278 lines
10 KiB
C++
278 lines
10 KiB
C++
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// Tencent is pleased to support the open source community by making ncnn available.
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//
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// Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
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//
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// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
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// in compliance with the License. You may obtain a copy of the License at
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//
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// https://opensource.org/licenses/BSD-3-Clause
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//
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// Unless required by applicable law or agreed to in writing, software distributed
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// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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// CONDITIONS OF ANY KIND, either express or implied. See the License for the
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// specific language governing permissions and limitations under the License.
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#include "layer/innerproduct.h"
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#include "testutil.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.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.c);
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if (bias)
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weights[1] = RandomMat(outch);
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int ret = test_layer<ncnn::InnerProduct>("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) 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_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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#if NCNN_INT8
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static int test_innerproduct_int8(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.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.c;
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weights[0] = RandomMat(outch * k);
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ncnn::Mat weight_scales = scales_mat(weights[0], outch, k, k);
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ncnn::Mat input_scales = scales_mat(a, 1, k, k);
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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 = TEST_LAYER_DISABLE_GPU_TESTING;
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int ret = test_layer<ncnn::InnerProduct>("InnerProduct", pd, weights, a, 0.001f, 0, flag);
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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) 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_3()
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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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}
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#endif // NCNN_INT8
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static int test_innerproduct_gemm(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);
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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<ncnn::InnerProduct>("InnerProduct", pd, weights, a);
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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_4()
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{
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return 0
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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, 12), 8, 1)
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|| test_innerproduct_gemm(RandomMat(16, 12), 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, 12), 20, 1)
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|| test_innerproduct_gemm(RandomMat(16, 12), 11, 1)
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|| test_innerproduct_gemm(RandomMat(19, 16), 16, 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)
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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, 12), 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)
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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] = RandomMat(outch * k);
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ncnn::Mat weight_scales = scales_mat(weights[0], outch, k, k);
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ncnn::Mat input_scales = scales_mat(a, 1, k, k);
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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 = TEST_LAYER_DISABLE_GPU_TESTING;
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int ret = test_layer<ncnn::InnerProduct>("InnerProduct", pd, weights, a, 0.001f, 0, flag);
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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\n", a.dims, a.w, a.h, a.c, outch, bias);
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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_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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}
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#endif // NCNN_INT8
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int main()
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{
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SRAND(7767517);
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#if NCNN_INT8
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return 0
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|| test_innerproduct_0()
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|| test_innerproduct_1()
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|| test_innerproduct_2()
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|| test_innerproduct_3()
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|| test_innerproduct_4()
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|| test_innerproduct_5();
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#else
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return 0
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|| test_innerproduct_0()
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|| test_innerproduct_1()
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|| test_innerproduct_2()
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|| test_innerproduct_4();
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#endif
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}
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