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Merge pull request #14682 from l-bat:axpy_layer

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* Add Axpy layer

* Fix test

* fix caffe importer
This commit is contained in:
Lubov Batanina 2019-06-05 00:18:06 +03:00 committed by Alexander Alekhin
parent f1fb002682
commit 3efd2df87f
3 changed files with 65 additions and 1 deletions
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21
modules/dnn/src/caffe/caffe_importer.cpp
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@ -388,6 +388,27 @@ public:
layerParams.blobs[1].setTo(1); // std
}
}
else if (type == "Axpy")
{
CV_Assert_N(layer.bottom_size() == 3, layer.top_size() == 1);
std::string scaleName = name + "/scale";
int repetitions = layerCounter[scaleName]++;
if (repetitions) {
scaleName += String("_") + toString(repetitions);
}
LayerParams scaleParams;
scaleParams.set("axis", 1);
scaleParams.set("has_bias", false);
int scaleId = dstNet.addLayer(scaleName, "Scale", scaleParams);
addInput(layer.bottom(2), scaleId, 0, dstNet);
addInput(layer.bottom(0), scaleId, 1, dstNet);
addOutput(layer, scaleId, 0);
net.mutable_layer(li)->set_bottom(0, layer.top(0));
net.mutable_layer(li)->mutable_bottom()->RemoveLast();
type = "Eltwise";
}
else if ("ConvolutionDepthwise" == type)
{
type = "Convolution";

2
modules/dnn/src/layers/eltwise_layer.cpp
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@ -143,7 +143,7 @@ public:
CV_Check(dst.dims, 1 < dst.dims && dst.dims <= 4, ""); CV_CheckTypeEQ(dst.type(), CV_32FC1, ""); CV_Assert(dst.isContinuous());
CV_Assert(coeffs.empty() || coeffs.size() == (size_t)nsrcs);
for( int i = 0; i > nsrcs; i++ )
for( int i = 0; i < nsrcs; i++ )
{
CV_Assert(srcs[i].size == dst.size &&
srcs[i].type() == dst.type() &&

43
modules/dnn/test/test_caffe_importer.cpp
View File

@ -109,6 +109,49 @@ TEST(Test_Caffe, read_googlenet)
ASSERT_FALSE(net.empty());
}
TEST_P(Test_Caffe_nets, Axpy)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
String proto = _tf("axpy.prototxt");
Net net = readNetFromCaffe(proto);
checkBackend();
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
int size[] = {1, 2, 3, 4};
int scale_size[] = {1, 2, 1, 1};
Mat scale(4, &scale_size[0], CV_32F);
Mat shift(4, &size[0], CV_32F);
Mat inp(4, &size[0], CV_32F);
randu(scale, -1.0f, 1.0f);
randu(shift, -1.0f, 1.0f);
randu(inp, -1.0f, 1.0f);
net.setInput(scale, "scale");
net.setInput(shift, "shift");
net.setInput(inp, "data");
Mat out = net.forward();
Mat ref(4, &size[0], inp.type());
for (int i = 0; i < inp.size[1]; i++) {
for (int h = 0; h < inp.size[2]; h++) {
for (int w = 0; w < inp.size[3]; w++) {
int idx[] = {0, i, h, w};
int scale_idx[] = {0, i, 0, 0};
ref.at<float>(idx) = inp.at<float>(idx) * scale.at<float>(scale_idx) +
shift.at<float>(idx);
}
}
}
float l1 = (target == DNN_TARGET_OPENCL_FP16) ? 2e-4 : 1e-5;
float lInf = (target == DNN_TARGET_OPENCL_FP16) ? 1e-3 : 1e-4;
normAssert(ref, out, "", l1, lInf);
}
typedef testing::TestWithParam<tuple<bool, Target> > Reproducibility_AlexNet;
TEST_P(Reproducibility_AlexNet, Accuracy)
{