mirror of
https://github.com/Tencent/ncnn.git
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nihui
41f031e045
* adjust tests * opt shader coverage op * update gpu_coverage.cpp atomicor coalesce
400 lines
12 KiB
C++
400 lines
12 KiB
C++
// Copyright 2020 Tencent
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// SPDX-License-Identifier: BSD-3-Clause
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#include "testutil.h"
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static int op_type = 0;
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static int test_binaryop(const ncnn::Mat& _a, const ncnn::Mat& _b)
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{
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ncnn::Mat a = _a;
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ncnn::Mat b = _b;
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ncnn::ParamDict pd;
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pd.set(0, op_type);
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pd.set(1, 0); // with_scalar
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pd.set(2, 0.f); // b
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std::vector<ncnn::Mat> weights(0);
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std::vector<ncnn::Mat> ab(2);
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ab[0] = a;
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ab[1] = b;
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int ret = test_layer("BinaryOp", pd, weights, ab, 1, 0.0001);
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if (ret != 0)
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{
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fprintf(stderr, "test_binaryop failed a.dims=%d a=(%d %d %d %d) b.dims=%d b=(%d %d %d %d) op_type=%d\n", a.dims, a.w, a.h, a.d, a.c, b.dims, b.w, b.h, b.d, b.c, op_type);
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}
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return ret;
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}
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static int test_binaryop(const ncnn::Mat& _a, float b)
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{
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ncnn::Mat a = _a;
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ncnn::ParamDict pd;
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pd.set(0, op_type);
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pd.set(1, 1); // with_scalar
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pd.set(2, b); // b
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std::vector<ncnn::Mat> weights(0);
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int ret = test_layer("BinaryOp", pd, weights, a, 0.0001);
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if (ret != 0)
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{
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fprintf(stderr, "test_binaryop failed a.dims=%d a=(%d %d %d %d) b=%f op_type=%d\n", a.dims, a.w, a.h, a.d, a.c, b, op_type);
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}
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return ret;
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}
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static int test_binaryop_1(int w)
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{
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// dense widths and scalar broadcasts retain each packing boundary
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ncnn::Mat a = RandomMat(w, 1.0f, 1.1f);
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ncnn::Mat b = RandomMat(w, 0.8f, 0.9f);
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ncnn::Mat scalar = RandomMat(1, 0.8f, 0.9f);
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return test_binaryop(a, b)
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|| test_binaryop(a, scalar)
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|| test_binaryop(scalar, a)
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|| test_binaryop(a, 0.7f);
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}
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static int test_binaryop_1()
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{
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// pack1, pack4, pack8 and pack16 include both vector loops and remainders
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return 0
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|| test_binaryop_1(31)
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|| test_binaryop_1(28)
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|| test_binaryop_1(24)
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|| test_binaryop_1(32);
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}
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static int test_binaryop_2(int w, int h)
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{
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// same-shape arithmetic, singleton axes and complementary output growth
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ncnn::Mat inputs[] = {
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RandomMat(w, h, 1.0f, 1.1f),
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RandomMat(1, h, 1.0f, 1.1f)
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};
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ncnn::Mat second_inputs[] = {
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RandomMat(w, h, 0.8f, 0.9f),
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RandomMat(1, h, 0.8f, 0.9f),
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RandomMat(w, 1, 0.8f, 0.9f),
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RandomMat(1, 1, 0.8f, 0.9f)
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};
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const int broadcast_pairs[][2] = {
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{0, 1}, {0, 2}, {0, 3}, {1, 2}
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};
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int ret = test_binaryop(inputs[0], second_inputs[0])
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|| test_binaryop(inputs[1], second_inputs[1]);
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if (ret != 0)
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return ret;
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for (size_t i = 0; i < sizeof(broadcast_pairs) / sizeof(broadcast_pairs[0]); i++)
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{
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const ncnn::Mat& a = inputs[broadcast_pairs[i][0]];
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const ncnn::Mat& b = second_inputs[broadcast_pairs[i][1]];
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int ret = test_binaryop(a, b) || test_binaryop(b, a);
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if (ret != 0)
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return ret;
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}
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// scalar parameters exercise the dense vector kernels
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return test_binaryop(inputs[0], 0.7f);
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}
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static int test_binaryop_2()
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{
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// singleton width includes a short row count before the packing boundaries
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ncnn::Mat rows = RandomMat(1, 3, 1.0f, 1.1f);
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ncnn::Mat second_rows = RandomMat(1, 3, 0.8f, 0.9f);
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return test_binaryop(rows, second_rows)
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|| test_binaryop_2(13, 31)
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|| test_binaryop_2(14, 28)
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|| test_binaryop_2(15, 24)
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|| test_binaryop_2(16, 32);
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}
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static int test_binaryop_3(int w, int h, int c)
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{
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// same-shape arithmetic, singleton axes and complementary output growth
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ncnn::Mat inputs[] = {
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RandomMat(w, h, c, 1.0f, 1.1f),
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RandomMat(1, h, c, 1.0f, 1.1f),
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RandomMat(w, 1, c, 1.0f, 1.1f),
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RandomMat(1, 1, c, 1.0f, 1.1f)
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};
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ncnn::Mat second_inputs[] = {
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RandomMat(w, h, c, 0.8f, 0.9f),
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RandomMat(1, h, c, 0.8f, 0.9f),
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RandomMat(w, 1, c, 0.8f, 0.9f),
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RandomMat(1, 1, c, 0.8f, 0.9f),
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RandomMat(w, h, 1, 0.8f, 0.9f),
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RandomMat(1, h, 1, 0.8f, 0.9f),
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RandomMat(w, 1, 1, 0.8f, 0.9f),
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RandomMat(1, 1, 1, 0.8f, 0.9f)
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};
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const int broadcast_pairs[][2] = {
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{1, 2}, {0, 1}, {0, 2}, {0, 3}, {0, 4}, {0, 5}, {0, 6}, {0, 7}, {1, 6}, {2, 5}, {3, 4}
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};
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int ret = test_binaryop(inputs[0], second_inputs[0])
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|| test_binaryop(inputs[1], second_inputs[1])
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|| test_binaryop(inputs[3], second_inputs[3]);
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if (ret != 0)
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return ret;
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for (size_t i = 0; i < sizeof(broadcast_pairs) / sizeof(broadcast_pairs[0]); i++)
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{
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const ncnn::Mat& a = inputs[broadcast_pairs[i][0]];
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const ncnn::Mat& b = second_inputs[broadcast_pairs[i][1]];
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int ret = test_binaryop(a, b) || test_binaryop(b, a);
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if (ret != 0)
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return ret;
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}
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// scalar parameters exercise the dense vector kernels
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return test_binaryop(inputs[0], 0.7f);
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}
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static int test_binaryop_channel_broadcast(int c)
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{
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// equal packing broadcasts the channel vector over width in both operand directions
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ncnn::Mat a = RandomMat(1, 9, c, 1.0f, 1.1f);
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ncnn::Mat b = RandomMat(7, 1, c, 0.8f, 0.9f);
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return test_binaryop(a, b) || test_binaryop(b, a);
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}
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static int test_binaryop_mixed_channel_broadcast()
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{
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// scalar row values broadcast over packed channels and width
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ncnn::Mat a = RandomMat(7, 3, 32, 1.0f, 1.1f);
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ncnn::Mat b = RandomMat(1, 3, 1, 0.8f, 0.9f);
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return test_binaryop(a, b) || test_binaryop(b, a);
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}
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static int test_binaryop_3()
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{
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// spatial broadcasting uses scalar and packed channels independently of vector widths
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return 0
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|| test_binaryop_3(7, 3, 31)
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|| test_binaryop_3(7, 9, 28)
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|| test_binaryop_channel_broadcast(24)
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|| test_binaryop_channel_broadcast(32)
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|| test_binaryop_mixed_channel_broadcast();
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}
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static int test_binaryop_4_dense(int w, int h, int d, int c)
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{
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// dense and scalar arithmetic retain both channel packing representatives
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ncnn::Mat a = RandomMat(w, h, d, c, 1.0f, 1.1f);
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ncnn::Mat b = RandomMat(w, h, d, c, 0.8f, 0.9f);
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return test_binaryop(a, b) || test_binaryop(a, 0.7f);
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}
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static int test_binaryop_4_dense()
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{
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return test_binaryop_4_dense(2, 7, 3, 31)
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|| test_binaryop_4_dense(3, 6, 4, 28);
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}
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static int test_binaryop_4_broadcast(int w, int h, int d, int c)
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{
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// inverse operation pairs retain both canonical arithmetic directions
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ncnn::Mat inputs[] = {
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RandomMat(w, h, d, c, 1.0f, 1.1f),
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RandomMat(1, h, d, c, 1.0f, 1.1f),
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RandomMat(w, 1, d, c, 1.0f, 1.1f),
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RandomMat(1, 1, d, c, 1.0f, 1.1f),
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RandomMat(w, h, 1, c, 1.0f, 1.1f),
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RandomMat(1, h, 1, c, 1.0f, 1.1f),
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RandomMat(w, 1, 1, c, 1.0f, 1.1f),
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RandomMat(1, 1, 1, c, 1.0f, 1.1f)
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};
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ncnn::Mat second_inputs[] = {
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RandomMat(1, h, d, c, 0.8f, 0.9f),
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RandomMat(w, 1, d, c, 0.8f, 0.9f),
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RandomMat(1, 1, d, c, 0.8f, 0.9f),
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RandomMat(w, h, 1, c, 0.8f, 0.9f),
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RandomMat(1, h, 1, c, 0.8f, 0.9f),
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RandomMat(w, 1, 1, c, 0.8f, 0.9f),
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RandomMat(1, 1, 1, c, 0.8f, 0.9f),
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RandomMat(w, h, d, 1, 0.8f, 0.9f),
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RandomMat(1, h, d, 1, 0.8f, 0.9f),
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RandomMat(w, 1, d, 1, 0.8f, 0.9f),
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RandomMat(1, 1, d, 1, 0.8f, 0.9f),
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RandomMat(w, h, 1, 1, 0.8f, 0.9f),
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RandomMat(1, h, 1, 1, 0.8f, 0.9f),
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RandomMat(w, 1, 1, 1, 0.8f, 0.9f),
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RandomMat(1, 1, 1, 1, 0.8f, 0.9f)
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};
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const int broadcast_pairs[][2] = {
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{0, 0}, {0, 1}, {0, 3}, {0, 7}, {0, 2}, {0, 6}, {0, 14}, {1, 13}, {2, 12}, {3, 11}, {4, 10}, {5, 9}, {6, 8}, {7, 7}
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};
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const int ordered_operations[][3] = {
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{15, 0, 1}, {16, 0, 1}, {17, 0, 1}, {18, 0, 1}
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};
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for (size_t j = 0; j < sizeof(ordered_operations) / sizeof(ordered_operations[0]); j++)
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{
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op_type = ordered_operations[j][0];
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for (size_t i = 0; i < sizeof(broadcast_pairs) / sizeof(broadcast_pairs[0]); i++)
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{
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const ncnn::Mat operands[] = {
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inputs[broadcast_pairs[i][0]],
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second_inputs[broadcast_pairs[i][1]]
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};
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int ret = test_binaryop(operands[ordered_operations[j][1]], operands[ordered_operations[j][2]]);
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if (ret != 0)
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return ret;
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}
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}
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return 0;
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}
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static int test_binaryop_4_broadcast()
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{
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// depth, spatial and channel expansion retain scalar and packed outputs
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return test_binaryop_4_broadcast(2, 7, 3, 31)
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|| test_binaryop_4_broadcast(3, 6, 4, 28);
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}
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static int test_binaryop_5(int w, int h, int d, int c)
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{
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ncnn::Mat a[4] = {
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RandomMat(c, 1.0f, 1.1f),
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RandomMat(d, c, 1.0f, 1.1f),
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RandomMat(h, d, c, 1.0f, 1.1f),
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RandomMat(w, h, d, c, 1.0f, 1.1f),
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};
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ncnn::Mat b[4] = {
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RandomMat(c, 0.8f, 0.9f),
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RandomMat(d, c, 0.8f, 0.9f),
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RandomMat(h, d, c, 0.8f, 0.9f),
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RandomMat(w, h, d, c, 0.8f, 0.9f),
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};
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// every implicit-rank expansion is checked in both operand directions
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const int rank_pairs[][2] = {
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{0, 1}, {0, 2}, {0, 3}, {1, 2}, {1, 3}, {2, 3}
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};
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for (size_t i = 0; i < sizeof(rank_pairs) / sizeof(rank_pairs[0]); i++)
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{
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const int j = rank_pairs[i][0];
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const int k = rank_pairs[i][1];
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int ret = test_binaryop(a[j], b[k]) || test_binaryop(a[k], b[j]);
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if (ret != 0)
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return ret;
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}
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return 0;
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}
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static int test_binaryop_5()
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{
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// implicit-rank broadcasts retain each packing boundary
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return 0
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|| test_binaryop_5(2, 7, 3, 31)
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|| test_binaryop_5(3, 6, 4, 28)
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|| test_binaryop_5(4, 5, 5, 24)
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|| test_binaryop_5(5, 4, 6, 32);
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}
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static int test_binaryop_6(int w, int h, int d, int c)
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{
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ncnn::Mat a[3] = {
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RandomMat(d, c, 1.0f, 1.1f),
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RandomMat(h, d, c, 1.0f, 1.1f),
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RandomMat(w, h, d, c, 1.0f, 1.1f),
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};
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for (int j = 0; j < 3; j++)
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{
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ncnn::Mat b = RandomMat(a[j].w, 0.8f, 0.9f);
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int ret = test_binaryop(a[j], b) || test_binaryop(b, a[j]);
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if (ret != 0)
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return ret;
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}
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ncnn::Mat aa[3] = {
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RandomMat(c, c, 1.0f, 1.1f),
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RandomMat(c, 3, c, 1.0f, 1.1f),
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RandomMat(c, 3, 2, c, 1.0f, 1.1f),
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};
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for (int j = 0; j < 3; j++)
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{
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ncnn::Mat b = RandomMat(aa[j].w, 0.8f, 0.9f);
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int ret = test_binaryop(aa[j], b) || test_binaryop(b, aa[j]);
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if (ret != 0)
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return ret;
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}
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return 0;
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}
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static int test_binaryop_6()
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{
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// keep the mixed packing conversions for one-dimensional broadcasts
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return 0
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|| test_binaryop_6(16, 15, 12, 31)
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|| test_binaryop_6(12, 16, 14, 28)
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|| test_binaryop_6(16, 15, 12, 24)
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|| test_binaryop_6(15, 12, 16, 32);
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}
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static int test_binaryop_singletons()
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{
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// rank-specific single-element layouts are independent of packing boundaries
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ncnn::Mat a1 = RandomMat(1, 1.0f, 1.1f);
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ncnn::Mat b1 = RandomMat(1, 0.8f, 0.9f);
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ncnn::Mat a2 = RandomMat(1, 1, 1.0f, 1.1f);
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ncnn::Mat a3 = RandomMat(1, 1, 1, 1.0f, 1.1f);
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ncnn::Mat a4 = RandomMat(1, 1, 1, 1, 1.0f, 1.1f);
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ncnn::Mat b4 = RandomMat(1, 1, 1, 1, 0.8f, 0.9f);
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return test_binaryop(a1, b1)
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|| test_binaryop(a1, 0.7f)
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|| test_binaryop(a2, 0.7f)
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|| test_binaryop(a3, 0.7f)
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|| test_binaryop(a4, b4)
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|| test_binaryop(a4, 0.7f);
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}
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static int test_binaryop_signed_remainders()
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{
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// binary fractions avoid rounding across integer quotient boundaries
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ncnn::Mat a(33);
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ncnn::Mat b(33);
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const float values[] = {-2.25f, -1.125f, -0.375f, 0.375f, 1.125f, 2.25f};
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for (int i = 0; i < 33; i++)
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{
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a[i] = values[i % 6];
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b[i] = i % 2 == 0 ? 0.875f : -0.875f;
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}
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return test_binaryop(a, b)
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|| test_binaryop(b, a)
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|| test_binaryop(a, 0.875f)
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|| test_binaryop(a, -0.875f);
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}
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int main()
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{
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SRAND(7767517);
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for (op_type = 15; op_type < 19; op_type++)
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{
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int ret = 0
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|| test_binaryop_1()
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|| test_binaryop_2()
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|| test_binaryop_3()
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|| test_binaryop_4_dense()
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|| test_binaryop_5()
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|| test_binaryop_6()
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|| test_binaryop_singletons()
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|| test_binaryop_signed_remainders();
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if (ret != 0)
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return ret;
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}
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return test_binaryop_4_broadcast();
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}
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