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Replace Slice layer to Crop in Faster-RCNN networks from Caffe

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This commit is contained in:
Dmitry Kurtaev 2018-08-29 16:33:54 +03:00
parent 087e9308ec
commit ea43e28a37
3 changed files with 69 additions and 11 deletions
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45
modules/dnn/src/layers/crop_layer.cpp
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;
};

22
modules/dnn/src/layers/fully_connected_layer.cpp
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);
}
}

13
modules/dnn/test/test_caffe_importer.cpp
View File

@ -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());