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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 2021-07-30 14:43:17 +00:00
commit 907743eee7
2 changed files with 17 additions and 18 deletions
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2
cmake/OpenCVDetectCUDA.cmake
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@ -112,7 +112,7 @@ if(CUDA_FOUND)
if(CUDA_GENERATION) if(CUDA_GENERATION)
if(NOT ";${_generations};" MATCHES ";${CUDA_GENERATION};") if(NOT ";${_generations};" MATCHES ";${CUDA_GENERATION};")
string(REPLACE ";" ", " _generations "${_generations}") string(REPLACE ";" ", " _generations "${_generations}")
message(FATAL_ERROR "ERROR: ${_generations} Generations are suppered.") message(FATAL_ERROR "ERROR: ${_generations} Generations are supported.")
endif() endif()
unset(CUDA_ARCH_BIN CACHE) unset(CUDA_ARCH_BIN CACHE)
unset(CUDA_ARCH_PTX CACHE) unset(CUDA_ARCH_PTX CACHE)

33
modules/dnn/src/onnx/onnx_importer.cpp
View File

@ -729,7 +729,7 @@ void ONNXImporter::parseAveragePool(LayerParams& layerParams, const opencv_onnx:
addLayer(layerParams, node_proto); addLayer(layerParams, node_proto);
} }
void ONNXImporter::parseReduce(LayerParams &layerParams, const opencv_onnx::NodeProto &node_proto_) void ONNXImporter::parseReduce(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_)
{ {
opencv_onnx::NodeProto node_proto = node_proto_; opencv_onnx::NodeProto node_proto = node_proto_;
const std::string& layer_type = node_proto.op_type(); const std::string& layer_type = node_proto.op_type();
@ -964,9 +964,9 @@ void ONNXImporter::parseSlice(LayerParams& layerParams, const opencv_onnx::NodeP
// Very strange application for Slice op with tensor reversing. // Very strange application for Slice op with tensor reversing.
// We just workaround it for 2d constants. // We just workaround it for 2d constants.
if (constBlobs.find(node_proto.input(0)) != constBlobs.end() && if (constBlobs.find(node_proto.input(0)) != constBlobs.end() &&
axis == 0 && axis == 0 &&
start_blob.at<int>(0) == -1 && step_blob.at<int>(0) == -1 && start_blob.at<int>(0) == -1 && step_blob.at<int>(0) == -1 &&
end_blob.at<int>(0) == std::numeric_limits<int32_t>::min()) end_blob.at<int>(0) == std::numeric_limits<int32_t>::min())
{ {
Mat inp = getBlob(node_proto, 0); Mat inp = getBlob(node_proto, 0);
if (inp.dims == 2) if (inp.dims == 2)
@ -1607,7 +1607,7 @@ void ONNXImporter::parseConv(LayerParams& layerParams, const opencv_onnx::NodePr
} }
} }
if (asymmetricPadding && pads.size() == 4) // [pad_t, pad_l, pad_b, pad_r] if (asymmetricPadding && pads.size() == 4) // [pad_t, pad_l, pad_b, pad_r]
{ {
layerParams.erase("pad"); layerParams.erase("pad");
// No paddings required for N, C axis // No paddings required for N, C axis
std::vector<int> paddings(4, 0); std::vector<int> paddings(4, 0);
@ -1628,7 +1628,7 @@ void ONNXImporter::parseConv(LayerParams& layerParams, const opencv_onnx::NodePr
addLayer(padLp, proto); addLayer(padLp, proto);
node_proto.set_input(0, padLp.name); node_proto.set_input(0, padLp.name);
} }
} }
addLayer(layerParams, node_proto); addLayer(layerParams, node_proto);
} }
@ -1666,7 +1666,7 @@ void ONNXImporter::parseConvTranspose(LayerParams& layerParams, const opencv_onn
int sz = outShape.get<int>(2 + i); int sz = outShape.get<int>(2 + i);
int stride = strides.get<int>(i); int stride = strides.get<int>(i);
adjust_pads.push_back(padMode == "SAME"? (sz - 1) % stride : adjust_pads.push_back(padMode == "SAME"? (sz - 1) % stride :
(sz - kernel.get<int>(i)) % stride); (sz - kernel.get<int>(i)) % stride);
} }
layerParams.set("adj", DictValue::arrayInt(&adjust_pads[0], adjust_pads.size())); layerParams.set("adj", DictValue::arrayInt(&adjust_pads[0], adjust_pads.size()));
} }
@ -1890,7 +1890,7 @@ void ONNXImporter::parseExpand(LayerParams& layerParams, const opencv_onnx::Node
} }
if (broadcast_axes.size() == 2 && if (broadcast_axes.size() == 2 &&
broadcast_axes[0] == broadcast_axes[1] - 1 && broadcast_axes[1] == inpShape.size() - 1) broadcast_axes[0] == broadcast_axes[1] - 1 && broadcast_axes[1] == inpShape.size() - 1)
{ {
LayerParams constParams; LayerParams constParams;
constParams.name = layerParams.name + "/const"; constParams.name = layerParams.name + "/const";
@ -1949,8 +1949,7 @@ void ONNXImporter::parseReshape(LayerParams& layerParams, const opencv_onnx::Nod
Mat blob = getBlob(node_proto, 1); Mat blob = getBlob(node_proto, 1);
CV_Assert(blob.type() == CV_32SC1); CV_Assert(blob.type() == CV_32SC1);
layerParams.set("dim", DictValue::arrayInt<int*>( layerParams.set("dim", DictValue::arrayInt<int*>(blob.ptr<int>(), blob.total()));
blob.ptr<int>(), blob.total() ));
if (layer_id.find(node_proto.input(0)) == layer_id.end()) { if (layer_id.find(node_proto.input(0)) == layer_id.end()) {
std::vector<Mat> inputs(1, getBlob(node_proto, 0)), outputs; std::vector<Mat> inputs(1, getBlob(node_proto, 0)), outputs;
@ -2026,10 +2025,10 @@ void ONNXImporter::parseCast(LayerParams& layerParams, const opencv_onnx::NodePr
case opencv_onnx::TensorProto_DataType_UINT16: type = CV_16U; break; case opencv_onnx::TensorProto_DataType_UINT16: type = CV_16U; break;
case opencv_onnx::TensorProto_DataType_FLOAT16: type = CV_16S; break; case opencv_onnx::TensorProto_DataType_FLOAT16: type = CV_16S; break;
case opencv_onnx::TensorProto_DataType_INT8: case opencv_onnx::TensorProto_DataType_INT8:
case opencv_onnx::TensorProto_DataType_INT16: case opencv_onnx::TensorProto_DataType_INT16:
case opencv_onnx::TensorProto_DataType_INT32: case opencv_onnx::TensorProto_DataType_INT32:
case opencv_onnx::TensorProto_DataType_INT64: type = CV_32S; break; case opencv_onnx::TensorProto_DataType_INT64: type = CV_32S; break;
default: type = blob.type(); default: type = blob.type();
} }
Mat dst; Mat dst;
blob.convertTo(dst, type); blob.convertTo(dst, type);
@ -2220,7 +2219,7 @@ void ONNXImporter::parseResize(LayerParams& layerParams, const opencv_onnx::Node
if (layerParams.get<String>("mode") == "linear") if (layerParams.get<String>("mode") == "linear")
{ {
layerParams.set("mode", interp_mode == "pytorch_half_pixel" ? layerParams.set("mode", interp_mode == "pytorch_half_pixel" ?
"opencv_linear" : "bilinear"); "opencv_linear" : "bilinear");
} }
} }
if (layerParams.get<String>("mode") == "linear" && framework_name == "pytorch") if (layerParams.get<String>("mode") == "linear" && framework_name == "pytorch")
@ -2228,7 +2227,7 @@ void ONNXImporter::parseResize(LayerParams& layerParams, const opencv_onnx::Node
// input = [X, scales], [X, roi, scales] or [x, roi, scales, sizes] // input = [X, scales], [X, roi, scales] or [x, roi, scales, sizes]
int foundScaleId = hasDynamicShapes ? node_proto.input_size() - 1 int foundScaleId = hasDynamicShapes ? node_proto.input_size() - 1
: node_proto.input_size() > 2 ? 2 : 1; : node_proto.input_size() > 2 ? 2 : 1;
Mat scales = getBlob(node_proto, foundScaleId); Mat scales = getBlob(node_proto, foundScaleId);
if (scales.total() == 4) if (scales.total() == 4)
@ -2267,7 +2266,7 @@ void ONNXImporter::parseUpsample(LayerParams& layerParams, const opencv_onnx::No
if (layerParams.get<String>("mode") == "linear") if (layerParams.get<String>("mode") == "linear")
{ {
layerParams.set("mode", interp_mode == "pytorch_half_pixel" ? layerParams.set("mode", interp_mode == "pytorch_half_pixel" ?
"opencv_linear" : "bilinear"); "opencv_linear" : "bilinear");
} }
} }
if (layerParams.get<String>("mode") == "linear" && framework_name == "pytorch") if (layerParams.get<String>("mode") == "linear" && framework_name == "pytorch")