perf_convolution1d.cpp
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "perf_precomp.hpp"
#include <opencv2/dnn/shape_utils.hpp>
namespace opencv_test {
struct Conv1DParam_t {
int kernel;
struct BlobShape { int dims[3]; } shapeIn;
int outCN;
int groups;
int stride;
int dilation;
int pad[2];
const char* padMode;
bool hasBias;
double declared_flops;
};
// Details: #12142
static const Conv1DParam_t testConvolution1DConfigs[] = {
{3, {{1, 6, 10}}, 6, 1, 1, 1, {0, 0}, "VALID", true, 1776.},
{3, {{1, 2, 19}}, 2, 2, 2, 1, {1, 1}, "", true, 260.},
{3, {{1, 2, 25}}, 2, 2, 1, 1, {2, 2}, "SAME", false, 650.},
};
struct Conv1DParamID
{
enum {
CONV_0 = 0,
CONV_LAST = sizeof(testConvolution1DConfigs) / sizeof(testConvolution1DConfigs[0])
};
int val_;
Conv1DParamID(int val = 0) : val_(val) {}
operator int() const { return val_; }
static ::testing::internal::ParamGenerator<Conv1DParamID> all()
{
enum { NUM = (int)CONV_LAST };
Conv1DParamID v_[NUM]; for (int i = 0; i < NUM; ++i) { v_[i] = Conv1DParamID(i); } // reduce generated code size
return ::testing::ValuesIn(v_, v_ + NUM);
}
};
static inline void PrintTo(const Conv1DParamID& v, std::ostream* os)
{
CV_Assert((int)v >= 0); CV_Assert((int)v < Conv1DParamID::CONV_LAST);
const Conv1DParam_t& p = testConvolution1DConfigs[(int)v];
*os << "GFLOPS=" << cv::format("%.3f", p.declared_flops * 1e-9)
<< ", K=[" << p.kernel << "]"
<< ", IN={" << p.shapeIn.dims[0] << ", " << p.shapeIn.dims[1] << ", " << p.shapeIn.dims[2] << "}"
<< ", OCN=" << p.outCN;
if (p.groups > 1)
*os << ", G=" << p.groups;
if (p.stride != 1)
*os << ", S=" << p.stride;
if (p.dilation != 1)
*os << ", D=" << p.dilation;
if (p.pad[0] != 0 && p.pad[1] != 0 )
*os << ", P=(" << p.pad[0] << ", " << p.pad[1] << ")";
if (!((std::string)p.padMode).empty())
*os << ", PM=" << ((std::string)p.padMode);
if (p.hasBias)
*os << ", BIAS";
}
typedef tuple<Conv1DParamID, tuple<Backend, Target> > Conv1DTestParam_t;
typedef TestBaseWithParam<Conv1DTestParam_t> Conv1D;
PERF_TEST_P_(Conv1D, conv1d)
{
int test_id = (int)get<0>(GetParam());
ASSERT_GE(test_id, 0); ASSERT_LT(test_id, Conv1DParamID::CONV_LAST);
const Conv1DParam_t& params = testConvolution1DConfigs[test_id];
double declared_flops = params.declared_flops;
DictValue kernel = DictValue::arrayInt(¶ms.kernel, 1);
DictValue stride = DictValue::arrayInt(¶ms.stride, 1);
DictValue pad = DictValue::arrayInt(¶ms.pad[0], 2);
DictValue dilation = DictValue::arrayInt(¶ms.dilation, 1);
MatShape inputShape = MatShape(params.shapeIn.dims, params.shapeIn.dims + 3);
int outChannels = params.outCN;
int groups = params.groups;
std::string padMode(params.padMode);
bool hasBias = params.hasBias;
Backend backendId = get<0>(get<1>(GetParam()));
Target targetId = get<1>(get<1>(GetParam()));
if (targetId != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
int inChannels = inputShape[1];
int sz[] = {outChannels, inChannels / groups, params.kernel};
Mat weights(3, &sz[0], CV_32F);
randu(weights, -1.0f, 1.0f);
LayerParams lp;
lp.set("kernel_size", kernel);
lp.set("pad", pad);
if (!padMode.empty())
lp.set("pad_mode", padMode);
lp.set("stride", stride);
lp.set("dilation", dilation);
lp.set("num_output", outChannels);
lp.set("group", groups);
lp.set("bias_term", hasBias);
lp.type = "Convolution";
lp.name = "testLayer";
lp.blobs.push_back(weights);
if (hasBias)
{
Mat bias(1, outChannels, CV_32F);
randu(bias, -1.0f, 1.0f);
lp.blobs.push_back(bias);
}
int inpSz[] = {1, inChannels, inputShape[2]};
Mat input(3, &inpSz[0], CV_32F);
randu(input, -1.0f, 1.0f);
Net net;
net.addLayerToPrev(lp.name, lp.type, lp);
net.setInput(input);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
// warmup
Mat output = net.forward();
MatShape netInputShape = shape(input);
size_t weightsMemory = 0, blobsMemory = 0;
net.getMemoryConsumption(netInputShape, weightsMemory, blobsMemory);
int64 flops = net.getFLOPS(netInputShape);
CV_Assert(flops > 0);
std::cout
<< "IN=" << divUp(input.total() * input.elemSize(), 1u<<10) << " Kb " << netInputShape
<< " OUT=" << divUp(output.total() * output.elemSize(), 1u<<10) << " Kb " << shape(output)
<< " Weights(parameters): " << divUp(weightsMemory, 1u<<10) << " Kb"
<< " MFLOPS=" << flops * 1e-6 << std::endl;
TEST_CYCLE()
{
Mat res = net.forward();
}
EXPECT_NEAR(flops, declared_flops, declared_flops * 1e-6);
SANITY_CHECK_NOTHING();
}
INSTANTIATE_TEST_CASE_P(/**/, Conv1D, Combine(
Conv1DParamID::all(),
dnnBackendsAndTargets(false, false) // defined in ../test/test_common.hpp
));
} // namespace