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Fix dnn::ResizeLayer to manage varying input shapes

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pull/16490/head
Dmitry Kurtaev 5 years ago
parent 4d291c06f6
commit 005f38fb45
2 changed files with 45 additions and 77 deletions
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  1. 91
      modules/dnn/src/layers/resize_layer.cpp
  2. 31
      modules/dnn/test/test_layers.cpp

91
modules/dnn/src/layers/resize_layer.cpp
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@ -19,7 +19,9 @@ namespace cv { namespace dnn {
class ResizeLayerImpl : public ResizeLayer
{
public:
ResizeLayerImpl(const LayerParams& params) : zoomFactorWidth(0), zoomFactorHeight(0), scaleWidth(0), scaleHeight(0)
ResizeLayerImpl(const LayerParams& params) : zoomFactorWidth(params.get<int>("zoom_factor_x", params.get<int>("zoom_factor", 0))),
zoomFactorHeight(params.get<int>("zoom_factor_y", params.get<int>("zoom_factor", 0))),
scaleWidth(0), scaleHeight(0)
{
setParamsFrom(params);
outWidth = params.get<float>("width", 0);
@ -27,13 +29,10 @@ public:
if (params.has("zoom_factor"))
{
CV_Assert(!params.has("zoom_factor_x") && !params.has("zoom_factor_y"));
zoomFactorWidth = zoomFactorHeight = params.get<int>("zoom_factor");
}
else if (params.has("zoom_factor_x") || params.has("zoom_factor_y"))
{
CV_Assert(params.has("zoom_factor_x") && params.has("zoom_factor_y"));
zoomFactorWidth = params.get<int>("zoom_factor_x");
zoomFactorHeight = params.get<int>("zoom_factor_y");
}
interpolation = params.get<String>("interpolation");
CV_Assert(interpolation == "nearest" || interpolation == "bilinear");
@ -48,8 +47,8 @@ public:
{
CV_Assert_N(inputs.size() == 1, inputs[0].size() == 4);
outputs.resize(1, inputs[0]);
outputs[0][2] = outHeight > 0 ? outHeight : (outputs[0][2] * zoomFactorHeight);
outputs[0][3] = outWidth > 0 ? outWidth : (outputs[0][3] * zoomFactorWidth);
outputs[0][2] = zoomFactorHeight > 0 ? (outputs[0][2] * zoomFactorHeight) : outHeight;
outputs[0][3] = zoomFactorWidth > 0 ? (outputs[0][3] * zoomFactorWidth) : 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]);
}
@ -73,11 +72,8 @@ public:
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
if (!outWidth && !outHeight)
{
outHeight = outputs[0].size[2];
outWidth = outputs[0].size[3];
}
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
@ -184,7 +180,7 @@ public:
ieLayer.setType("Interp");
ieLayer.getParameters()["pad_beg"] = 0;
ieLayer.getParameters()["pad_end"] = 0;
ieLayer.getParameters()["align_corners"] = false;
ieLayer.getParameters()["align_corners"] = alignCorners;
}
else
CV_Error(Error::StsNotImplemented, "Unsupported interpolation: " + interpolation);
@ -208,7 +204,7 @@ public:
attrs.pads_begin.push_back(0);
attrs.pads_end.push_back(0);
attrs.axes = ngraph::AxisSet{2, 3};
attrs.align_corners = false;
attrs.align_corners = alignCorners;
if (interpolation == "nearest") {
attrs.mode = "nearest";
@ -227,7 +223,8 @@ public:
#endif // HAVE_DNN_NGRAPH
protected:
int outWidth, outHeight, zoomFactorWidth, zoomFactorHeight;
int outWidth, outHeight;
const int zoomFactorWidth, zoomFactorHeight;
String interpolation;
float scaleWidth, scaleHeight;
bool alignCorners;
@ -251,78 +248,18 @@ public:
{
CV_Assert_N(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));
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]);
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
|| backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
#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);
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;
}
#ifdef HAVE_INF_ENGINE
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::Layer ieLayer(name);
ieLayer.setName(name);
ieLayer.setType("Interp");
ieLayer.getParameters()["pad_beg"] = 0;
ieLayer.getParameters()["pad_end"] = 0;
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_INF_ENGINE
#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;
ngraph::op::InterpolateAttrs attrs;
attrs.pads_begin.push_back(0);
attrs.pads_end.push_back(0);
attrs.axes = ngraph::AxisSet{2, 3};
attrs.mode = "linear";
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);
return Ptr<BackendNode>(new InfEngineNgraphNode(interp));
}
#endif // HAVE_DNN_NGRAPH
};
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));
}

31
modules/dnn/test/test_layers.cpp
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@ -1715,4 +1715,35 @@ INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_Eltwise_unequal, Combine(
dnnBackendsAndTargets()
));
typedef testing::TestWithParam<tuple<Backend, Target> > Layer_Test_Resize;
TEST_P(Layer_Test_Resize, change_input)
{
int backendId = get<0>(GetParam());
int targetId = get<1>(GetParam());
Net net;
LayerParams lp;
lp.type = "Resize";
lp.name = "testLayer";
lp.set("zoom_factor", 2);
lp.set("interpolation", "nearest");
net.addLayerToPrev(lp.name, lp.type, lp);
for (int i = 0; i < 2; ++i)
{
Mat inp(4 + i, 5 + i, CV_8UC3), ref;
randu(inp, 0, 255);
resize(inp, ref, Size(0, 0), 2, 2, INTER_NEAREST);
ref = blobFromImage(ref);
net.setInput(blobFromImage(inp));
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat out = net.forward();
normAssert(out, ref);
}
}
INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_Resize, dnnBackendsAndTargets());
}} // namespace

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