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opencv/modules/dnn/src/layers/resize_layer.cpp

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// 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) 2017, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
#include "../precomp.hpp"
#include "layers_common.hpp"
#include "../op_cuda.hpp"
#include "../op_inf_engine.hpp"
#include <opencv2/imgproc.hpp>
#ifdef HAVE_DNN_NGRAPH
#include "../ie_ngraph.hpp"
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4)
#include <ngraph/op/interpolate.hpp>
#else
#include <ngraph/op/experimental/layers/interpolate.hpp>
#endif
#endif
#ifdef HAVE_CUDA
#include "../cuda4dnn/primitives/resize.hpp"
using namespace cv::dnn::cuda4dnn;
#endif
namespace cv { namespace dnn {
class ResizeLayerImpl : public ResizeLayer
{
public:
ResizeLayerImpl(const LayerParams& params) : zoomFactorWidth(params.get<float>("zoom_factor_x", params.get<float>("zoom_factor", 0))),
zoomFactorHeight(params.get<float>("zoom_factor_y", params.get<float>("zoom_factor", 0))),
scaleWidth(0), scaleHeight(0)
{
setParamsFrom(params);
outWidth = params.get<float>("width", 0);
outHeight = params.get<float>("height", 0);
if (params.has("zoom_factor"))
{
CV_Assert(!params.has("zoom_factor_x") && !params.has("zoom_factor_y"));
}
else if (params.has("zoom_factor_x") || params.has("zoom_factor_y"))
{
CV_Assert(params.has("zoom_factor_x") && params.has("zoom_factor_y"));
}
interpolation = params.get<String>("interpolation");
CV_Check(interpolation, interpolation == "nearest" || interpolation == "opencv_linear" || interpolation == "bilinear", "");
alignCorners = params.get<bool>("align_corners", false);
halfPixelCenters = params.get<bool>("half_pixel_centers", false);
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert_N(inputs.size() == 1 || inputs.size() == 2, inputs[0].size() == 4);
outputs.resize(1, inputs[0]);
if (inputs.size() == 1) {
outputs[0][2] = zoomFactorHeight > 0 ? (outputs[0][2] * zoomFactorHeight) : outHeight;
outputs[0][3] = zoomFactorWidth > 0 ? (outputs[0][3] * zoomFactorWidth) : outWidth;
} else {
outputs[0][2] = inputs[1][2];
outputs[0][3] = inputs[1][3];
}
// We can work in-place (do nothing) if input shape == output shape.
return (outputs[0][2] == inputs[0][2]) && (outputs[0][3] == inputs[0][3]);
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
if (backendId == DNN_BACKEND_CUDA)
return interpolation == "nearest" || interpolation == "bilinear" || interpolation == "opencv_linear";
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
return (interpolation == "nearest" && scaleWidth == scaleHeight) ||
(interpolation == "bilinear");
}
#endif
return backendId == DNN_BACKEND_OPENCV;
}
virtual void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE
{
std::vector<Mat> inputs, outputs;
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
outHeight = outputs[0].size[2];
outWidth = outputs[0].size[3];
if (alignCorners && outHeight > 1)
scaleHeight = static_cast<float>(inputs[0].size[2] - 1) / (outHeight - 1);
else
scaleHeight = static_cast<float>(inputs[0].size[2]) / outHeight;
if (alignCorners && outWidth > 1)
scaleWidth = static_cast<float>(inputs[0].size[3] - 1) / (outWidth - 1);
else
scaleWidth = static_cast<float>(inputs[0].size[3]) / outWidth;
}
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());
if (inputs_arr.depth() == CV_16S)
{
forward_fallback(inputs_arr, outputs_arr, internals_arr);
return;
}
std::vector<Mat> inputs, outputs, internals;
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
internals_arr.getMatVector(internals);
if (outHeight == inputs[0].size[2] && outWidth == inputs[0].size[3])
{
// outputs[0] = inputs[0] doesn't work due to BlobManager optimizations
if (inputs[0].data != outputs[0].data)
{
inputs[0].copyTo(outputs[0]);
}
return;
}
Mat& inp = inputs[0];
Mat& out = outputs[0];
if ((interpolation == "nearest" && !alignCorners && !halfPixelCenters) || interpolation == "opencv_linear" || (interpolation == "bilinear" && halfPixelCenters))
{
InterpolationFlags mode = interpolation == "nearest" ? INTER_NEAREST : INTER_LINEAR;
for (size_t n = 0; n < inputs[0].size[0]; ++n)
{
for (size_t ch = 0; ch < inputs[0].size[1]; ++ch)
{
resize(getPlane(inp, n, ch), getPlane(out, n, ch),
Size(outWidth, outHeight), 0, 0, mode);
}
}
}
else if (interpolation == "nearest")
{
const int inpHeight = inp.size[2];
const int inpWidth = inp.size[3];
const int inpSpatialSize = inpHeight * inpWidth;
const int outSpatialSize = outHeight * outWidth;
const int numPlanes = inp.size[0] * inp.size[1];
CV_Assert_N(inp.isContinuous(), out.isContinuous());
Mat inpPlanes = inp.reshape(1, numPlanes * inpHeight);
Mat outPlanes = out.reshape(1, numPlanes * outHeight);
float heightOffset = 0.0f;
float widthOffset = 0.0f;
if (halfPixelCenters)
{
heightOffset = 0.5f * scaleHeight;
widthOffset = 0.5f * scaleWidth;
}
for (int y = 0; y < outHeight; ++y)
{
float input_y = y * scaleHeight + heightOffset;
int y0 = halfPixelCenters ? std::floor(input_y) : lroundf(input_y);
y0 = std::min(y0, inpHeight - 1);
const float* inpData_row = inpPlanes.ptr<float>(y0);
for (int x = 0; x < outWidth; ++x)
{
float input_x = x * scaleWidth + widthOffset;
int x0 = halfPixelCenters ? std::floor(input_x) : lroundf(input_x);
x0 = std::min(x0, inpWidth - 1);
float* outData = outPlanes.ptr<float>(y, x);
const float* inpData_row_c = inpData_row;
for (int c = 0; c < numPlanes; ++c)
{
*outData = inpData_row_c[x0];
inpData_row_c += inpSpatialSize;
outData += outSpatialSize;
}
}
}
}
else if (interpolation == "bilinear")
{
const int inpHeight = inp.size[2];
const int inpWidth = inp.size[3];
const int inpSpatialSize = inpHeight * inpWidth;
const int outSpatialSize = outHeight * outWidth;
const int numPlanes = inp.size[0] * inp.size[1];
CV_Assert_N(inp.isContinuous(), out.isContinuous());
Mat inpPlanes = inp.reshape(1, numPlanes * inpHeight);
Mat outPlanes = out.reshape(1, numPlanes * outHeight);
for (int y = 0; y < outHeight; ++y)
{
float input_y = y * scaleHeight;
int y0 = static_cast<int>(input_y);
const float* inpData_row0 = inpPlanes.ptr<float>(y0);
const float* inpData_row1 = inpPlanes.ptr<float>(std::min(y0 + 1, inpHeight - 1));
for (int x = 0; x < outWidth; ++x)
{
float input_x = x * scaleWidth;
int x0 = static_cast<int>(input_x);
int x1 = std::min(x0 + 1, inpWidth - 1);
float* outData = outPlanes.ptr<float>(y, x);
const float* inpData_row0_c = inpData_row0;
const float* inpData_row1_c = inpData_row1;
for (int c = 0; c < numPlanes; ++c)
{
*outData = inpData_row0_c[x0] +
(input_y - y0) * (inpData_row1_c[x0] - inpData_row0_c[x0]) +
(input_x - x0) * (inpData_row0_c[x1] - inpData_row0_c[x0] +
(input_y - y0) * (inpData_row1_c[x1] - inpData_row0_c[x1] - inpData_row1_c[x0] + inpData_row0_c[x0]));
inpData_row0_c += inpSpatialSize;
inpData_row1_c += inpSpatialSize;
outData += outSpatialSize;
}
}
}
}
else
CV_Error(Error::StsNotImplemented, "Unknown interpolation: " + interpolation);
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::Layer ieLayer(name);
ieLayer.setName(name);
if (interpolation == "nearest")
{
ieLayer.setType("Resample");
ieLayer.getParameters()["type"] = std::string("caffe.ResampleParameter.NEAREST");
ieLayer.getParameters()["antialias"] = false;
if (scaleWidth != scaleHeight)
CV_Error(Error::StsNotImplemented, "resample with sw != sh");
ieLayer.getParameters()["factor"] = 1.0f / scaleWidth;
}
else if (interpolation == "bilinear")
{
ieLayer.setType("Interp");
ieLayer.getParameters()["pad_beg"] = 0;
ieLayer.getParameters()["pad_end"] = 0;
ieLayer.getParameters()["align_corners"] = alignCorners;
}
else
CV_Error(Error::StsNotImplemented, "Unsupported interpolation: " + interpolation);
ieLayer.getParameters()["width"] = outWidth;
ieLayer.getParameters()["height"] = outHeight;
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(1));
ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2021_2)
ngraph::op::InterpolateAttrs attrs;
attrs.pads_begin.push_back(0);
attrs.pads_end.push_back(0);
attrs.axes = ngraph::AxisSet{2, 3};
attrs.align_corners = alignCorners;
if (interpolation == "nearest") {
attrs.mode = "nearest";
attrs.antialias = false;
} else if (interpolation == "bilinear") {
attrs.mode = "linear";
} else {
CV_Error(Error::StsNotImplemented, "Unsupported interpolation: " + interpolation);
}
std::vector<int64_t> shape = {outHeight, outWidth};
auto out_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, shape.data());
auto interp = std::make_shared<ngraph::op::Interpolate>(ieInpNode, out_shape, attrs);
#else
ngraph::op::v4::Interpolate::InterpolateAttrs attrs;
if (interpolation == "nearest") {
attrs.mode = ngraph::op::v4::Interpolate::InterpolateMode::nearest;
attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::half_pixel;
} else if (interpolation == "bilinear") {
attrs.mode = ngraph::op::v4::Interpolate::InterpolateMode::linear_onnx;
attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::asymmetric;
} else {
CV_Error(Error::StsNotImplemented, format("Unsupported interpolation: %s", interpolation.c_str()));
}
attrs.shape_calculation_mode = ngraph::op::v4::Interpolate::ShapeCalcMode::sizes;
if (alignCorners) {
attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::align_corners;
}
attrs.nearest_mode = ngraph::op::v4::Interpolate::NearestMode::round_prefer_floor;
std::vector<int64_t> shape = {outHeight, outWidth};
auto out_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, shape.data());
auto& input_shape = ieInpNode->get_shape();
CV_Assert_N(input_shape[2] != 0, input_shape[3] != 0);
std::vector<float> scales = {static_cast<float>(outHeight) / input_shape[2], static_cast<float>(outWidth) / input_shape[3]};
auto scales_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{2}, scales.data());
auto axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{2, 3});
auto interp = std::make_shared<ngraph::op::v4::Interpolate>(ieInpNode, out_shape, scales_shape, axes, attrs);
#endif
return Ptr<BackendNode>(new InfEngineNgraphNode(interp));
}
#endif // HAVE_DNN_NGRAPH
#ifdef HAVE_CUDA
Ptr<BackendNode> initCUDA(
void *context_,
const std::vector<Ptr<BackendWrapper>>& inputs,
const std::vector<Ptr<BackendWrapper>>& outputs
) override
{
auto context = reinterpret_cast<csl::CSLContext*>(context_);
cuda4dnn::ResizeConfiguration config;
if (interpolation == "nearest")
{
config.type = InterpolationType::NEAREST_NEIGHBOUR;
config.align_corners = alignCorners;
config.half_pixel_centers = halfPixelCenters;
}
else if (interpolation == "bilinear")
{
config.type = InterpolationType::BILINEAR;
config.align_corners = alignCorners;
config.half_pixel_centers = halfPixelCenters;
}
else if (interpolation == "opencv_linear")
{
config.type = InterpolationType::BILINEAR;
config.align_corners = false;
config.half_pixel_centers = true;
}
else
CV_Error(Error::StsNotImplemented, "Requested interpolation mode is not available in resize layer.");
return make_cuda_node<cuda4dnn::ResizeOp>(preferableTarget, std::move(context->stream), config);
}
#endif
protected:
int outWidth, outHeight;
const float zoomFactorWidth, zoomFactorHeight;
String interpolation;
float scaleWidth, scaleHeight;
bool alignCorners;
bool halfPixelCenters;
};
Ptr<ResizeLayer> ResizeLayer::create(const LayerParams& params)
{
return Ptr<ResizeLayer>(new ResizeLayerImpl(params));
}
class InterpLayerImpl CV_FINAL : public ResizeLayerImpl
{
public:
InterpLayerImpl(const LayerParams& params) : ResizeLayerImpl(params) {}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert_N(inputs.size() == 1, inputs[0].size() == 4);
outputs.resize(1, inputs[0]);
outputs[0][2] = zoomFactorHeight > 0 ? (1 + zoomFactorHeight * (outputs[0][2] - 1)) : outHeight;
outputs[0][3] = zoomFactorWidth > 0 ? (1 + zoomFactorWidth * (outputs[0][3] - 1)) : outWidth;
// We can work in-place (do nothing) if input shape == output shape.
return (outputs[0][2] == inputs[0][2]) && (outputs[0][3] == inputs[0][3]);
}
};
Ptr<Layer> InterpLayer::create(const LayerParams& params)
{
LayerParams lp(params);
lp.set("interpolation", "bilinear");
lp.set("align_corners", true);
return Ptr<Layer>(new InterpLayerImpl(lp));
}
} // namespace dnn
} // namespace cv
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