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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

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This commit is contained in:
Alexander Alekhin
2018-09-04 19:35:38 +03:00
committed by Alexander Alekhin
parent e0bfe44cac 351ee2e3a5
commit d74b98c3d9
117 changed files with 6913 additions and 3713 deletions
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modules/dnn/CMakeLists.txt
+1 -1
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@@ -95,7 +95,7 @@ ocv_glob_module_sources(${sources_options} SOURCES ${fw_srcs})
ocv_create_module(${libs} ${INF_ENGINE_TARGET})
ocv_add_samples()
ocv_add_accuracy_tests(${INF_ENGINE_TARGET})
ocv_add_perf_tests()
ocv_add_perf_tests(${INF_ENGINE_TARGET})
ocv_option(${the_module}_PERF_CAFFE "Add performance tests of Caffe framework" OFF)
ocv_option(${the_module}_PERF_CLCAFFE "Add performance tests of clCaffe framework" OFF)
modules/dnn/include/opencv2/dnn/dnn.hpp
+8
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@@ -878,6 +878,14 @@ CV__DNN_INLINE_NS_BEGIN
CV_EXPORTS_W void shrinkCaffeModel(const String& src, const String& dst,
const std::vector<String>& layersTypes = std::vector<String>());
/** @brief Create a text representation for a binary network stored in protocol buffer format.
* @param[in] model A path to binary network.
* @param[in] output A path to output text file to be created.
*
* @note To reduce output file size, trained weights are not included.
*/
CV_EXPORTS_W void writeTextGraph(const String& model, const String& output);
/** @brief Performs non maximum suppression given boxes and corresponding scores.
* @param bboxes a set of bounding boxes to apply NMS.
modules/dnn/misc/java/test/DnnListRegressionTest.java
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@@ -0,0 +1,119 @@
package org.opencv.test.dnn;
import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfInt;
import org.opencv.core.MatOfFloat;
import org.opencv.core.MatOfByte;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.dnn.DictValue;
import org.opencv.dnn.Dnn;
import org.opencv.dnn.Layer;
import org.opencv.dnn.Net;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.opencv.test.OpenCVTestCase;
/*
* regression test for #12324,
* testing various java.util.List invocations,
* which use the LIST_GET macro
*/
public class DnnListRegressionTest extends OpenCVTestCase {
private final static String ENV_OPENCV_DNN_TEST_DATA_PATH = "OPENCV_DNN_TEST_DATA_PATH";
private final static String ENV_OPENCV_TEST_DATA_PATH = "OPENCV_TEST_DATA_PATH";
String modelFileName = "";
String sourceImageFile = "";
Net net;
@Override
protected void setUp() throws Exception {
super.setUp();
String envDnnTestDataPath = System.getenv(ENV_OPENCV_DNN_TEST_DATA_PATH);
if(envDnnTestDataPath == null){
isTestCaseEnabled = false;
return;
}
File dnnTestDataPath = new File(envDnnTestDataPath);
modelFileName = new File(dnnTestDataPath, "dnn/tensorflow_inception_graph.pb").toString();
String envTestDataPath = System.getenv(ENV_OPENCV_TEST_DATA_PATH);
if(envTestDataPath == null) throw new Exception(ENV_OPENCV_TEST_DATA_PATH + " has to be defined!");
File testDataPath = new File(envTestDataPath);
File f = new File(testDataPath, "dnn/grace_hopper_227.png");
sourceImageFile = f.toString();
if(!f.exists()) throw new Exception("Test image is missing: " + sourceImageFile);
net = Dnn.readNetFromTensorflow(modelFileName);
Mat image = Imgcodecs.imread(sourceImageFile);
assertNotNull("Loading image from file failed!", image);
Mat inputBlob = Dnn.blobFromImage(image, 1.0, new Size(224, 224), new Scalar(0), true, true);
assertNotNull("Converting image to blob failed!", inputBlob);
net.setInput(inputBlob, "input");
}
public void testSetInputsNames() {
List<String> inputs = new ArrayList();
inputs.add("input");
try {
net.setInputsNames(inputs);
} catch(Exception e) {
fail("Net setInputsNames failed: " + e.getMessage());
}
}
public void testForward() {
List<Mat> outs = new ArrayList();
List<String> outNames = new ArrayList();
outNames.add("softmax2");
try {
net.forward(outs,outNames);
} catch(Exception e) {
fail("Net forward failed: " + e.getMessage());
}
}
public void testGetMemoryConsumption() {
int layerId = 1;
List<MatOfInt> netInputShapes = new ArrayList();
netInputShapes.add(new MatOfInt(1, 3, 224, 224));
long[] weights=null;
long[] blobs=null;
try {
net.getMemoryConsumption(layerId, netInputShapes, weights, blobs);
} catch(Exception e) {
fail("Net getMemoryConsumption failed: " + e.getMessage());
}
}
public void testGetFLOPS() {
int layerId = 1;
List<MatOfInt> netInputShapes = new ArrayList();
netInputShapes.add(new MatOfInt(1, 3, 224, 224));
try {
net.getFLOPS(layerId, netInputShapes);
} catch(Exception e) {
fail("Net getFLOPS failed: " + e.getMessage());
}
}
}
modules/dnn/perf/opencl/perf_convolution.cpp
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@@ -1,107 +0,0 @@
#include "../perf_precomp.hpp"
#include "opencv2/ts/ocl_perf.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#ifdef HAVE_OPENCL
namespace opencv_test { namespace ocl {
using namespace ::perf;
namespace {
enum {STRIDE_OFF = 1, STRIDE_ON = 2};
CV_ENUM(StrideSize, STRIDE_OFF, STRIDE_ON);
enum {GROUP_OFF = 1, GROUP_2 = 2};
CV_ENUM(GroupSize, GROUP_OFF, GROUP_2);
} // namespace
//Squared Size
#define SSZ(n) cv::Size(n, n)
typedef std::pair<MatShape, int> InpShapeNumOut;
typedef tuple<Size, InpShapeNumOut, GroupSize, StrideSize> ConvParam; //kernel_size, inp shape, groups, stride
typedef TestBaseWithParam<ConvParam> ConvolutionPerfTest;
static inline MatShape blobShape(int count, int nplanes, int height, int width)
{
int data[] = {count, nplanes, height, width};
return MatShape(data, data+4);
}
OCL_PERF_TEST_P( ConvolutionPerfTest, perf, Combine(
Values(Size(1, 1), Size(3, 3), Size(5, 5), Size(11, 11)),
Values(make_pair(blobShape(1, 4, 224, 224), 64),
make_pair(blobShape(1, 64, 112, 122), 128),
make_pair(blobShape(1, 256, 28, 28), 512)),
GroupSize::all(),
StrideSize::all())
)
{
RNG rng(0);
ConvParam params = GetParam();
int ksz = get<0>(params).width;
MatShape inpShape = get<1>(params).first;
int outCn = get<1>(params).second;
int groups = get<2>(params);
int stride = (ksz >= 11) ? 4 : (int)get<3>(params);
int inpCn = inpShape[1];
int wgtSize[] = { outCn, inpCn/groups, ksz, ksz };
int biasSize[] = { outCn, 1, 1, 1 };
const int wtype = CV_32F;
Mat wgtBlob(4, wgtSize, wtype), biasBlob(4, biasSize, wtype);
Mat inpBlob(4, &inpShape[0], wtype);
rng.fill(biasBlob, RNG::UNIFORM, -1, +1);
rng.fill(wgtBlob, RNG::UNIFORM, -1, +1);
rng.fill(inpBlob, RNG::UNIFORM, -1, +1);
LayerParams lp;
lp.set("num_output", outCn);
lp.set("group", groups);
lp.set("stride", stride);
lp.set("kernel_size", ksz);
lp.blobs.reserve(2);
lp.blobs.push_back(wgtBlob);
lp.blobs.push_back(biasBlob);
std::vector<Mat*> inpBlobs(1, &inpBlob);
std::vector<Mat> outBlobs, internalBlobs;
Ptr<Layer> layer = cv::dnn::LayerFactory::createLayerInstance("Convolution", lp);
std::vector<MatShape> inputShapes(1, shape(inpBlob)), outShapes, internals;
layer->getMemoryShapes(inputShapes, 0, outShapes, internals);
for (size_t i = 0; i < outShapes.size(); i++)
{
outBlobs.push_back(Mat(outShapes[i], CV_32F));
}
for (size_t i = 0; i < internals.size(); i++)
{
internalBlobs.push_back(Mat());
if (total(internals[i]))
internalBlobs.back().create(internals[i], CV_32F);
}
layer->finalize(inpBlobs, outBlobs);
layer->preferableTarget = DNN_TARGET_OPENCL;
Mat inpBlob2D = inpBlob.reshape(1, outCn);
Mat wgtBlob2D = wgtBlob.reshape(1, outCn*(inpCn/groups));
Mat outBlob2D = outBlobs[0].reshape(1, outBlobs[0].size[0]);
declare.in(inpBlob2D, wgtBlob2D, WARMUP_RNG).out(outBlob2D);
// warmup
layer->forward(inpBlobs, outBlobs, internalBlobs);
TEST_CYCLE()
{
layer->forward(inpBlobs, outBlobs, internalBlobs);
}
SANITY_CHECK_NOTHING();
}
}
}
#endif
modules/dnn/perf/perf_convolution.cpp
+651 -69
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@@ -1,92 +1,674 @@
// 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 {
enum {STRIDE_OFF = 1, STRIDE_ON = 2};
CV_ENUM(StrideSize, STRIDE_OFF, STRIDE_ON);
enum {GROUP_OFF = 1, GROUP_2 = 2};
CV_ENUM(GroupSize, GROUP_OFF, GROUP_2);
typedef std::pair<MatShape, int> InpShapeNumOut;
typedef tuple<Size, InpShapeNumOut, GroupSize, StrideSize> ConvParam; //kernel_size, inp shape, groups, stride
typedef TestBaseWithParam<ConvParam> ConvolutionPerfTest;
static inline MatShape blobShape(int count, int nplanes, int height, int width)
// Flops_Kernel_Input_OutCN_Group_Stride_Pad_Dilation_PadAdjust_PadMode_Bias
struct TestSize_ {
int width, height;
operator Size() const { return Size(width, height); }
};
struct ConvParam_t {
struct TestSize_ kernel;
struct BlobShape { int dims[4]; } shapeIn;
int outCN;
int groups;
struct TestSize_ stride;
struct TestSize_ dilation;
struct TestSize_ pad;
struct TestSize_ padAdjust;
const char* padMode;
bool hasBias;
double declared_flops;
};
// Details: #12142
static const ConvParam_t testConvolutionConfigs[] = {
/* GFLOPS 10.087 x 1 = 10.087 */ {{3, 3}, {{1, 576, 38, 50}}, 512, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 10086963200.},
/* GFLOPS 1.704 x 5 = 8.518 */ {{3, 3}, {{1, 512, 19, 19}}, 512, 512, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 1703596544.},
/* GFLOPS 1.704 x 5 = 8.518 */ {{3, 3}, {{1, 512, 19, 19}}, 512, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 1703596544.},
/* GFLOPS 6.641 x 1 = 6.641 */ {{3, 3}, {{1, 64, 150, 200}}, 192, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 6641280000.},
/* GFLOPS 1.659 x 3 = 4.977 */ {{3, 3}, {{1, 960, 10, 10}}, 960, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 1658976000.},
/* GFLOPS 2.156 x 2 = 4.312 */ {{3, 3}, {{1, 576, 19, 19}}, 576, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 2156088384.},
/* GFLOPS 0.958 x 4 = 3.833 */ {{3, 3}, {{1, 384, 19, 19}}, 384, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 958307712.},
/* GFLOPS 0.830 x 4 = 3.321 */ {{3, 3}, {{1, 64, 75, 100}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 830160000.},
/* GFLOPS 1.245 x 2 = 2.490 */ {{3, 3}, {{1, 96, 75, 100}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 1244880000.},
/* GFLOPS 2.100 x 1 = 2.100 */ {{3, 3}, {{1, 144, 75, 75}}, 144, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 2100330000.},
/* GFLOPS 1.022 x 2 = 2.044 */ {{3, 3}, {{1, 576, 19, 19}}, 273, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 1021896057.},
/* GFLOPS 0.958 x 2 = 1.917 */ {{3, 3}, {{1, 192, 38, 38}}, 192, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 958446336.},
/* GFLOPS 1.888 x 1 = 1.888 */ {{3, 3}, {{1, 1024, 10, 10}}, 1024, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 1887539200.},
/* GFLOPS 1.888 x 1 = 1.888 */ {{3, 3}, {{1, 1024, 10, 10}}, 1024, 1024, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 1887539200.},
/* GFLOPS 1.704 x 1 = 1.704 */ {{3, 3}, {{1, 256, 38, 38}}, 256, 256, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 1703781376.},
/* GFLOPS 1.704 x 1 = 1.704 */ {{3, 3}, {{1, 256, 38, 38}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 1703781376.},
/* GFLOPS 1.660 x 1 = 1.660 */ {{3, 3}, {{1, 128, 75, 75}}, 128, 128, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 1659600000.},
/* GFLOPS 1.660 x 1 = 1.660 */ {{3, 3}, {{1, 128, 75, 75}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 1659600000.},
/* GFLOPS 0.280 x 5 = 1.402 */ {{1, 1}, {{1, 576, 38, 50}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 280409600.},
/* GFLOPS 0.701 x 2 = 1.401 */ {{3, 3}, {{1, 128, 38, 50}}, 160, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 700720000.},
/* GFLOPS 0.231 x 6 = 1.388 */ {{3, 3}, {{1, 128, 56, 56}}, 32, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", false, 231311360.},
/* GFLOPS 0.231 x 6 = 1.388 */ {{3, 3}, {{1, 256, 14, 14}}, 256, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", false, 231261184.},
/* GFLOPS 0.210 x 6 = 1.262 */ {{1, 1}, {{1, 576, 38, 50}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 210307200.},
/* GFLOPS 0.420 x 3 = 1.261 */ {{3, 3}, {{1, 96, 38, 50}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 420492800.},
/* GFLOPS 1.261 x 1 = 1.261 */ {{3, 3}, {{1, 192, 38, 50}}, 192, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 1261113600.},
/* GFLOPS 1.258 x 1 = 1.258 */ {{3, 3}, {{1, 1280, 10, 10}}, 546, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 1258038600.},
/* GFLOPS 1.245 x 1 = 1.245 */ {{3, 3}, {{1, 64, 75, 75}}, 192, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 1245240000.},
/* GFLOPS 0.561 x 2 = 1.121 */ {{3, 3}, {{1, 128, 38, 50}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 560576000.},
/* GFLOPS 1.051 x 1 = 1.051 */ {{3, 3}, {{1, 160, 38, 50}}, 192, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 1050988800.},
/* GFLOPS 1.006 x 1 = 1.006 */ {{3, 3}, {{1, 1024, 10, 10}}, 546, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 1006441800.},
/* GFLOPS 0.246 x 4 = 0.985 */ {{1, 1}, {{1, 256, 75, 100}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 246240000.},
/* GFLOPS 0.189 x 5 = 0.947 */ {{1, 1}, {{1, 512, 19, 19}}, 512, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 189452800.},
/* GFLOPS 0.189 x 5 = 0.947 */ {{1, 1}, {{1, 512, 19, 19}}, 512, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 189452800.},
/* GFLOPS 0.934 x 1 = 0.934 */ {{3, 3}, {{1, 96, 150, 150}}, 96, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 933660000.},
/* GFLOPS 0.231 x 4 = 0.925 */ {{3, 3}, {{1, 128, 28, 28}}, 128, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", false, 231311360.},
/* GFLOPS 0.896 x 1 = 0.896 */ {{5, 5}, {{1, 96, 27, 27}}, 256, 2, {1, 1}, {1, 1}, {2, 2}, {0, 0}, "", true, 895981824.},
/* GFLOPS 0.876 x 1 = 0.876 */ {{3, 3}, {{1, 160, 38, 50}}, 160, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 875824000.},
/* GFLOPS 0.850 x 1 = 0.850 */ {{7, 7}, {{1, 3, 600, 800}}, 24, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 849600000.},
/* GFLOPS 0.841 x 1 = 0.841 */ {{3, 3}, {{1, 128, 38, 50}}, 192, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 840864000.},
/* GFLOPS 0.415 x 2 = 0.831 */ {{3, 3}, {{1, 32, 150, 150}}, 32, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 415440000.},
/* GFLOPS 0.351 x 2 = 0.701 */ {{1, 1}, {{1, 576, 38, 50}}, 160, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 350512000.},
/* GFLOPS 0.701 x 1 = 0.701 */ {{3, 3}, {{1, 128, 75, 100}}, 160, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 700720000.},
/* GFLOPS 0.694 x 1 = 0.694 */ {{3, 3}, {{1, 64, 56, 56}}, 192, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 694235136.},
/* GFLOPS 0.694 x 1 = 0.694 */ {{3, 3}, {{1, 64, 56, 56}}, 192, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 694235136.},
/* GFLOPS 0.231 x 3 = 0.694 */ {{3, 3}, {{1, 64, 56, 56}}, 64, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", false, 231411712.},
/* GFLOPS 0.058 x 12 = 0.694 */ {{3, 3}, {{1, 128, 28, 28}}, 32, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", false, 57827840.},
/* GFLOPS 0.231 x 3 = 0.694 */ {{3, 3}, {{1, 512, 7, 7}}, 512, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", false, 231236096.},
/* GFLOPS 0.160 x 4 = 0.639 */ {{3, 3}, {{1, 64, 38, 38}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 159833472.},
/* GFLOPS 0.103 x 6 = 0.618 */ {{1, 1}, {{1, 256, 14, 14}}, 1024, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 102961152.},
/* GFLOPS 0.615 x 1 = 0.615 */ {{1, 1}, {{1, 320, 75, 100}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 615360000.},
/* GFLOPS 0.597 x 1 = 0.597 */ {{3, 3}, {{1, 576, 19, 19}}, 576, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 597254400.},
/* GFLOPS 0.185 x 3 = 0.554 */ {{1, 1}, {{1, 192, 75, 100}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 184800000.},
/* GFLOPS 0.553 x 1 = 0.553 */ {{3, 3}, {{1, 64, 75, 100}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 553440000.},
/* GFLOPS 0.539 x 1 = 0.539 */ {{3, 3}, {{1, 144, 75, 75}}, 144, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 539178048.},
/* GFLOPS 0.103 x 5 = 0.514 */ {{1, 1}, {{1, 1024, 14, 14}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 102810624.},
/* GFLOPS 0.491 x 1 = 0.491 */ {{1, 1}, {{1, 576, 38, 50}}, 224, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 490716800.},
/* GFLOPS 0.240 x 2 = 0.479 */ {{3, 3}, {{1, 96, 38, 38}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 239680896.},
/* GFLOPS 0.237 x 2 = 0.474 */ {{7, 7}, {{1, 3, 224, 224}}, 64, 1, {2, 2}, {1, 1}, {3, 3}, {0, 0}, "", true, 236830720.},
/* GFLOPS 0.472 x 1 = 0.472 */ {{3, 3}, {{1, 512, 19, 19}}, 512, 512, {2, 2}, {1, 1}, {1, 1}, {0, 0}, "", true, 471910400.},
/* GFLOPS 0.472 x 1 = 0.472 */ {{3, 3}, {{1, 512, 19, 19}}, 512, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 471910400.},
/* GFLOPS 0.449 x 1 = 0.449 */ {{3, 3}, {{1, 384, 13, 13}}, 384, 2, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 448626048.},
/* GFLOPS 0.426 x 1 = 0.426 */ {{3, 3}, {{1, 128, 75, 75}}, 128, 128, {2, 2}, {1, 1}, {1, 1}, {0, 0}, "", true, 426037760.},
/* GFLOPS 0.426 x 1 = 0.426 */ {{3, 3}, {{1, 128, 75, 75}}, 128, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 426037760.},
/* GFLOPS 0.426 x 1 = 0.426 */ {{3, 3}, {{1, 128, 38, 38}}, 128, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", false, 426037760.},
/* GFLOPS 0.426 x 1 = 0.426 */ {{3, 3}, {{1, 256, 38, 38}}, 256, 256, {2, 2}, {1, 1}, {1, 1}, {0, 0}, "", true, 425945344.},
/* GFLOPS 0.426 x 1 = 0.426 */ {{3, 3}, {{1, 256, 38, 38}}, 256, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 425945344.},
/* GFLOPS 0.426 x 1 = 0.426 */ {{3, 3}, {{1, 256, 19, 19}}, 256, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", false, 425945344.},
/* GFLOPS 0.421 x 1 = 0.421 */ {{1, 1}, {{1, 576, 38, 50}}, 192, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 420614400.},
/* GFLOPS 0.415 x 1 = 0.415 */ {{3, 3}, {{1, 32, 150, 150}}, 32, 32, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 415440000.},
/* GFLOPS 0.415 x 1 = 0.415 */ {{3, 3}, {{1, 64, 150, 150}}, 64, 64, {2, 2}, {1, 1}, {1, 1}, {0, 0}, "", true, 415080000.},
/* GFLOPS 0.415 x 1 = 0.415 */ {{3, 3}, {{1, 64, 150, 150}}, 64, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 415080000.},
/* GFLOPS 0.104 x 4 = 0.414 */ {{1, 1}, {{1, 64, 56, 56}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 103563264.},
/* GFLOPS 0.103 x 4 = 0.413 */ {{1, 1}, {{1, 128, 28, 28}}, 512, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 103161856.},
/* GFLOPS 0.376 x 1 = 0.376 */ {{1, 1}, {{1, 24, 300, 400}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "VALID", true, 376320000.},
/* GFLOPS 0.347 x 1 = 0.347 */ {{3, 3}, {{1, 128, 28, 28}}, 192, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 346967040.},
/* GFLOPS 0.347 x 1 = 0.347 */ {{3, 3}, {{1, 128, 28, 28}}, 192, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 346967040.},
/* GFLOPS 0.014 x 24 = 0.347 */ {{3, 3}, {{1, 128, 14, 14}}, 32, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", false, 14456960.},
/* GFLOPS 0.053 x 6 = 0.320 */ {{1, 1}, {{1, 576, 19, 19}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 53277824.},
/* GFLOPS 0.319 x 1 = 0.319 */ {{3, 3}, {{1, 192, 19, 19}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 319482112.},
/* GFLOPS 0.315 x 1 = 0.315 */ {{3, 3}, {{1, 96, 75, 100}}, 96, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 315369600.},
/* GFLOPS 0.103 x 3 = 0.309 */ {{1, 1}, {{1, 512, 7, 7}}, 2048, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 102860800.},
/* GFLOPS 0.103 x 3 = 0.309 */ {{1, 1}, {{1, 512, 28, 28}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 102860800.},
/* GFLOPS 0.308 x 1 = 0.308 */ {{1, 1}, {{1, 320, 75, 100}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 307680000.},
/* GFLOPS 0.299 x 1 = 0.299 */ {{3, 3}, {{1, 256, 13, 13}}, 384, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 299105664.},
/* GFLOPS 0.299 x 1 = 0.299 */ {{3, 3}, {{1, 384, 13, 13}}, 256, 2, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 299084032.},
/* GFLOPS 0.017 x 17 = 0.290 */ {{1, 1}, {{1, 32, 32, 64}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 17039360.},
/* GFLOPS 0.017 x 16 = 0.269 */ {{1, 1}, {{1, 128, 32, 64}}, 32, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 16842752.},
/* GFLOPS 0.133 x 2 = 0.266 */ {{3, 3}, {{1, 128, 19, 19}}, 160, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 133136800.},
/* GFLOPS 0.038 x 7 = 0.265 */ {{3, 3}, {{1, 16, 64, 128}}, 16, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 37879808.},
/* GFLOPS 0.126 x 2 = 0.252 */ {{3, 3}, {{1, 512, 5, 5}}, 546, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 125812050.},
/* GFLOPS 0.248 x 1 = 0.248 */ {{1, 1}, {{1, 64, 150, 200}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 247680000.},
/* GFLOPS 0.040 x 6 = 0.240 */ {{1, 1}, {{1, 576, 19, 19}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 39958368.},
/* GFLOPS 0.080 x 3 = 0.240 */ {{3, 3}, {{1, 96, 19, 19}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 79893632.},
/* GFLOPS 0.240 x 1 = 0.240 */ {{3, 3}, {{1, 192, 38, 38}}, 192, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 239611584.},
/* GFLOPS 0.240 x 1 = 0.240 */ {{3, 3}, {{1, 192, 19, 19}}, 192, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 239611584.},
/* GFLOPS 0.237 x 1 = 0.237 */ {{7, 7}, {{1, 3, 224, 224}}, 64, 1, {2, 2}, {1, 1}, {3, 3}, {0, 0}, "", false, 236830720.},
/* GFLOPS 0.237 x 1 = 0.237 */ {{7, 7}, {{1, 3, 224, 224}}, 64, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 236830720.},
/* GFLOPS 0.111 x 2 = 0.221 */ {{3, 3}, {{1, 192, 10, 10}}, 320, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 110624000.},
/* GFLOPS 0.213 x 1 = 0.213 */ {{3, 3}, {{1, 128, 38, 38}}, 256, 1, {2, 2}, {1, 1}, {1, 1}, {0, 0}, "", false, 213018880.},
/* GFLOPS 0.213 x 1 = 0.213 */ {{3, 3}, {{1, 128, 19, 19}}, 256, 1, {1, 1}, {2, 2}, {2, 2}, {0, 0}, "", false, 213018880.},
/* GFLOPS 0.107 x 2 = 0.213 */ {{3, 3}, {{1, 128, 19, 19}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 106509440.},
/* GFLOPS 0.213 x 1 = 0.213 */ {{3, 3}, {{1, 256, 19, 19}}, 128, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", false, 212972672.},
/* GFLOPS 0.212 x 1 = 0.212 */ {{7, 7}, {{1, 3, 300, 300}}, 32, 1, {2, 2}, {1, 1}, {3, 3}, {0, 0}, "", true, 212400000.},
/* GFLOPS 0.211 x 1 = 0.211 */ {{11, 11}, {{1, 3, 227, 227}}, 96, 1, {4, 4}, {1, 1}, {0, 0}, {0, 0}, "", true, 211120800.},
/* GFLOPS 0.210 x 1 = 0.210 */ {{3, 3}, {{1, 64, 38, 50}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 210307200.},
/* GFLOPS 0.210 x 1 = 0.210 */ {{1, 1}, {{1, 1024, 10, 10}}, 1024, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 209817600.},
/* GFLOPS 0.210 x 1 = 0.210 */ {{1, 1}, {{1, 1024, 10, 10}}, 1024, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 209817600.},
/* GFLOPS 0.104 x 2 = 0.208 */ {{3, 3}, {{1, 32, 75, 75}}, 32, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", false, 103860000.},
/* GFLOPS 0.206 x 1 = 0.206 */ {{1, 1}, {{1, 256, 56, 56}}, 512, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "", false, 205922304.},
/* GFLOPS 0.206 x 1 = 0.206 */ {{1, 1}, {{1, 256, 56, 56}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 205922304.},
/* GFLOPS 0.103 x 2 = 0.206 */ {{1, 1}, {{1, 256, 56, 56}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 102961152.},
/* GFLOPS 0.206 x 1 = 0.206 */ {{1, 1}, {{1, 512, 28, 28}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 205721600.},
/* GFLOPS 0.206 x 1 = 0.206 */ {{1, 1}, {{1, 512, 28, 28}}, 1024, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "", false, 205721600.},
/* GFLOPS 0.206 x 1 = 0.206 */ {{1, 1}, {{1, 1024, 14, 14}}, 512, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 205621248.},
/* GFLOPS 0.206 x 1 = 0.206 */ {{1, 1}, {{1, 1024, 14, 14}}, 2048, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "", false, 205621248.},
/* GFLOPS 0.103 x 2 = 0.206 */ {{1, 1}, {{1, 2048, 7, 7}}, 512, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 102785536.},
/* GFLOPS 0.201 x 1 = 0.201 */ {{1, 1}, {{1, 512, 14, 14}}, 1000, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 200900000.},
/* GFLOPS 0.200 x 1 = 0.200 */ {{3, 3}, {{1, 160, 19, 19}}, 192, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 199687872.},
/* GFLOPS 0.190 x 1 = 0.190 */ {{1, 1}, {{1, 256, 38, 38}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 189637632.},
/* GFLOPS 0.190 x 1 = 0.190 */ {{1, 1}, {{1, 256, 38, 38}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 189637632.},
/* GFLOPS 0.047 x 4 = 0.190 */ {{1, 1}, {{1, 256, 38, 38}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 47409408.},
/* GFLOPS 0.038 x 5 = 0.189 */ {{3, 3}, {{1, 32, 32, 64}}, 32, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 37814272.},
/* GFLOPS 0.185 x 1 = 0.185 */ {{1, 1}, {{1, 128, 75, 75}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 185040000.},
/* GFLOPS 0.185 x 1 = 0.185 */ {{1, 1}, {{1, 128, 75, 75}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 185040000.},
/* GFLOPS 0.181 x 1 = 0.181 */ {{3, 3}, {{1, 160, 14, 14}}, 320, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 180696320.},
/* GFLOPS 0.181 x 1 = 0.181 */ {{3, 3}, {{1, 160, 14, 14}}, 320, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 180696320.},
/* GFLOPS 0.090 x 2 = 0.181 */ {{3, 3}, {{1, 224, 10, 10}}, 224, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 90339200.},
/* GFLOPS 0.180 x 1 = 0.180 */ {{1, 1}, {{1, 224, 56, 56}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 180232192.},
/* GFLOPS 0.174 x 1 = 0.174 */ {{3, 3}, {{1, 96, 28, 28}}, 128, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 173508608.},
/* GFLOPS 0.174 x 1 = 0.174 */ {{3, 3}, {{1, 96, 28, 28}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 173508608.},
/* GFLOPS 0.166 x 1 = 0.166 */ {{3, 3}, {{1, 160, 19, 19}}, 160, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 166406560.},
/* GFLOPS 0.080 x 2 = 0.160 */ {{1, 1}, {{1, 576, 19, 19}}, 192, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 79916736.},
/* GFLOPS 0.160 x 1 = 0.160 */ {{3, 3}, {{1, 128, 19, 19}}, 192, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 159764160.},
/* GFLOPS 0.159 x 1 = 0.159 */ {{7, 7}, {{1, 3, 300, 300}}, 24, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 159300000.},
/* GFLOPS 0.155 x 1 = 0.155 */ {{1, 1}, {{1, 192, 56, 56}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 154542080.},
/* GFLOPS 0.146 x 1 = 0.146 */ {{3, 3}, {{1, 144, 14, 14}}, 288, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 146369664.},
/* GFLOPS 0.146 x 1 = 0.146 */ {{3, 3}, {{1, 144, 14, 14}}, 288, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 146369664.},
/* GFLOPS 0.072 x 2 = 0.144 */ {{1, 1}, {{1, 1024, 10, 10}}, 352, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 72124800.},
/* GFLOPS 0.140 x 1 = 0.140 */ {{1, 1}, {{1, 576, 38, 50}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 140204800.},
/* GFLOPS 0.017 x 8 = 0.138 */ {{1, 1}, {{1, 16, 64, 128}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 17301504.},
/* GFLOPS 0.067 x 2 = 0.133 */ {{1, 1}, {{1, 576, 19, 19}}, 160, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 66597280.},
/* GFLOPS 0.133 x 1 = 0.133 */ {{3, 3}, {{1, 128, 38, 38}}, 160, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 133136800.},
/* GFLOPS 0.129 x 1 = 0.129 */ {{1, 1}, {{1, 160, 56, 56}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 128851968.},
/* GFLOPS 0.128 x 1 = 0.128 */ {{3, 3}, {{1, 64, 24, 24}}, 192, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 127512576.},
/* GFLOPS 0.120 x 1 = 0.120 */ {{5, 5}, {{1, 32, 28, 28}}, 96, 1, {1, 1}, {1, 1}, {2, 2}, {0, 0}, "", true, 120497664.},
/* GFLOPS 0.120 x 1 = 0.120 */ {{5, 5}, {{1, 32, 28, 28}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 120497664.},
/* GFLOPS 0.040 x 3 = 0.120 */ {{1, 1}, {{1, 96, 19, 19}}, 576, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 40131648.},
/* GFLOPS 0.118 x 1 = 0.118 */ {{1, 1}, {{1, 320, 38, 38}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 118477312.},
/* GFLOPS 0.017 x 7 = 0.118 */ {{1, 1}, {{1, 64, 64, 128}}, 16, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 16908288.},
/* GFLOPS 0.039 x 3 = 0.118 */ {{1, 1}, {{1, 1024, 10, 10}}, 192, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 39340800.},
/* GFLOPS 0.118 x 1 = 0.118 */ {{3, 3}, {{1, 256, 19, 19}}, 256, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 117990400.},
/* GFLOPS 0.058 x 2 = 0.116 */ {{3, 3}, {{1, 16, 56, 56}}, 64, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 58003456.},
/* GFLOPS 0.058 x 2 = 0.116 */ {{3, 3}, {{1, 32, 28, 28}}, 128, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 57903104.},
/* GFLOPS 0.058 x 2 = 0.116 */ {{3, 3}, {{1, 64, 14, 14}}, 256, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 57852928.},
/* GFLOPS 0.116 x 1 = 0.116 */ {{3, 3}, {{1, 128, 14, 14}}, 256, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 115655680.},
/* GFLOPS 0.116 x 1 = 0.116 */ {{3, 3}, {{1, 128, 14, 14}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 115655680.},
/* GFLOPS 0.112 x 1 = 0.112 */ {{1, 1}, {{1, 1024, 10, 10}}, 546, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 111875400.},
/* GFLOPS 0.036 x 3 = 0.107 */ {{1, 1}, {{1, 192, 38, 38}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 35580160.},
/* GFLOPS 0.107 x 1 = 0.107 */ {{3, 3}, {{1, 32, 75, 75}}, 128, 1, {2, 2}, {1, 1}, {1, 1}, {0, 0}, "", false, 106648064.},
/* GFLOPS 0.107 x 1 = 0.107 */ {{3, 3}, {{1, 64, 38, 38}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 106555648.},
/* GFLOPS 0.105 x 1 = 0.105 */ {{1, 1}, {{1, 512, 10, 10}}, 1024, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 104960000.},
/* GFLOPS 0.105 x 1 = 0.105 */ {{1, 1}, {{1, 512, 10, 10}}, 1024, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 104960000.},
/* GFLOPS 0.103 x 1 = 0.103 */ {{1, 1}, {{1, 128, 56, 56}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 103161856.},
/* GFLOPS 0.051 x 2 = 0.103 */ {{1, 1}, {{1, 256, 28, 28}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 51480576.},
/* GFLOPS 0.051 x 2 = 0.103 */ {{1, 1}, {{1, 256, 28, 28}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 51480576.},
/* GFLOPS 0.101 x 1 = 0.101 */ {{1, 1}, {{1, 512, 19, 19}}, 273, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 101016825.},
/* GFLOPS 0.096 x 1 = 0.096 */ {{1, 1}, {{1, 480, 28, 28}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 96438272.},
/* GFLOPS 0.095 x 1 = 0.095 */ {{1, 1}, {{1, 128, 38, 38}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 95003648.},
/* GFLOPS 0.095 x 1 = 0.095 */ {{1, 1}, {{1, 128, 38, 38}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 95003648.},
/* GFLOPS 0.095 x 1 = 0.095 */ {{1, 1}, {{1, 256, 19, 19}}, 512, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 94818816.},
/* GFLOPS 0.095 x 1 = 0.095 */ {{1, 1}, {{1, 256, 19, 19}}, 512, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 94818816.},
/* GFLOPS 0.094 x 1 = 0.094 */ {{1, 1}, {{1, 32, 150, 150}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 93600000.},
/* GFLOPS 0.094 x 1 = 0.094 */ {{1, 1}, {{1, 32, 150, 150}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 93600000.},
/* GFLOPS 0.093 x 1 = 0.093 */ {{1, 1}, {{1, 512, 38, 50}}, 48, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 93480000.},
/* GFLOPS 0.093 x 1 = 0.093 */ {{1, 1}, {{1, 576, 19, 19}}, 224, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 93236192.},
/* GFLOPS 0.093 x 1 = 0.093 */ {{1, 1}, {{1, 64, 75, 75}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 92880000.},
/* GFLOPS 0.093 x 1 = 0.093 */ {{1, 1}, {{1, 64, 75, 75}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 92880000.},
/* GFLOPS 0.031 x 3 = 0.092 */ {{1, 1}, {{1, 160, 10, 10}}, 960, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 30816000.},
/* GFLOPS 0.092 x 1 = 0.092 */ {{1, 1}, {{1, 192, 75, 100}}, 32, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 92400000.},
/* GFLOPS 0.090 x 1 = 0.090 */ {{1, 1}, {{1, 448, 28, 28}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 90015744.},
/* GFLOPS 0.045 x 2 = 0.090 */ {{3, 3}, {{1, 576, 19, 19}}, 12, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 44918508.},
/* GFLOPS 0.089 x 1 = 0.089 */ {{3, 3}, {{1, 112, 14, 14}}, 224, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 88554368.},
/* GFLOPS 0.089 x 1 = 0.089 */ {{3, 3}, {{1, 112, 14, 14}}, 224, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 88554368.},
/* GFLOPS 0.021 x 4 = 0.084 */ {{5, 1}, {{1, 32, 32, 64}}, 32, 1, {1, 1}, {1, 1}, {2, 0}, {0, 0}, "", false, 21037056.},
/* GFLOPS 0.021 x 4 = 0.084 */ {{1, 5}, {{1, 32, 32, 64}}, 32, 1, {1, 1}, {1, 1}, {0, 2}, {0, 0}, "", true, 21037056.},
/* GFLOPS 0.084 x 1 = 0.084 */ {{1, 1}, {{1, 416, 28, 28}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 83593216.},
/* GFLOPS 0.082 x 1 = 0.082 */ {{1, 1}, {{1, 320, 10, 10}}, 1280, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 82048000.},
/* GFLOPS 0.040 x 2 = 0.080 */ {{1, 1}, {{1, 576, 19, 19}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 39958368.},
/* GFLOPS 0.040 x 2 = 0.079 */ {{1, 1}, {{1, 24, 75, 75}}, 144, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 39690000.},
/* GFLOPS 0.040 x 2 = 0.079 */ {{3, 3}, {{1, 3, 300, 300}}, 32, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 39600000.},
/* GFLOPS 0.077 x 1 = 0.077 */ {{1, 1}, {{1, 96, 56, 56}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 77471744.},
/* GFLOPS 0.077 x 1 = 0.077 */ {{3, 3}, {{1, 192, 10, 10}}, 224, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 77436800.},
/* GFLOPS 0.077 x 1 = 0.077 */ {{1, 1}, {{1, 384, 28, 28}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 77170688.},
/* GFLOPS 0.038 x 2 = 0.076 */ {{3, 3}, {{1, 32, 32, 64}}, 32, 1, {1, 1}, {8, 8}, {8, 8}, {0, 0}, "", true, 37814272.},
/* GFLOPS 0.038 x 2 = 0.076 */ {{3, 3}, {{1, 32, 32, 64}}, 32, 1, {1, 1}, {4, 4}, {4, 4}, {0, 0}, "", true, 37814272.},
/* GFLOPS 0.038 x 2 = 0.076 */ {{3, 3}, {{1, 32, 32, 64}}, 32, 1, {1, 1}, {2, 2}, {2, 2}, {0, 0}, "", true, 37814272.},
/* GFLOPS 0.038 x 2 = 0.076 */ {{3, 3}, {{1, 32, 32, 64}}, 32, 1, {1, 1}, {16, 16}, {16, 16}, {0, 0}, "", true, 37814272.},
/* GFLOPS 0.018 x 4 = 0.072 */ {{1, 1}, {{1, 64, 19, 19}}, 384, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 17882496.},
/* GFLOPS 0.071 x 1 = 0.071 */ {{1, 1}, {{1, 16, 150, 150}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 71280000.},
/* GFLOPS 0.071 x 1 = 0.071 */ {{1, 1}, {{1, 352, 28, 28}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 70748160.},
/* GFLOPS 0.071 x 1 = 0.071 */ {{1, 1}, {{1, 24, 150, 150}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "VALID", true, 70560000.},
/* GFLOPS 0.070 x 1 = 0.070 */ {{3, 3}, {{1, 96, 14, 14}}, 208, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 70487872.},
/* GFLOPS 0.069 x 1 = 0.069 */ {{3, 3}, {{1, 96, 14, 14}}, 204, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 69132336.},
/* GFLOPS 0.066 x 1 = 0.066 */ {{1, 1}, {{1, 1280, 10, 10}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 65561600.},
/* GFLOPS 0.033 x 2 = 0.065 */ {{3, 3}, {{1, 48, 14, 14}}, 192, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 32551680.},
/* GFLOPS 0.065 x 1 = 0.065 */ {{3, 3}, {{1, 192, 7, 7}}, 384, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 65046912.},
/* GFLOPS 0.065 x 1 = 0.065 */ {{3, 3}, {{1, 192, 7, 7}}, 384, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 65046912.},
/* GFLOPS 0.065 x 1 = 0.065 */ {{3, 3}, {{1, 160, 10, 10}}, 224, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 64534400.},
/* GFLOPS 0.064 x 1 = 0.064 */ {{1, 1}, {{1, 320, 28, 28}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 64325632.},
/* GFLOPS 0.032 x 2 = 0.064 */ {{3, 3}, {{1, 96, 12, 12}}, 128, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 31868928.},
/* GFLOPS 0.061 x 1 = 0.061 */ {{1, 1}, {{1, 960, 10, 10}}, 320, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 61472000.},
/* GFLOPS 0.031 x 2 = 0.061 */ {{1, 1}, {{1, 960, 10, 10}}, 160, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 30736000.},
/* GFLOPS 0.060 x 1 = 0.060 */ {{3, 3}, {{1, 96, 38, 38}}, 96, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 59920224.},
/* GFLOPS 0.059 x 1 = 0.059 */ {{1, 1}, {{1, 320, 38, 38}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 59238656.},
/* GFLOPS 0.059 x 1 = 0.059 */ {{3, 3}, {{1, 128, 19, 19}}, 256, 1, {2, 2}, {1, 1}, {1, 1}, {0, 0}, "", true, 59008000.},
/* GFLOPS 0.059 x 1 = 0.059 */ {{3, 3}, {{1, 256, 10, 10}}, 512, 1, {2, 2}, {1, 1}, {1, 1}, {0, 0}, "", true, 58995200.},
/* GFLOPS 0.059 x 1 = 0.059 */ {{3, 3}, {{1, 256, 10, 10}}, 512, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 58995200.},
/* GFLOPS 0.059 x 1 = 0.059 */ {{3, 3}, {{1, 256, 10, 10}}, 512, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 58995200.},
/* GFLOPS 0.058 x 1 = 0.058 */ {{1, 1}, {{1, 288, 28, 28}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 57903104.},
/* GFLOPS 0.004 x 16 = 0.058 */ {{3, 3}, {{1, 128, 7, 7}}, 32, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", false, 3614240.},
/* GFLOPS 0.055 x 1 = 0.055 */ {{3, 3}, {{1, 1280, 10, 10}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 55298400.},
/* GFLOPS 0.018 x 3 = 0.054 */ {{1, 1}, {{1, 32, 38, 38}}, 192, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 18021120.},
/* GFLOPS 0.018 x 3 = 0.053 */ {{1, 1}, {{1, 384, 19, 19}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 17766976.},
/* GFLOPS 0.053 x 1 = 0.053 */ {{3, 3}, {{1, 128, 38, 38}}, 16, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 53254720.},
/* GFLOPS 0.053 x 1 = 0.053 */ {{1, 1}, {{1, 528, 14, 14}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 53036032.},
/* GFLOPS 0.053 x 1 = 0.053 */ {{1, 1}, {{1, 528, 14, 14}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 53036032.},
/* GFLOPS 0.052 x 1 = 0.052 */ {{1, 1}, {{1, 1024, 10, 10}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 52454400.},
/* GFLOPS 0.052 x 1 = 0.052 */ {{1, 1}, {{1, 1024, 10, 10}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 52454400.},
/* GFLOPS 0.052 x 1 = 0.052 */ {{1, 1}, {{1, 1024, 10, 10}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 52454400.},
/* GFLOPS 0.026 x 2 = 0.052 */ {{1, 1}, {{1, 1024, 10, 10}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 26227200.},
/* GFLOPS 0.052 x 1 = 0.052 */ {{1, 1}, {{1, 64, 56, 56}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 51781632.},
/* GFLOPS 0.051 x 1 = 0.051 */ {{1, 1}, {{1, 256, 56, 56}}, 128, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "", false, 51480576.},
/* GFLOPS 0.051 x 1 = 0.051 */ {{1, 1}, {{1, 256, 28, 28}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 51480576.},
/* GFLOPS 0.051 x 1 = 0.051 */ {{1, 1}, {{1, 512, 28, 28}}, 256, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "", false, 51430400.},
/* GFLOPS 0.026 x 2 = 0.051 */ {{1, 1}, {{1, 512, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 25715200.},
/* GFLOPS 0.026 x 2 = 0.051 */ {{1, 1}, {{1, 512, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 25715200.},
/* GFLOPS 0.013 x 4 = 0.051 */ {{1, 1}, {{1, 512, 14, 14}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 12857600.},
/* GFLOPS 0.051 x 1 = 0.051 */ {{1, 1}, {{1, 1024, 14, 14}}, 512, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "", false, 51405312.},
/* GFLOPS 0.050 x 1 = 0.050 */ {{1, 1}, {{1, 992, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 49799680.},
/* GFLOPS 0.048 x 1 = 0.048 */ {{1, 1}, {{1, 960, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 48194048.},
/* GFLOPS 0.047 x 1 = 0.047 */ {{1, 1}, {{1, 256, 19, 19}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 47409408.},
/* GFLOPS 0.047 x 1 = 0.047 */ {{1, 1}, {{1, 512, 38, 50}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 46740000.},
/* GFLOPS 0.047 x 1 = 0.047 */ {{1, 1}, {{1, 928, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 46588416.},
/* GFLOPS 0.046 x 1 = 0.046 */ {{1, 1}, {{1, 64, 75, 75}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 46440000.},
/* GFLOPS 0.023 x 2 = 0.045 */ {{3, 3}, {{1, 256, 3, 3}}, 546, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 22648626.},
/* GFLOPS 0.045 x 1 = 0.045 */ {{3, 3}, {{1, 160, 7, 7}}, 320, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 45174080.},
/* GFLOPS 0.045 x 1 = 0.045 */ {{3, 3}, {{1, 160, 7, 7}}, 320, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 45174080.},
/* GFLOPS 0.045 x 1 = 0.045 */ {{1, 1}, {{1, 224, 28, 28}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 45058048.},
/* GFLOPS 0.023 x 2 = 0.045 */ {{1, 1}, {{1, 512, 14, 14}}, 112, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 22500800.},
/* GFLOPS 0.045 x 1 = 0.045 */ {{1, 1}, {{1, 896, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 44982784.},
/* GFLOPS 0.045 x 1 = 0.045 */ {{3, 3}, {{1, 3, 227, 227}}, 64, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "", true, 44946880.},
/* GFLOPS 0.044 x 1 = 0.044 */ {{3, 3}, {{1, 128, 19, 19}}, 192, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 44256000.},
/* GFLOPS 0.044 x 1 = 0.044 */ {{3, 3}, {{1, 1024, 10, 10}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 44239200.},
/* GFLOPS 0.043 x 1 = 0.043 */ {{7, 7}, {{1, 3, 96, 96}}, 64, 1, {2, 2}, {1, 1}, {3, 3}, {0, 0}, "", true, 43499520.},
/* GFLOPS 0.043 x 1 = 0.043 */ {{1, 1}, {{1, 864, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 43377152.},
/* GFLOPS 0.042 x 1 = 0.042 */ {{1, 1}, {{1, 832, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 41771520.},
/* GFLOPS 0.040 x 1 = 0.040 */ {{5, 5}, {{1, 32, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {2, 2}, {0, 0}, "", true, 40165888.},
/* GFLOPS 0.040 x 1 = 0.040 */ {{5, 5}, {{1, 32, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 40165888.},
/* GFLOPS 0.040 x 1 = 0.040 */ {{1, 1}, {{1, 800, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 40165888.},
/* GFLOPS 0.040 x 1 = 0.040 */ {{3, 3}, {{1, 64, 19, 19}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 39958368.},
/* GFLOPS 0.040 x 1 = 0.040 */ {{3, 3}, {{1, 256, 19, 19}}, 24, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 39932376.},
/* GFLOPS 0.040 x 1 = 0.040 */ {{3, 3}, {{1, 3, 300, 300}}, 32, 1, {2, 2}, {1, 1}, {1, 1}, {0, 0}, "", true, 39600000.},
/* GFLOPS 0.039 x 1 = 0.039 */ {{1, 1}, {{1, 144, 75, 75}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 39015000.},
/* GFLOPS 0.039 x 1 = 0.039 */ {{1, 1}, {{1, 192, 28, 28}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 38635520.},
/* GFLOPS 0.039 x 1 = 0.039 */ {{1, 1}, {{1, 768, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 38560256.},
/* GFLOPS 0.037 x 1 = 0.037 */ {{1, 1}, {{1, 736, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 36954624.},
/* GFLOPS 0.036 x 1 = 0.036 */ {{1, 1}, {{1, 480, 14, 14}}, 192, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 36164352.},
/* GFLOPS 0.036 x 1 = 0.036 */ {{1, 1}, {{1, 480, 14, 14}}, 192, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 36164352.},
/* GFLOPS 0.018 x 2 = 0.036 */ {{1, 1}, {{1, 192, 38, 38}}, 32, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 17790080.},
/* GFLOPS 0.035 x 1 = 0.035 */ {{1, 1}, {{1, 704, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 35348992.},
/* GFLOPS 0.034 x 1 = 0.034 */ {{1, 1}, {{1, 672, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 33743360.},
/* GFLOPS 0.034 x 1 = 0.034 */ {{1, 1}, {{1, 128, 32, 64}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 33685504.},
/* GFLOPS 0.034 x 1 = 0.034 */ {{2, 2}, {{1, 64, 64, 128}}, 32, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "", false, 33619968.},
/* GFLOPS 0.033 x 1 = 0.033 */ {{1, 1}, {{1, 528, 14, 14}}, 160, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 33147520.},
/* GFLOPS 0.033 x 1 = 0.033 */ {{1, 1}, {{1, 528, 14, 14}}, 160, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 33147520.},
/* GFLOPS 0.033 x 1 = 0.033 */ {{1, 1}, {{1, 1024, 10, 10}}, 160, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 32784000.},
/* GFLOPS 0.032 x 1 = 0.032 */ {{1, 1}, {{1, 160, 28, 28}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 32212992.},
/* GFLOPS 0.032 x 1 = 0.032 */ {{1, 1}, {{1, 512, 14, 14}}, 160, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 32144000.},
/* GFLOPS 0.032 x 1 = 0.032 */ {{1, 1}, {{1, 640, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 32137728.},
/* GFLOPS 0.032 x 1 = 0.032 */ {{1, 1}, {{1, 508, 14, 14}}, 160, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 31893120.},
/* GFLOPS 0.031 x 1 = 0.031 */ {{1, 1}, {{1, 832, 7, 7}}, 384, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 31328640.},
/* GFLOPS 0.031 x 1 = 0.031 */ {{1, 1}, {{1, 832, 7, 7}}, 384, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 31328640.},
/* GFLOPS 0.031 x 1 = 0.031 */ {{1, 1}, {{1, 608, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 30532096.},
/* GFLOPS 0.015 x 2 = 0.030 */ {{5, 5}, {{1, 24, 14, 14}}, 64, 1, {1, 1}, {1, 1}, {2, 2}, {0, 0}, "", true, 15065344.},
/* GFLOPS 0.015 x 2 = 0.030 */ {{5, 5}, {{1, 24, 14, 14}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 15065344.},
/* GFLOPS 0.015 x 2 = 0.030 */ {{5, 5}, {{1, 48, 7, 7}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 15059072.},
/* GFLOPS 0.029 x 1 = 0.029 */ {{3, 3}, {{1, 256, 10, 10}}, 256, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 29497600.},
/* GFLOPS 0.029 x 1 = 0.029 */ {{1, 1}, {{1, 192, 28, 28}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 28976640.},
/* GFLOPS 0.029 x 1 = 0.029 */ {{1, 1}, {{1, 192, 28, 28}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 28976640.},
/* GFLOPS 0.029 x 1 = 0.029 */ {{1, 1}, {{1, 512, 14, 14}}, 144, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 28929600.},
/* GFLOPS 0.029 x 1 = 0.029 */ {{1, 1}, {{1, 512, 14, 14}}, 144, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 28929600.},
/* GFLOPS 0.029 x 1 = 0.029 */ {{1, 1}, {{1, 576, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 28926464.},
/* GFLOPS 0.027 x 1 = 0.027 */ {{1, 1}, {{1, 544, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 27320832.},
/* GFLOPS 0.027 x 1 = 0.027 */ {{1, 1}, {{1, 384, 19, 19}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 26650464.},
/* GFLOPS 0.027 x 1 = 0.027 */ {{1, 1}, {{1, 576, 19, 19}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 26638912.},
/* GFLOPS 0.027 x 1 = 0.027 */ {{3, 3}, {{1, 128, 38, 38}}, 8, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 26627360.},
/* GFLOPS 0.027 x 1 = 0.027 */ {{1, 1}, {{1, 528, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 26518016.},
/* GFLOPS 0.027 x 1 = 0.027 */ {{1, 1}, {{1, 528, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 26518016.},
/* GFLOPS 0.026 x 1 = 0.026 */ {{1, 1}, {{1, 96, 75, 75}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 26055000.},
/* GFLOPS 0.026 x 1 = 0.026 */ {{1, 1}, {{1, 64, 56, 56}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "VALID", true, 25890816.},
/* GFLOPS 0.026 x 1 = 0.026 */ {{1, 1}, {{1, 64, 56, 56}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 25890816.},
/* GFLOPS 0.026 x 1 = 0.026 */ {{1, 1}, {{1, 64, 56, 56}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 25890816.},
/* GFLOPS 0.026 x 1 = 0.026 */ {{1, 1}, {{1, 1024, 10, 10}}, 126, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 25817400.},
/* GFLOPS 0.026 x 1 = 0.026 */ {{1, 1}, {{1, 128, 28, 28}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 25790464.},
/* GFLOPS 0.026 x 1 = 0.026 */ {{1, 1}, {{1, 256, 28, 28}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 25740288.},
/* GFLOPS 0.026 x 1 = 0.026 */ {{1, 1}, {{1, 256, 28, 28}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 25740288.},
/* GFLOPS 0.013 x 2 = 0.026 */ {{1, 1}, {{1, 256, 28, 28}}, 32, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 12870144.},
/* GFLOPS 0.026 x 1 = 0.026 */ {{1, 1}, {{1, 512, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 25715200.},
/* GFLOPS 0.013 x 2 = 0.026 */ {{1, 1}, {{1, 512, 14, 14}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 12857600.},
/* GFLOPS 0.024 x 1 = 0.024 */ {{1, 1}, {{1, 480, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 24109568.},
/* GFLOPS 0.024 x 1 = 0.024 */ {{1, 1}, {{1, 128, 38, 38}}, 256, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "", false, 23750912.},
/* GFLOPS 0.024 x 1 = 0.024 */ {{1, 1}, {{1, 256, 19, 19}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 23704704.},
/* GFLOPS 0.023 x 1 = 0.023 */ {{3, 3}, {{1, 3, 256, 512}}, 13, 1, {2, 2}, {1, 1}, {1, 1}, {0, 0}, "", true, 23429120.},
/* GFLOPS 0.023 x 1 = 0.023 */ {{1, 1}, {{1, 32, 150, 150}}, 16, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 23400000.},
/* GFLOPS 0.023 x 1 = 0.023 */ {{1, 1}, {{1, 512, 19, 19}}, 63, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 23311575.},
/* GFLOPS 0.023 x 1 = 0.023 */ {{1, 1}, {{1, 448, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 22503936.},
/* GFLOPS 0.023 x 1 = 0.023 */ {{1, 1}, {{1, 512, 14, 14}}, 112, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 22500800.},
/* GFLOPS 0.022 x 1 = 0.022 */ {{1, 1}, {{1, 508, 14, 14}}, 112, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 22325184.},
/* GFLOPS 0.021 x 1 = 0.021 */ {{3, 3}, {{1, 128, 12, 12}}, 256, 1, {2, 2}, {1, 1}, {1, 1}, {0, 0}, "", true, 21242880.},
/* GFLOPS 0.021 x 1 = 0.021 */ {{1, 1}, {{1, 416, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 20898304.},
/* GFLOPS 0.021 x 1 = 0.021 */ {{1, 1}, {{1, 832, 7, 7}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 20885760.},
/* GFLOPS 0.021 x 1 = 0.021 */ {{1, 1}, {{1, 832, 7, 7}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 20885760.},
/* GFLOPS 0.010 x 2 = 0.021 */ {{1, 1}, {{1, 832, 7, 7}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 10442880.},
/* GFLOPS 0.010 x 2 = 0.021 */ {{1, 1}, {{1, 832, 7, 7}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 10442880.},
/* GFLOPS 0.010 x 2 = 0.020 */ {{3, 3}, {{1, 256, 2, 2}}, 546, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 10066056.},
/* GFLOPS 0.020 x 1 = 0.020 */ {{5, 5}, {{1, 16, 28, 28}}, 32, 1, {1, 1}, {1, 1}, {2, 2}, {0, 0}, "", true, 20095488.},
/* GFLOPS 0.020 x 1 = 0.020 */ {{5, 5}, {{1, 16, 28, 28}}, 32, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 20095488.},
/* GFLOPS 0.020 x 1 = 0.020 */ {{5, 5}, {{1, 32, 14, 14}}, 64, 1, {1, 1}, {1, 1}, {2, 2}, {0, 0}, "", true, 20082944.},
/* GFLOPS 0.020 x 1 = 0.020 */ {{5, 5}, {{1, 32, 14, 14}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 20082944.},
/* GFLOPS 0.020 x 1 = 0.020 */ {{3, 3}, {{1, 256, 19, 19}}, 12, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 19966188.},
/* GFLOPS 0.019 x 1 = 0.019 */ {{1, 1}, {{1, 192, 28, 28}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 19317760.},
/* GFLOPS 0.019 x 1 = 0.019 */ {{1, 1}, {{1, 192, 28, 28}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 19317760.},
/* GFLOPS 0.019 x 1 = 0.019 */ {{1, 1}, {{1, 384, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 19292672.},
/* GFLOPS 0.018 x 1 = 0.018 */ {{1, 1}, {{1, 576, 10, 10}}, 160, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 18448000.},
/* GFLOPS 0.018 x 1 = 0.018 */ {{1, 1}, {{1, 480, 14, 14}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 18082176.},
/* GFLOPS 0.018 x 1 = 0.018 */ {{1, 1}, {{1, 480, 14, 14}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 18082176.},
/* GFLOPS 0.018 x 1 = 0.018 */ {{1, 1}, {{1, 192, 38, 38}}, 32, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 17790080.},
/* GFLOPS 0.018 x 1 = 0.018 */ {{1, 1}, {{1, 352, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 17687040.},
/* GFLOPS 0.017 x 1 = 0.017 */ {{2, 2}, {{1, 16, 128, 256}}, 16, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "", false, 16908288.},
/* GFLOPS 0.016 x 1 = 0.016 */ {{1, 1}, {{1, 320, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 16081408.},
/* GFLOPS 0.016 x 1 = 0.016 */ {{1, 1}, {{1, 832, 7, 7}}, 192, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 15664320.},
/* GFLOPS 0.016 x 1 = 0.016 */ {{1, 1}, {{1, 832, 7, 7}}, 192, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 15664320.},
/* GFLOPS 0.015 x 1 = 0.015 */ {{5, 5}, {{1, 48, 7, 7}}, 128, 1, {1, 1}, {1, 1}, {2, 2}, {0, 0}, "", true, 15059072.},
/* GFLOPS 0.015 x 1 = 0.015 */ {{5, 5}, {{1, 32, 12, 12}}, 64, 1, {1, 1}, {1, 1}, {2, 2}, {0, 0}, "", true, 14754816.},
/* GFLOPS 0.014 x 1 = 0.014 */ {{1, 1}, {{1, 288, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 14475776.},
/* GFLOPS 0.014 x 1 = 0.014 */ {{1, 1}, {{1, 512, 5, 5}}, 546, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 13991250.},
/* GFLOPS 0.013 x 1 = 0.013 */ {{1, 1}, {{1, 144, 38, 38}}, 32, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 13354112.},
/* GFLOPS 0.007 x 2 = 0.013 */ {{1, 1}, {{1, 16, 56, 56}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 6623232.},
/* GFLOPS 0.013 x 1 = 0.013 */ {{1, 1}, {{1, 832, 7, 7}}, 160, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 13053600.},
/* GFLOPS 0.013 x 1 = 0.013 */ {{1, 1}, {{1, 832, 7, 7}}, 160, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 13053600.},
/* GFLOPS 0.007 x 2 = 0.013 */ {{1, 1}, {{1, 32, 28, 28}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 6522880.},
/* GFLOPS 0.006 x 2 = 0.013 */ {{1, 1}, {{1, 64, 14, 14}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 6472704.},
/* GFLOPS 0.013 x 1 = 0.013 */ {{1, 1}, {{1, 128, 56, 56}}, 16, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 12895232.},
/* GFLOPS 0.013 x 1 = 0.013 */ {{1, 1}, {{1, 256, 28, 28}}, 32, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 12870144.},
/* GFLOPS 0.013 x 1 = 0.013 */ {{1, 1}, {{1, 256, 14, 14}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 12870144.},
/* GFLOPS 0.013 x 1 = 0.013 */ {{1, 1}, {{1, 508, 14, 14}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 12757248.},
/* GFLOPS 0.012 x 1 = 0.012 */ {{1, 1}, {{1, 992, 7, 7}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 12449920.},
/* GFLOPS 0.012 x 1 = 0.012 */ {{1, 1}, {{1, 480, 14, 14}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 12054784.},
/* GFLOPS 0.012 x 1 = 0.012 */ {{1, 1}, {{1, 480, 14, 14}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 12054784.},
/* GFLOPS 0.012 x 1 = 0.012 */ {{1, 1}, {{1, 960, 7, 7}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 12048512.},
/* GFLOPS 0.012 x 1 = 0.012 */ {{1, 1}, {{1, 32, 75, 75}}, 128, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "", false, 12014080.},
/* GFLOPS 0.012 x 1 = 0.012 */ {{3, 3}, {{1, 96, 6, 6}}, 192, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 11950848.},
/* GFLOPS 0.006 x 2 = 0.012 */ {{3, 3}, {{1, 96, 3, 3}}, 384, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 5975424.},
/* GFLOPS 0.012 x 1 = 0.012 */ {{1, 1}, {{1, 320, 12, 12}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 11814912.},
/* GFLOPS 0.012 x 1 = 0.012 */ {{1, 1}, {{1, 640, 6, 6}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 11805696.},
/* GFLOPS 0.012 x 1 = 0.012 */ {{1, 1}, {{1, 928, 7, 7}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 11647104.},
/* GFLOPS 0.011 x 1 = 0.011 */ {{1, 1}, {{1, 896, 7, 7}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 11245696.},
/* GFLOPS 0.011 x 1 = 0.011 */ {{3, 3}, {{1, 256, 10, 10}}, 24, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 11061600.},
/* GFLOPS 0.006 x 2 = 0.011 */ {{3, 3}, {{1, 512, 5, 5}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 5530200.},
/* GFLOPS 0.011 x 1 = 0.011 */ {{1, 1}, {{1, 864, 7, 7}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 10844288.},
/* GFLOPS 0.010 x 1 = 0.010 */ {{1, 1}, {{1, 832, 7, 7}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 10442880.},
/* GFLOPS 0.010 x 1 = 0.010 */ {{5, 5}, {{1, 32, 7, 7}}, 128, 1, {1, 1}, {1, 1}, {2, 2}, {0, 0}, "", true, 10041472.},
/* GFLOPS 0.010 x 1 = 0.010 */ {{1, 1}, {{1, 800, 7, 7}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 10041472.},
/* GFLOPS 0.010 x 1 = 0.010 */ {{1, 1}, {{1, 192, 28, 28}}, 32, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 9658880.},
/* GFLOPS 0.010 x 1 = 0.010 */ {{1, 1}, {{1, 192, 28, 28}}, 32, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 9658880.},
/* GFLOPS 0.010 x 1 = 0.010 */ {{1, 1}, {{1, 384, 14, 14}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 9646336.},
/* GFLOPS 0.005 x 2 = 0.010 */ {{1, 1}, {{1, 512, 14, 14}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 4821600.},
/* GFLOPS 0.010 x 1 = 0.010 */ {{1, 1}, {{1, 768, 7, 7}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 9640064.},
/* GFLOPS 0.010 x 1 = 0.010 */ {{3, 3}, {{1, 4, 128, 256}}, 4, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 9568256.},
/* GFLOPS 0.005 x 2 = 0.009 */ {{1, 1}, {{1, 4, 128, 256}}, 16, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 4718592.},
/* GFLOPS 0.009 x 1 = 0.009 */ {{1, 1}, {{1, 736, 7, 7}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 9238656.},
/* GFLOPS 0.009 x 1 = 0.009 */ {{1, 1}, {{1, 192, 19, 19}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 8895040.},
/* GFLOPS 0.009 x 1 = 0.009 */ {{1, 1}, {{1, 704, 7, 7}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 8837248.},
/* GFLOPS 0.008 x 1 = 0.008 */ {{1, 1}, {{1, 672, 7, 7}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 8435840.},
/* GFLOPS 0.008 x 1 = 0.008 */ {{1, 1}, {{1, 128, 32, 64}}, 16, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 8421376.},
/* GFLOPS 0.008 x 1 = 0.008 */ {{1, 1}, {{1, 640, 7, 7}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 8034432.},
/* GFLOPS 0.004 x 2 = 0.008 */ {{1, 1}, {{1, 832, 7, 7}}, 48, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 3916080.},
/* GFLOPS 0.008 x 1 = 0.008 */ {{1, 1}, {{1, 608, 7, 7}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 7633024.},
/* GFLOPS 0.008 x 1 = 0.008 */ {{5, 5}, {{1, 16, 14, 14}}, 48, 1, {1, 1}, {1, 1}, {2, 2}, {0, 0}, "", true, 7535808.},
/* GFLOPS 0.008 x 1 = 0.008 */ {{5, 5}, {{1, 16, 14, 14}}, 48, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 7535808.},
/* GFLOPS 0.004 x 2 = 0.007 */ {{3, 3}, {{1, 64, 5, 5}}, 128, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 3689600.},
/* GFLOPS 0.007 x 1 = 0.007 */ {{1, 1}, {{1, 640, 6, 6}}, 160, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 7378560.},
/* GFLOPS 0.004 x 2 = 0.007 */ {{1, 1}, {{1, 48, 14, 14}}, 192, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 3650304.},
/* GFLOPS 0.007 x 1 = 0.007 */ {{1, 1}, {{1, 384, 14, 14}}, 48, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 7234752.},
/* GFLOPS 0.007 x 1 = 0.007 */ {{1, 1}, {{1, 576, 7, 7}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 7231616.},
/* GFLOPS 0.007 x 1 = 0.007 */ {{1, 1}, {{1, 256, 12, 12}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 7091712.},
/* GFLOPS 0.007 x 1 = 0.007 */ {{1, 1}, {{1, 544, 7, 7}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 6830208.},
/* GFLOPS 0.007 x 1 = 0.007 */ {{3, 3}, {{1, 160, 6, 6}}, 256, 1, {2, 2}, {1, 1}, {1, 1}, {0, 0}, "", true, 6637824.},
/* GFLOPS 0.007 x 1 = 0.007 */ {{1, 1}, {{1, 528, 14, 14}}, 32, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 6629504.},
/* GFLOPS 0.007 x 1 = 0.007 */ {{1, 1}, {{1, 528, 14, 14}}, 32, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 6629504.},
/* GFLOPS 0.007 x 1 = 0.007 */ {{1, 1}, {{1, 256, 5, 5}}, 512, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 6566400.},
/* GFLOPS 0.003 x 2 = 0.007 */ {{1, 1}, {{1, 512, 5, 5}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 3280000.},
/* GFLOPS 0.006 x 1 = 0.006 */ {{1, 1}, {{1, 64, 56, 56}}, 16, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 6472704.},
/* GFLOPS 0.006 x 1 = 0.006 */ {{1, 1}, {{1, 128, 28, 28}}, 32, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 6447616.},
/* GFLOPS 0.006 x 1 = 0.006 */ {{1, 1}, {{1, 512, 7, 7}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 6428800.},
/* GFLOPS 0.006 x 1 = 0.006 */ {{1, 1}, {{1, 512, 14, 14}}, 32, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 6428800.},
/* GFLOPS 0.006 x 1 = 0.006 */ {{1, 1}, {{1, 512, 14, 14}}, 32, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 6428800.},
/* GFLOPS 0.006 x 1 = 0.006 */ {{3, 3}, {{1, 256, 10, 10}}, 12, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 5530800.},
/* GFLOPS 0.005 x 1 = 0.005 */ {{1, 1}, {{1, 192, 12, 12}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 5322240.},
/* GFLOPS 0.005 x 1 = 0.005 */ {{3, 3}, {{1, 128, 5, 5}}, 256, 1, {2, 2}, {1, 1}, {1, 1}, {0, 0}, "", true, 5310720.},
/* GFLOPS 0.005 x 1 = 0.005 */ {{3, 3}, {{1, 128, 5, 5}}, 256, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 5310720.},
/* GFLOPS 0.005 x 1 = 0.005 */ {{3, 3}, {{1, 128, 5, 5}}, 256, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 5310720.},
/* GFLOPS 0.005 x 1 = 0.005 */ {{1, 1}, {{1, 1024, 10, 10}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 4917600.},
/* GFLOPS 0.005 x 1 = 0.005 */ {{1, 1}, {{1, 1024, 10, 10}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 4917600.},
/* GFLOPS 0.005 x 1 = 0.005 */ {{1, 1}, {{1, 192, 28, 28}}, 16, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 4829440.},
/* GFLOPS 0.005 x 1 = 0.005 */ {{1, 1}, {{1, 192, 28, 28}}, 16, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 4829440.},
/* GFLOPS 0.005 x 1 = 0.005 */ {{1, 1}, {{1, 256, 14, 14}}, 48, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 4826304.},
/* GFLOPS 0.005 x 1 = 0.005 */ {{1, 1}, {{1, 512, 14, 14}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 4821600.},
/* GFLOPS 0.005 x 1 = 0.005 */ {{1, 1}, {{1, 508, 14, 14}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 4783968.},
/* GFLOPS 0.005 x 1 = 0.005 */ {{1, 1}, {{1, 64, 24, 24}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 4755456.},
/* GFLOPS 0.005 x 1 = 0.005 */ {{1, 1}, {{1, 256, 12, 12}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 4727808.},
/* GFLOPS 0.005 x 1 = 0.005 */ {{1, 1}, {{1, 1024, 3, 3}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 4720896.},
/* GFLOPS 0.004 x 1 = 0.004 */ {{1, 1}, {{1, 512, 19, 19}}, 12, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 4440300.},
/* GFLOPS 0.004 x 1 = 0.004 */ {{1, 1}, {{1, 512, 19, 19}}, 12, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 4440300.},
/* GFLOPS 0.004 x 1 = 0.004 */ {{1, 1}, {{1, 640, 6, 6}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 4427136.},
/* GFLOPS 0.004 x 1 = 0.004 */ {{1, 1}, {{1, 16, 128, 256}}, 4, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 4325376.},
/* GFLOPS 0.004 x 1 = 0.004 */ {{1, 1}, {{1, 64, 64, 128}}, 4, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", false, 4227072.},
/* GFLOPS 0.004 x 1 = 0.004 */ {{1, 1}, {{1, 832, 7, 7}}, 48, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 3916080.},
/* GFLOPS 0.004 x 1 = 0.004 */ {{5, 5}, {{1, 16, 12, 12}}, 32, 1, {1, 1}, {1, 1}, {2, 2}, {0, 0}, "", true, 3691008.},
/* GFLOPS 0.004 x 1 = 0.004 */ {{3, 3}, {{1, 64, 10, 10}}, 128, 1, {2, 2}, {1, 1}, {1, 1}, {0, 0}, "", true, 3689600.},
/* GFLOPS 0.004 x 1 = 0.004 */ {{5, 5}, {{1, 32, 6, 6}}, 64, 1, {1, 1}, {1, 1}, {2, 2}, {0, 0}, "", true, 3688704.},
/* GFLOPS 0.004 x 1 = 0.004 */ {{5, 5}, {{1, 32, 12, 12}}, 64, 1, {2, 2}, {1, 1}, {2, 2}, {0, 0}, "", true, 3688704.},
/* GFLOPS 0.004 x 1 = 0.004 */ {{5, 5}, {{1, 64, 6, 6}}, 128, 1, {2, 2}, {1, 1}, {2, 2}, {0, 0}, "", true, 3687552.},
/* GFLOPS 0.004 x 1 = 0.004 */ {{1, 1}, {{1, 192, 12, 12}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 3548160.},
/* GFLOPS 0.003 x 1 = 0.003 */ {{1, 1}, {{1, 736, 3, 3}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 3393792.},
/* GFLOPS 0.003 x 1 = 0.003 */ {{1, 1}, {{1, 256, 10, 10}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 3283200.},
/* GFLOPS 0.003 x 1 = 0.003 */ {{1, 1}, {{1, 512, 5, 5}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 3280000.},
/* GFLOPS 0.003 x 1 = 0.003 */ {{1, 1}, {{1, 512, 5, 5}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 3280000.},
/* GFLOPS 0.003 x 1 = 0.003 */ {{1, 1}, {{1, 512, 5, 5}}, 126, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 3228750.},
/* GFLOPS 0.003 x 1 = 0.003 */ {{1, 1}, {{1, 480, 14, 14}}, 16, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 3013696.},
/* GFLOPS 0.003 x 1 = 0.003 */ {{1, 1}, {{1, 480, 14, 14}}, 16, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 3013696.},
/* GFLOPS 0.003 x 1 = 0.003 */ {{1, 1}, {{1, 320, 12, 12}}, 32, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 2953728.},
/* GFLOPS 0.003 x 1 = 0.003 */ {{1, 1}, {{1, 640, 6, 6}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 2951424.},
/* GFLOPS 0.003 x 1 = 0.003 */ {{3, 3}, {{1, 128, 5, 5}}, 128, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 2655360.},
/* GFLOPS 0.003 x 1 = 0.003 */ {{1, 1}, {{1, 832, 7, 7}}, 32, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 2610720.},
/* GFLOPS 0.003 x 1 = 0.003 */ {{1, 1}, {{1, 256, 3, 3}}, 546, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 2520882.},
/* GFLOPS 0.001 x 2 = 0.003 */ {{3, 3}, {{1, 128, 1, 1}}, 546, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 1258530.},
/* GFLOPS 0.002 x 1 = 0.002 */ {{1, 1}, {{1, 256, 12, 12}}, 32, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 2363904.},
/* GFLOPS 0.002 x 1 = 0.002 */ {{3, 3}, {{1, 128, 3, 3}}, 256, 1, {2, 2}, {1, 1}, {1, 1}, {0, 0}, "", true, 2360320.},
/* GFLOPS 0.002 x 1 = 0.002 */ {{3, 3}, {{1, 128, 3, 3}}, 256, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 2360320.},
/* GFLOPS 0.002 x 1 = 0.002 */ {{3, 3}, {{1, 128, 3, 3}}, 256, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 2360320.},
/* GFLOPS 0.002 x 1 = 0.002 */ {{1, 1}, {{1, 528, 4, 4}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 2164736.},
/* GFLOPS 0.002 x 1 = 0.002 */ {{1, 1}, {{1, 508, 4, 4}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 2082816.},
/* GFLOPS 0.002 x 1 = 0.002 */ {{1, 1}, {{1, 1024, 1, 1}}, 1000, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 2049000.},
/* GFLOPS 0.001 x 2 = 0.002 */ {{3, 3}, {{1, 256, 3, 3}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 995544.},
/* GFLOPS 0.001 x 2 = 0.002 */ {{3, 3}, {{1, 128, 5, 5}}, 16, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 922000.},
/* GFLOPS 0.002 x 1 = 0.002 */ {{1, 1}, {{1, 1024, 3, 3}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 1770336.},
/* GFLOPS 0.001 x 1 = 0.001 */ {{1, 1}, {{1, 640, 6, 6}}, 32, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 1475712.},
/* GFLOPS 0.001 x 1 = 0.001 */ {{3, 3}, {{1, 128, 5, 5}}, 24, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 1383000.},
/* GFLOPS 0.001 x 1 = 0.001 */ {{1, 1}, {{1, 736, 3, 3}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 1272672.},
/* GFLOPS 0.001 x 2 = 0.001 */ {{1, 1}, {{1, 256, 3, 3}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 590976.},
/* GFLOPS 0.001 x 1 = 0.001 */ {{3, 3}, {{1, 128, 3, 3}}, 128, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 1180160.},
/* GFLOPS 0.001 x 1 = 0.001 */ {{1, 1}, {{1, 256, 2, 2}}, 546, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 1120392.},
/* GFLOPS 0.000 x 2 = 0.001 */ {{3, 3}, {{1, 128, 5, 5}}, 8, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 461000.},
/* GFLOPS 0.001 x 1 = 0.001 */ {{1, 1}, {{1, 192, 12, 12}}, 16, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 887040.},
/* GFLOPS 0.000 x 2 = 0.001 */ {{3, 3}, {{1, 256, 2, 2}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 442464.},
/* GFLOPS 0.000 x 2 = 0.001 */ {{1, 1}, {{1, 128, 5, 5}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 411200.},
/* GFLOPS 0.001 x 1 = 0.001 */ {{3, 3}, {{1, 128, 5, 5}}, 12, 1, {1, 1}, {1, 1}, {1, 1}, {0, 0}, "", true, 691500.},
/* GFLOPS 0.001 x 1 = 0.001 */ {{1, 1}, {{1, 640, 2, 2}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 655872.},
/* GFLOPS 0.001 x 1 = 0.001 */ {{1, 1}, {{1, 512, 5, 5}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 615000.},
/* GFLOPS 0.001 x 1 = 0.001 */ {{1, 1}, {{1, 512, 5, 5}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 615000.},
/* GFLOPS 0.001 x 1 = 0.001 */ {{1, 1}, {{1, 128, 3, 3}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 592128.},
/* GFLOPS 0.001 x 1 = 0.001 */ {{1, 1}, {{1, 256, 3, 3}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 590976.},
/* GFLOPS 0.001 x 1 = 0.001 */ {{1, 1}, {{1, 256, 3, 3}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 590976.},
/* GFLOPS 0.001 x 1 = 0.001 */ {{1, 1}, {{1, 256, 3, 3}}, 126, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 581742.},
/* GFLOPS 0.001 x 1 = 0.001 */ {{1, 1}, {{1, 256, 4, 4}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 525312.},
/* GFLOPS 0.000 x 1 = 0.000 */ {{1, 1}, {{1, 192, 5, 5}}, 32, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 308000.},
/* GFLOPS 0.000 x 1 = 0.000 */ {{1, 1}, {{1, 128, 2, 2}}, 256, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 263168.},
/* GFLOPS 0.000 x 2 = 0.000 */ {{1, 1}, {{1, 256, 2, 2}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 131328.},
/* GFLOPS 0.000 x 1 = 0.000 */ {{1, 1}, {{1, 256, 2, 2}}, 126, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 258552.},
/* GFLOPS 0.000 x 1 = 0.000 */ {{1, 1}, {{1, 1024, 1, 1}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 196704.},
/* GFLOPS 0.000 x 1 = 0.000 */ {{3, 3}, {{1, 64, 2, 2}}, 128, 1, {2, 2}, {1, 1}, {1, 1}, {0, 0}, "", true, 147584.},
/* GFLOPS 0.000 x 1 = 0.000 */ {{3, 3}, {{1, 64, 2, 2}}, 128, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 147584.},
/* GFLOPS 0.000 x 1 = 0.000 */ {{3, 3}, {{1, 64, 2, 2}}, 128, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 147584.},
/* GFLOPS 0.000 x 1 = 0.000 */ {{1, 1}, {{1, 736, 1, 1}}, 96, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 141408.},
/* GFLOPS 0.000 x 1 = 0.000 */ {{1, 1}, {{1, 128, 1, 1}}, 546, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 140322.},
/* GFLOPS 0.000 x 1 = 0.000 */ {{1, 1}, {{1, 256, 2, 2}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 131328.},
/* GFLOPS 0.000 x 1 = 0.000 */ {{1, 1}, {{1, 256, 2, 2}}, 64, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 131328.},
/* GFLOPS 0.000 x 1 = 0.000 */ {{1, 1}, {{1, 256, 3, 3}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 110808.},
/* GFLOPS 0.000 x 1 = 0.000 */ {{1, 1}, {{1, 256, 3, 3}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 110808.},
/* GFLOPS 0.000 x 2 = 0.000 */ {{3, 3}, {{1, 128, 1, 1}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 55320.},
/* GFLOPS 0.000 x 1 = 0.000 */ {{3, 3}, {{1, 64, 2, 2}}, 64, 1, {2, 2}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 73792.},
/* GFLOPS 0.000 x 1 = 0.000 */ {{1, 1}, {{1, 256, 2, 2}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 49248.},
/* GFLOPS 0.000 x 1 = 0.000 */ {{1, 1}, {{1, 256, 2, 2}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 49248.},
/* GFLOPS 0.000 x 1 = 0.000 */ {{1, 1}, {{1, 128, 1, 1}}, 126, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 32382.},
/* GFLOPS 0.000 x 1 = 0.000 */ {{1, 1}, {{1, 64, 1, 1}}, 128, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", false, 16512.},
/* GFLOPS 0.000 x 1 = 0.000 */ {{1, 1}, {{1, 128, 1, 1}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "", true, 6168.},
/* GFLOPS 0.000 x 1 = 0.000 */ {{1, 1}, {{1, 128, 1, 1}}, 24, 1, {1, 1}, {1, 1}, {0, 0}, {0, 0}, "SAME", true, 6168.}
};
struct ConvParamID
{
int data[] = {count, nplanes, height, width};
return MatShape(data, data+4);
enum {
CONV_0 = 0,
CONV_100 = 100,
CONV_LAST = sizeof(testConvolutionConfigs) / sizeof(testConvolutionConfigs[0])
};
int val_; \
ConvParamID(int val = 0) : val_(val) {}
operator int() const { return val_; }
static ::testing::internal::ParamGenerator<ConvParamID> all()
{
#if 0
enum { NUM = (int)CONV_LAST };
#else
enum { NUM = (int)CONV_100 };
#endif
ConvParamID v_[NUM]; for (int i = 0; i < NUM; ++i) { v_[i] = ConvParamID(i); } // reduce generated code size
return ::testing::ValuesIn(v_, v_ + NUM);
}
}; \
static inline void PrintTo(const ConvParamID& v, std::ostream* os)
{
CV_Assert((int)v >= 0); CV_Assert((int)v < ConvParamID::CONV_LAST);
const ConvParam_t& p = testConvolutionConfigs[(int)v];
*os << "GFLOPS=" << cv::format("%.3f", p.declared_flops * 1e-9)
<< ", K=" << (Size)p.kernel
<< ", IN={" << p.shapeIn.dims[0] << ", " << p.shapeIn.dims[1] << ", " << p.shapeIn.dims[2] << ", " << p.shapeIn.dims[3] << "}"
<< ", OCN=" << p.outCN;
if (p.groups > 1)
*os << ", G=" << p.groups;
if (((Size)p.stride).area() != 1)
*os << ", S=" << ((Size)p.stride);
if (((Size)p.dilation).area() != 1)
*os << ", D=" << ((Size)p.dilation);
if (((Size)p.pad).area() != 0)
*os << ", P=" << ((Size)p.pad);
if (((Size)p.padAdjust).area() != 0)
*os << ", PAdj=" << ((Size)p.padAdjust);
if (!((std::string)p.padMode).empty())
*os << ", PM=" << ((std::string)p.padMode);
if (p.hasBias)
*os << ", BIAS";
}
PERF_TEST_P( ConvolutionPerfTest, perf, Combine(
Values(Size(1, 1), Size(3, 3), Size(5, 5), Size(11, 11)),
Values(make_pair(blobShape(1, 4, 224, 224), 64),
make_pair(blobShape(1, 64, 112, 122), 128),
make_pair(blobShape(1, 256, 28, 28), 512)),
GroupSize::all(),
StrideSize::all())
)
typedef tuple<ConvParamID, tuple<Backend, Target> > ConvTestParam_t;
typedef TestBaseWithParam<ConvTestParam_t> Conv;
PERF_TEST_P_(Conv, conv)
{
RNG rng(0);
int test_id = (int)get<0>(GetParam());
ASSERT_GE(test_id, 0); ASSERT_LT(test_id, ConvParamID::CONV_LAST);
const ConvParam_t& params = testConvolutionConfigs[test_id];
double declared_flops = params.declared_flops;
Size kernel = params.kernel;
MatShape inputShape = MatShape(params.shapeIn.dims, params.shapeIn.dims + 4);
int outChannels = params.outCN;
int groups = params.groups;
Size stride = params.stride;
Size dilation = params.dilation;
Size pad = params.pad;
Size padAdjust = params.padAdjust;
std::string padMode(params.padMode);
bool hasBias = params.hasBias;
Backend backendId = get<0>(get<1>(GetParam()));
Target targetId = get<1>(get<1>(GetParam()));
ConvParam params = GetParam();
int ksz = get<0>(params).width;
MatShape inpShape = get<1>(params).first;
int outCn = get<1>(params).second;
int groups = get<2>(params);
int stride = (ksz >= 11) ? 4 : (int)get<3>(params);
int inChannels = inputShape[1];
Size inSize(inputShape[3], inputShape[2]);
int inpCn = inpShape[1];
int wgtSize[] = { outCn, inpCn/groups, ksz, ksz };
int biasSize[] = { outCn, 1, 1, 1 };
const int wtype = CV_32F;
Mat wgtBlob(4, wgtSize, wtype), biasBlob(4, biasSize, wtype);
Mat inpBlob(4, &inpShape[0], wtype);
rng.fill(biasBlob, RNG::UNIFORM, -1, +1);
rng.fill(wgtBlob, RNG::UNIFORM, -1, +1);
rng.fill(inpBlob, RNG::UNIFORM, -1, +1);
int sz[] = {outChannels, inChannels / groups, kernel.height, kernel.width};
Mat weights(4, &sz[0], CV_32F);
randu(weights, -1.0f, 1.0f);
LayerParams lp;
lp.set("num_output", outCn);
lp.set("kernel_w", kernel.width);
lp.set("kernel_h", kernel.height);
lp.set("pad_w", pad.width);
lp.set("pad_h", pad.height);
if (padAdjust.width > 0 || padAdjust.height > 0)
{
lp.set("adj_w", padAdjust.width);
lp.set("adj_h", padAdjust.height);
}
if (!padMode.empty())
lp.set("pad_mode", padMode);
lp.set("stride_w", stride.width);
lp.set("stride_h", stride.height);
lp.set("dilation_w", dilation.width);
lp.set("dilation_h", dilation.height);
lp.set("num_output", outChannels);
lp.set("group", groups);
lp.set("stride", stride);
lp.set("kernel_size", ksz);
lp.blobs.reserve(2);
lp.blobs.push_back(wgtBlob);
lp.blobs.push_back(biasBlob);
std::vector<Mat*> inpBlobs(1, &inpBlob);
std::vector<Mat> outBlobs, internalBlobs;
Ptr<Layer> layer = cv::dnn::LayerFactory::createLayerInstance("Convolution", lp);
std::vector<MatShape> inputShapes(1, shape(inpBlob)), outShapes, internals;
layer->getMemoryShapes(inputShapes, 0, outShapes, internals);
for (size_t i = 0; i < outShapes.size(); i++)
lp.set("bias_term", hasBias);
lp.type = "Convolution";
lp.name = "testLayer";
lp.blobs.push_back(weights);
if (hasBias)
{
outBlobs.push_back(Mat(outShapes[i], CV_32F));
Mat bias(1, outChannels, CV_32F);
randu(bias, -1.0f, 1.0f);
lp.blobs.push_back(bias);
}
for (size_t i = 0; i < internals.size(); i++)
int inpSz[] = {1, inChannels, inSize.height, inSize.width};
Mat input(4, &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()
{
internalBlobs.push_back(Mat());
if (total(internals[i]))
internalBlobs.back().create(internals[i], CV_32F);
Mat res = net.forward();
}
layer->finalize(inpBlobs, outBlobs);
Mat inpBlob2D = inpBlob.reshape(1, outCn);
Mat wgtBlob2D = wgtBlob.reshape(1, outCn*(inpCn/groups));
Mat outBlob2D = outBlobs[0].reshape(1, outBlobs[0].size[0]);
declare.in(inpBlob2D, wgtBlob2D, WARMUP_RNG).out(outBlob2D);
layer->forward(inpBlobs, outBlobs, internalBlobs); /// warmup
PERF_SAMPLE_BEGIN()
layer->forward(inpBlobs, outBlobs, internalBlobs);
PERF_SAMPLE_END()
EXPECT_NEAR(flops, declared_flops, declared_flops * 1e-6);
SANITY_CHECK_NOTHING();
}
INSTANTIATE_TEST_CASE_P(/**/, Conv, Combine(
ConvParamID::all(),
dnnBackendsAndTargets(false, false) // defined in ../test/test_common.hpp
));
} // namespace
modules/dnn/perf/perf_net.cpp
+2 -21
View File
@@ -14,10 +14,7 @@
namespace opencv_test {
CV_ENUM(DNNBackend, DNN_BACKEND_DEFAULT, DNN_BACKEND_HALIDE, DNN_BACKEND_INFERENCE_ENGINE, DNN_BACKEND_OPENCV)
CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16, DNN_TARGET_MYRIAD)
class DNNTestNetwork : public ::perf::TestBaseWithParam< tuple<DNNBackend, DNNTarget> >
class DNNTestNetwork : public ::perf::TestBaseWithParam< tuple<Backend, Target> >
{
public:
dnn::Backend backend;
@@ -269,22 +266,6 @@ PERF_TEST_P_(DNNTestNetwork, Inception_v2_Faster_RCNN)
Mat(cv::Size(800, 600), CV_32FC3));
}
const tuple<DNNBackend, DNNTarget> testCases[] = {
#ifdef HAVE_HALIDE
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL),
#endif
#ifdef HAVE_INF_ENGINE
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
#endif
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
};
INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, testing::ValuesIn(testCases));
INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, dnnBackendsAndTargets());
} // namespace
modules/dnn/perf/perf_precomp.hpp
+2
View File
@@ -4,6 +4,8 @@
#include <opencv2/ts.hpp>
#include <opencv2/dnn.hpp>
#include "../test/test_common.hpp"
namespace opencv_test {
using namespace perf;
using namespace cv::dnn;
modules/dnn/src/dnn.cpp
+9 -11
View File
@@ -1676,14 +1676,6 @@ struct Net::Impl
// with the current layer if they follow it. Normally, the are fused with the convolution layer,
// but some of them (like activation) may be fused with fully-connected, elemwise (+) and
// some other layers.
// TODO: OpenCL target support more fusion styles.
if ( preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget) &&
(!cv::ocl::useOpenCL() || (ld.layerInstance->type != "Convolution" &&
ld.layerInstance->type != "MVN" && ld.layerInstance->type != "Pooling" &&
ld.layerInstance->type != "Concat")) )
continue;
Ptr<Layer>& currLayer = ld.layerInstance;
if( ld.consumers.size() == 1 && pinsToKeep.count(LayerPin(lid, 0)) == 0 )
{
@@ -1717,6 +1709,13 @@ struct Net::Impl
if (preferableBackend != DNN_BACKEND_OPENCV)
continue; // Go to the next layer.
// TODO: OpenCL target support more fusion styles.
if ( preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget) &&
(!cv::ocl::useOpenCL() || (ld.layerInstance->type != "Convolution" &&
ld.layerInstance->type != "MVN" && ld.layerInstance->type != "Pooling" &&
ld.layerInstance->type != "Concat")) )
continue;
while (nextData)
{
// For now, OpenCL target support fusion with activation of ReLU/ChannelsPReLU/Power/Tanh
@@ -2693,8 +2692,7 @@ void Net::setInput(InputArray blob, const String& name, double scalefactor, cons
Mat Net::getParam(LayerId layer, int numParam)
{
LayerData &ld = impl->getLayerData(layer);
std::vector<Mat> &layerBlobs = ld.layerInstance->blobs;
std::vector<Mat> &layerBlobs = ld.getLayerInstance()->blobs;
CV_Assert(numParam < (int)layerBlobs.size());
return layerBlobs[numParam];
}
@@ -2703,7 +2701,7 @@ void Net::setParam(LayerId layer, int numParam, const Mat &blob)
{
LayerData &ld = impl->getLayerData(layer);
std::vector<Mat> &layerBlobs = ld.layerInstance->blobs;
std::vector<Mat> &layerBlobs = ld.getLayerInstance()->blobs;
CV_Assert(numParam < (int)layerBlobs.size());
//we don't make strong checks, use this function carefully
layerBlobs[numParam] = blob;
modules/dnn/src/layers/convolution_layer.cpp
+3 -1
View File
@@ -350,12 +350,14 @@ public:
return false;
}
void fuseWeights(const Mat& w, const Mat& b)
void fuseWeights(const Mat& w_, const Mat& b_)
{
// Convolution weights have OIHW data layout. Parameters fusion in case of
// (conv(I) + b1 ) * w + b2
// means to replace convolution's weights to [w*conv(I)] and bias to [b1 * w + b2]
const int outCn = weightsMat.size[0];
Mat w = w_.total() == 1 ? Mat(1, outCn, CV_32F, Scalar(w_.at<float>(0))) : w_;
Mat b = b_.total() == 1 ? Mat(1, outCn, CV_32F, Scalar(b_.at<float>(0))) : b_;
CV_Assert_N(!weightsMat.empty(), biasvec.size() == outCn + 2,
w.empty() || outCn == w.total(), b.empty() || outCn == b.total());
modules/dnn/src/layers/crop_layer.cpp
+44 -1
View File
@@ -41,6 +41,7 @@
//M*/
#include "../precomp.hpp"
#include "../op_inf_engine.hpp"
#include "layers_common.hpp"
namespace cv
@@ -64,6 +65,12 @@ public:
}
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && crop_ranges.size() == 4;
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
@@ -109,7 +116,11 @@ public:
offset_final[i] = offset[i - start_axis];
}
crop_ranges.resize(dims, Range::all());
crop_ranges.resize(dims);
for (int i = 0; i < start_axis; i++)
{
crop_ranges[i] = Range(0, inpBlob.size[i]);
}
for (int i = start_axis; i < dims; i++)
{
if (offset_final[i] < 0 || offset_final[i] + inpSzBlob.size[i] > inpBlob.size[i])
@@ -138,6 +149,38 @@ public:
input(&crop_ranges[0]).copyTo(output);
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "Crop";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::CropLayer> ieLayer(new InferenceEngine::CropLayer(lp));
CV_Assert(crop_ranges.size() == 4);
ieLayer->axis.push_back(0); // batch
ieLayer->offset.push_back(crop_ranges[0].start);
ieLayer->dim.push_back(crop_ranges[0].end - crop_ranges[0].start);
ieLayer->axis.push_back(1); // channels
ieLayer->offset.push_back(crop_ranges[1].start);
ieLayer->dim.push_back(crop_ranges[1].end - crop_ranges[1].start);
ieLayer->axis.push_back(3); // height
ieLayer->offset.push_back(crop_ranges[2].start);
ieLayer->dim.push_back(crop_ranges[2].end - crop_ranges[2].start);
ieLayer->axis.push_back(2); // width
ieLayer->offset.push_back(crop_ranges[3].start);
ieLayer->dim.push_back(crop_ranges[3].end - crop_ranges[3].start);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();
}
std::vector<Range> crop_ranges;
};
modules/dnn/src/layers/elementwise_layers.cpp
+84 -6
View File
@@ -161,6 +161,16 @@ public:
return Ptr<BackendNode>();
}
virtual bool tryFuse(Ptr<dnn::Layer>& top) CV_OVERRIDE
{
return func.tryFuse(top);
}
void getScaleShift(Mat& scale_, Mat& shift_) const CV_OVERRIDE
{
func.getScaleShift(scale_, shift_);
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
@@ -343,6 +353,10 @@ struct ReLUFunctor
}
#endif // HAVE_INF_ENGINE
bool tryFuse(Ptr<dnn::Layer>&) { return false; }
void getScaleShift(Mat&, Mat&) const {}
int64 getFLOPSPerElement() const { return 1; }
};
@@ -448,6 +462,10 @@ struct ReLU6Functor
}
#endif // HAVE_INF_ENGINE
bool tryFuse(Ptr<dnn::Layer>&) { return false; }
void getScaleShift(Mat&, Mat&) const {}
int64 getFLOPSPerElement() const { return 2; }
};
@@ -518,6 +536,10 @@ struct TanHFunctor
}
#endif // HAVE_INF_ENGINE
bool tryFuse(Ptr<dnn::Layer>&) { return false; }
void getScaleShift(Mat&, Mat&) const {}
int64 getFLOPSPerElement() const { return 1; }
};
@@ -588,6 +610,10 @@ struct SigmoidFunctor
}
#endif // HAVE_INF_ENGINE
bool tryFuse(Ptr<dnn::Layer>&) { return false; }
void getScaleShift(Mat&, Mat&) const {}
int64 getFLOPSPerElement() const { return 3; }
};
@@ -659,6 +685,10 @@ struct ELUFunctor
}
#endif // HAVE_INF_ENGINE
bool tryFuse(Ptr<dnn::Layer>&) { return false; }
void getScaleShift(Mat&, Mat&) const {}
int64 getFLOPSPerElement() const { return 2; }
};
@@ -727,6 +757,10 @@ struct AbsValFunctor
}
#endif // HAVE_INF_ENGINE
bool tryFuse(Ptr<dnn::Layer>&) { return false; }
void getScaleShift(Mat&, Mat&) const {}
int64 getFLOPSPerElement() const { return 1; }
};
@@ -775,6 +809,10 @@ struct BNLLFunctor
}
#endif // HAVE_INF_ENGINE
bool tryFuse(Ptr<dnn::Layer>&) { return false; }
void getScaleShift(Mat&, Mat&) const {}
int64 getFLOPSPerElement() const { return 5; }
};
@@ -875,15 +913,51 @@ struct PowerFunctor
#ifdef HAVE_INF_ENGINE
InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp)
{
lp.type = "Power";
std::shared_ptr<InferenceEngine::PowerLayer> ieLayer(new InferenceEngine::PowerLayer(lp));
ieLayer->power = power;
ieLayer->scale = scale;
ieLayer->offset = shift;
return ieLayer;
if (power == 1.0f && scale == 1.0f && shift == 0.0f)
{
// It looks like there is a bug in Inference Engine for DNN_TARGET_OPENCL and DNN_TARGET_OPENCL_FP16
// if power layer do nothing so we replace it to Identity.
lp.type = "Split";
return std::shared_ptr<InferenceEngine::SplitLayer>(new InferenceEngine::SplitLayer(lp));
}
else
{
lp.type = "Power";
std::shared_ptr<InferenceEngine::PowerLayer> ieLayer(new InferenceEngine::PowerLayer(lp));
ieLayer->power = power;
ieLayer->scale = scale;
ieLayer->offset = shift;
return ieLayer;
}
}
#endif // HAVE_INF_ENGINE
bool tryFuse(Ptr<dnn::Layer>& top)
{
if (power != 1.0f && shift != 0.0f)
return false;
Mat w, b;
top->getScaleShift(w, b);
if ((w.empty() && b.empty()) || w.total() > 1 || b.total() > 1)
return false;
float nextScale = w.empty() ? 1.0f : w.at<float>(0);
float nextShift = b.empty() ? 0.0f : b.at<float>(0);
scale = std::pow(scale, power) * nextScale;
shift = nextScale * shift + nextShift;
return true;
}
void getScaleShift(Mat& _scale, Mat& _shift) const
{
if (power == 1.0f)
{
_scale = Mat(1, 1, CV_32F, Scalar(scale));
_shift = Mat(1, 1, CV_32F, Scalar(shift));
}
}
int64 getFLOPSPerElement() const { return power == 1 ? 2 : 10; }
};
@@ -989,6 +1063,10 @@ struct ChannelsPReLUFunctor
}
#endif // HAVE_INF_ENGINE
bool tryFuse(Ptr<dnn::Layer>&) { return false; }
void getScaleShift(Mat&, Mat&) const {}
int64 getFLOPSPerElement() const { return 1; }
};
modules/dnn/src/layers/flatten_layer.cpp
-6
View File
@@ -83,12 +83,6 @@ public:
int startAxis = clamp(_startAxis, numAxes);
int endAxis = clamp(_endAxis, numAxes);
for (size_t i = 1; i < inputs.size(); i++)
{
CV_Assert(inputs[i] == inputs[0]);
}
CV_Assert(startAxis >= 0);
CV_Assert(endAxis >= startAxis && endAxis < (int)numAxes);
modules/dnn/src/layers/fully_connected_layer.cpp
+19 -3
View File
@@ -350,17 +350,33 @@ public:
inshape = shape(outerSize, innerSize);
outshape = shape(outerSize, numOutput);
UMat srcMat, dstMat;
UMat srcMat, dstMat, srcMat_fp32, dstMat_fp32;
srcMat = inputs[i].reshape(1, inshape.size(), &inshape[0]);
dstMat = outputs[i].reshape(1, outshape.size(), &outshape[0]);
cv::gemm(srcMat, weights, 1, noArray(), 0, dstMat, GEMM_2_T);
if (use_half)
{
convertFp16(srcMat, srcMat_fp32);
convertFp16(dstMat, dstMat_fp32);
}
else
{
srcMat_fp32 = srcMat;
dstMat_fp32 = dstMat;
}
cv::gemm(srcMat_fp32, weights, 1, noArray(), 0, dstMat_fp32, GEMM_2_T);
if (bias)
{
UMat biasOnesMat = UMat::ones(outerSize, 1, umat_blobs[0].type());
UMat& biases = umat_blobs[1];
cv::gemm(biasOnesMat, biases, 1, dstMat, 1, dstMat, 0);
cv::gemm(biasOnesMat, biases, 1, dstMat_fp32, 1, dstMat_fp32, 0);
}
if (use_half)
{
convertFp16(srcMat_fp32, srcMat);
convertFp16(dstMat_fp32, dstMat);
}
}
modules/dnn/src/layers/prior_box_layer.cpp
+2 -2
View File
@@ -453,8 +453,8 @@ public:
outputPtr = outputs[0].ptr<float>(0, 1);
if(_variance.size() == 1)
{
Mat secondChannel(outputs[0].size[2], outputs[0].size[3], CV_32F, outputPtr);
secondChannel.setTo(Scalar(_variance[0]));
Mat secondChannel(1, outputs[0].size[2], CV_32F, outputPtr);
secondChannel.setTo(Scalar::all(_variance[0]));
}
else
{
modules/dnn/src/op_inf_engine.cpp
+1
View File
@@ -161,6 +161,7 @@ InfEngineBackendNet::InfEngineBackendNet(InferenceEngine::CNNNetwork& net)
inputs = net.getInputsInfo();
outputs = net.getOutputsInfo();
layers.resize(net.layerCount()); // A hack to execute InfEngineBackendNet::layerCount correctly.
netOwner = net;
}
void InfEngineBackendNet::Release() noexcept
modules/dnn/src/op_inf_engine.hpp
+2
View File
@@ -131,6 +131,8 @@ private:
InferenceEngine::InferencePlugin plugin;
InferenceEngine::ExecutableNetwork netExec;
InferenceEngine::InferRequest infRequest;
// In case of models from Model Optimizer we need to manage their lifetime.
InferenceEngine::CNNNetwork netOwner;
std::string name;
modules/dnn/src/tensorflow/tf_graph_simplifier.cpp
+102
View File
@@ -782,6 +782,108 @@ void releaseTensor(tensorflow::TensorProto* tensor)
}
}
static void permute(google::protobuf::RepeatedPtrField<tensorflow::NodeDef>* data,
const std::vector<int>& indices)
{
const int num = data->size();
CV_Assert(num == indices.size());
std::vector<int> elemIdToPos(num);
std::vector<int> posToElemId(num);
for (int i = 0; i < num; ++i)
{
elemIdToPos[i] = i;
posToElemId[i] = i;
}
for (int i = 0; i < num; ++i)
{
int elemId = indices[i];
int pos = elemIdToPos[elemId];
if (pos != i)
{
data->SwapElements(i, pos);
const int swappedElemId = posToElemId[i];
elemIdToPos[elemId] = i;
elemIdToPos[swappedElemId] = pos;
posToElemId[i] = elemId;
posToElemId[pos] = swappedElemId;
}
}
}
// Is based on tensorflow::graph_transforms::SortByExecutionOrder
void sortByExecutionOrder(tensorflow::GraphDef& net)
{
// Maps node's name to index at net.node() list.
std::map<std::string, int> nodesMap;
std::map<std::string, int>::iterator nodesMapIt;
for (int i = 0; i < net.node_size(); ++i)
{
const tensorflow::NodeDef& node = net.node(i);
nodesMap.insert(std::make_pair(node.name(), i));
}
// Indices of nodes which use specific node as input.
std::vector<std::vector<int> > edges(nodesMap.size());
std::vector<int> numRefsToAdd(nodesMap.size(), 0);
std::vector<int> nodesToAdd;
for (int i = 0; i < net.node_size(); ++i)
{
const tensorflow::NodeDef& node = net.node(i);
for (int j = 0; j < node.input_size(); ++j)
{
std::string inpName = node.input(j);
inpName = inpName.substr(0, inpName.rfind(':'));
inpName = inpName.substr(inpName.find('^') + 1);
nodesMapIt = nodesMap.find(inpName);
CV_Assert(nodesMapIt != nodesMap.end());
edges[nodesMapIt->second].push_back(i);
}
if (node.input_size() == 0)
nodesToAdd.push_back(i);
else
{
if (node.op() == "Merge" || node.op() == "RefMerge")
{
int numControlEdges = 0;
for (int j = 0; j < node.input_size(); ++j)
numControlEdges += node.input(j)[0] == '^';
numRefsToAdd[i] = numControlEdges + 1;
}
else
numRefsToAdd[i] = node.input_size();
}
}
std::vector<int> permIds;
permIds.reserve(net.node_size());
while (!nodesToAdd.empty())
{
int nodeToAdd = nodesToAdd.back();
nodesToAdd.pop_back();
permIds.push_back(nodeToAdd);
// std::cout << net.node(nodeToAdd).name() << '\n';
for (int i = 0; i < edges[nodeToAdd].size(); ++i)
{
int consumerId = edges[nodeToAdd][i];
if (numRefsToAdd[consumerId] > 0)
{
if (numRefsToAdd[consumerId] == 1)
nodesToAdd.push_back(consumerId);
else
CV_Assert(numRefsToAdd[consumerId] >= 0);
numRefsToAdd[consumerId] -= 1;
}
}
}
CV_Assert(permIds.size() == net.node_size());
permute(net.mutable_node(), permIds);
}
CV__DNN_INLINE_NS_END
}} // namespace dnn, namespace cv
modules/dnn/src/tensorflow/tf_graph_simplifier.hpp
+2
View File
@@ -25,6 +25,8 @@ Mat getTensorContent(const tensorflow::TensorProto &tensor);
void releaseTensor(tensorflow::TensorProto* tensor);
void sortByExecutionOrder(tensorflow::GraphDef& net);
CV__DNN_INLINE_NS_END
}} // namespace dnn, namespace cv
modules/dnn/src/tensorflow/tf_importer.cpp
+29
View File
@@ -1950,5 +1950,34 @@ Net readNetFromTensorflow(const std::vector<uchar>& bufferModel, const std::vect
bufferConfigPtr, bufferConfig.size());
}
void writeTextGraph(const String& _model, const String& output)
{
String model = _model;
const std::string modelExt = model.substr(model.rfind('.') + 1);
if (modelExt != "pb")
CV_Error(Error::StsNotImplemented, "Only TensorFlow models support export to text file");
tensorflow::GraphDef net;
ReadTFNetParamsFromBinaryFileOrDie(model.c_str(), &net);
sortByExecutionOrder(net);
RepeatedPtrField<tensorflow::NodeDef>::iterator it;
for (it = net.mutable_node()->begin(); it != net.mutable_node()->end(); ++it)
{
if (it->op() == "Const")
{
it->mutable_attr()->at("value").mutable_tensor()->clear_tensor_content();
}
}
std::string content;
google::protobuf::TextFormat::PrintToString(net, &content);
std::ofstream ofs(output.c_str());
ofs << content;
ofs.close();
}
CV__DNN_INLINE_NS_END
}} // namespace
modules/dnn/test/test_backends.cpp
+5 -20
View File
@@ -161,7 +161,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow)
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.011 : 0.0;
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.06 : 0.0;
processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt",
@@ -173,7 +173,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.011 : 0.0;
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.062 : 0.0;
processNet("dnn/ssd_mobilenet_v2_coco_2018_03_29.pb", "dnn/ssd_mobilenet_v2_coco_2018_03_29.pbtxt",
@@ -247,8 +247,8 @@ TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.008 : 0.0;
Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.015 : 0.0;
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0731 : 0.0;
processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "dnn/ssd_inception_v2_coco_2017_11_17.pbtxt",
inp, "detection_out", "", l1, lInf);
@@ -285,21 +285,6 @@ TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16)
processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", inp, "", "", l1, lInf);
}
const tuple<Backend, Target> testCases[] = {
#ifdef HAVE_HALIDE
tuple<Backend, Target>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU),
tuple<Backend, Target>(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL),
#endif
#ifdef HAVE_INF_ENGINE
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
#endif
tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
};
INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, testing::ValuesIn(testCases));
INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, dnnBackendsAndTargets(true, true, false));
}} // namespace
modules/dnn/test/test_caffe_importer.cpp
+7 -8
View File
@@ -417,7 +417,7 @@ TEST_P(Test_Caffe_nets, DenseNet_121)
float l1 = default_l1, lInf = default_lInf;
if (target == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.017; lInf = 0.067;
l1 = 0.017; lInf = 0.0795;
}
else if (target == DNN_TARGET_MYRIAD)
{
@@ -490,8 +490,7 @@ INSTANTIATE_TEST_CASE_P(Test_Caffe, opencv_face_detector,
TEST_P(Test_Caffe_nets, FasterRCNN_vgg16)
{
if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD) ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
static Mat ref = (Mat_<float>(3, 7) << 0, 2, 0.949398, 99.2454, 210.141, 601.205, 462.849,
0, 7, 0.997022, 481.841, 92.3218, 722.685, 175.953,
@@ -502,8 +501,7 @@ TEST_P(Test_Caffe_nets, FasterRCNN_vgg16)
TEST_P(Test_Caffe_nets, FasterRCNN_zf)
{
if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16) ||
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD) ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
throw SkipTestException("");
static Mat ref = (Mat_<float>(3, 7) << 0, 2, 0.90121, 120.407, 115.83, 570.586, 528.395,
0, 7, 0.988779, 469.849, 75.1756, 718.64, 186.762,
@@ -514,12 +512,13 @@ TEST_P(Test_Caffe_nets, FasterRCNN_zf)
TEST_P(Test_Caffe_nets, RFCN)
{
if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16) ||
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD) ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
throw SkipTestException("");
double scoreDiff = (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) ? 4e-3 : default_l1;
double iouDiff = (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) ? 8e-2 : default_lInf;
static Mat ref = (Mat_<float>(2, 7) << 0, 7, 0.991359, 491.822, 81.1668, 702.573, 178.234,
0, 12, 0.94786, 132.093, 223.903, 338.077, 566.16);
testFaster("rfcn_pascal_voc_resnet50.prototxt", "resnet50_rfcn_final.caffemodel", ref);
testFaster("rfcn_pascal_voc_resnet50.prototxt", "resnet50_rfcn_final.caffemodel", ref, scoreDiff, iouDiff);
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Caffe_nets, dnnBackendsAndTargets());
modules/dnn/test/test_common.hpp
+91
View File
@@ -42,6 +42,47 @@
#ifndef __OPENCV_TEST_COMMON_HPP__
#define __OPENCV_TEST_COMMON_HPP__
#ifdef HAVE_OPENCL
#include "opencv2/core/ocl.hpp"
#endif
namespace cv { namespace dnn {
CV__DNN_INLINE_NS_BEGIN
static inline void PrintTo(const cv::dnn::Backend& v, std::ostream* os)
{
switch (v) {
case DNN_BACKEND_DEFAULT: *os << "DEFAULT"; return;
case DNN_BACKEND_HALIDE: *os << "HALIDE"; return;
case DNN_BACKEND_INFERENCE_ENGINE: *os << "DLIE"; return;
case DNN_BACKEND_OPENCV: *os << "OCV"; return;
} // don't use "default:" to emit compiler warnings
*os << "DNN_BACKEND_UNKNOWN(" << v << ")";
}
static inline void PrintTo(const cv::dnn::Target& v, std::ostream* os)
{
switch (v) {
case DNN_TARGET_CPU: *os << "CPU"; return;
case DNN_TARGET_OPENCL: *os << "OCL"; return;
case DNN_TARGET_OPENCL_FP16: *os << "OCL_FP16"; return;
case DNN_TARGET_MYRIAD: *os << "MYRIAD"; return;
} // don't use "default:" to emit compiler warnings
*os << "DNN_TARGET_UNKNOWN(" << v << ")";
}
using opencv_test::tuple;
using opencv_test::get;
static inline void PrintTo(const tuple<cv::dnn::Backend, cv::dnn::Target> v, std::ostream* os)
{
PrintTo(get<0>(v), os);
*os << "/";
PrintTo(get<1>(v), os);
}
CV__DNN_INLINE_NS_END
}} // namespace
static inline const std::string &getOpenCVExtraDir()
{
return cvtest::TS::ptr()->get_data_path();
@@ -190,4 +231,54 @@ static inline bool readFileInMemory(const std::string& filename, std::string& co
return true;
}
namespace opencv_test {
using namespace cv::dnn;
static testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargets(
bool withInferenceEngine = true,
bool withHalide = false,
bool withCpuOCV = true
)
{
std::vector<tuple<Backend, Target> > targets;
#ifdef HAVE_HALIDE
if (withHalide)
{
targets.push_back(make_tuple(DNN_BACKEND_HALIDE, DNN_TARGET_CPU));
#ifdef HAVE_OPENCL
if (cv::ocl::useOpenCL())
targets.push_back(make_tuple(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL));
#endif
}
#endif
#ifdef HAVE_INF_ENGINE
if (withInferenceEngine)
{
targets.push_back(make_tuple(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU));
#ifdef HAVE_OPENCL
if (cv::ocl::useOpenCL())
{
targets.push_back(make_tuple(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL));
targets.push_back(make_tuple(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16));
}
#endif
if (checkMyriadTarget())
targets.push_back(make_tuple(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD));
}
#endif
if (withCpuOCV)
targets.push_back(make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU));
#ifdef HAVE_OPENCL
if (cv::ocl::useOpenCL())
{
targets.push_back(make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL));
targets.push_back(make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16));
}
#endif
return testing::ValuesIn(targets);
}
} // namespace
#endif
modules/dnn/test/test_halide_layers.cpp
+2 -16
View File
@@ -44,23 +44,9 @@ static void test(LayerParams& params, Mat& input, Backend backendId, Target targ
test(input, net, backendId, targetId, skipCheck);
}
static testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargetsWithHalide()
static inline testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargetsWithHalide()
{
static const tuple<Backend, Target> testCases[] = {
#ifdef HAVE_HALIDE
tuple<Backend, Target>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU),
tuple<Backend, Target>(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL),
#endif
#ifdef HAVE_INF_ENGINE
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
#endif
tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
};
return testing::ValuesIn(testCases);
return dnnBackendsAndTargets(true, true, false); // OpenCV/CPU is used as reference
}
class Test_Halide_layers : public DNNTestLayer {};
modules/dnn/test/test_ie_models.cpp
-4
View File
@@ -177,10 +177,6 @@ TEST_P(DNNTestOpenVINO, models)
Target target = (dnn::Target)(int)get<0>(GetParam());
std::string modelName = get<1>(GetParam());
if ((modelName == "semantic-segmentation-adas-0001" && target == DNN_TARGET_OPENCL_FP16) ||
(modelName == "vehicle-license-plate-detection-barrier-0106"))
throw SkipTestException("");
std::string precision = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? "FP16" : "FP32";
std::string prefix = utils::fs::join("intel_models",
utils::fs::join(modelName,
modules/dnn/test/test_precomp.hpp
-45
View File
@@ -49,35 +49,6 @@
#include "opencv2/dnn.hpp"
#include "test_common.hpp"
namespace cv {
namespace dnn {
CV__DNN_INLINE_NS_BEGIN
static inline void PrintTo(const cv::dnn::Backend& v, std::ostream* os)
{
switch (v) {
case DNN_BACKEND_DEFAULT: *os << "DNN_BACKEND_DEFAULT"; return;
case DNN_BACKEND_HALIDE: *os << "DNN_BACKEND_HALIDE"; return;
case DNN_BACKEND_INFERENCE_ENGINE: *os << "DNN_BACKEND_INFERENCE_ENGINE"; return;
case DNN_BACKEND_OPENCV: *os << "DNN_BACKEND_OPENCV"; return;
} // don't use "default:" to emit compiler warnings
*os << "DNN_BACKEND_UNKNOWN(" << v << ")";
}
static inline void PrintTo(const cv::dnn::Target& v, std::ostream* os)
{
switch (v) {
case DNN_TARGET_CPU: *os << "DNN_TARGET_CPU"; return;
case DNN_TARGET_OPENCL: *os << "DNN_TARGET_OPENCL"; return;
case DNN_TARGET_OPENCL_FP16: *os << "DNN_TARGET_OPENCL_FP16"; return;
case DNN_TARGET_MYRIAD: *os << "DNN_TARGET_MYRIAD"; return;
} // don't use "default:" to emit compiler warnings
*os << "DNN_TARGET_UNKNOWN(" << v << ")";
}
CV__DNN_INLINE_NS_END
}} // namespace
namespace opencv_test {
using namespace cv::dnn;
@@ -95,22 +66,6 @@ static testing::internal::ParamGenerator<Target> availableDnnTargets()
return testing::ValuesIn(targets);
}
static testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargets()
{
static const tuple<Backend, Target> testCases[] = {
#ifdef HAVE_INF_ENGINE
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
#endif
tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_CPU),
tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
};
return testing::ValuesIn(testCases);
}
class DNNTestLayer : public TestWithParam<tuple<Backend, Target> >
{
public:
modules/dnn/test/test_tf_importer.cpp
+48 -17
View File
@@ -296,7 +296,7 @@ TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
Net net = readNetFromTensorflow(model, proto);
Mat img = imread(findDataFile("dnn/street.png", false));
Mat blob = blobFromImage(img, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), true, false);
Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
@@ -310,32 +310,61 @@ TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
0, 3, 0.75838411, 0.44668293, 0.45907149, 0.49459291, 0.52197015,
0, 10, 0.95932811, 0.38349164, 0.32528657, 0.40387636, 0.39165527,
0, 10, 0.93973452, 0.66561931, 0.37841269, 0.68074018, 0.42907384);
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 5e-3 : default_l1;
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0097 : default_l1;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.09 : default_lInf;
normAssertDetections(ref, out, "", 0.5, scoreDiff, iouDiff);
}
TEST_P(Test_TensorFlow_nets, Inception_v2_Faster_RCNN)
TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD)
{
checkBackend();
std::string model = findDataFile("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", false);
std::string proto = findDataFile("dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt", false);
Net net = readNetFromTensorflow(model, proto);
Mat img = imread(findDataFile("dnn/dog416.png", false));
Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(blob);
Mat out = net.forward();
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco_2017_11_17.detection_out.npy"));
float scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 7e-3 : 1e-5;
float iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0098 : 1e-3;
normAssertDetections(ref, out, "", 0.3, scoreDiff, iouDiff);
}
TEST_P(Test_TensorFlow_nets, Faster_RCNN)
{
static std::string names[] = {"faster_rcnn_inception_v2_coco_2018_01_28",
"faster_rcnn_resnet50_coco_2018_01_28"};
checkBackend();
if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU) ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
std::string proto = findDataFile("dnn/faster_rcnn_inception_v2_coco_2018_01_28.pbtxt", false);
std::string model = findDataFile("dnn/faster_rcnn_inception_v2_coco_2018_01_28.pb", false);
for (int i = 1; i < 2; ++i)
{
std::string proto = findDataFile("dnn/" + names[i] + ".pbtxt", false);
std::string model = findDataFile("dnn/" + names[i] + ".pb", false);
Net net = readNetFromTensorflow(model, proto);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat img = imread(findDataFile("dnn/dog416.png", false));
Mat blob = blobFromImage(img, 1.0f / 127.5, Size(800, 600), Scalar(127.5, 127.5, 127.5), true, false);
Net net = readNetFromTensorflow(model, proto);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat img = imread(findDataFile("dnn/dog416.png", false));
Mat blob = blobFromImage(img, 1.0f, Size(800, 600), Scalar(), true, false);
net.setInput(blob);
Mat out = net.forward();
net.setInput(blob);
Mat out = net.forward();
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/faster_rcnn_inception_v2_coco_2018_01_28.detection_out.npy"));
normAssertDetections(ref, out, "", 0.3);
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/" + names[i] + ".detection_out.npy"));
normAssertDetections(ref, out, names[i].c_str(), 0.3);
}
}
TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN)
@@ -347,15 +376,17 @@ TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN)
Net net = readNetFromTensorflow(model, proto);
Mat img = imread(findDataFile("dnn/dog416.png", false));
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_ppn_coco.detection_out.npy", false));
Mat blob = blobFromImage(img, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), true, false);
Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(blob);
Mat out = net.forward();
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.006 : default_l1;
normAssertDetections(ref, out, "", 0.4, scoreDiff, default_lInf);
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.011 : default_l1;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.021 : default_lInf;
normAssertDetections(ref, out, "", 0.4, scoreDiff, iouDiff);
}
TEST_P(Test_TensorFlow_nets, opencv_face_detector_uint8)
modules/dnn/test/test_torch_importer.cpp
+2 -2
View File
@@ -301,14 +301,14 @@ TEST_P(Test_Torch_nets, ENet_accuracy)
// Due to numerical instability in Pooling-Unpooling layers (indexes jittering)
// thresholds for ENet must be changed. Accuracy of results was checked on
// Cityscapes dataset and difference in mIOU with Torch is 10E-4%
normAssert(ref, out, "", 0.00044, 0.44);
normAssert(ref, out, "", 0.00044, target == DNN_TARGET_CPU ? 0.453 : 0.44);
const int N = 3;
for (int i = 0; i < N; i++)
{
net.setInput(inputBlob, "");
Mat out = net.forward();
normAssert(ref, out, "", 0.00044, 0.44);
normAssert(ref, out, "", 0.00044, target == DNN_TARGET_CPU ? 0.453 : 0.44);
}
}