Added ResizeBilinear op for tf (#11050)
* Added ResizeBilinear op for tf Combined ResizeNearestNeighbor and ResizeBilinear layers into Resize (with an interpolation param). Minor changes to tf_importer and resize layer to save some code lines Minor changes in init.cpp Minor changes in tf_importer.cpp * Replaced implementation of a custom ResizeBilinear layer to all layers * Use Mat::ptr. Replace interpolation flagspull/11714/head
David
7 years ago
committed by
Vadim Pisarevsky
parent
60fa6bea70
commit
7175f257b5
10 changed files with 253 additions and 284 deletions
@ -0,0 +1,176 @@ |
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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// Copyright (C) 2017, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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#include "../precomp.hpp" |
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#include "layers_common.hpp" |
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#include "../op_inf_engine.hpp" |
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#include <opencv2/imgproc.hpp> |
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namespace cv { namespace dnn { |
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class ResizeLayerImpl CV_FINAL : public ResizeLayer |
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{ |
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public: |
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ResizeLayerImpl(const LayerParams& params) |
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{ |
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setParamsFrom(params); |
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outWidth = params.get<float>("width", 0); |
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outHeight = params.get<float>("height", 0); |
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if (params.has("zoom_factor")) |
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{ |
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CV_Assert(!params.has("zoom_factor_x") && !params.has("zoom_factor_y")); |
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zoomFactorWidth = zoomFactorHeight = params.get<int>("zoom_factor"); |
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} |
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else if (params.has("zoom_factor_x") || params.has("zoom_factor_y")) |
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{ |
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CV_Assert(params.has("zoom_factor_x") && params.has("zoom_factor_y")); |
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zoomFactorWidth = params.get<int>("zoom_factor_x"); |
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zoomFactorHeight = params.get<int>("zoom_factor_y"); |
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} |
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interpolation = params.get<String>("interpolation"); |
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CV_Assert(interpolation == "nearest" || interpolation == "bilinear"); |
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alignCorners = params.get<bool>("align_corners", false); |
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if (alignCorners) |
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CV_Error(Error::StsNotImplemented, "Resize with align_corners=true is not implemented"); |
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} |
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bool getMemoryShapes(const std::vector<MatShape> &inputs, |
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const int requiredOutputs, |
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std::vector<MatShape> &outputs, |
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std::vector<MatShape> &internals) const CV_OVERRIDE |
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{ |
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CV_Assert(inputs.size() == 1, inputs[0].size() == 4); |
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outputs.resize(1, inputs[0]); |
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outputs[0][2] = outHeight > 0 ? outHeight : (outputs[0][2] * zoomFactorHeight); |
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outputs[0][3] = outWidth > 0 ? outWidth : (outputs[0][3] * zoomFactorWidth); |
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// We can work in-place (do nothing) if input shape == output shape.
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return (outputs[0][2] == inputs[0][2]) && (outputs[0][3] == inputs[0][3]); |
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} |
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virtual bool supportBackend(int backendId) CV_OVERRIDE |
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{ |
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return backendId == DNN_BACKEND_OPENCV || |
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backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && interpolation == "nearest"; |
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} |
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virtual void finalize(const std::vector<Mat*>& inputs, std::vector<Mat> &outputs) CV_OVERRIDE |
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{ |
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if (!outWidth && !outHeight) |
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{ |
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outHeight = outputs[0].size[2]; |
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outWidth = outputs[0].size[3]; |
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} |
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} |
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void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE |
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{ |
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CV_TRACE_FUNCTION(); |
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CV_TRACE_ARG_VALUE(name, "name", name.c_str()); |
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Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr); |
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} |
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void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals) CV_OVERRIDE |
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{ |
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CV_TRACE_FUNCTION(); |
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CV_TRACE_ARG_VALUE(name, "name", name.c_str()); |
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if (outHeight == inputs[0]->size[2] && outWidth == inputs[0]->size[3]) |
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return; |
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Mat& inp = *inputs[0]; |
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Mat& out = outputs[0]; |
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if (interpolation == "nearest") |
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{ |
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for (size_t n = 0; n < inputs[0]->size[0]; ++n) |
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{ |
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for (size_t ch = 0; ch < inputs[0]->size[1]; ++ch) |
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{ |
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resize(getPlane(inp, n, ch), getPlane(out, n, ch), |
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Size(outWidth, outHeight), 0, 0, INTER_NEAREST); |
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} |
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} |
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} |
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else if (interpolation == "bilinear") |
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{ |
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const int inpHeight = inp.size[2]; |
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const int inpWidth = inp.size[3]; |
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const int inpSpatialSize = inpHeight * inpWidth; |
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const int outSpatialSize = outHeight * outWidth; |
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const float heightScale = static_cast<float>(inpHeight) / (outHeight); |
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const float widthScale = static_cast<float>(inpWidth) / (outWidth); |
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const int numPlanes = inp.size[0] * inp.size[1]; |
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CV_Assert(inp.isContinuous(), out.isContinuous()); |
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Mat inpPlanes = inp.reshape(1, numPlanes * inpHeight); |
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Mat outPlanes = out.reshape(1, numPlanes * outHeight); |
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for (int y = 0; y < outHeight; ++y) |
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{ |
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float input_y = y * heightScale; |
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int y0 = static_cast<int>(input_y); |
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const float* inpData_row0 = inpPlanes.ptr<float>(y0); |
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const float* inpData_row1 = inpPlanes.ptr<float>(std::min(y0 + 1, inpHeight - 1)); |
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for (int x = 0; x < outWidth; ++x) |
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{ |
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float input_x = x * widthScale; |
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int x0 = static_cast<int>(input_x); |
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int x1 = std::min(x0 + 1, inpWidth - 1); |
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float* outData = outPlanes.ptr<float>(y, x); |
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const float* inpData_row0_c = inpData_row0; |
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const float* inpData_row1_c = inpData_row1; |
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for (int c = 0; c < numPlanes; ++c) |
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{ |
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*outData = inpData_row0_c[x0] + |
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(input_y - y0) * (inpData_row1_c[x0] - inpData_row0_c[x0]) + |
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(input_x - x0) * (inpData_row0_c[x1] - inpData_row0_c[x0] + |
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(input_y - y0) * (inpData_row1_c[x1] - inpData_row0_c[x1] - inpData_row1_c[x0] + inpData_row0_c[x0])); |
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inpData_row0_c += inpSpatialSize; |
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inpData_row1_c += inpSpatialSize; |
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outData += outSpatialSize; |
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} |
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} |
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} |
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} |
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else |
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CV_Error(Error::StsNotImplemented, "Unknown interpolation: " + interpolation); |
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} |
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virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE |
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{ |
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#ifdef HAVE_INF_ENGINE |
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InferenceEngine::LayerParams lp; |
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lp.name = name; |
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lp.type = "Resample"; |
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lp.precision = InferenceEngine::Precision::FP32; |
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std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp)); |
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ieLayer->params["type"] = "caffe.ResampleParameter.NEAREST"; |
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ieLayer->params["antialias"] = "0"; |
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ieLayer->params["width"] = cv::format("%d", outWidth); |
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ieLayer->params["height"] = cv::format("%d", outHeight); |
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return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); |
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#endif // HAVE_INF_ENGINE
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return Ptr<BackendNode>(); |
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} |
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private: |
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int outWidth, outHeight, zoomFactorWidth, zoomFactorHeight; |
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String interpolation; |
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bool alignCorners; |
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}; |
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Ptr<ResizeLayer> ResizeLayer::create(const LayerParams& params) |
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{ |
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return Ptr<ResizeLayer>(new ResizeLayerImpl(params)); |
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} |
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} // namespace dnn
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} // namespace cv
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@ -1,117 +0,0 @@ |
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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// Copyright (C) 2017, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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#include "../precomp.hpp" |
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#include "layers_common.hpp" |
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#include "../op_inf_engine.hpp" |
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#include <opencv2/imgproc.hpp> |
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namespace cv { namespace dnn { |
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class ResizeNearestNeighborLayerImpl CV_FINAL : public ResizeNearestNeighborLayer |
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{ |
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public: |
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ResizeNearestNeighborLayerImpl(const LayerParams& params) |
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{ |
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setParamsFrom(params); |
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CV_Assert(params.has("width") && params.has("height") || params.has("zoom_factor")); |
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CV_Assert(!params.has("width") && !params.has("height") || !params.has("zoom_factor")); |
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outWidth = params.get<float>("width", 0); |
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outHeight = params.get<float>("height", 0); |
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zoomFactor = params.get<int>("zoom_factor", 1); |
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alignCorners = params.get<bool>("align_corners", false); |
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if (alignCorners) |
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CV_Error(Error::StsNotImplemented, "Nearest neighborhood resize with align_corners=true is not implemented"); |
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} |
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bool getMemoryShapes(const std::vector<MatShape> &inputs, |
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const int requiredOutputs, |
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std::vector<MatShape> &outputs, |
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std::vector<MatShape> &internals) const CV_OVERRIDE |
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{ |
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CV_Assert(inputs.size() == 1, inputs[0].size() == 4); |
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outputs.resize(1, inputs[0]); |
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outputs[0][2] = outHeight > 0 ? outHeight : (outputs[0][2] * zoomFactor); |
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outputs[0][3] = outWidth > 0 ? outWidth : (outputs[0][3] * zoomFactor); |
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// We can work in-place (do nothing) if input shape == output shape.
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return (outputs[0][2] == inputs[0][2]) && (outputs[0][3] == inputs[0][3]); |
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} |
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virtual bool supportBackend(int backendId) CV_OVERRIDE |
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{ |
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return backendId == DNN_BACKEND_OPENCV || |
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backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine(); |
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} |
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virtual void finalize(const std::vector<Mat*>& inputs, std::vector<Mat> &outputs) CV_OVERRIDE |
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{ |
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if (!outWidth && !outHeight) |
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{ |
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outHeight = outputs[0].size[2]; |
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outWidth = outputs[0].size[3]; |
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} |
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} |
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void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE |
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{ |
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CV_TRACE_FUNCTION(); |
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CV_TRACE_ARG_VALUE(name, "name", name.c_str()); |
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Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr); |
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} |
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void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals) CV_OVERRIDE |
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{ |
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CV_TRACE_FUNCTION(); |
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CV_TRACE_ARG_VALUE(name, "name", name.c_str()); |
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if (outHeight == inputs[0]->size[2] && outWidth == inputs[0]->size[3]) |
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return; |
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Mat& inp = *inputs[0]; |
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Mat& out = outputs[0]; |
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for (size_t n = 0; n < inputs[0]->size[0]; ++n) |
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{ |
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for (size_t ch = 0; ch < inputs[0]->size[1]; ++ch) |
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{ |
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resize(getPlane(inp, n, ch), getPlane(out, n, ch), |
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Size(outWidth, outHeight), 0, 0, INTER_NEAREST); |
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} |
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} |
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} |
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virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE |
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{ |
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#ifdef HAVE_INF_ENGINE |
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InferenceEngine::LayerParams lp; |
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lp.name = name; |
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lp.type = "Resample"; |
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lp.precision = InferenceEngine::Precision::FP32; |
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std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp)); |
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ieLayer->params["type"] = "caffe.ResampleParameter.NEAREST"; |
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ieLayer->params["antialias"] = "0"; |
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ieLayer->params["width"] = cv::format("%d", outWidth); |
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ieLayer->params["height"] = cv::format("%d", outHeight); |
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return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); |
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#endif // HAVE_INF_ENGINE
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return Ptr<BackendNode>(); |
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} |
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private: |
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int outWidth, outHeight, zoomFactor; |
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bool alignCorners; |
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}; |
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Ptr<ResizeNearestNeighborLayer> ResizeNearestNeighborLayer::create(const LayerParams& params) |
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{ |
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return Ptr<ResizeNearestNeighborLayer>(new ResizeNearestNeighborLayerImpl(params)); |
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} |
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} // namespace dnn
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} // namespace cv
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