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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

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This commit is contained in:
Alexander Alekhin
2021-01-25 22:34:41 +00:00
parent f54f6627c3 573812d0ff
commit e85b41f9be
9 changed files with 419 additions and 189 deletions
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modules/dnn/misc/python/test/test_dnn.py
+1 -1
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@@ -177,7 +177,7 @@ class dnn_test(NewOpenCVTests):
cv.rectangle(frame, box, (0, 255, 0))
cv.rectangle(frame, np.array(box), (0, 255, 0))
cv.rectangle(frame, tuple(box), (0, 255, 0))
# FIXIT never properly work: cv.rectangle(frame, list(box), (0, 255, 0))
cv.rectangle(frame, list(box), (0, 255, 0))
def test_classification_model(self):
modules/dnn/src/onnx/onnx_importer.cpp
+64 -14
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@@ -1162,6 +1162,53 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_)
layerParams.type = "Scale";
}
}
else if (!haveVariables)
{
Mat inp0 = getBlob(node_proto, 0);
Mat inp1 = getBlob(node_proto, 1);
if (inp0.size != inp1.size && (inp0.total() != 1 || inp1.total() != 1))
CV_Error_(Error::StsNotImplemented, ("Different shapes case is not supported with constant inputs: %s", layer_type.c_str()));
if (inp0.total() == 1 && inp1.total() == 1 && inp0.dims != inp1.dims)
{
if (inp0.dims < inp1.dims)
{
inp0 = inp0.reshape(1, inp1.dims, inp1.size);
inp0.dims = inp1.dims;
}
else
{
inp1 = inp1.reshape(1, inp0.dims, inp0.size);
inp1.dims = inp0.dims;
}
}
Mat out;
if (inp0.total() != inp1.total())
{
if (inp0.total() == 1)
{
float coeff = isDiv ? 1.0 / inp0.at<float>(0) : inp0.at<float>(0);
multiply(inp1, coeff, out);
}
else
{
float coeff = isDiv ? 1.0 / inp1.at<float>(0) : inp1.at<float>(0);
multiply(inp0, coeff, out);
}
}
else
{
out = isDiv ? inp0 / inp1 : inp0.mul(inp1);
}
if (inp0.dims == 1 && inp1.dims == 1)
out.dims = 1; // to workaround dims == 1
addConstant(layerParams.name, out);
return;
}
else if (outShapes[node_proto.input(0)] == outShapes[node_proto.input(1)])
{
layerParams.type = "Eltwise";
@@ -1201,20 +1248,6 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_)
}
layerParams.type = "Scale";
}
if (!haveVariables)
{
Mat inp0 = getBlob(node_proto, 0);
Mat inp1 = getBlob(node_proto, 1);
if (inp0.size != inp1.size && inp1.total() != 1)
CV_Error(Error::StsNotImplemented, "Constant multiply with different shapes");
Mat out = isDiv ? inp0 / inp1 : inp0.mul(inp1);
out = out.reshape(1, inp0.dims, inp0.size);
out.dims = inp0.dims; // to workaround dims == 1
addConstant(layerParams.name, out);
return;
}
}
else if (layer_type == "Conv")
{
@@ -1733,9 +1766,26 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_)
if (!hasVariableInps)
{
std::vector<Mat> inputs(node_proto.input_size()), concatenated;
// Due constant folding we can get inputs with different number of dimensions
// Insert the missing dimension to inputs
MatShape inputShape;
for (size_t i = 0; i < inputs.size(); ++i)
{
inputs[i] = getBlob(node_proto, i);
if (inputs[i].size.dims() > inputShape.size())
{
inputShape = shape(inputs[i]);
}
}
// Concat-1 has default value for axis is 1: https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Concat-1
int axis = layerParams.get<int>("axis", 1);
for (size_t i = 0; i < inputs.size(); ++i)
{
MatShape targetShape = inputShape;
targetShape[axis] = shape(inputs[i])[axis];
CV_CheckEQ(total(targetShape), total(shape(inputs[i])), "");
inputs[i] = inputs[i].reshape(0, targetShape);
}
runLayer(layerParams, inputs, concatenated);
modules/dnn/src/tensorflow/tf_importer.cpp
+12 -2
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@@ -1228,8 +1228,18 @@ void TFImporter::parseNode(const tensorflow::NodeDef& layer_)
int kernel_blob_index = -1;
const tensorflow::TensorProto& kernelTensor = getConstBlob(layer, value_id, -1, &kernel_blob_index);
blobFromTensor(kernelTensor, layerParams.blobs[0]);
releaseTensor(const_cast<tensorflow::TensorProto*>(&kernelTensor));
const String kernelTensorName = layer.input(kernel_blob_index);
std::map<String, Mat>::iterator sharedWeightsIt = sharedWeights.find(kernelTensorName);
if (sharedWeightsIt == sharedWeights.end())
{
blobFromTensor(kernelTensor, layerParams.blobs[0]);
releaseTensor(const_cast<tensorflow::TensorProto*>(&kernelTensor));
sharedWeights[kernelTensorName] = layerParams.blobs[0];
}
else
{
layerParams.blobs[0] = sharedWeightsIt->second;
}
if (kernel_blob_index == 1) { // In this case output is computed by x*W formula - W should be transposed
Mat data = layerParams.blobs[0].t();
modules/dnn/test/test_onnx_importer.cpp
+5
View File
@@ -706,6 +706,11 @@ TEST_P(Test_ONNX_layers, Mish)
testONNXModels("mish");
}
TEST_P(Test_ONNX_layers, CalculatePads)
{
testONNXModels("calc_pads");
}
TEST_P(Test_ONNX_layers, Conv1d)
{
testONNXModels("conv1d");