setParamsFrom(params);
hasBias = params.get<bool>("bias_term", false);
axis = params.get<int>("axis", 1);
+ hasWeights = false;
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
- CV_Assert(inputs.size() == 2 && blobs.empty() || blobs.size() == 1 + hasBias);
outputs.assign(1, inputs[0]);
return true;
}
+ virtual void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs) CV_OVERRIDE
+ {
+ hasWeights = blobs.size() == 2 || (blobs.size() == 1 && !hasBias);
+ CV_Assert(inputs.size() == 2 && blobs.empty() || blobs.size() == (int)hasWeights + (int)hasBias);
+ }
+
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
Mat &inpBlob = *inputs[0];
Mat &outBlob = outputs[0];
- Mat &weights = blobs.empty() ? *inputs[1] : blobs[0];
- Mat bias = hasBias ? blobs.back() : Mat();
+ // There is a mode when we multiply a first blob by a second one
+ // instead of trainable weights.
+ Mat weights = blobs.empty() ? *inputs[1] : (hasWeights ? blobs[0] : Mat());
+ Mat bias = hasBias ? blobs.back().reshape(1, 1) : Mat();
+ if (!weights.empty())
+ weights = weights.reshape(1, 1);
MatShape inpShape = shape(inpBlob);
- const int numWeights = weights.total();
+ const int numWeights = !weights.empty() ? weights.total() : bias.total();
+ CV_Assert(numWeights != 0, !hasWeights || !hasBias || weights.total() == bias.total());
int endAxis;
for (endAxis = axis + 1; endAxis <= inpBlob.dims; ++endAxis)
if (endAxis != inpBlob.dims)
{
- float* weightsData = (float*)weights.data;
+ float* weightsData = !weights.empty() ? (float*)weights.data : 0;
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;
+ float w = weightsData ? weightsData[j] : 1;
+ float b = biasesData ? biasesData[j] : 0;
Mat inpSlice(1, spatialSize, CV_32F, inpData);
Mat outSlice(1, spatialSize, CV_32F, outData);
inpSlice.convertTo(outSlice, CV_32F, w, b);
{
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);
-
+ Mat inpSlice(1, numWeights, CV_32F, inpData);
+ Mat outSlice(1, numWeights, CV_32F, outData);
+ if (!weights.empty())
+ {
+ multiply(inpSlice, weights, outSlice);
+ if (hasBias)
+ add(outSlice, bias, outSlice);
+ }
+ else if (hasBias)
+ add(inpSlice, bias, outSlice);
inpData += numWeights;
outData += numWeights;
}
const int numChannels = blobs[0].total();
- auto weights = wrapToHalideBuffer(blobs[0], {numChannels});
- Halide::Expr topExpr = input * weights(c);
+ Halide::Expr topExpr = input;
+ if (hasWeights)
+ {
+ auto weights = wrapToHalideBuffer(blobs[0], {numChannels});
+ topExpr *= weights(c);
+ }
if (hasBias)
{
- auto bias = wrapToHalideBuffer(blobs[1], {numChannels});
+ auto bias = wrapToHalideBuffer(blobs.back(), {numChannels});
topExpr += bias(c);
}
top(x, y, c, n) = topExpr;
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::ScaleShiftLayer> ieLayer(new InferenceEngine::ScaleShiftLayer(lp));
+ CV_Assert(!blobs.empty());
const size_t numChannels = blobs[0].total();
- ieLayer->_weights = wrapToInfEngineBlob(blobs[0], {numChannels}, InferenceEngine::Layout::C);
+ if (hasWeights)
+ {
+ ieLayer->_weights = wrapToInfEngineBlob(blobs[0], {numChannels}, InferenceEngine::Layout::C);
+ }
+ else
+ {
+ auto weights = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32,
+ {numChannels});
+ weights->allocate();
+
+ std::vector<float> ones(numChannels, 1);
+ weights->set(ones);
+ ieLayer->_weights = weights;
+ }
if (hasBias)
- ieLayer->_biases = wrapToInfEngineBlob(blobs[1], {numChannels}, InferenceEngine::Layout::C);
+ ieLayer->_biases = wrapToInfEngineBlob(blobs.back(), {numChannels}, InferenceEngine::Layout::C);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE
void getScaleShift(Mat& scale, Mat& shift) const CV_OVERRIDE
{
- scale = !blobs.empty() ? blobs[0] : Mat();
- shift = hasBias ? blobs[1] : Mat();
+ scale = hasWeights ? blobs[0] : Mat();
+ shift = hasBias ? blobs.back() : Mat();
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
}
return flops;
}
+
+private:
+ bool hasWeights;
};
return Ptr<ScaleLayer>(new ScaleLayerImpl(params));
}
+Ptr<Layer> ShiftLayer::create(const LayerParams& params)
+{
+ LayerParams scaleParams;
+ scaleParams.name = params.name;
+ scaleParams.type = "Scale";
+ scaleParams.blobs = params.blobs;
+ scaleParams.set("bias_term", true);
+ scaleParams.set("axis", 0);
+ return Ptr<ScaleLayer>(new ScaleLayerImpl(scaleParams));
+}
+
} // namespace dnn
} // namespace cv
+++ /dev/null
-// 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.
-
-// Copyright (C) 2016, Intel Corporation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-
-/*
-Implementation of shift layer, which adds up const values to blob.
-*/
-
-#include "../precomp.hpp"
-#include "../op_inf_engine.hpp"
-#include <opencv2/dnn/shape_utils.hpp>
-
-namespace cv
-{
-namespace dnn
-{
-
-class ShiftLayerImpl CV_FINAL : public ShiftLayer
-{
-public:
- ShiftLayerImpl(const LayerParams ¶ms)
- {
- setParamsFrom(params);
- CV_Assert(blobs.size() == 1);
- }
-
- virtual bool supportBackend(int backendId) CV_OVERRIDE
- {
- return backendId == DNN_BACKEND_DEFAULT ||
- backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
- }
-
- bool getMemoryShapes(const std::vector<MatShape> &inputs,
- const int requiredOutputs,
- std::vector<MatShape> &outputs,
- std::vector<MatShape> &internals) const CV_OVERRIDE
- {
- Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
- internals.assign(1, shape(1, total(inputs[0], 2)));
- return true;
- }
-
- void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
- {
- CV_TRACE_FUNCTION();
- CV_TRACE_ARG_VALUE(name, "name", name.c_str());
-
- Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
- }
-
- virtual void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals) CV_OVERRIDE
- {
- CV_TRACE_FUNCTION();
- CV_TRACE_ARG_VALUE(name, "name", name.c_str());
-
- CV_Assert(inputs.size() > 0);
- CV_Assert(blobs.size() > 0);
-
- if(inputs[0]->dims == blobs[0].dims)
- {
- for (size_t ii = 0; ii < outputs.size(); ii++)
- {
- Mat &inpBlob = *inputs[ii];
- Mat &outBlob = outputs[ii];
-
- outBlob = inpBlob + blobs[0];
- }
- }
- else
- {
- Mat biasOnesMat = internals[0];
- biasOnesMat.setTo(1);
- for (size_t ii = 0; ii < outputs.size(); ii++)
- {
- Mat &inpBlob = *inputs[ii];
- Mat &outBlob = outputs[ii];
-
- inpBlob.copyTo(outBlob);
-
- for (int n = 0; n < inpBlob.size[0]; n++)
- {
- Mat dstMat(inpBlob.size[1], inpBlob.size[2] * inpBlob.size[3],
- outBlob.type(), outBlob.ptr(n));
- gemm(blobs[0], biasOnesMat, 1, dstMat, 1, dstMat); //TODO: gemv
- }
- }
- }
- }
-
- virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
- {
-#ifdef HAVE_INF_ENGINE
- // Inference Engine has no layer just for biases. Create a linear
- // transformation layer with ones weights.
- InferenceEngine::LayerParams lp;
- lp.name = name;
- lp.type = "ScaleShift";
- lp.precision = InferenceEngine::Precision::FP32;
- std::shared_ptr<InferenceEngine::ScaleShiftLayer> ieLayer(new InferenceEngine::ScaleShiftLayer(lp));
-
- auto weights = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32,
- {blobs[0].total()});
- weights->allocate();
-
- std::vector<float> ones(blobs[0].total(), 1);
- weights->set(ones);
- ieLayer->_weights = weights;
-
- ieLayer->_biases = wrapToInfEngineBlob(blobs[0]);
- return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-#endif // HAVE_INF_ENGINE
- return Ptr<BackendNode>();
- }
-
- void getScaleShift(Mat& scale, Mat& shift) const CV_OVERRIDE
- {
- scale = Mat();
- shift = blobs[0];
- }
-
- virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
- const std::vector<MatShape> &outputs) const CV_OVERRIDE
- {
- (void)outputs; // suppress unused variable warning
- long flops = 0;
-
- for(int i= 0; i < inputs.size(); i++)
- {
- flops += total(inputs[i]);
- }
-
- return flops;
- }
-};
-
-Ptr<ShiftLayer> ShiftLayer::create(const LayerParams& params)
-{
- return Ptr<ShiftLayer>(new ShiftLayerImpl(params));
-}
-
-}
-}
if (haveConst)
{
- layerParams.blobs.resize(1);
- blobFromTensor(getConstBlob(layer, value_id), layerParams.blobs[0]);
+ Mat values = getTensorContent(getConstBlob(layer, value_id));
+ CV_Assert(values.type() == CV_32FC1);
- int id = dstNet.addLayer(name, "Shift", layerParams);
+ int id;
+ if (values.total() == 1) // is a scalar.
+ {
+ layerParams.set("shift", values.at<float>(0));
+ id = dstNet.addLayer(name, "Power", layerParams);
+ }
+ else // is a vector
+ {
+ layerParams.blobs.resize(1, values);
+ id = dstNet.addLayer(name, "Shift", layerParams);
+ }
layer_id[name] = id;
// one input only
}
CV_Assert(haveConst);
- layerParams.blobs.resize(1);
- blobFromTensor(getConstBlob(layer, value_id), layerParams.blobs[0]);
- layerParams.blobs[0] *= -1;
+ Mat values = getTensorContent(getConstBlob(layer, value_id));
+ CV_Assert(values.type() == CV_32FC1);
+ values *= -1.0f;
- int id = dstNet.addLayer(name, "Shift", layerParams);
+ int id;
+ if (values.total() == 1) // is a scalar.
+ {
+ layerParams.set("shift", values.at<float>(0));
+ id = dstNet.addLayer(name, "Power", layerParams);
+ }
+ else // is a vector
+ {
+ layerParams.blobs.resize(1, values);
+ id = dstNet.addLayer(name, "Shift", layerParams);
+ }
layer_id[name] = id;
// one input only