mirror/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2025-06-07 17:44:04 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Implement Interp layer using Resize layer

Browse Source
This commit is contained in:
Dmitry Kurtaev 2018-06-22 19:26:47 +03:00
parent 1894f1a37f
commit e8e9d1d021
4 changed files with 65 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

11
modules/dnn/include/opencv2/dnn/all_layers.hpp
View File

@ -592,6 +592,17 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
static Ptr<ResizeLayer> create(const LayerParams& params);
};
/**
* @brief Bilinear resize layer from https://github.com/cdmh/deeplab-public
*
* It differs from @ref ResizeLayer in output shape and resize scales computations.
*/
class CV_EXPORTS InterpLayer : public Layer
{
public:
static Ptr<Layer> create(const LayerParams& params);
};
class CV_EXPORTS ProposalLayer : public Layer
{
public:

1
modules/dnn/src/init.cpp
View File

@ -84,6 +84,7 @@ void initializeLayerFactory()
CV_DNN_REGISTER_LAYER_CLASS(Reshape, ReshapeLayer);
CV_DNN_REGISTER_LAYER_CLASS(Flatten, FlattenLayer);
CV_DNN_REGISTER_LAYER_CLASS(Resize, ResizeLayer);
CV_DNN_REGISTER_LAYER_CLASS(Interp, InterpLayer);
CV_DNN_REGISTER_LAYER_CLASS(CropAndResize, CropAndResizeLayer);
CV_DNN_REGISTER_LAYER_CLASS(Convolution, ConvolutionLayer);

55
modules/dnn/src/layers/resize_layer.cpp
View File

@ -11,7 +11,7 @@
namespace cv { namespace dnn {
class ResizeLayerImpl CV_FINAL : public ResizeLayer
class ResizeLayerImpl : public ResizeLayer
{
public:
ResizeLayerImpl(const LayerParams& params)
@ -33,7 +33,7 @@ public:
interpolation = params.get<String>("interpolation");
CV_Assert(interpolation == "nearest" || interpolation == "bilinear");
alignCorners = params.get<bool>("align_corners", false);
bool alignCorners = params.get<bool>("align_corners", false);
if (alignCorners)
CV_Error(Error::StsNotImplemented, "Resize with align_corners=true is not implemented");
}
@ -66,6 +66,8 @@ public:
outHeight = outputs[0].size[2];
outWidth = outputs[0].size[3];
}
scaleHeight = static_cast<float>(inputs[0]->size[2]) / outHeight;
scaleWidth = static_cast<float>(inputs[0]->size[3]) / outWidth;
}
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
@ -103,8 +105,6 @@ public:
const int inpWidth = inp.size[3];
const int inpSpatialSize = inpHeight * inpWidth;
const int outSpatialSize = outHeight * outWidth;
const float heightScale = static_cast<float>(inpHeight) / (outHeight);
const float widthScale = static_cast<float>(inpWidth) / (outWidth);
const int numPlanes = inp.size[0] * inp.size[1];
CV_Assert(inp.isContinuous(), out.isContinuous());
@ -112,13 +112,13 @@ public:
Mat outPlanes = out.reshape(1, numPlanes * outHeight);
for (int y = 0; y < outHeight; ++y)
{
float input_y = y * heightScale;
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 * widthScale;
float input_x = x * scaleWidth;
int x0 = static_cast<int>(input_x);
int x1 = std::min(x0 + 1, inpWidth - 1);
@ -162,10 +162,10 @@ public:
return Ptr<BackendNode>();
}
private:
protected:
int outWidth, outHeight, zoomFactorWidth, zoomFactorHeight;
String interpolation;
bool alignCorners;
float scaleWidth, scaleHeight;
};
@ -174,5 +174,44 @@ 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(inputs.size() == 1, inputs[0].size() == 4);
outputs.resize(1, inputs[0]);
outputs[0][2] = outHeight > 0 ? outHeight : (1 + zoomFactorHeight * (outputs[0][2] - 1));
outputs[0][3] = outWidth > 0 ? outWidth : (1 + zoomFactorWidth * (outputs[0][3] - 1));
// 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 void finalize(const std::vector<Mat*>& inputs, std::vector<Mat> &outputs) CV_OVERRIDE
{
if (!outWidth && !outHeight)
{
outHeight = outputs[0].size[2];
outWidth = outputs[0].size[3];
}
int inpHeight = inputs[0]->size[2];
int inpWidth = inputs[0]->size[3];
scaleHeight = (outHeight > 1) ? (static_cast<float>(inpHeight - 1) / (outHeight - 1)) : 0.f;
scaleWidth = (outWidth > 1) ? (static_cast<float>(inpWidth - 1) / (outWidth - 1)) : 0.f;
}
};
Ptr<Layer> InterpLayer::create(const LayerParams& params)
{
LayerParams lp(params);
lp.set("interpolation", "bilinear");
return Ptr<Layer>(new InterpLayerImpl(lp));
}
} // namespace dnn
} // namespace cv

7
modules/dnn/test/test_layers.cpp
View File

@ -1137,13 +1137,18 @@ private:
int outWidth, outHeight, zoomFactor;
};
TEST(Layer_Test_Interp, Accuracy)
TEST(Layer_Test_Interp_custom, Accuracy)
{
CV_DNN_REGISTER_LAYER_CLASS(Interp, InterpLayer);
testLayerUsingCaffeModels("layer_interp", DNN_TARGET_CPU, false, false);
LayerFactory::unregisterLayer("Interp");
}
TEST(Layer_Test_Interp, Accuracy)
{
testLayerUsingCaffeModels("layer_interp", DNN_TARGET_CPU, false, false);
}
TEST(Layer_Test_PoolingIndices, Accuracy)
{
Net net;