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Remove Shift deep learning layer

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This commit is contained in:
Dmitry Kurtaev 2018-05-28 18:13:32 +03:00
parent 29421d7def
commit 2c3c59d018
4 changed files with 95 additions and 173 deletions
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2
modules/dnn/include/opencv2/dnn/all_layers.hpp
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@ -503,7 +503,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
class CV_EXPORTS ShiftLayer : public Layer
{
public:
static Ptr<ShiftLayer> create(const LayerParams& params);
static Ptr<Layer> create(const LayerParams& params);
};
class CV_EXPORTS PriorBoxLayer : public Layer

87
modules/dnn/src/layers/scale_layer.cpp
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@ -28,6 +28,7 @@ public:
setParamsFrom(params);
hasBias = params.get<bool>("bias_term", false);
axis = params.get<int>("axis", 1);
hasWeights = false;
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -35,11 +36,16 @@ public:
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 ||
@ -63,10 +69,15 @@ public:
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)
@ -84,15 +95,15 @@ public:
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);
@ -105,12 +116,16 @@ public:
{
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;
}
@ -157,11 +172,15 @@ public:
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;
@ -178,10 +197,24 @@ public:
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
@ -190,8 +223,8 @@ public:
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,
@ -205,6 +238,9 @@ public:
}
return flops;
}
private:
bool hasWeights;
};
@ -213,5 +249,16 @@ Ptr<ScaleLayer> ScaleLayer::create(const LayerParams& params)
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

145
modules/dnn/src/layers/shift_layer.cpp
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@ -1,145 +0,0 @@
// 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 &params)
{
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));
}
}
}

34
modules/dnn/src/tensorflow/tf_importer.cpp
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@ -743,10 +743,20 @@ void TFImporter::populateNet(Net dstNet)
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
@ -777,11 +787,21 @@ void TFImporter::populateNet(Net dstNet)
}
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