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Untrainable version of Scale layer from Caffe

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This commit is contained in:
Dmitry Kurtaev
2018-01-12 11:59:05 +03:00
parent 57dc28fe99
commit 1f4fdfd599
5 changed files with 137 additions and 24 deletions
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modules/dnn/src/layers/scale_layer.cpp
+50 -17
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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;
}
@@ -56,30 +57,62 @@ 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];
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);
CV_Assert(inpBlob.size[1] == blobs[0].total());
if (hasBias)
CV_Assert(inpBlob.size[1] == blobs[1].total());
int numSlices = total(inpShape, 0, axis);
float* inpData = (float*)inpBlob.data;
float* outData = (float*)outBlob.data;
CV_Assert(inpBlob.type() == CV_32F && outBlob.type() == CV_32F);
for( int cn = 0; cn < inpBlob.size[0]; cn++ )
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 n = 0; n < inpBlob.size[1]; n++)
for (int j = 0; j < numWeights; ++j)
{
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);
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;
}
}
}
virtual Ptr<BackendNode> tryAttach(const Ptr<BackendNode>& node)