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test_operation_convolution.cc
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test_operation_convolution.cc
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/************************************************************
*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* 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 "singa/singa_config.h"
#ifdef USE_CBLAS
#include <chrono>
#include <iostream>
#include "../src/model/operation/convolution.h"
#include "gtest/gtest.h"
using namespace singa;
#ifdef USE_DNNL
#include <stdio.h>
TEST(DNNLOperation_Convolution, Forward) {
const size_t batch_size = 2, c = 1, h = 3, w = 3;
const float x[batch_size * c * h * w] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f,
7.0f, 8.0f, 9.0f, 1.0f, 2.0f, 3.0f,
4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f};
Tensor in(Shape{batch_size, c, h, w});
in.CopyDataFromHostPtr(x, batch_size * c * h * w);
const size_t num_filters = 1;
const size_t kernel_w = 3;
const size_t kernel_h = 3;
const std::vector<size_t> stride = {2, 2};
const std::vector<size_t> padding = {1, 1};
const bool bias_flag = true;
const float we[num_filters * kernel_w * kernel_h] = {
1.0f, 1.0f, 0.0f, 0.0f, 0.0f, -1.0f, 0.0f, 1.0f, 0.0f};
Tensor weight(Shape{num_filters, num_filters, 3, 3});
weight.CopyDataFromHostPtr(we,
num_filters * num_filters * kernel_w * kernel_h);
const float b[num_filters] = {1.0f};
Tensor bias(Shape{num_filters});
bias.CopyDataFromHostPtr(b, num_filters);
ConvHandle conv_handle(in, {kernel_w, kernel_h}, stride, padding, c,
num_filters, bias_flag);
Tensor out1 = CpuConvForward(in, weight, bias, conv_handle);
const float *out_ptr1 = out1.data<float>();
// Input: 3*3; kernel: 3*3; stride: 2*2; padding: 1*1.
EXPECT_EQ(8u, out1.Size());
EXPECT_EQ(3.0f, out_ptr1[0]);
EXPECT_EQ(7.0f, out_ptr1[1]);
EXPECT_EQ(-3.0f, out_ptr1[2]);
EXPECT_EQ(12.0f, out_ptr1[3]);
EXPECT_EQ(3.0f, out_ptr1[4]);
EXPECT_EQ(7.0f, out_ptr1[5]);
EXPECT_EQ(-3.0f, out_ptr1[6]);
EXPECT_EQ(12.0f, out_ptr1[7]);
}
TEST(DNNLOperation_Convolution, Performance) {
const int batch = 64;
const int image_h = 28;
const int in_chan = 1;
const int out_chan = 20;
const int ker = 5;
const int stride = 1;
const int out_size = 24;
const bool bias_flag = true;
Tensor grad(Shape{batch, out_chan, out_size, out_size});
Tensor in(Shape{batch, in_chan, image_h, image_h});
Tensor weight(Shape{out_chan, in_chan, ker, ker});
Tensor bias(Shape{out_chan});
Gaussian(0.0f, 1.0f, &grad);
Gaussian(0.0f, 1.0f, &in);
Gaussian(0.0f, 1.0f, &weight);
Gaussian(0.0f, 1.0f, &bias);
ConvHandle conv_handle(in, {ker, ker}, {stride, stride}, {0, 0}, in_chan,
out_chan, bias_flag);
const int times = 100;
{
std::chrono::steady_clock::time_point begin =
std::chrono::steady_clock::now();
for (int i = 0; i < times; i++) {
Tensor out = CpuConvForward(in, weight, bias, conv_handle);
}
std::chrono::steady_clock::time_point end =
std::chrono::steady_clock::now();
std::cout << "[avg]forward Time difference = "
<< (std::chrono::duration_cast<std::chrono::microseconds>(end -
begin)
.count()) /
times
<< "[microsec]" << std::endl;
}
{
std::chrono::steady_clock::time_point begin =
std::chrono::steady_clock::now();
for (int i = 0; i < times; i++) {
Tensor in_grad = CpuConvBackwardx(grad, weight, in, conv_handle);
}
std::chrono::steady_clock::time_point end =
std::chrono::steady_clock::now();
std::cout << "[avg]backwardx Time difference = "
<< (std::chrono::duration_cast<std::chrono::microseconds>(end -
begin)
.count()) /
times
<< "[microsec]" << std::endl;
}
{
std::chrono::steady_clock::time_point begin =
std::chrono::steady_clock::now();
for (int i = 0; i < times; i++) {
Tensor dw = CpuConvBackwardW(grad, in, weight, conv_handle);
}
std::chrono::steady_clock::time_point end =
std::chrono::steady_clock::now();
std::cout << "[avg]backwardW Time difference = "
<< (std::chrono::duration_cast<std::chrono::microseconds>(end -
begin)
.count()) /
times
<< "[microsec]" << std::endl;
}
}
TEST(DNNLOperation_Convolution, Backward) {
const size_t batch_size = 2, c = 1, h = 3, w = 3;
const float x[batch_size * c * h * w] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f,
7.0f, 8.0f, 9.0f, 1.0f, 2.0f, 3.0f,
4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f};
Tensor in(Shape{batch_size, c, h, w});
in.CopyDataFromHostPtr(x, batch_size * c * h * w);
const size_t num_filters = 1;
const size_t kernel_w = 3;
const size_t kernel_h = 3;
const std::vector<size_t> stride = {2, 2};
const std::vector<size_t> padding = {1, 1};
const bool bias_flag = true;
const float we[num_filters * kernel_w * kernel_h] = {
1.0f, 1.0f, 0.0f, 0.0f, 0.0f, -1.0f, 0.0f, 1.0f, 0.0f};
Tensor weight(Shape{num_filters, num_filters, 3, 3});
weight.CopyDataFromHostPtr(we,
num_filters * num_filters * kernel_w * kernel_h);
const float b[num_filters] = {1.0f};
Tensor bias(Shape{num_filters});
bias.CopyDataFromHostPtr(b, num_filters);
ConvHandle conv_handle(in, {kernel_w, kernel_h}, stride, padding, c,
num_filters, bias_flag);
Tensor out1 = CpuConvForward(in, weight, bias, conv_handle);
// grad
const size_t grad_h = 2, grad_w = 2;
const float dy[batch_size * num_filters * grad_h * grad_w] = {
0.1f, 0.2f, 0.3f, 0.4f, 0.1f, 0.2f, 0.3f, 0.4f};
Tensor grad(Shape{batch_size, num_filters, grad_h, grad_w});
grad.CopyDataFromHostPtr(dy, batch_size * num_filters * grad_h * grad_w);
Tensor in_grad = CpuConvBackwardx(grad, weight, in, conv_handle);
const float *dx = in_grad.data<float>();
const float *wptr = we;
EXPECT_EQ(18u, in_grad.Size());
EXPECT_EQ(dy[0] * wptr[4], dx[0]);
EXPECT_EQ(dy[0] * wptr[5] + dy[1] * wptr[3], dx[1]);
EXPECT_EQ(dy[1] * wptr[4], dx[2]);
EXPECT_EQ(dy[0] * wptr[7] + dy[2] * wptr[1], dx[3]);
EXPECT_EQ(
dy[0] * wptr[8] + dy[1] * wptr[6] + dy[2] * wptr[2] + dy[3] * wptr[0],
dx[4]);
EXPECT_EQ(dy[1] * wptr[7] + dy[3] * wptr[1], dx[5]);
EXPECT_EQ(dy[2] * wptr[4], dx[6]);
EXPECT_EQ(dy[2] * wptr[5] + dy[3] * wptr[3], dx[7]);
EXPECT_EQ(dy[3] * wptr[4], dx[8]);
EXPECT_EQ(dy[4] * wptr[4], dx[9]);
EXPECT_EQ(dy[4] * wptr[5] + dy[1] * wptr[3], dx[10]);
EXPECT_EQ(dy[5] * wptr[4], dx[11]);
EXPECT_EQ(dy[4] * wptr[7] + dy[2] * wptr[1], dx[12]);
EXPECT_EQ(
dy[4] * wptr[8] + dy[5] * wptr[6] + dy[6] * wptr[2] + dy[7] * wptr[0],
dx[13]);
EXPECT_EQ(dy[5] * wptr[7] + dy[7] * wptr[1], dx[14]);
EXPECT_EQ(dy[6] * wptr[4], dx[15]);
EXPECT_EQ(dy[6] * wptr[5] + dy[7] * wptr[3], dx[16]);
EXPECT_EQ(dy[7] * wptr[4], dx[17]);
Tensor dw = CpuConvBackwardW(grad, in, weight, conv_handle);
Tensor db = CpuConvBackwardb(grad, bias, conv_handle);
const float *dbptr = db.data<float>();
EXPECT_FLOAT_EQ(dy[0] + dy[1] + dy[2] + dy[3] + dy[4] + dy[5] + dy[6] + dy[7],
dbptr[0]);
const float *dwptr = dw.data<float>();
EXPECT_EQ(9u, dw.Size());
EXPECT_FLOAT_EQ(dy[3] * x[4] + dy[7] * x[13], dwptr[0]);
EXPECT_FLOAT_EQ(dy[3] * x[5] + dy[7] * x[14] + dy[2] * x[3] + dy[6] * x[12],
dwptr[1]);
EXPECT_FLOAT_EQ(dy[2] * x[4] + dy[6] * x[13], dwptr[2]);
EXPECT_FLOAT_EQ(dy[1] * x[1] + dy[5] * x[10] + dy[3] * x[7] + dy[7] * x[16],
dwptr[3]);
EXPECT_FLOAT_EQ(dy[0] * x[0] + dy[4] * x[9] + dy[1] * x[2] + dy[5] * x[11] +
dy[2] * x[6] + dy[6] * x[15] + dy[3] * x[8] +
dy[7] * x[17],
dwptr[4]);
EXPECT_FLOAT_EQ(dy[0] * x[1] + dy[4] * x[10] + dy[2] * x[7] + dy[6] * x[16],
dwptr[5]);
EXPECT_FLOAT_EQ(dy[1] * x[4] + dy[5] * x[13], dwptr[6]);
EXPECT_FLOAT_EQ(dy[0] * x[3] + dy[4] * x[12] + dy[1] * x[5] + dy[5] * x[14],
dwptr[7]);
EXPECT_FLOAT_EQ(dy[0] * x[4] + dy[4] * x[13], dwptr[8]);
}
#endif // USE_DNNL
#endif // USE_CBLAS