1, 1, -2, }; float expected_same[] = { -1125, 2926, 6406, 631, -1244, 97, -1454, 2526, 1065, 3292, 3464, 2553, -330, 532, 1038, 1182, -402, 3758, 3392, 9854, 4365, 1408, 4736, 3134, 3838, 2409, 3221, 4350, 6750, 4045, 815, 1188, 2959, 9802, 9590, 4572, 5740, 4253, 1701, 7974, 7012, 6854, 7093, 3907, 4539, 3886, 4267, 3505, 465, 7824, 9219, 10026, 7968, 957, 2295, 5594, 10811, 9641, 5950, 10043, 8783, 3132, 1421, 1110, 4108, 13929, 10660, -84, -61, 3932, -180, 6811, 13393, 15147, 15640, 9337, 6961, 3808, 1604, 1398, 1047, 6739, 10144, 6517, 4698, 2678, 7389, 2595, 5248, 12075, 11272, 13951, 8820, 1090, 2199, 2206, 2788, 12116, 6683, 2612, -291, 3183, 9414, 12316, 14524, 12333, 13208, 7832, 4664, 4657, 3534, 1298, -666, 4250, 7707, 9103, 5760, 688, 9571, 15782, 14203, 14878, 17339, 14684, 8690, 5671, 875, 1429, 1531, 6173, 2984, 5558, 2996, 7928, 6733, 16117, 15262, 12757, 7980, 3923, 4795, 5973, 2051, 455, -1922, 1816, 5906, 3321, 10908, 10910, 7377, 12204, 12809, 11195, 7451, 6666, 74, -1645, -35, -391, 3813, 7324, 892, 1656, 6095, 12193, 14648, 12156, 14663, 10251, 10325, 7821, 3925, 323, 697, 442, 1324, 4669, 7002, 5485, 5171, 5086, 10582, 11053, 9709, 11353, 8543, 5256, 2873, 235, -628, 1496, 1878, -867, 3420, 6865, 5937, 10182, 13277, 10069, 10789, 5998, 624, -2082, 4417, 1258, -1080, -819, -1430, 1033, 5220, 6335, 8471, 8980, 11908, 14430, 12584, 8404, 1576, -803, 985, 1481, 1367, -193, 873, 3684, 2288, 6676, 9477, 11155, 9602, 9707, 10507, 4739, 3174, -575, -178, 3002, 1710, 423, -477, 554, 3088, 2029, 5113, 5000, 3771, 6090, 5365, 1185, 2855, 399, -312, -1577, 176, 955, }; float expected_replicate[] = { 13768, 13528, 12999, 6906, 4618, 4043, 2611, 9955, 6685, 4776, 2753, 1036, 3063, 4544, 5183, 7349, 12451, 12501, 9131, 12753, 8908, 4058, 6299, 7542, 7115, 3307, 3360, 3543, 9754, 7808, 5991, 9019, 14320, 14919, 12492, 6871, 7373, 3336, 2085, 10604, 9377, 6882, 5009, 3103, 6220, 6278, 7588, 10196, 11045, 11563, 11842, 11911, 8279, 2030, 1858, 6368, 12123, 9909, 6347, 10345, 9365, 4038, 1673, 3051, 16492, 16649, 12276, 408, -301, 4122, -654, 7864, 14038, 15279, 15315, 9744, 8243, 5298, 746, 380, 9824, 9124, 10895, 6640, 4712, 2669, 6980, 2759, 5385, 12345, 11336, 13129, 8600, 2370, 3682, 5219, 12407, 13123, 6784, 2612, -291, 3183, 9414, 12316, 14524, 12333, 13397, 7543, 3916, 4153, 4477, 4314, 7983, 8418, 9163, 9103, 5760, 688, 9571, 15782, 14203, 14878, 17718, 14570, 7940, 6642, 5094, 7133, 9964, 10219, 3224, 5558, 2996, 7928, 6733, 16117, 15262, 12757, 7958, 4401, 5187, 5476, 5529, 6055, 2206, 3909, 6015, 3321, 10908, 10910, 7377, 12204, 12809, 11195, 6967, 6840, 481, -1600, 274, 1, 10373, 8514, 1123, 2117, 6758, 12736, 16223, 13585, 15988, 11771, 10600, 7918, 4156, 2840, 3111, 3287, 6359, 7652, 8813, 6530, 6967, 7789, 13671, 13990, 13247, 13241, 9836, 5251, 3024, 2313, 1834, 4187, 2637, -1312, 2139, 7378, 7665, 11933, 15591, 15314, 15678, 9531, 2820, -1516, 3400, 1314, 22, 363, -2896, -898, 5906, 7308, 10650, 12975, 16978, 20370, 18817, 12381, 4118, -861, -137, 236, 1802, 1632, -350, 2334, 3400, 8680, 14064, 18216, 18675, 21765, 22871, 11491, 4937, -1555, -11, 1669, 2392, 3265, -5254, -217, 5001, 8063, 13444, 18884, 19706, 22794, 21064, 9545, 6689, -7, 289, -2021, 504, 2347, }; float expected_valid[] = { 2612, -291, 3183, 9414, 12316, 14524, 12333, 9103, 5760, 688, 9571, 15782, 14203, 14878, 5558, 2996, 7928, 6733, 16117, 15262, 12757, 3321, 10908, 10910, 7377, 12204, 12809, 11195, }; CNN_CONFIG cnn_config = { 3, 0, 0, 0, 0, { { 1, filter_width, filter_height, 3, 1, 1, 0, nullptr, nullptr, PADDING_SAME_ZERO, NONE, 0, 0, BRANCH_NO_COPY, BRANCH_NOC, {}, {}, -1, }, { 3, filter_width, filter_height, 3, 1, 1, 0, nullptr, nullptr, PADDING_SAME_ZERO, NONE, 0, 0, BRANCH_NO_COPY, BRANCH_NOC, {}, {}, -1, }, { 3, filter_width, filter_height, 1, 1, 1, 0, nullptr, nullptr, PADDING_SAME_ZERO, NONE, 0, 0, BRANCH_NO_COPY, BRANCH_NOC, {}, {}, 0, }, } }; // Weights and biases need to be specified separately because // of the offset. AssignLayerWeightsBiases(&cnn_config, weights, bias); CNN_THREAD_DATA thread_data = { 1, nullptr }; RunCNNTest(image_width, image_height, input, expected_same, &cnn_config, image_width, &thread_data, MSE_INT_TOL); for (int i = 0; i < cnn_config.num_layers; ++i) { cnn_config.layer_config[i].pad = PADDING_SAME_REPLICATE; } RunCNNTest(image_width, image_height, input, expected_replicate, &cnn_config, image_width, &thread_data, MSE_INT_TOL); for (int i = 0; i < cnn_config.num_layers; ++i) { cnn_config.layer_config[i].pad = PADDING_VALID; } RunCNNTest(image_width, image_height, input, expected_valid, &cnn_config, image_width, &thread_data, MSE_INT_TOL); } TEST_F(CNNTest, TestRELUSingleLayer) { int image_width = 8; int image_height = 8; int filter_height = 5; int filter_width = 4; float input[] = { 0, -2, -3, 1, -1, 2, -2, 1, -3, -1, 0, 1, -2, -3, -2, -2, 1, -3, 2, -3, -1, -1, 2, 0, -2, -3, 0, -2, -3, 1, -1, -1, 2, -2, 0, -2, -3, -3, 1, 1, -1, 1, 0, 1, -3, 0, 2, 2, 0, -3, 1, -3, 2, -2, 1, -1, -1, -2, -3, -2, -1, -3, -2, -1, }; float expected_same[] = { 9, 0, 1, 1, 0, 3, 0, 19, 0, 12, 10, 0, 0, 0, 5, 0, 0, 18, 21, 7, 19, 4, 3, 0, 0, 9, 16, 0, 11, 16, 0, 11, 12, 2, 0, 11, 0, 16, 6, 0, 8, 22, 13, 10, 12, 0, 0, 0, 0, 1, 2, 12, 29, 6, 10, 0, 13, 0, 0, 5, 8, 10, 0, 0, }; float expected_replicate[] = { 18, 17, 12, 2, 0, 0, 5, 11, 0, 17, 22, 6, 0, 0, 17, 0, 0, 18, 21, 7, 19, 4, 3, 5, 3, 9, 16, 0, 11, 16, 0, 3, 3, 2, 0, 11, 0, 16, 6, 0, 17, 22, 13, 10, 12, 0, 0, 0, 0, 4, 1, 10, 30, 7, 10, 0, 23, 8, 0, 13, 15, 19, 8, 10, }; float expected_valid[] = { 18, 21, 7, 19, 4, 9, 16, 0, 11, 16, 2, 0, 11, 0, 16, 22, 13, 10, 12, 0, }; float weights[] = { -2, -3, 1, 2, 2, -2, -3, 0, -3, 2, 2, -3, -3, -2, 0, 1, 2, 0, -1, -1, }; float bias[] = { -3 }; CNN_CONFIG cnn_config = { 1, 0, 0, 0, 0, { { 1, filter_width, filter_height, 1, 1, 1, 0, weights, bias, PADDING_SAME_ZERO, RELU, 0, 0, BRANCH_NO_COPY, BRANCH_NOC, {}, {}, 0, } } }; CNN_THREAD_DATA thread_data = { 1, nullptr }; RunCNNTest(image_width, image_height, input, expected_same, &cnn_config, image_width, &thread_data, MSE_INT_TOL); cnn_config.layer_config[0].pad = PADDING_SAME_REPLICATE; RunCNNTest(image_width, image_height, input, expected_replicate, &cnn_config, image_width, &thread_data, MSE_INT_TOL); cnn_config.layer_config[0].pad = PADDING_VALID; RunCNNTest(image_width, image_height, input, expected_valid, &cnn_config, image_width, &thread_data, MSE_INT_TOL); } TEST_F(CNNTest, TestVaryingStridesVaryingDimImages) { float weights[] = { 1, -5, -3, -4, -1, 1, 2, -3, 2, 2, -1, 1, -5, 1, 1, -3, -5, 3, 1, 4, -2, -5, -2, -3, -5, 0, -1, -5, 2, -2, -2, 1, -2, -4, 1, 3, -2, 2, 0, -3, 2, -3, -2, -3, }; float bias[] = { 2 }; CNN_CONFIG cnn_config = { 1, 0, 0, 0, 0, { { 1, 4, 11, 1, 7, 6, 0, weights, bias, PADDING_SAME_ZERO, NONE, 0, 0, BRANCH_NO_COPY, BRANCH_NOC, {}, {}, 0, }, } }; int image_height = 24; int image_width = 17; float input[] = { -1, -3, 4, 4, -5, 4, 3, -5, -1, -3, 4, -4, 2, -3, 3, -5, 2, -1, -5, 1, -1, 3, 1, -3, -3, 4, 0, 2, -3, -5, -5, -4, 0, -5, -2, -3, -1, -2, 2, -5, 4, 4, 0, -4, -3, 1, -3, -5, -4, -4, 1, -2, -3, 3, -3, -3, -1, -5, -5, -2, 3, 1, -1, -5, -5, 1, -4, -2, -1, -2, -4, -4, 2, -2, 2, 1, -2, -4, -1, 1, -2, -5, 3, -2, -1, -1, -5, -3, 1, -2, -2, -3, -1, -2, -4, -2, 1, -4, -1, 4, 3, -4, 0, 4, 2, 2, 4, -3, -5, 2, 2, 1, -1, -4, -2, 1, 3, 2, 0, 4, -1, -3, 2, 1, -4, 2, 2, -4, -2, 0, -2, -1, 4, 4, 2, 3, -4, 2, -4, -5, 4, -1, -3, -1, 0, -4, 1, 3, -1, -3, -5, 3, -2, -4, 1, 2, -2, -3, -3, -5, 1, -3, -1, 0, -1, 3, -4, -1, -5, -5, 1, 0, 0, -2, -2, 2, -2, 0, 0, 2, 0, -3, 0, -1, -4, -4, -1, 3, -4, -4, -1, 0, -5, -3, -2, 4, -3, -4, -4, 0, -5, 1, -2, -3, -3, -4, 4, 3, 4, 3, 3, -1, 3, 1, -3, -2, 3, 3, 0, 2, -4, -3, 2, 2, 0, -2, 4, -2, 2, -2, -1, -4, -2, 2, -4, 3, -1, 4, 1, 1, 4, -1, -4, -4, 1, 1, -2, 4, -1, 3, 2, -3, 4, 3, 1, 4, 0, -4, 2, 0, 2, 4, -2, -2, 4, 2, -1, -2, 1, -3, 2, 3, -5, -3, 4, 4, 2, -5, -4, -5, -2, -4, 2, 0, 2, -5, 4, -4, -2, -5, 2, 1, 0, 4, 1, -2, -3, -4, -3, -4, 3, 3, 2, 0, -3, 1, -5, 4, 0, 4, -1, 3, -5, -5, -2, -1, -1, 4, 3, 3, 4, 3, -4, 4, -3, -3, -1, -4, -1, -4, -1, -2, 4, -2, -4, 4, 4, -3, -4, -1, 1, 2, -1, -2, -2, 3, 2, 2, -3, 0, -1, 0, 3, 2, -5, 0, -4, 0, 0, 2, -4, -1, -1, 0, -2, 0, 1, 0, 0, 4, -5, -1, -5, 2, -1, 0, 2, -1, 1, 3, -3, -5, -2, -3, 4, -2, -2, -1, -3, -4, -1, -2, -4, 1, 4, -3, -2, -1, 3, -3, -2, 3, 2, 1, -4, -3, -5, 1, }; float expected_1[] = { 41, -26, 5, 76, 13, 83, -21, 53, -54, -14, 21, 121, }; CNN_THREAD_DATA thread_data = { 1, nullptr }; RunCNNTest(image_width, image_height, input, expected_1, &cnn_config, image_width, &thread_data, MSE_INT_TOL); cnn_config.layer_config[0].skip_width = 6; cnn_config.layer_config[0].skip_height = 7; float expected_2[] = { 21, -50, 41, 20, 72, 127, -21, 103, 62, -37, 83, -3, }; RunCNNTest(image_width, image_height, input, expected_2, &cnn_config, image_width, &thread_data, MSE_INT_TOL); cnn_config.layer_config[0].skip_width = 3; cnn_config.layer_config[0].skip_height = 10; float expected_3[] = { -26, -21, -35, 69, 49, 4, -51, -43, -56, -41, 15, -44, 40, -62, 63, 38, 27, 47, }; RunCNNTest(image_width, image_height, input, expected_3, &cnn_config, image_width, &thread_data, MSE_INT_TOL); cnn_config.layer_config[0].skip_width = 10; cnn_config.layer_config[0].skip_height = 3; float expected_4[] = { 21, 49, 28, 87, 50, 40, 102, 81, 58, 85, 51, 66, 36, 19, -37, -45, }; RunCNNTest(image_width, image_height, input, expected_4, &cnn_config, image_width, &thread_data, MSE_INT_TOL); } TEST_F(CNNTest, TestMaxPool) { int image_width = 8; int image_height = 8; int stride = 3; float input[] = { 1, -4, -4, 8, 0, 7, -5, -2, 8, 2, 2, 8, 5, -1, -1, 9, -3, 0, -2, 0, 6, 3, -4, 8, 7, 8, 7, -1, 4, -1, 0, 2, -5, -2, 8, 5, 5, 4, 2, 7, 4, 6, 2, 8, 8, -4, -3, -4, -3, -1, 2, 3, 3, 6, -5, 8, 9, 5, 0, -2, -1, 6, 5, 7, }; float expected[] = { 49, 58, 70, 68, 68, 70, 48, 57, 88, }; float weights[] = { 3, 1, 3, 4, -1, 5, -2, 1, -4, }; float bias[] = { -3, }; CNN_CONFIG cnn_config = { 1, 0, 0, 0, 0, { { 1, 3, 3, 1, stride, stride, 1, weights, bias, PADDING_SAME_ZERO, NONE, 0, 0, BRANCH_NO_COPY, BRANCH_NOC, {}, {}, 0, } } }; CNN_THREAD_DATA thread_data = { 1, nullptr }; RunCNNTest(image_width, image_height, input, expected, &cnn_config, image_width, &thread_data, MSE_INT_TOL); } TEST_F(CNNTest, TestDeconvolveNonActivationSingleLayerSingleKernel) { int image_width = 4; int image_height = 7; float input[] = { 9, 6, 181, 9, 218, 30, 80, 108, 68, 216, 70, 128, 179, 228, 33, 212, 34, 14, 48, 27, 230, 23, 202, 113, 80, 56, 122, 112, }; float expected_1_same[] = { 15, -30, 36, -525, 377, -193, 558, 531, 6, -24, -15, 124, 166, -561, -356, -754, -3, -3, -3, -3, -3, -3, -3, -3, 433, -311, 711, 381, 247, -317, 453, 129, 215, -627, -409, -885, 17, -255, -55, -647, -3, -3, -3, -3, -3, -3, -3, -3, 133, -719, 633, -225, 785, 191, 463, 79, 65, 9, 77, -853, -365, -949, -15, -667, -3, -3, -3, -3, -3, -3, -3, -3, 355, -866, 990, 207, 747, 12, 520, -116, 176, -312, -133, -1370, -426, -802, 143, -771, -3, -3, -3, -3, -3, -3, -3, -3, 65, -79, 127, -59, 135, -90, 195, 114, 31, -91, -57, -133, 17, -176, -72, -276, -3, -3, -3, -3, -3, -3, -3, -3, 457, -302, 733, 58, 470, -475, 829, 490, 227, -670, -440, -790, 153, -588, -294, -1150, -3, -3, -3, -3, -3, -3, -3, -3, 157, -251, 349, -185, 409, -293, 587, 251, 77, -187, -107, -369, 7, -481, -135, -827, -3, -3, -3, -3, -3, -3, -3, -3, }; float expected_1_valid[] = { -30, 15, -30, 36, -525, 377, -193, 558, 531, 24, 24, 6, 6, -24, -15, 124, 166, -561, -356, -754, -21, -39, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -657, 433, -311, 711, 381, 247, -317, 453, 129, 321, 321, 215, 215, -627, -409, -885, 17, -255, -55, -647, -219, -435, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -207, 133, -719, 633, -225, 785, 191, 463, 79, 381, 381, 65, 65, 9, 77, -853, -365, -949, -15, -667, -259, -515, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -540, 355, -866, 990, 207, 747, 12, 520, -116, 633, 633, 176, 176, -312, -133, -1370, -426, -802, 143, -771, -427, -851, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -105, 65, -79, 127, -59, 135, -90, 195, 114, 78, 78, 31, 31, -91, -57, -133, 17, -176, -72, -276, -57, -111, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -693, 457, -302, 733, 58, 470, -475, 829, 490, 336, 336, 227, 227, -670, -440, -790, 153, -588, -294, -1150, -229, -455, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -243, 157, -251, 349, -185, 409, -293, 587, 251, 333, 333, 77, 77, -187, -107, -369, 7, -481, -135, -827, -227, -451, }; float weights_1[] = { -3, 2, -1, 3, 3, 1, 1, -3, -2, -4 }; float bias_1[] = { -3 }; CNN_CONFIG cnn_config = { 1, 0, 0, 0, 0, { { 1, 5, 2, 1, 2, 3, 0, weights_1, bias_1, PADDING_SAME_ZERO, NONE, 1, 0, BRANCH_NO_COPY, BRANCH_NOC, {}, {}, 0, } } }; CNN_THREAD_DATA thread_data = { 1, nullptr }; RunCNNTest(image_width, image_height, input, expected_1_same, &cnn_config, image_width, &thread_data, MSE_INT_TOL); // Change padding to valid cnn_config.layer_config[0].pad = PADDING_VALID; RunCNNTest(image_width, image_height, input, expected_1_valid, &cnn_config, image_width, &thread_data, MSE_INT_TOL); float expected_12_same[] = { 15, -12, 6, 36, -9, -528, 377, -184, 513, 558, -12, 24, 6, -30, -15, -33, -21, 166, 154, -546, -356, -718, -30, -21, 433, -221, 561, 711, -33, -153, 247, -83, -87, 453, -111, 321, 215, -657, -409, -845, -93, 17, -43, -243, -55, -215, -327, -219, 133, -71, -447, 633, -219, 435, 785, -73, -177, 463, -131, 381, 65, -207, 77, -59, -651, -365, -797, -213, -15, -155, -387, -259, 355, -182, -150, 990, -231, 582, 747, -36, -540, 520, -215, 633, 176, -540, -133, -491, -687, -426, -882, -102, 143, 77, -639, -427, 65, -37, 57, 127, -17, -105, 135, -51, 60, 195, -30, 78, 31, -105, -57, -125, -45, 17, -11, -147, -72, -168, -84, -57, 457, -233, 618, 733, -26, -540, 470, -205, 264, 829, -116, 336, 227, -693, -440, -900, -72, 153, 107, -609, -294, -698, -342, -229, 157, -83, 69, 349, -59, -201, 409, -125, 27, 587, -115, 333, 77, -243, -107, -267, -171, 7, -105, -369, -135, -379, -339, -227, }; float expected_12_valid[] = { -30, 15, -12, 6, 36, -9, -528, 377, -184, 513, 558, -12, 24, 24, 6, 6, -30, -15, -33, -21, 166, 154, -546, -356, -718, -30, -21, -39, -657, 433, -221, 561, 711, -33, -153, 247, -83, -87, 453, -111, 321, 321, 215, 215, -657, -409, -845, -93, 17, -43, -243, -55, -215, -327, -219, -435, -207, 133, -71, -447, 633, -219, 435, 785, -73, -177, 463, -131, 381, 381, 65, 65, -207, 77, -59, -651, -365, -797, -213, -15, -155, -387, -259, -515, -540, 355, -182, -150, 990, -231, 582, 747, -36, -540, 520, -215, 633, 633, 176, 176, -540, -133, -491, -687, -426, -882, -102, 143, 77, -639, -427, -851, -105, 65, -37, 57, 127, -17, -105, 135, -51, 60, 195, -30, 78, 78, 31, 31, -105, -57, -125, -45, 17, -11, -147, -72, -168, -84, -57, -111, -693, 457, -233, 618, 733, -26, -540, 470, -205, 264, 829, -116, 336, 336, 227, 227, -693, -440, -900, -72, 153, 107, -609, -294, -698, -342, -229, -455, -243, 157, -83, 69, 349, -59, -201, 409, -125, 27, 587, -115, 333, 333, 77, 77, -243, -107, -267, -171, 7, -105, -369, -135, -379, -339, -227, -451, }; // Change skip_width, skip_height to {2, 3} cnn_config.layer_config[0].skip_width = 3; cnn_config.layer_config[0].skip_height = 2; // Set padding to same cnn_config.layer_config[0].pad = PADDING_SAME_ZERO; RunCNNTest(image_width, image_height, input, expected_12_same, &cnn_config, image_width, &thread_data, MSE_INT_TOL); // Change padding to valid cnn_config.layer_config[0].pad = PADDING_VALID; RunCNNTest(image_width, image_height, input, expected_12_valid, &cnn_config, image_width, &thread_data, MSE_INT_TOL); cnn_config.layer_config[0].filter_width = 4; cnn_config.layer_config[0].filter_height = 3; float weights_2[] = { -1, -3, -1, -3, 0, 2, -2, 4, 3, 0, 1, 4 }; float bias_2[] = { -4 }; cnn_config.layer_config[0].weights = weights_2; cnn_config.layer_config[0].bias = bias_2; cnn_config.layer_config[0].skip_width = 5; cnn_config.layer_config[0].skip_height = 2; float expected_2_same[] = { -13, -31, -13, -31, -4, -10, -22, -10, -22, -4, -185, -547, -185, -547, -4, -13, -31, -13, -31, -4, -4, 14, -22, 32, -4, -4, 8, -16, 20, -4, -4, 358, -366, 720, -4, -4, 14, -22, 32, -4, -195, -658, -213, -622, -4, -16, -94, -28, -70, -4, 459, -244, 97, 480, -4, -85, -328, -103, -292, -4, -4, 432, -440, 868, -4, -4, 56, -64, 116, -4, -4, 156, -164, 316, -4, -4, 212, -220, 428, -4, 582, -208, 146, 664, -4, -130, -652, -190, -532, -4, 166, -214, 6, 106, -4, 192, -388, -24, 44, -4, -4, 132, -140, 268, -4, -4, 428, -436, 860, -4, -4, 136, -144, 276, -4, -4, 252, -260, 508, -4, 21, -541, -115, -269, -4, 416, -688, -16, 176, -4, 173, -103, 33, 177, -4, 168, -640, -88, -128, -4, -4, 354, -362, 712, -4, -4, 452, -460, 908, -4, -4, 62, -70, 128, -4, -4, 420, -428, 844, -4, 499, -106, 141, 610, -4, 666, -46, 210, 866, -4, 47, -148, -19, -16, -4, 605, -85, 181, 763, -4, -4, 64, -72, 132, -4, -4, 24, -32, 52, -4, -4, 92, -100, 188, -4, -4, 50, -58, 104, -4, -132, -694, -200, -558, -4, 15, -73, -13, -17, -4, -62, -610, -158, -418, -4, -36, -343, -90, -235, -4, -4, 456, -464, 916, -4, -4, 42, -50, 88, -4, -4, 400, -408, 804, -4, -4, 222, -230, 448, -4, 606, -244, 146, 676, -4, 9, -172, -37, -80, -4, 480, -370, 76, 438, -4, 223, -340, -3, 112, -4, -4, 156, -164, 316, -4, -4, 108, -116, 220, -4, -4, 240, -248, 484, -4, -4, 220, -228, 444, -4, }; float expected_2_valid[] = { -13, -31, -13, -31, -4, -10, -22, -10, -22, -4, -185, -547, -185, -547, -4, -13, -31, -13, -31, -4, 14, -22, 32, -4, -4, 8, -16, 20, -4, -4, 358, -366, 720, -4, -4, 14, -22, 32, -195, -658, -213, -622, -4, -16, -94, -28, -70, -4, 459, -244, 97, 480, -4, -85, -328, -103, -292, -4, 432, -440, 868, -4, -4, 56, -64, 116, -4, -4, 156, -164, 316, -4, -4, 212, -220, 428, 582, -208, 146, 664, -4, -130, -652, -190, -532, -4, 166, -214, 6, 106, -4, 192, -388, -24, 44, -4, 132, -140, 268, -4, -4, 428, -436, 860, -4, -4, 136, -144, 276, -4, -4, 252, -260, 508, 21, -541, -115, -269, -4, 416, -688, -16, 176, -4, 173, -103, 33, 177, -4, 168, -640, -88, -128, -4, 354, -362, 712, -4, -4, 452, -460, 908, -4, -4, 62, -70, 128, -4, -4, 420, -428, 844, 499, -106, 141, 610, -4, 666, -46, 210, 866, -4, 47, -148, -19, -16, -4, 605, -85, 181, 763, -4, 64, -72, 132, -4, -4, 24, -32, 52, -4, -4, 92, -100, 188, -4, -4, 50, -58, 104, -132, -694, -200, -558, -4, 15, -73, -13, -17, -4, -62, -610, -158, -418, -4, -36, -343, -90, -235, -4, 456, -464, 916, -4, -4, 42, -50, 88, -4, -4, 400, -408, 804, -4, -4, 222, -230, 448, 606, -244, 146, 676, -4, 9, -172, -37, -80, -4, 480, -370, 76, 438, -4, 223, -340, -3, 112, -4, 156, -164, 316, -4, -4, 108, -116, 220, -4, -4, 240, -248, 484, -4, -4, 220, -228, 444, 236, -4, 76, 316, -4, 164, -4, 52, 220, -4, 362, -4, 118, 484, -4, 332, -4, 108, 444, }; // Set padding to same cnn_config.layer_config[0].pad = PADDING_SAME_ZERO; RunCNNTest(image_width, image_height, input, expected_2_same, &cnn_config, image_width, &thread_data, MSE_INT_TOL); cnn_config.layer_config[0].pad = PADDING_VALID; RunCNNTest(image_width, image_height, input, expected_2_valid, &cnn_config, image_width, &thread_data, MSE_INT_TOL); cnn_config.layer_config[0].skip_width = 2; cnn_config.layer_config[0].skip_height = 5; float expected_21_same[] = { -31, -19, -49, -191, -565, -194, -574, -13, 14, -22, 44, -16, 382, -366, 738, -22, -4, 23, 32, 545, 20, 204, 720, 5, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -658, -252, -748, -114, -334, -192, -568, -112, 432, -440, 928, -64, 276, -164, 532, -220, -4, 304, 868, 266, 116, 400, 316, 104, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -208, -288, -856, -290, -862, -202, -598, -132, 132, -140, 700, -436, 1000, -144, 532, -260, -4, 712, 268, 422, 860, 450, 276, 124, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -541, -411, -1225, -265, -787, -249, -739, -216, 354, -362, 1168, -460, 974, -70, 552, -428, -4, 859, 712, 323, 908, 665, 128, 208, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -106, -52, -148, -66, -190, -79, -229, -31, 64, -72, 160, -32, 148, -100, 242, -58, -4, 72, 132, 154, 52, 125, 188, 23, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -694, -257, -763, -229, -679, -319, -949, -117, 456, -464, 962, -50, 492, -408, 1030, -230, -4, 295, 916, 625, 88, 537, 804, 109, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -244, -140, -412, -182, -538, -238, -706, -116, 156, -164, 428, -116, 464, -248, 708, -228, -4, 244, 316, 418, 220, 454, 484, 108, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, }; float expected_21_valid[] = { -13, -31, -19, -49, -191, -565, -194, -574, -13, -31, -4, 14, -22, 44, -16, 382, -366, 738, -22, 32, 23, -4, 23, 32, 545, 20, 204, 720, 5, 32, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -222, -658, -252, -748, -114, -334, -192, -568, -112, -328, -4, 432, -440, 928, -64, 276, -164, 532, -220, 428, 650, -4, 304, 868, 266, 116, 400, 316, 104, 428, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -72, -208, -288, -856, -290, -862, -202, -598, -132, -388, -4, 132, -140, 700, -436, 1000, -144, 532, -260, 508, 200, -4, 712, 268, 422, 860, 450, 276, 124, 508, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -183, -541, -411, -1225, -265, -787, -249, -739, -216, -640, -4, 354, -362, 1168, -460, 974, -70, 552, -428, 844, 533, -4, 859, 712, 323, 908, 665, 128, 208, 844, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -38, -106, -52, -148, -66, -190, -79, -229, -31, -85, -4, 64, -72, 160, -32, 148, -100, 242, -58, 104, 98, -4, 72, 132, 154, 52, 125, 188, 23, 104, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -234, -694, -257, -763, -229, -679, -319, -949, -117, -343, -4, 456, -464, 962, -50, 492, -408, 1030, -230, 448, 686, -4, 295, 916, 625, 88, 537, 804, 109, 448, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -84, -244, -140, -412, -182, -538, -238, -706, -116, -340, -4, 156, -164, 428, -116, 464, -248, 708, -228, 444, 236, -4, 244, 316, 418, 220, 454, 484, 108, 444, }; cnn_config.layer_config[0].pad = PADDING_SAME_ZERO; RunCNNTest(image_width, image_height, input, expected_21_same, &cnn_config, image_width, &thread_data, MSE_INT_TOL); cnn_config.layer_config[0].pad = PADDING_VALID; RunCNNTest(image_width, image_height, input, expected_21_valid, &cnn_config, image_width, &thread_data, MSE_INT_TOL); } TEST_F(CNNTest, TestLargeKernelsAndStrides) { float input_10x11[] = { 4, 4, 2, 4, 2, -5, -2, 3, -1, 0, 0, 1, 2, 0, -5, -2, -5, 1, -3, -1, 4, -3, 2, -2, 1, 0, 1, -3, -3, -4, -2, -2, 1, -4, -1, 4, 1, -4, -4, -4, 3, 2, -5, 3, -5, 1, 2, -4, 1, -1, 3, 4, -2, 3, -3, 3, 0, 2, -4, -5, -5, -2, -1, -2, 1, 1, 1, -2, 4, -5, 4, -1, -1, 2, 3, -4, 2, 2, 3, 0, 0, 1, 0, 3, 2, 3, 1, -2, 3, -4, 3, 2, 4, -2, 0, 4, -4, 1, -3, -3, -3, -5, 1, -3, -5, 0, 4, -1, -3, 2, }; float weights_10x11[] = { -3, 4, -4, -3, -5, 1, -2, 3, 1, -4, -4, 0, -1, 0, 3, 1, -3, -2, 0, -1, 1, 3, -4, -4, -3, -3, -2, 4, 3, -5, 4, 2, -3, 4, -2, -1, 2, -1, -5, 0, -3, 0, 3, -5, -5, 3, -4, -1, -5, 3, 4, 0, 4, -5, 2, -1, 2, -1, -1, -1, -5, 0, -4, 3, -1, 1, 1, -1, 3, 2, -5, -4, 0, -4, 4, -5, -3, 4, -5, 2, -5, -4, -4, -1, 3, 3, 0, 2, -4, 1, -2, 1, 1, 0, 3, -2, 0, 1, 2, 4, -3, -1, -5, -5, 2, -4, 1, 1, 2, -4, -2, -2, 2, 1, 3, 4, -5, 1, -1, -3, -3, -1, -2, -5, 1, -1, 0, 1, 4, 4, 0, 0, 4, -3, -1, -5, -3, 0, 1, 1, 1, -5, 3, 4, 3, -5, 3, -2, -2, 0, -4, 0, 0, -2, 1, -4, -1, 0, -5, -2, -2, -5, -3, -3, 1, 1, -3, 2, 4, 2, 4, -4, -3, 3, 1, 1, 3, -4, 4, -2, -3, -3, -3, -3, -4, -2, 3, -5, 2, 4, -1, -4, -4, 4, -2, -1, 3, -3, -4, -4, -2, 4, 1, 0, 2, -1, 4, -3, 1, 4, -3, 4, 4, 0, -4, 3, -2, -3, 2, 3, -1, -3, 2, 1, 4, -2, -3, 1, 4, -2, 2, -2, -5, -2, 1, 4, -1, -4, 4, -5, 2, -5, -4, -1, -2, 3, 1, 2, 1, -5, 1, -5, -4, -1, -2, 2, -2, -4, -3, -2, -2, 4, -1, 2, 2, -4, 2, -2, 4, -4, -2, -2, 1, -1, 1, 1, 1, -4, -5, -2, 3, -4, -1, 3, -2, 3, 2, -5, -4, 0, 3, -2, -4, -5, 3, -2, -4, 2, -2, 1, -4, 0, 2, -5, 1, -4, -1, -1, 4, -5, -4, 0, -5, -4, -3, -5, -4, 0, 2, 0, -4, 2, -2, 1, 1, -3, 2, 0, -4, 0, -4, 1, 0, -5, -1, -1, -1, -5, 4, 2, 2, -4, 3, -2, -2, 2, -3, -2, -1, 2, -4, -5, 2, -2, -4, -5, -5, -1, 2, -1, 0, -5, -2, -2, -5, 0, 1, -1, -5, 0, 3, 2, 3, 0, -3, -2, 0, -5, -1, -2, 2, -4, -1, 2, 2, -5, 2, -4, 0, 3, -3, 1, 0, 0, 1, -5, -3, 1, -1, 0, -4, -3, 2, -4, -4, 4, -1, 0, 1, 2, -4, -5, 4, -2, 1, -4, -4, -3, -1, -1, 1, -1, -4, -1, -4, -3, 2, -1, -2, -4, 1, 1, 0, -2, 0, -4, 3, -3, 0, -4, -1, -4, 2, -1, -2, -5, -1, -2, -3, 3, -1, 0, -3, 0, 1, -5, 1, -5, 0, 1, }; float bias_10x11[] = { 3 }; float expected_10x11[] = { 118, }; CNN_CONFIG cnn_config = { 1, 0, 0, 0, 0, { { 1, 23, 20, 1, 15, 20, 0, weights_10x11, bias_10x11, PADDING_SAME_ZERO, NONE, 0, 0, BRANCH_NO_COPY, BRANCH_NOC, {}, {}, 0, } } }; int image_height = 10; int image_width = 11; CNN_THREAD_DATA thread_data = { 1, nullptr }; RunCNNTest(image_width, image_height, input_10x11, expected_10x11, &cnn_config, image_width, &thread_data, MSE_INT_TOL); float input_11x10[] = { -2, -2, 3, -5, -1, -3, 1, 3, 2, 1, 1, -5, 4, 1, 3, -5, 3, -3, -5, 0, -1, -3, -3, 1, 1, -5, -1, -5, -5, -3, 0, 1, -3, -1, -3, -3, 0, 3, 4, -4, -1, 3, -3, -1, -3, 1, -3, -2, -1, -4, -3, 2, -4, 1, -4, -1, -3, -5, -1, 2, 3, 0, 2, 2, -5, 4, 1, 2, -1, -4, 4, -4, -4, 0, -1, 1, -1, 1, -3, -3, -2, 1, 2, 4, 4, 4, -3, -3, 0, 1, 0, 1, 4, 1, 3, 4, -3, -2, -4, 4, 2, 0, 3, 4, -1, 2, -2, 1, -3, -2, }; float weights_11x10[] = { 4, -1, 1, -1, 2, 4, 3, 3, -4, 3, -5, 1, -1, -1, -2, -2, 0, 2, -3, -2, 3, -5, -1, 0, -1, -2, -2, -1, 2, 4, 3, 1, 0, 0, -3, 3, -4, -1, -5, 4, -2, -2, 1, 2, -1, -3, 1, 2, -5, 1, -3, 3, 3, 0, -4, -4, -5, -3, -4, -4, 4, -2, 4, 4, -2, 2, -5, -1, -2, -5, -1, 4, -3, 3, -2, 0, -4, -3, 0, -1, -2, 4, 2, 0, -2, -5, -4, 1, 4, -4, -2, 2, -2, 1, 1, -4, 1, -4, -4, -2, 4, 2, -1, -5, -5, 1, -3, -3, 3, -3, -5, -3, 4, -1, -1, -3, 0, -4, 3, -1, 0, -2, 0, -5, -2, -5, 2, 0, -5, 2, 3, -2, 2, 4, -1, 1, -3, 2, 3, 2, 0, -5, -4, -5, 2, 1, 1, -1, -2, 3, 4, 2, -2, 4, -2, 3, 1, -4, -3, -1, 4, 4, -3, -5, -2, 2, 0, 3, -2, 3, -1, -4, 0, -2, 0, 3, 4, -2, -3, -2, 0, 3, 4, 2, -4, 0, 1, 2, 2, -1, -1, 4, 1, 4, -2, -1, -1, -5, 1, -3, 3, 3, -1, -4, 3, -5, 0, 0, -1, -4, -1, -2, 4, -2, 3, 3, -3, 1, -1, 2, -1, 4, 4, -2, -2, 4, -2, 0, 3, -3, -5, -1, -2, 4, -4, 2, -4, 0, -2, 3, -3, 2, 2, -2, -5, -1, 4, 3, -2, -1, 3, 3, -1, 3, 0, -3, 0, 4, 2, 0, -1, 4, 1, 1, 2, 1, 3, 1, 1, 1, -3, -5, -4, 4, -4, 2, 0, 0, -4, 1, 4, -5, 4, 4, 0, 1, 0, -2, -4, -4, -3, 0, 1, -5, 4, 0, -3, -2, -4, 2, 4, 1, -5, 1, -4, 1, 0, -3, -3, 0, 2, -5, 4, 3, -2, -5, 3, 1, -1, 0, 3, -2, -2, 3, -2, -5, 4, 1, -2, 2, -1, 0, 4, 0, -5, 3, -2, 1, 2, 1, -5, -3, -2, -5, 4, -4, 0, 3, 2, -1, -4, -1, 2, 1, -2, 3, -1, -4, 2, 0, -3, 1, -1, 2, -5, -4, -1, -5, 1, 4, 3, 4, 2, -3, 1, -5, -1, 3, 0, -1, -4, 3, 4, -5, 4, 4, -3, 2, -3, -1, -3, -5, -3, 2, -3, -2, 1, 1, 0, -5, 3, 2, 1, -5, 1, 1, 1, 3, 4, -4, -1, -2, 0, -5, -3, -5, -2, -4, 3, 3, 3, 4, 0, -4, -1, -5, 0, -3, 1, 4, 4, -4, 4, -5, -5, -1, -2, -5, 3, -4, 4, 3, 0, -3, 2, -2, 0, 0, 4, 4, 0, -2, 1, -1, -3, 2, -1, 1, -3, -5, }; float bias_11x10[] = { -5, }; float expected_11x10[] = { 36, -84, 95, 45, 18, 46, 77, -54, -99, -149, 66, 49, 161, 11, 39, 61, -66, 61, 4, -3, 34, -44, -23, 31, 64, 29, 47, 72, -27, -27, 121, -3, 100, 1, 30, -78, -12, -89, -59, 8, -16, 112, 91, -102, -26, -4, 30, 54, 4, -84, -24, -58, 27, -53, -33, 5, 53, -26, 63, 50, -103, -130, -23, 6, -104, -207, 73, 23, 77, 132, 38, 32, -130, -44, -60, 7, 27, 176, 45, -32, -2, 99, -97, 63, 69, 126, 47, 63, 136, -57, 5, 16, -40, -157, 8, 38, -44, -10, 91, 7, 122, 140, 30, -105, 4, -1, 113, 64, 180, 141, }; cnn_config.layer_config[0].weights = weights_11x10; cnn_config.layer_config[0].bias = bias_11x10; cnn_config.layer_config[0].filter_width = 20; cnn_config.layer_config[0].filter_height = 23; cnn_config.layer_config[0].skip_width = 1; cnn_config.layer_config[0].skip_height = 1; image_height = 11; image_width = 10; RunCNNTest(image_width, image_height, input_11x10, expected_11x10, &cnn_config, image_width, &thread_data, MSE_INT_TOL); } TEST_F(CNNTest, TestSoftsignSingleLayer) { int image_width = 8; int image_height = 8; int filter_height = 5; int filter_width = 4; float input[] = { -0.5220f, 0.8410f, -0.8990f, -0.0090f, 0.6710f, -0.9470f, -0.8240f, -0.0870f, 0.5380f, 0.4750f, 0.570f, -0.3760f, -0.6960f, -0.5940f, -0.3830f, 0.080f, -0.0980f, -0.4940f, -0.4030f, 0.9460f, -0.6020f, 0.4220f, 0.6190f, 0.6640f, -0.9210f, -0.1470f, -0.2480f, -0.1120f, -0.580f, -0.0650f, 0.3330f, 0.9860f, -0.7430f, 0.7610f, 0.4840f, 0.1030f, 0.9570f, 0.6120f, -0.5240f, -0.1220f, -0.5850f, -0.270f, 0.7840f, -0.9790f, 0.7290f, -0.30f, -0.6460f, 0.0780f, 0.4750f, -0.0510f, 0.4550f, 0.3850f, -0.7230f, 0.4460f, -0.6260f, -0.810f, 0.8720f, -0.2120f, -0.580f, -0.9510f, -0.8430f, -0.1340f, -0.0850f, 0.9190f, }; float expected_same[] = { 0.430f, 0.660f, 0.5510f, -0.610f, 0.450f, -0.1610f, 0.0520f, 0.3240f, 0.6820f, 0.3820f, 0.6360f, 0.7480f, 0.3080f, 0.090f, 0.3910f, 0.1730f, 0.340f, 0.6660f, -0.4990f, 0.4280f, 0.1540f, 0.120f, 0.4670f, 0.6150f, -0.3880f, 0.7590f, 0.4190f, 0.7350f, 0.5310f, -0.5160f, -0.1760f, 0.6790f, -0.6780f, 0.5470f, 0.5750f, -0.6420f, 0.7210f, -0.4620f, 0.5430f, 0.770f, -0.1990f, 0.3950f, 0.7860f, -0.4380f, 0.7540f, 0.2640f, -0.6430f, 0.4510f, -0.1260f, 0.1590f, -0.2110f, -0.0560f, 0.6570f, 0.680f, 0.5870f, 0.4720f, 0.4040f, 0.3630f, 0.670f, 0.2360f, 0.410f, 0.6980f, -0.5350f, 0.3940f, }; float expected_replicate[] = { 0.540f, 0.7230f, -0.3530f, -0.2130f, 0.7440f, -0.4470f, -0.6260f, -0.2050f, 0.7230f, 0.4630f, 0.5920f, 0.7440f, 0.6080f, 0.3130f, -0.5670f, -0.4720f, 0.5480f, 0.6660f, -0.4990f, 0.4280f, 0.1540f, 0.120f, 0.3390f, 0.6090f, 0.4160f, 0.7590f, 0.4190f, 0.7350f, 0.5310f, -0.5160f, -0.490f, 0.4450f, -0.610f, 0.5470f, 0.5750f, -0.6420f, 0.7210f, -0.4620f, 0.3150f, 0.7370f, -0.5820f, 0.3950f, 0.7860f, -0.4380f, 0.7540f, 0.2640f, -0.7430f, -0.5340f, -0.6270f, 0.4430f, 0.4730f, 0.4570f, 0.7450f, 0.630f, 0.2620f, 0.3140f, -0.1840f, 0.1810f, 0.7210f, 0.2760f, 0.6430f, 0.6720f, -0.4390f, 0.2040f, }; float expected_valid[] = { 0.6660f, -0.4990f, 0.4280f, 0.1540f, 0.120f, 0.7590f, 0.4190f, 0.7350f, 0.5310f, -0.5160f, 0.5470f, 0.5750f, -0.6420f, 0.7210f, -0.4620f, 0.3950f, 0.7860f, -0.4380f, 0.7540f, 0.2640f, }; float weights[] = { 0.6210f, 0.3710f, -0.2770f, -0.7230f, -0.2450f, 0.6770f, 0.3080f, -0.9880f, -0.080f, 0.7190f, -0.6760f, -0.0170f, -0.8970f, 0.8260f, 0.7390f, -0.4550f, -0.4260f, -0.6330f, 0.0880f, -0.9390f, }; float bias[] = { 0.750f, }; CNN_CONFIG cnn_config = { 1, 0, 0, 0, 0, { { 1, filter_width, filter_height, 1, 1, 1, 0, weights, bias, PADDING_SAME_ZERO, SOFTSIGN, 0, 0, BRANCH_NO_COPY, BRANCH_NOC, {}, {}, 0, } } }; CNN_THREAD_DATA thread_data = { 1, nullptr }; RunCNNTest(image_width, image_height, input, expected_same, &cnn_config, image_width, &thread_data, MSE_FLOAT_TOL); cnn_config.layer_config[0].pad = PADDING_SAME_REPLICATE; RunCNNTest(image_width, image_height, input, expected_replicate, &cnn_config, image_width, &thread_data, MSE_FLOAT_TOL); cnn_config.layer_config[0].pad = PADDING_VALID; RunCNNTest(image_width, image_height, input, expected_valid, &cnn_config, image_width, &thread_data, MSE_FLOAT_TOL); } TEST_F(CNNTest, TestBranchTensorAdd) { int filter_width = 2; int filter_height = 3; int image_width = 4; int image_height = 4; float input[] = { -3, -2, -2, 0, -1, 3, 2, -2, 1, 3, 4, 0, 2, -5, -4, 0, }; float weights[] = { -3, -1, 4, -1, -3, 3, 3, 0, 2, 0, 3, 2, 4, 4, 4, -5, 1, -4, 2, -4, 1, -3, 0, 4, -5, 4, 0, -4, -3, -1, 0, 0, -2, 0, 0, 2, -5, -1, 1, -3, 3, 4, 3, 0, 1, -1, 1, 1, 2, 4, -2, -5, 2, -2, 3, -2, 4, -1, 0, 2, 3, 2, -2, -1, -3, 1, 3, 4, -1, -3, 0, -4, 4, 2, -3, -3, -1, 0, 1, 0, 3, 3, -3, 0, 3, 2, -5, -3, 4, -5, 3, -1, -1, -3, 0, 1, -1, -4, 2, 4, -1, 4, -1, 1, 3, 4, 4, 4, 0, -1, -3, -3, -3, -3, 2, -3, -2, 2, 3, -3, }; float bias[] = { 3, 4, -1, -1, 2, 1, -2, 1, 4, 1, 3, }; float expected[] = { -11502, -4101, -3424, 668, -17950, -5470, -5504, 626, 4835, 446, 1779, -3483, 3679, -4214, 4578, -105, }; int channels = 2; CNN_CONFIG cnn_config = { 6, 0, 0, 0, 0, { { 1, filter_width, filter_height, channels, 1, 1, 0, weights, bias, PADDING_SAME_ZERO, NONE, 0, 0, BRANCH_NO_COPY, BRANCH_NOC, {}, {}, -1, }, { channels, filter_width, filter_height, channels, 1, 1, 0, nullptr, nullptr, PADDING_SAME_ZERO, NONE, 0, 0, BRANCH_INPUT, BRANCH_NOC, { 0x02, 0, 0x00, }, {}, -1, }, { channels, filter_width, filter_height, channels, 1, 1, 0, nullptr, nullptr, PADDING_SAME_ZERO, NONE, 0, 1, BRANCH_NO_COPY, BRANCH_NOC, {}, {}, -1, }, { channels, filter_width, filter_height, channels, 1, 1, 0, nullptr, nullptr, PADDING_SAME_ZERO, NONE, 0, 1, BRANCH_NO_COPY, BRANCH_NOC, {}, {}, -1, }, { channels, filter_width, filter_height, channels, 1, 1, 0, nullptr, nullptr, PADDING_SAME_ZERO, NONE, 0, 0, BRANCH_NO_COPY, BRANCH_ADD, { 0x00, 0, 0x02, }, {}, -1, }, { channels, filter_width, filter_height, 1, 1, 1, 0, nullptr, nullptr, PADDING_SAME_ZERO, NONE, 0, 0, BRANCH_NO_COPY, BRANCH_NOC, {}, {}, 0, } } }; // Weights and biases need to be specified separately because // of the offset. AssignLayerWeightsBiases(&cnn_config, weights, bias); CNN_THREAD_DATA thread_data = { 1, nullptr }; RunCNNTest(image_width, image_height, input, expected, &cnn_config, image_width, &thread_data, MSE_INT_TOL); } TEST_F(CNNTest, TestBranchTensorConcatenation) { int filter_width = 2; int filter_height = 3; int image_width = 4; int image_height = 4; float input[] = { -3, -2, -2, 0, -1, 3, 2, -2, 1, 3, 4, 0, 2, -5, -4, 0, }; float weights[] = { 3, 0, 2, 0, 2, 3, 1, -3, 1, -5, -3, 0, -4, 4, 0, -5, 0, -5, -1, -2, -5, 0, -3, 2, -4, 2, 0, 2, -1, 0, -4, 3, 0, 0, -1, -5, 2, -1, 4, -4, -2, -3, -3, 3, 4, -2, -1, -4, -1, 4, 4, -1, 4, 3, -4, 2, -2, -4, -3, -2, 3, -3, -5, -1, 3, -2, 4, 1, -4, -3, -5, -5, -3, 4, -2, -2, -1, -5, -5, 0, -1, -2, -3, 3, -4, -5, 2, -3, 1, 0, -5, 2, 2, -2, 0, 2, 2, -2, 4, 2, 2, 0, 1, -5, -3, 0, 2, -2, 1, 2, -5, 2, 3, 3, -1, 3, 0, -3, 3, -4, -4, 3, 3, -4, -2, 2, -2, 2, -2, -1, 3, 0, }; float bias[] = { -3, -5, 4, -4, -3, -2, 0, 3, -4, 4, -3, }; float expected[] = { -33533, -32087, -6741, -2124, 39979, 41453, 14034, 689, -22611, -42203, -14882, -239, 15781, 15963, 9524, 837, }; int channels = 2; CNN_CONFIG cnn_config = { 6, 0, 0, 0, 0, { { 1, filter_width, filter_height, channels, 1, 1, 0, weights, bias, PADDING_SAME_ZERO, NONE, 0, 0, BRANCH_NO_COPY, BRANCH_NOC, {}, {}, -1, }, { channels, filter_width, filter_height, channels, 1, 1, 0, nullptr, nullptr, PADDING_SAME_ZERO, NONE, 0, 0, BRANCH_INPUT, BRANCH_NOC, { 0x02, 0, 0x00, }, {}, -1, }, { channels, filter_width, filter_height, channels, 1, 1, 0, nullptr, nullptr, PADDING_SAME_ZERO, NONE, 0, 1, BRANCH_NO_COPY, BRANCH_NOC, {}, {}, -1, }, { channels, filter_width, filter_height, channels, 1, 1, 0, nullptr, nullptr, PADDING_SAME_ZERO, NONE, 0, 1, BRANCH_NO_COPY, BRANCH_NOC, {}, {}, -1, }, { channels, filter_width, filter_height, channels, 1, 1, 0, nullptr, nullptr, PADDING_SAME_ZERO, NONE, 0, 0, BRANCH_NO_COPY, BRANCH_CAT, { 0x00, 0, 0x02, }, {}, -1, }, { channels + channels, filter_width, filter_height, 1, 1, 1, 0, nullptr, nullptr, PADDING_SAME_ZERO, NONE, 0, 0, BRANCH_NO_COPY, BRANCH_NOC, {}, {}, 0, } } }; // Weights and biases need to be specified separately because // of the offset. AssignLayerWeightsBiases(&cnn_config, weights, bias); CNN_THREAD_DATA thread_data = { 1, nullptr }; RunCNNTest(image_width, image_height, input, expected, &cnn_config, image_width, &thread_data, MSE_INT_TOL); } --> -------------------- --> maximum size reached --> --------------------
¤ Dauer der Verarbeitung: 0.26 Sekunden (vorverarbeitet) ¤*© Formatika GbR, Deutschland |
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2026-04-02