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Merge pull request #10585 from dkurt:dnn_weightless_scale

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This commit is contained in:
Alexander Alekhin 2018-01-15 06:07:50 +00:00
commit 1255bd8d4b
5 changed files with 137 additions and 24 deletions
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1
modules/dnn/include/opencv2/dnn/all_layers.hpp
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@ -490,6 +490,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
{
public:
bool hasBias;
int axis;
static Ptr<ScaleLayer> create(const LayerParams& params);
};

4
modules/dnn/src/layers/convolution_layer.cpp
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@ -268,6 +268,8 @@ public:
bool setScale(const Ptr<ScaleLayer>& layer)
{
if (layer.empty() || layer->blobs.empty())
return false;
scaleLayer = layer;
// we will need to re-compute the weights with the scaling
// coefficients taken into account
@ -276,7 +278,7 @@ public:
newWeightAndBias = true;
fusedBias = false;
#endif
return !scaleLayer.empty();
return true;
}
virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs)

75
modules/dnn/src/layers/scale_layer.cpp
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@ -26,6 +26,7 @@ public:
{
setParamsFrom(params);
hasBias = params.get<bool>("bias_term", false);
axis = params.get<int>("axis", 1);
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -33,8 +34,8 @@ public:
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const
{
CV_Assert(blobs.size() == 1 + hasBias);
Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
CV_Assert(inputs.size() == 2 && blobs.empty() || blobs.size() == 1 + hasBias);
outputs.assign(1, inputs[0]);
return true;
}
@ -56,28 +57,60 @@ public:
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_Assert(outputs.size() == 1, !blobs.empty() || inputs.size() == 2);
for (size_t ii = 0; ii < outputs.size(); ii++)
Mat &inpBlob = *inputs[0];
Mat &outBlob = outputs[0];
Mat &weights = blobs.empty() ? *inputs[1] : blobs[0];
Mat bias = hasBias ? blobs.back() : Mat();
MatShape inpShape = shape(inpBlob);
const int numWeights = weights.total();
int endAxis;
for (endAxis = axis + 1; endAxis <= inpBlob.dims; ++endAxis)
{
Mat &inpBlob = *inputs[ii];
Mat &outBlob = outputs[ii];
CV_Assert(inpBlob.size[1] == blobs[0].total());
if (hasBias)
CV_Assert(inpBlob.size[1] == blobs[1].total());
CV_Assert(inpBlob.type() == CV_32F && outBlob.type() == CV_32F);
for( int cn = 0; cn < inpBlob.size[0]; cn++ )
{
for (int n = 0; n < inpBlob.size[1]; n++)
{
float w = blobs[0].at<float>(n);
float b = hasBias ? blobs[1].at<float>(n) : 0;
Mat outBlobPlane = slice(outBlob, cn, n);
Mat inpBlobPlane = slice(inpBlob, cn, n);
inpBlobPlane.convertTo(outBlobPlane, CV_32F, w, b);
if (total(inpShape, axis, endAxis) == numWeights)
break;
}
CV_Assert(total(inpShape, axis, endAxis) == numWeights,
!hasBias || numWeights == bias.total(),
inpBlob.type() == CV_32F && outBlob.type() == CV_32F);
int numSlices = total(inpShape, 0, axis);
float* inpData = (float*)inpBlob.data;
float* outData = (float*)outBlob.data;
if (endAxis != inpBlob.dims)
{
float* weightsData = (float*)weights.data;
float* biasesData = hasBias ? (float*)bias.data : 0;
int spatialSize = total(inpShape, endAxis); // spatialSize != 1
for (int i = 0; i < numSlices; ++i)
{
for (int j = 0; j < numWeights; ++j)
{
float w = weightsData[j];
float b = hasBias ? biasesData[j] : 0;
Mat inpSlice(1, spatialSize, CV_32F, inpData);
Mat outSlice(1, spatialSize, CV_32F, outData);
inpSlice.convertTo(outSlice, CV_32F, w, b);
inpData += spatialSize;
outData += spatialSize;
}
}
}
else
{
for (int i = 0; i < numSlices; ++i)
{
Mat inpSlice(weights.dims, weights.size, CV_32F, inpData);
Mat outSlice(weights.dims, weights.size, CV_32F, outData);
multiply(inpSlice, weights, outSlice);
if (hasBias)
add(outSlice, bias, outSlice);
inpData += numWeights;
outData += numWeights;
}
}
}

12
modules/dnn/test/test_caffe_importer.cpp
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@ -87,10 +87,10 @@ TEST(Test_Caffe, read_googlenet)
ASSERT_FALSE(net.empty());
}
typedef testing::TestWithParam<tuple<bool> > Reproducibility_AlexNet;
typedef testing::TestWithParam<bool> Reproducibility_AlexNet;
TEST_P(Reproducibility_AlexNet, Accuracy)
{
bool readFromMemory = get<0>(GetParam());
bool readFromMemory = GetParam();
Net net;
{
const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
@ -119,12 +119,12 @@ TEST_P(Reproducibility_AlexNet, Accuracy)
normAssert(ref, out);
}
INSTANTIATE_TEST_CASE_P(Test_Caffe, Reproducibility_AlexNet, testing::Values(true, false));
INSTANTIATE_TEST_CASE_P(Test_Caffe, Reproducibility_AlexNet, testing::Bool());
typedef testing::TestWithParam<tuple<bool> > Reproducibility_OCL_AlexNet;
typedef testing::TestWithParam<bool> Reproducibility_OCL_AlexNet;
OCL_TEST_P(Reproducibility_OCL_AlexNet, Accuracy)
{
bool readFromMemory = get<0>(GetParam());
bool readFromMemory = GetParam();
Net net;
{
const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
@ -156,7 +156,7 @@ OCL_TEST_P(Reproducibility_OCL_AlexNet, Accuracy)
normAssert(ref, out);
}
OCL_INSTANTIATE_TEST_CASE_P(Test_Caffe, Reproducibility_OCL_AlexNet, testing::Values(true, false));
OCL_INSTANTIATE_TEST_CASE_P(Test_Caffe, Reproducibility_OCL_AlexNet, testing::Bool());
#if !defined(_WIN32) || defined(_WIN64)
TEST(Reproducibility_FCN, Accuracy)

77
modules/dnn/test/test_layers.cpp
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@ -627,4 +627,81 @@ OCL_TEST(Layer_Test_FasterRCNN_Proposal, Accuracy)
EXPECT_EQ(countNonZero(out.rowRange(numDets, out.size[0])), 0);
}
typedef testing::TestWithParam<tuple<Vec4i, Vec2i, bool> > Scale_untrainable;
TEST_P(Scale_untrainable, Accuracy)
{
Vec4i inpShapeVec = get<0>(GetParam());
int axis = get<1>(GetParam())[0];
int weightsDims = get<1>(GetParam())[1];
bool testFusion = get<2>(GetParam());
const int inpShape[] = {inpShapeVec[0], inpShapeVec[1], inpShapeVec[2], inpShapeVec[3]};
// Create a network with two inputs. Scale layer multiplies a first input to
// a second one. See http://caffe.berkeleyvision.org/tutorial/layers/scale.html
Net net;
// Check that this version of Scale layer won't be fused with Convolution layer.
if (testFusion)
{
LayerParams lp;
lp.set("kernel_size", 1);
lp.set("num_output", 3);
lp.set("group", 3);
lp.set("bias_term", false);
lp.type = "Convolution";
lp.name = "testConv";
std::vector<int> weightsShape(4);
weightsShape[0] = 3; // #outChannels
weightsShape[1] = 1; // #inpChannels / group
weightsShape[2] = 1; // height
weightsShape[3] = 1; // width
Mat weights(weightsShape, CV_32F);
weights.setTo(1);
lp.blobs.push_back(weights);
net.addLayerToPrev(lp.name, lp.type, lp);
}
LayerParams lp;
lp.type = "Scale";
lp.name = "testLayer";
lp.set("axis", axis);
int id = net.addLayerToPrev(lp.name, lp.type, lp);
net.connect(0, 1, id, 1);
Mat input(4, inpShape, CV_32F);
Mat weights(weightsDims, &inpShape[axis], CV_32F);
randu(input, -1, 1);
randu(weights, -1, 1);
std::vector<String> inpNames(2);
inpNames[0] = "scale_input";
inpNames[1] = "scale_weights";
net.setInputsNames(inpNames);
net.setInput(input, inpNames[0]);
net.setInput(weights, inpNames[1]);
Mat out = net.forward();
Mat ref(input.dims, input.size, CV_32F);
float* inpData = (float*)input.data;
float* refData = (float*)ref.data;
float* weightsData = (float*)weights.data;
int spatialSize = 1;
for (int i = axis + weightsDims; i < 4; ++i)
spatialSize *= inpShape[i];
for (int i = 0; i < ref.total(); ++i)
{
float w = weightsData[(i / spatialSize) % weights.total()];
refData[i] = inpData[i] * w;
}
normAssert(out, ref);
}
INSTANTIATE_TEST_CASE_P(Layer_Test, Scale_untrainable, Combine(
/*input size*/ Values(Vec4i(2, 3, 4, 5)),
/*axis, #dims*/ Values(Vec2i(0, 1), Vec2i(0, 2), Vec2i(0, 3), Vec2i(0, 4),
Vec2i(1, 1), Vec2i(1, 2), Vec2i(1, 3),
Vec2i(2, 1), Vec2i(2, 2),
Vec2i(3, 1)),
/*conv fusion*/ testing::Bool()
));
}