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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
2021-10-15 13:35:03 +00:00
parent 39c3334147 bd0732b1d0
commit 31c40fa4cc
8 changed files with 154 additions and 32 deletions
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modules/dnn/src/onnx/onnx_importer.cpp
+58 -23
View File
@@ -58,7 +58,6 @@ class ONNXImporter
Mat getBlob(const std::string& input_name);
LayerParams getLayerParams(const opencv_onnx::NodeProto& node_proto);
bool isCeilMode(const LayerParams& layerParams);
void addConstant(const std::string& name, const Mat& blob);
void addLayer(LayerParams& layerParams,
@@ -68,6 +67,7 @@ class ONNXImporter
void expandMid(const std::string& prefix, opencv_onnx::NodeProto& node_proto,
const std::string& input, size_t n);
void addNegation(const LayerParams& layerParams, opencv_onnx::NodeProto& node_proto, int input_id);
public:
ONNXImporter(Net& net, const char *onnxFile);
ONNXImporter(Net& net, const char* buffer, size_t sizeBuffer);
@@ -530,6 +530,32 @@ void ONNXImporter::expandMid(const std::string& prefix, opencv_onnx::NodeProto&
}
}
/** @brief Multiply one of node_proto inputs by -1
* @param layerParams parameters of the node
* @param node_proto node which input will be replaced
* @param input_id id of input to be multiplied by -1
*/
void ONNXImporter::addNegation(const LayerParams& layerParams, opencv_onnx::NodeProto& node_proto, int input_id)
{
LayerParams powerParams;
powerParams.name = layerParams.name + "/neg";
powerParams.type = "Power";
powerParams.set("scale", -1.f);
//Create Power layer
int id = dstNet.addLayer(powerParams.name, powerParams.type, powerParams);
//Connect to input
IterLayerId_t layerId = layer_id.find(node_proto.input(input_id));
CV_Assert(layerId != layer_id.end());
dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, 0);
//Add shape
layer_id.insert(std::make_pair(powerParams.name, LayerInfo(id, 0)));
outShapes[powerParams.name] = outShapes[node_proto.input(input_id)];
//Replace input to Power
node_proto.set_input(input_id, powerParams.name);
}
void ONNXImporter::addConstant(const std::string& name, const Mat& blob)
{
constBlobs.insert(std::make_pair(name, blob));
@@ -1057,7 +1083,19 @@ void ONNXImporter::parseBias(LayerParams& layerParams, const opencv_onnx::NodePr
opencv_onnx::NodeProto node_proto = node_proto_;
const std::string& layer_type = node_proto.op_type();
bool isSub = layer_type == "Sub";
CV_CheckEQ(node_proto.input_size(), 2, "");
CV_Assert((node_proto.input_size() == 2) || (layer_type == "Sum" && node_proto.input_size() > 2));
if (layer_type == "Sum" && node_proto.input_size() > 2)
{
for (int i = 0; i < node_proto.input_size(); ++i)
{
if (layer_id.find(node_proto.input(i)) == layer_id.end())
{
CV_Error(Error::StsNotImplemented, "Sum of constants is not implemented for inputs > 2");
}
}
}
bool is_const_0 = layer_id.find(node_proto.input(0)) == layer_id.end();
bool is_const_1 = layer_id.find(node_proto.input(1)) == layer_id.end();
if (is_const_0 && is_const_1)
@@ -1072,29 +1110,42 @@ void ONNXImporter::parseBias(LayerParams& layerParams, const opencv_onnx::NodePr
else if (is_const_0 || is_const_1)
{
int const_blob_id = is_const_0 ? 0 : 1;
int input_id = 1 - const_blob_id;
Mat blob = getBlob(node_proto, const_blob_id);
int blob_total = blob.total();
const float inputScale = isSub && is_const_0 ? -1.f : 1.f;
const float constScale = isSub && is_const_1 ? -1.f : 1.f;
if (blob_total == 1) {
layerParams.type = "Power";
layerParams.set("shift", (isSub ? -1 : 1) * blob.ptr<float>()[0]);
layerParams.set("scale", inputScale);
layerParams.set("shift", constScale * blob.ptr<float>()[0]);
}
else {
MatShape inpShape = outShapes[node_proto.input(1 - const_blob_id)];
MatShape inpShape = outShapes[node_proto.input(input_id)];
if (shape(blob) == inpShape)
{
LayerParams constParams;
constParams.name = layerParams.name + "/const";
constParams.type = "Const";
constParams.blobs.push_back((isSub ? -1 : 1) * blob);
constParams.blobs.push_back(blob);
int id = dstNet.addLayer(constParams.name, constParams.type, constParams);
layer_id.insert(std::make_pair(constParams.name, LayerInfo(id, 0)));
outShapes[constParams.name] = shape(blob);
layerParams.type = "Eltwise";
float coeffs[] = {1., isSub ? -1.f : 1.f};
layerParams.set("coeff", DictValue::arrayReal<float*>(coeffs, 2));
node_proto.set_input(const_blob_id, constParams.name);
}
else
{
if (inputScale < 0.f)
{
addNegation(layerParams, node_proto, input_id);
}
layerParams.type = "Scale";
layerParams.set("bias_term", true);
int axis = 1;
@@ -1109,7 +1160,7 @@ void ONNXImporter::parseBias(LayerParams& layerParams, const opencv_onnx::NodePr
}
layerParams.set("axis", axis);
blob = blob.reshape(1, 1);
layerParams.blobs.push_back((isSub ? -1 : 1) * blob);
layerParams.blobs.push_back(constScale * blob);
}
}
}
@@ -1126,23 +1177,7 @@ void ONNXImporter::parseBias(LayerParams& layerParams, const opencv_onnx::NodePr
{
if (isSub)
{
LayerParams powerParams;
powerParams.name = layerParams.name + "/neg";
powerParams.type = "Power";
powerParams.set("scale", -1);
//Create Power layer
int id = dstNet.addLayer(powerParams.name, powerParams.type, powerParams);
//Connect to input
IterLayerId_t layerId = layer_id.find(node_proto.input(1));
CV_Assert(layerId != layer_id.end());
dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, 0);
//Add shape
layer_id.insert(std::make_pair(powerParams.name, LayerInfo(id, 0)));
outShapes[powerParams.name] = outShapes[node_proto.input(1)];
//Replace input to Power
node_proto.set_input(1, powerParams.name);
addNegation(layerParams, node_proto, 1);
}
layerParams.type = "Scale";
layerParams.set("bias_term", true);
modules/dnn/test/test_onnx_importer.cpp
+8
View File
@@ -991,6 +991,14 @@ TEST_P(Test_ONNX_layers, ConvResizePool1d)
testONNXModels("conv_resize_pool_1d");
}
TEST_P(Test_ONNX_layers, SubFromConst)
{
testONNXModels("sub_from_const1");
testONNXModels("sub_from_const_eltwise");
testONNXModels("sub_from_const_broadcast");
}
TEST_P(Test_ONNX_layers, Quantized_Convolution)
{
testONNXModels("quantized_conv_uint8_weights", npy, 0.004, 0.02);