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e050d596b08bb389cf3c88b4f5278e0213fe30c0
ncnn/tests/test_convolution.cpp
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nihui e050d596b0 fix convolution int8 requant test on x86
2020-01-27 21:07:07 +08:00

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// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.
#include "testutil.h"
#include "layer/convolution.h"
static int test_convolution(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias, bool use_packing_layout)
{
ncnn::Mat a = RandomMat(w, h, c);
ncnn::ParamDict pd;
pd.set(0, outch);// num_output
pd.set(1, kernel);// kernel_w
pd.set(2, dilation);// dilation_w
pd.set(3, stride);// stride_w
pd.set(4, pad);// pad_w
pd.set(5, bias);// bias_term
pd.set(6, outch*c*kernel*kernel);
std::vector<ncnn::Mat> weights(bias ? 2 : 1);
weights[0] = RandomMat(outch*c*kernel*kernel);
if (bias)
weights[1] = RandomMat(outch);
ncnn::ModelBinFromMatArray mb(weights.data());
ncnn::Option opt;
opt.num_threads = 1;
opt.use_vulkan_compute = true;
opt.use_int8_inference = false;
opt.use_fp16_packed = false;
opt.use_fp16_storage = false;
opt.use_fp16_arithmetic = false;
opt.use_int8_storage = false;
opt.use_int8_arithmetic = false;
opt.use_packing_layout = use_packing_layout;
int ret = test_layer<ncnn::Convolution>("Convolution", pd, mb, opt, a);
if (ret != 0)
{
fprintf(stderr, "test_convolution failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d use_packing_layout=%d\n", w, h, c, outch, kernel, dilation, stride, pad, bias, use_packing_layout);
}
return ret;
}
static int test_convolution_0()
{
static const int kdsp[24][4] = {
{1, 1, 1, 0},
{1, 1, 2, 0},
{2, 1, 1, 1},
{2, 1, 2, 1},
{2, 2, 1, 1},
{2, 2, 2, 1},
{3, 1, 1, 1},
{3, 1, 2, 1},
{3, 2, 1, 1},
{3, 2, 2, 1},
{4, 1, 1, 2},
{4, 1, 2, 2},
{4, 2, 1, 2},
{4, 2, 2, 2},
{5, 1, 1, 2},
{5, 1, 2, 2},
{5, 2, 1, 2},
{5, 2, 2, 2},
{7, 1, 1, 3},
{7, 1, 2, 3},
{7, 1, 3, 3},
{7, 2, 1, 3},
{7, 2, 2, 3},
{7, 2, 3, 3},
};
for (int i=0; i<24; i++)
{
int ret = 0
|| test_convolution(13, 11, 1, 1, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, false)
|| test_convolution(13, 11, 2, 2, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, false)
|| test_convolution(13, 11, 3, 3, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, false)
|| test_convolution(13, 11, 4, 4, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, false)
|| test_convolution(13, 11, 7, 7, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, false)
|| test_convolution(13, 11, 8, 8, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, false)
|| test_convolution(13, 11, 15, 15, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, false)
|| test_convolution(13, 11, 16, 16, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, false)
|| test_convolution(13, 11, 1, 1, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution(13, 11, 2, 2, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution(13, 11, 3, 3, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution(13, 11, 3, 12, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution(13, 11, 4, 4, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution(13, 11, 8, 3, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution(13, 11, 8, 8, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution(13, 11, 16, 4, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution(13, 11, 16, 16, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
;
if (ret != 0)
return -1;
}
return 0;
}
void set_param(ncnn::Convolution* layer)
{
layer->use_int8_requantize = true;
layer->top_blob_int8_scale = 64.f;
return;
}
static int test_convolution_int8(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias, bool requant = false)
{
ncnn::Mat a = RandomMat(w, h, c);
ncnn::ParamDict pd;
pd.set(0, outch);// num_output
pd.set(1, kernel);// kernel_w
pd.set(2, dilation);// dilation_w
pd.set(3, stride);// stride_w
pd.set(4, pad);// pad_w
pd.set(5, bias);// bias_term
pd.set(6, outch*c*kernel*kernel);
pd.set(8, 1);// int8_scale_term
std::vector<ncnn::Mat> weights(bias ? 4 : 3);
weights[0] = RandomMat(outch*c*kernel*kernel);
if (bias)
{
weights[1] = RandomMat(outch);
weights[2] = RandomMat(outch);
weights[3] = RandomMat(1);
}
else
{
weights[1] = RandomMat(outch);
weights[2] = RandomMat(1);
}
ncnn::ModelBinFromMatArray mb(weights.data());
ncnn::Option opt;
opt.num_threads = 1;
opt.use_vulkan_compute = false;
opt.use_int8_inference = true;
opt.use_fp16_packed = false;
opt.use_fp16_storage = false;
opt.use_fp16_arithmetic = false;
opt.use_int8_storage = false;
opt.use_int8_arithmetic = false;
opt.use_packing_layout = false;
int ret = test_layer<ncnn::Convolution>("Convolution", pd, mb, opt, a, 0.001f, requant ? set_param : 0);
if (ret != 0)
{
fprintf(stderr, "test_convolution_int8 failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d requant=%d\n", w, h, c, outch, kernel, dilation, stride, pad, bias, requant);
}
return 0;
}
static int test_convolution_1()
{
static const int kdsp[24][4] = {
{1, 1, 1, 0},
{1, 1, 2, 0},
{2, 1, 1, 1},
{2, 1, 2, 1},
{2, 2, 1, 1},
{2, 2, 2, 1},
{3, 1, 1, 1},
{3, 1, 2, 1},
{3, 2, 1, 1},
{3, 2, 2, 1},
{4, 1, 1, 2},
{4, 1, 2, 2},
{4, 2, 1, 2},
{4, 2, 2, 2},
{5, 1, 1, 2},
{5, 1, 2, 2},
{5, 2, 1, 2},
{5, 2, 2, 2},
{7, 1, 1, 3},
{7, 1, 2, 3},
{7, 1, 3, 3},
{7, 2, 1, 3},
{7, 2, 2, 3},
{7, 2, 3, 3},
};
for (int i=0; i<24; i++)
{
int ret = 0
|| test_convolution_int8(13, 11, 1, 1, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
|| test_convolution_int8(13, 11, 2, 2, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
|| test_convolution_int8(13, 11, 3, 3, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
|| test_convolution_int8(13, 11, 4, 4, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
|| test_convolution_int8(13, 11, 7, 7, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
|| test_convolution_int8(13, 11, 8, 8, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
|| test_convolution_int8(13, 11, 15, 15, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
|| test_convolution_int8(13, 11, 16, 16, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
|| test_convolution_int8(13, 11, 1, 1, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
|| test_convolution_int8(13, 11, 2, 2, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
|| test_convolution_int8(13, 11, 3, 3, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
|| test_convolution_int8(13, 11, 3, 12, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
|| test_convolution_int8(13, 11, 4, 4, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
|| test_convolution_int8(13, 11, 8, 3, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
|| test_convolution_int8(13, 11, 8, 8, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
|| test_convolution_int8(13, 11, 16, 4, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
|| test_convolution_int8(13, 11, 16, 16, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
;
if (ret != 0)
return -1;
}
for (int i=0; i<20; i++)
{
int ret = 0
|| test_convolution_int8(13, 11, 1, 1, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution_int8(13, 11, 1, 1, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution_int8(13, 11, 2, 2, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution_int8(13, 11, 3, 3, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution_int8(13, 11, 4, 4, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution_int8(13, 11, 7, 7, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution_int8(13, 11, 8, 8, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution_int8(13, 11, 15, 15, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution_int8(13, 11, 16, 16, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution_int8(13, 11, 1, 1, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution_int8(13, 11, 2, 2, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution_int8(13, 11, 3, 3, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution_int8(13, 11, 3, 12, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution_int8(13, 11, 4, 4, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution_int8(13, 11, 8, 3, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution_int8(13, 11, 8, 8, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution_int8(13, 11, 16, 4, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
|| test_convolution_int8(13, 11, 16, 16, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
;
if (ret != 0)
return -1;
}
return 0;
}
int main()
{
SRAND(7767517);
return test_convolution_0() || test_convolution_1();
}
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