mirror of
https://github.com/Tencent/ncnn.git
synced 2026-10-18 05:36:58 +08:00
nihui
579 lines
21 KiB
C++
579 lines
21 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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#include "layer_type.h"
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static int test_innerproduct(const ncnn::Mat& a, int outch, int bias)
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{
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ncnn::ParamDict pd;
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pd.set(0, outch); // num_output
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pd.set(1, bias); // bias_term
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pd.set(2, outch * a.w * a.h * a.d * a.c);
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int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
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ncnn::Mat activation_params(2);
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activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
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activation_params[1] = RandomFloat(0, 1); // beta
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pd.set(9, activation_type);
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pd.set(10, activation_params);
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std::vector<ncnn::Mat> weights(bias ? 2 : 1);
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weights[0] = RandomMat(outch * a.w * a.h * a.d * a.c);
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if (bias)
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weights[1] = RandomMat(outch);
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int ret = test_layer("InnerProduct", pd, weights, a);
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if (ret != 0)
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{
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fprintf(stderr, "test_innerproduct failed a.dims=%d a=(%d %d %d %d) outch=%d bias=%d act=%d actparams=[%f,%f]\n", a.dims, a.w, a.h, a.d, a.c, outch, bias, activation_type, activation_params[0], activation_params[1]);
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}
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return ret;
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}
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static int test_innerproduct_0()
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{
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return 0
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|| test_innerproduct(RandomMat(1, 3, 1), 1, 1)
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|| test_innerproduct(RandomMat(3, 2, 2), 2, 0)
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|| test_innerproduct(RandomMat(9, 3, 8), 7, 1)
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|| test_innerproduct(RandomMat(2, 2, 8), 8, 0)
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|| test_innerproduct(RandomMat(4, 3, 15), 8, 1)
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|| test_innerproduct(RandomMat(6, 2, 16), 16, 0)
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|| test_innerproduct(RandomMat(6, 2, 16), 7, 1)
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|| test_innerproduct(RandomMat(6, 2, 5), 16, 1);
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}
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static int test_innerproduct_1()
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{
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return 0
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|| test_innerproduct(RandomMat(1, 1), 1, 1)
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|| test_innerproduct(RandomMat(3, 2), 2, 0)
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|| test_innerproduct(RandomMat(9, 8), 7, 1)
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|| test_innerproduct(RandomMat(2, 8), 8, 0)
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|| test_innerproduct(RandomMat(4, 15), 8, 1)
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|| test_innerproduct(RandomMat(6, 16), 16, 0)
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|| test_innerproduct(RandomMat(6, 16), 7, 1)
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|| test_innerproduct(RandomMat(6, 5), 16, 1);
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}
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static int test_innerproduct_2()
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{
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return 0
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|| test_innerproduct(RandomMat(1), 1, 1)
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|| test_innerproduct(RandomMat(2), 2, 0)
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|| test_innerproduct(RandomMat(8), 7, 1)
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|| test_innerproduct(RandomMat(8), 8, 0)
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|| test_innerproduct(RandomMat(15), 8, 1)
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|| test_innerproduct(RandomMat(15), 15, 1)
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|| test_innerproduct(RandomMat(16), 16, 0)
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|| test_innerproduct(RandomMat(16), 7, 1)
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|| test_innerproduct(RandomMat(5), 16, 0)
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|| test_innerproduct(RandomMat(32), 16, 1)
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|| test_innerproduct(RandomMat(12), 16, 0)
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|| test_innerproduct(RandomMat(16), 12, 1)
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|| test_innerproduct(RandomMat(24), 32, 1);
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}
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static int test_innerproduct_3()
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{
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return 0
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|| test_innerproduct(RandomMat(2, 2, 3, 1), 8, 1)
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|| test_innerproduct(RandomMat(5, 3, 2, 3), 7, 0)
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|| test_innerproduct(RandomMat(3, 2, 2, 5), 8, 1)
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|| test_innerproduct(RandomMat(4, 3, 3, 4), 15, 1)
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|| test_innerproduct(RandomMat(2, 2, 3, 16), 16, 0);
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}
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#if NCNN_INT8
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static int test_innerproduct_int8(const ncnn::Mat& a, int outch, int bias, bool input_int8 = false, bool weight_int8 = false)
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{
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ncnn::ParamDict pd;
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pd.set(0, outch); // num_output
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pd.set(1, bias); // bias_term
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pd.set(2, outch * a.w * a.h * a.d * a.c);
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pd.set(8, 1); // int8_scale_term
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int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
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ncnn::Mat activation_params(2);
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activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
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activation_params[1] = RandomFloat(0, 1); // beta
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pd.set(9, activation_type);
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pd.set(10, activation_params);
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std::vector<ncnn::Mat> weights(bias ? 4 : 3);
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const int k = a.w * a.h * a.d * a.c;
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weights[0] = weight_int8 ? RandomS8Mat(outch * k) : RandomMat(outch * k);
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ncnn::Mat weight_scales = weight_int8 ? RandomMat(outch, 10.f, 20.f) : scales_mat(weights[0], outch, k, k);
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if (!weight_int8)
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{
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for (int q = 0; q < outch; q++)
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{
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weight_scales[q] = std::min(weight_scales[q], 127.f);
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}
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}
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ncnn::Mat input_scales = scales_mat(a, 1, k, k);
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input_scales[0] = std::min(input_scales[0], 127.f);
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ncnn::Mat a_int8 = a;
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if (input_int8)
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{
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_packing_layout = false;
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ncnn::quantize_to_int8(a, a_int8, input_scales, opt);
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}
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if (bias)
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{
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weights[1] = RandomMat(outch);
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weights[2] = weight_scales;
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weights[3] = input_scales;
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}
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else
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{
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weights[1] = weight_scales;
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weights[2] = input_scales;
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}
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int flag = input_int8 ? TEST_LAYER_DISABLE_AUTO_INPUT_CASTING : 0;
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int ret = 0;
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if (input_int8)
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{
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_packing_layout = true;
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opt.use_fp16_packed = false;
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opt.use_fp16_storage = false;
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opt.use_fp16_arithmetic = false;
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opt.use_bf16_packed = false;
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opt.use_bf16_storage = false;
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ret = test_layer_opt("InnerProduct", pd, weights, opt, a_int8, 0.001f, flag);
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}
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else
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{
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ret = test_layer("InnerProduct", pd, weights, a_int8, 0.001f, flag);
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}
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if (ret != 0)
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{
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fprintf(stderr, "test_innerproduct_int8 failed a.dims=%d a=(%d %d %d %d) outch=%d bias=%d input_int8=%d weight_int8=%d act=%d actparams=[%f,%f]\n", a.dims, a.w, a.h, a.d, a.c, outch, bias, input_int8, weight_int8, activation_type, activation_params[0], activation_params[1]);
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}
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return ret;
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}
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static int test_innerproduct_4()
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{
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return 0
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|| test_innerproduct_int8(RandomMat(1, 3, 1), 1, 1)
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|| test_innerproduct_int8(RandomMat(3, 2, 2), 2, 1)
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|| test_innerproduct_int8(RandomMat(5, 3, 3), 3, 1)
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|| test_innerproduct_int8(RandomMat(7, 2, 3), 12, 1)
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|| test_innerproduct_int8(RandomMat(9, 3, 4), 4, 1)
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|| test_innerproduct_int8(RandomMat(2, 2, 7), 7, 1)
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|| test_innerproduct_int8(RandomMat(4, 3, 8), 3, 1)
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|| test_innerproduct_int8(RandomMat(6, 2, 8), 8, 1)
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|| test_innerproduct_int8(RandomMat(8, 3, 15), 15, 1)
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|| test_innerproduct_int8(RandomMat(7, 2, 16), 4, 1)
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|| test_innerproduct_int8(RandomMat(6, 3, 16), 16, 1)
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|| test_innerproduct_int8(RandomMat(16), 16, 1, true)
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|| test_innerproduct_int8(RandomMat(32), 16, 1, false, true)
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|| test_innerproduct_int8(RandomMat(16), 12, 1, true, true)
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|| test_innerproduct_int8(RandomMat(2, 2, 1), 7, 1, true)
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|| test_innerproduct_int8(RandomMat(2, 2, 2), 7, 1, true)
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|| test_innerproduct_int8(RandomMat(2, 2, 3), 7, 1, true)
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|| test_innerproduct_int8(RandomMat(2, 2, 4), 8, 1, true);
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}
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#endif // NCNN_INT8
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static int test_innerproduct_gemm(const ncnn::Mat& a, int outch, int bias, int flag = 0)
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{
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ncnn::ParamDict pd;
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pd.set(0, outch);
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pd.set(1, bias);
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pd.set(2, outch * a.w);
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int activation_type = RAND() % 7;
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ncnn::Mat activation_params(2);
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activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
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activation_params[1] = RandomFloat(0, 1);
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pd.set(9, activation_type);
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pd.set(10, activation_params);
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std::vector<ncnn::Mat> weights(bias ? 2 : 1);
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weights[0] = RandomMat(outch * a.w);
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if (bias)
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weights[1] = RandomMat(outch);
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int ret = test_layer("InnerProduct", pd, weights, a, 0.001, flag);
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if (ret != 0)
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{
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fprintf(stderr, "test_innerproduct_gemm failed a.dims=%d a=(%d %d %d) outch=%d bias=%d act=%d actparams=[%f,%f]\n", a.dims, a.w, a.h, a.c, outch, bias, activation_type, activation_params[0], activation_params[1]);
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}
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return ret;
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}
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static int test_innerproduct_5()
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{
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return 0
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|| test_innerproduct_gemm(RandomMat(1, 1), 1, 1)
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|| test_innerproduct_gemm(RandomMat(48, 1), 11, 1)
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|| test_innerproduct_gemm(RandomMat(1, 5), 1, 1)
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|| test_innerproduct_gemm(RandomMat(3, 2), 2, 0)
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|| test_innerproduct_gemm(RandomMat(9, 8), 7, 1)
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|| test_innerproduct_gemm(RandomMat(2, 8), 8, 0)
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|| test_innerproduct_gemm(RandomMat(13, 20), 8, 1)
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|| test_innerproduct_gemm(RandomMat(16, 20), 16, 0)
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|| test_innerproduct_gemm(RandomMat(11, 24), 8, 0)
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|| test_innerproduct_gemm(RandomMat(13, 24), 12, 1)
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|| test_innerproduct_gemm(RandomMat(15, 20), 20, 1)
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|| test_innerproduct_gemm(RandomMat(16, 20), 11, 1)
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|| test_innerproduct_gemm(RandomMat(19, 16), 16, 1)
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|| test_innerproduct_gemm(RandomMat(15, 15), 15, 1)
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|| test_innerproduct_gemm(RandomMat(14, 15), 8, 1)
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|| test_innerproduct_gemm(RandomMat(17, 15), 12, 1)
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|| test_innerproduct_gemm(RandomMat(12, 16), 7, 1)
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|| test_innerproduct_gemm(RandomMat(11, 32), 32, 1)
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|| test_innerproduct_gemm(RandomMat(12, 32), 24, 1)
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|| test_innerproduct_gemm(RandomMat(13, 32), 12, 1, TEST_LAYER_DISABLE_GPU_TESTING)
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|| test_innerproduct_gemm(RandomMat(14, 32), 14, 1)
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|| test_innerproduct_gemm(RandomMat(15, 32), 32, 1)
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|| test_innerproduct_gemm(RandomMat(16, 24), 32, 1)
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|| test_innerproduct_gemm(RandomMat(17, 20), 32, 1)
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|| test_innerproduct_gemm(RandomMat(18, 14), 32, 1);
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}
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#if NCNN_INT8
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static int test_innerproduct_gemm_int8(const ncnn::Mat& a, int outch, int bias, bool input_int8 = false, bool weight_int8 = false)
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{
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ncnn::ParamDict pd;
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pd.set(0, outch);
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pd.set(1, bias);
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pd.set(2, outch * a.w);
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pd.set(8, 1); // int8_scale_term
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std::vector<ncnn::Mat> weights(bias ? 4 : 3);
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const int k = a.w;
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weights[0] = weight_int8 ? RandomS8Mat(outch * k) : RandomMat(outch * k);
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ncnn::Mat weight_scales = weight_int8 ? RandomMat(outch, 10.f, 20.f) : scales_mat(weights[0], outch, k, k);
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if (!weight_int8)
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{
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for (int q = 0; q < outch; q++)
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{
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weight_scales[q] = std::min(weight_scales[q], 127.f);
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}
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}
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ncnn::Mat input_scales = scales_mat(a, 1, k, k);
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input_scales[0] = std::min(input_scales[0], 127.f);
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ncnn::Mat a_int8 = a;
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if (input_int8)
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{
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_packing_layout = false;
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ncnn::quantize_to_int8(a, a_int8, input_scales, opt);
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}
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if (bias)
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{
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weights[1] = RandomMat(outch);
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weights[2] = weight_scales;
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weights[3] = input_scales;
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}
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else
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{
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weights[1] = weight_scales;
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weights[2] = input_scales;
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}
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int flag = input_int8 ? TEST_LAYER_DISABLE_AUTO_INPUT_CASTING : 0;
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int ret = 0;
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if (input_int8)
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{
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_packing_layout = true;
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opt.use_fp16_packed = false;
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opt.use_fp16_storage = false;
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opt.use_fp16_arithmetic = false;
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opt.use_bf16_packed = false;
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opt.use_bf16_storage = false;
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ret = test_layer_opt("InnerProduct", pd, weights, opt, a_int8, 0.001f, flag);
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}
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else
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{
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ret = test_layer("InnerProduct", pd, weights, a_int8, 0.001f, flag);
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}
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if (ret != 0)
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{
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fprintf(stderr, "test_innerproduct_gemm_int8 failed a.dims=%d a=(%d %d %d) outch=%d bias=%d input_int8=%d weight_int8=%d\n", a.dims, a.w, a.h, a.c, outch, bias, input_int8, weight_int8);
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}
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return ret;
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}
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static int test_innerproduct_6()
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{
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return 0
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|| test_innerproduct_gemm_int8(RandomMat(1, 5), 1, 1)
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|| test_innerproduct_gemm_int8(RandomMat(3, 2), 2, 0)
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|| test_innerproduct_gemm_int8(RandomMat(9, 8), 7, 1)
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|| test_innerproduct_gemm_int8(RandomMat(2, 8), 8, 0)
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|| test_innerproduct_gemm_int8(RandomMat(13, 12), 8, 1)
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|| test_innerproduct_gemm_int8(RandomMat(16, 12), 16, 0)
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|| test_innerproduct_gemm_int8(RandomMat(4, 15), 8, 1)
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|| test_innerproduct_gemm_int8(RandomMat(6, 16), 16, 0)
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|| test_innerproduct_gemm_int8(RandomMat(12, 16), 7, 1)
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|| test_innerproduct_gemm_int8(RandomMat(11, 16), 8, 1, false, true)
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|| test_innerproduct_gemm_int8(RandomMat(13, 15), 7, 1, true)
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|| test_innerproduct_gemm_int8(RandomMat(12, 16), 7, 1, true)
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|| test_innerproduct_gemm_int8(RandomMat(12, 16), 7, 1, true, true);
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}
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static int test_innerproduct_7()
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{
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return 0
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|| test_innerproduct_int8(RandomMat(2, 2, 2, 1), 5, 1)
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|| test_innerproduct_int8(RandomMat(3, 2, 2, 3), 7, 1)
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|| test_innerproduct_int8(RandomMat(2, 2, 3, 4), 8, 1);
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}
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#endif // NCNN_INT8
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static int test_innerproduct_activation_params(const ncnn::ParamDict& pd, float value, float expected)
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{
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std::vector<ncnn::Mat> weights(1);
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weights[0].create(1);
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weights[0][0] = 1.f;
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ncnn::Mat a(1);
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a[0] = value;
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ncnn::Mat reference(1);
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reference[0] = expected;
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ncnn::Mat b;
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int ret = test_layer_naive(ncnn::LayerType::InnerProduct, pd, weights, a, b, 0);
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if (ret == 0)
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ret = CompareMat(reference, b, 0.f);
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if (ret != 0)
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{
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fprintf(stderr, "test_innerproduct_activation_params failed value=%f expected=%f ret=%d\n", value, expected, ret);
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return ret;
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}
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return 0;
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}
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#if NCNN_STRING
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static int test_innerproduct_activation_params_text(float value, float expected)
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{
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TestParamDict pd;
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if (pd.load_param("0=8 2=64 9=3 -23310=2,-1,2") != 0)
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return -1;
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pd.set(0, 1);
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pd.set(2, 1);
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int ret = test_innerproduct_activation_params(pd, value, expected);
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if (ret != 0)
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return ret;
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const ncnn::Mat original = pd.get(10, ncnn::Mat());
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const int* p = original;
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if (p[0] != -1 || p[1] != 2)
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{
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fprintf(stderr, "test_innerproduct_activation_params_text modified params=[%d,%d]\n", p[0], p[1]);
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return -1;
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}
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return 0;
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}
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#endif
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static int test_innerproduct_activation_params_text()
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{
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#if NCNN_STRING
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return 0
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|| test_innerproduct_activation_params_text(3.f, 2.f)
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|| test_innerproduct_activation_params_text(-3.f, -1.f);
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#else
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return 0;
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#endif
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}
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static int test_innerproduct_activation_params_binary()
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{
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// ncnn2mem output for 0=1 2=1 9=3 -23310=2,-1.0,2.0 in little-endian byte order
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const unsigned char binary[] = {
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0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
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0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
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0x09, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
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0xf2, 0xa4, 0xff, 0xff, 0x02, 0x00, 0x00, 0x00,
|
|
0x00, 0x00, 0x80, 0xbf, 0x00, 0x00, 0x00, 0x40,
|
|
0x17, 0xff, 0xff, 0xff
|
|
};
|
|
TestParamDict pd;
|
|
if (pd.load_param_bin(binary) != 0)
|
|
return -1;
|
|
|
|
int ret = 0
|
|
|| test_innerproduct_activation_params(pd, 3.f, 2.f)
|
|
|| test_innerproduct_activation_params(pd, -3.f, -1.f);
|
|
if (ret != 0)
|
|
return ret;
|
|
|
|
// binary arrays preserve bits without retaining the text integer type
|
|
const unsigned char integer_binary[] = {
|
|
0xf2, 0xa4, 0xff, 0xff, 0x02, 0x00, 0x00, 0x00,
|
|
0xff, 0xff, 0xff, 0xff, 0x02, 0x00, 0x00, 0x00,
|
|
0x17, 0xff, 0xff, 0xff
|
|
};
|
|
if (pd.load_param_bin(integer_binary) != 0)
|
|
return -1;
|
|
|
|
const ncnn::Mat params = pd.get(10, ncnn::Mat());
|
|
const unsigned int expected_bits[] = {0xffffffffu, 0x00000002u};
|
|
if (pd.type(10) != 4 || params.w != 2 || params.empty() || memcmp(params.data, expected_bits, sizeof(expected_bits)) != 0)
|
|
{
|
|
fprintf(stderr, "test_innerproduct_activation_params_binary changed untyped array bits\n");
|
|
return -1;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
#if NCNN_VALIDATION
|
|
static int test_innerproduct_load_param_activation(const ncnn::ParamDict& base, int activation_type, const ncnn::Mat& activation_params, int expected_ret)
|
|
{
|
|
ncnn::ParamDict pd = base;
|
|
pd.set(9, activation_type);
|
|
pd.set(10, activation_params);
|
|
|
|
return test_layer_param(ncnn::LayerType::InnerProduct, pd, expected_ret);
|
|
}
|
|
|
|
static int test_innerproduct_load_param()
|
|
{
|
|
ncnn::ParamDict base;
|
|
base.set(0, 8);
|
|
base.set(2, 64);
|
|
if (test_layer_param(ncnn::LayerType::InnerProduct, base, 0) != 0)
|
|
return -1;
|
|
|
|
ncnn::Mat params(2);
|
|
params[0] = 0.1f;
|
|
params[1] = 0.5f;
|
|
|
|
int ret = 0
|
|
|| test_innerproduct_load_param_activation(base, 0, ncnn::Mat(), 0)
|
|
|| test_innerproduct_load_param_activation(base, 0, ncnn::Mat(0), 0)
|
|
|| test_innerproduct_load_param_activation(base, 0, params, 0)
|
|
|| test_innerproduct_load_param_activation(base, 0, params.range(0, 1), 0)
|
|
|| test_innerproduct_load_param_activation(base, 1, ncnn::Mat(), 0)
|
|
|| test_innerproduct_load_param_activation(base, 1, ncnn::Mat(0), 0)
|
|
|| test_innerproduct_load_param_activation(base, 1, params, 0)
|
|
|| test_innerproduct_load_param_activation(base, 1, params.range(0, 1), 0)
|
|
|| test_innerproduct_load_param_activation(base, 2, ncnn::Mat(), -1)
|
|
|| test_innerproduct_load_param_activation(base, 2, ncnn::Mat(0), -1)
|
|
|| test_innerproduct_load_param_activation(base, 2, params, 0)
|
|
|| test_innerproduct_load_param_activation(base, 2, params.range(0, 1), 0)
|
|
|| test_innerproduct_load_param_activation(base, 3, ncnn::Mat(), -1)
|
|
|| test_innerproduct_load_param_activation(base, 3, ncnn::Mat(0), -1)
|
|
|| test_innerproduct_load_param_activation(base, 3, params, 0)
|
|
|| test_innerproduct_load_param_activation(base, 3, params.range(0, 1), -1)
|
|
|| test_innerproduct_load_param_activation(base, 4, ncnn::Mat(), 0)
|
|
|| test_innerproduct_load_param_activation(base, 4, ncnn::Mat(0), 0)
|
|
|| test_innerproduct_load_param_activation(base, 4, params, 0)
|
|
|| test_innerproduct_load_param_activation(base, 4, params.range(0, 1), 0)
|
|
|| test_innerproduct_load_param_activation(base, 5, ncnn::Mat(), 0)
|
|
|| test_innerproduct_load_param_activation(base, 5, ncnn::Mat(0), 0)
|
|
|| test_innerproduct_load_param_activation(base, 5, params, 0)
|
|
|| test_innerproduct_load_param_activation(base, 5, params.range(0, 1), 0)
|
|
|| test_innerproduct_load_param_activation(base, 6, ncnn::Mat(), -1)
|
|
|| test_innerproduct_load_param_activation(base, 6, ncnn::Mat(0), -1)
|
|
|| test_innerproduct_load_param_activation(base, 6, params, 0)
|
|
|| test_innerproduct_load_param_activation(base, 6, params.range(0, 1), -1)
|
|
|| test_innerproduct_load_param_activation(base, 7, ncnn::Mat(), -1);
|
|
if (ret != 0)
|
|
return ret;
|
|
|
|
ncnn::Mat missing_data(0);
|
|
missing_data.w = 1;
|
|
|
|
const ncnn::Mat bad[] = {ncnn::Mat(2, (size_t)1u), ncnn::Mat(2, (size_t)2u), ncnn::Mat(2, 2), ncnn::Mat(2, (size_t)16u, 4), missing_data};
|
|
for (int i = 0; i < 5; i++)
|
|
{
|
|
if (test_layer_param(ncnn::LayerType::InnerProduct, base, 10, bad[i], -1) != 0)
|
|
return -1;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
static int test_innerproduct_load_param_text()
|
|
{
|
|
#if NCNN_STRING
|
|
TestParamDict pd;
|
|
if (pd.load_param("0=8 2=64 9=3 -23310=2,-1,2") != 0)
|
|
return -1;
|
|
|
|
if (test_layer_param(ncnn::LayerType::InnerProduct, pd, 0) != 0)
|
|
return -1;
|
|
|
|
pd.set(0, 1);
|
|
pd.set(2, 1);
|
|
return test_layer_param(ncnn::LayerType::InnerProduct, pd, 0);
|
|
#else
|
|
return 0;
|
|
#endif
|
|
}
|
|
|
|
static int test_innerproduct_load_param_binary()
|
|
{
|
|
// ncnn2mem output for 0=1 2=1 9=3 -23310=2,-1.0,2.0 in little-endian byte order
|
|
const unsigned char binary[] = {
|
|
0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
|
|
0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
|
|
0x09, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
|
|
0xf2, 0xa4, 0xff, 0xff, 0x02, 0x00, 0x00, 0x00,
|
|
0x00, 0x00, 0x80, 0xbf, 0x00, 0x00, 0x00, 0x40,
|
|
0x17, 0xff, 0xff, 0xff
|
|
};
|
|
TestParamDict pd;
|
|
if (pd.load_param_bin(binary) != 0)
|
|
return -1;
|
|
|
|
return test_layer_param(ncnn::LayerType::InnerProduct, pd, 0);
|
|
}
|
|
#endif // NCNN_VALIDATION
|
|
|
|
int main()
|
|
{
|
|
SRAND(7767517);
|
|
|
|
return 0
|
|
|| test_innerproduct_0()
|
|
|| test_innerproduct_1()
|
|
|| test_innerproduct_2()
|
|
|| test_innerproduct_3()
|
|
#if NCNN_INT8
|
|
|| test_innerproduct_4()
|
|
#endif // NCNN_INT8
|
|
|| test_innerproduct_5()
|
|
#if NCNN_INT8
|
|
|| test_innerproduct_6()
|
|
|| test_innerproduct_7()
|
|
#endif // NCNN_INT8
|
|
|| test_innerproduct_activation_params_text()
|
|
|| test_innerproduct_activation_params_binary()
|
|
#if NCNN_VALIDATION
|
|
|| test_innerproduct_load_param()
|
|
|| test_innerproduct_load_param_text()
|
|
|| test_innerproduct_load_param_binary()
|
|
#endif // NCNN_VALIDATION
|
|
;
|
|
}
|