Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

Merge pull request #18096 from l-bat:update_onnx_importer

Browse Source 
* Added ReduceSum to ONNX importer

* Fix comments

* Fix Mul
pull/18211/head
Liubov Batanina 5 years ago committed by GitHub
parent 3b5813c035
commit ad63d24dba
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 124 additions and 34 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 1
      modules/dnn/src/layers/fully_connected_layer.cpp
  2. 19
      modules/dnn/src/onnx/onnx_graph_simplifier.cpp
  3. 132
      modules/dnn/src/onnx/onnx_importer.cpp
  4. 6
      modules/dnn/test/test_onnx_importer.cpp

1
modules/dnn/src/layers/fully_connected_layer.cpp
Unescape View File

@ -116,7 +116,6 @@ public:
CV_CheckEQ(inputs.size(), (size_t)2, "");
numOutput = inputs[1].back();
cAxis = inputs[0].size() - 1;
CV_CheckEQ(numOutput, inputs[0][cAxis - 1], "");
int dims = inputs[0].size();
CV_CheckEQ(inputs[1].size(), (size_t)dims, "");
CV_CheckGE(dims, 2, "");

19
modules/dnn/src/onnx/onnx_graph_simplifier.cpp
Unescape View File

@ -262,6 +262,24 @@ public:
}
};
class ExpandSubgraph : public Subgraph
{
public:
ExpandSubgraph()
{
int input = addNodeToMatch("");
int values = addNodeToMatch("");
int init = addNodeToMatch("ConstantOfShape", values);
int coeff = addNodeToMatch("Constant");
int mul = addNodeToMatch("Mul", init, coeff);
int shape = addNodeToMatch("Constant");
int condition = addNodeToMatch("Equal", shape, mul);
int where = addNodeToMatch("Where", condition, init, addNodeToMatch("Constant"));
addNodeToMatch("Expand", input, where);
setFusedNode("Expand", input, shape);
}
};
class MulCastSubgraph : public Subgraph
{
public:
@ -459,6 +477,7 @@ void simplifySubgraphs(opencv_onnx::GraphProto& net)
subgraphs.push_back(makePtr<NormalizeSubgraph3>());
subgraphs.push_back(makePtr<BatchNormalizationSubgraph1>());
subgraphs.push_back(makePtr<BatchNormalizationSubgraph2>());
subgraphs.push_back(makePtr<ExpandSubgraph>());
simplifySubgraphs(Ptr<ImportGraphWrapper>(new ONNXGraphWrapper(net)), subgraphs);
}

132
modules/dnn/src/onnx/onnx_importer.cpp
Unescape View File

@ -387,26 +387,42 @@ void ONNXImporter::populateNet(Net dstNet)
layerParams.set("ceil_mode", layerParams.has("pad_mode"));
layerParams.set("ave_pool_padded_area", framework_name == "pytorch");
}
else if (layer_type == "GlobalAveragePool" || layer_type == "GlobalMaxPool" || layer_type == "ReduceMean")
else if (layer_type == "GlobalAveragePool" || layer_type == "GlobalMaxPool" ||
layer_type == "ReduceMean" || layer_type == "ReduceSum")
{
CV_Assert(node_proto.input_size() == 1);
layerParams.type = "Pooling";
layerParams.set("pool", layer_type == "GlobalMaxPool"? "MAX" : "AVE");
String pool;
if (layer_type == "GlobalMaxPool")
pool = "MAX";
else if (layer_type == "ReduceSum")
pool = "SUM";
else
pool = "AVE";
layerParams.set("pool", pool);
layerParams.set("global_pooling", layer_type == "GlobalAveragePool" || layer_type == "GlobalMaxPool");
if (layer_type == "ReduceMean")
if (layer_type == "ReduceMean" || layer_type == "ReduceSum")
{
if (layerParams.get<int>("keepdims") == 0 || !layerParams.has("axes"))
CV_Error(Error::StsNotImplemented, "Unsupported mode of ReduceMean operation.");
if (!layerParams.has("axes"))
CV_Error(Error::StsNotImplemented, "Unsupported mode of " + layer_type + " operation.");
MatShape inpShape = outShapes[node_proto.input(0)];
DictValue axes = layerParams.get("axes");
bool keepdims = layerParams.get<int>("keepdims");
MatShape targetShape = inpShape;
for (int i = 0; i < axes.size(); i++) {
int axis = clamp(axes.get<int>(i), inpShape.size());
if (keepdims) {
targetShape[axis] = 1;
} else {
targetShape.erase(targetShape.begin() + axis);
}
}
if (inpShape.size() == 3 && axes.size() <= 2)
{
int axis = axes.get<int>(0);
int axis = clamp(axes.get<int>(0), inpShape.size());
CV_CheckNE(axis, 0, "");
outShapes[layerParams.name] = inpShape;
outShapes[layerParams.name][axis] = 1;
LayerParams reshapeLp;
reshapeLp.name = layerParams.name + "/reshape";
@ -426,13 +442,12 @@ void ONNXImporter::populateNet(Net dstNet)
avgLp.name = layerParams.name + "/avg";
avgLp.type = "Pooling";
CV_Assert(layer_id.find(avgLp.name) == layer_id.end());
avgLp.set("pool", "ave");
avgLp.set("pool", pool);
if (axes.size() == 2)
{
CV_CheckEQ(axes.get<int>(0), 1, "Unsupported ReduceMean mode");
CV_CheckEQ(axes.get<int>(1), 2, "Unsupported ReduceMean mode");
CV_CheckEQ(clamp(axes.get<int>(0), inpShape.size()), 1, ("Unsupported " + layer_type + " mode").c_str());
CV_CheckEQ(clamp(axes.get<int>(1), inpShape.size()), 2, ("Unsupported " + layer_type + " mode").c_str());
avgLp.set("global_pooling", true);
outShapes[layerParams.name][axes.get<int>(1)] = 1;
}
else
{
@ -443,28 +458,33 @@ void ONNXImporter::populateNet(Net dstNet)
node_proto.set_input(0, reshapeLp.name);
node_proto.set_output(0, avgLp.name);
addLayer(dstNet, avgLp, node_proto, layer_id, outShapes);
layerParams.type = "Flatten";
layerParams.set("axis", 0);
layerParams.set("end_axis", 1);
node_proto.set_input(0, avgLp.name);
node_proto.set_output(0, layerParams.name);
}
else
{
if (inpShape.size() != 4 && inpShape.size() != 5)
CV_Error(Error::StsNotImplemented, "Unsupported input shape of reduce_mean operation.");
CV_Error(Error::StsNotImplemented, "Unsupported input shape of " + layer_type + " operation.");
CV_Assert(axes.size() <= inpShape.size() - 2);
std::vector<int> kernel_size(inpShape.size() - 2, 1);
for (int i = 0; i < axes.size(); i++) {
int axis = axes.get<int>(i);
int axis = clamp(axes.get<int>(i), inpShape.size());
CV_Assert_N(axis >= 2 + i, axis < inpShape.size());
kernel_size[axis - 2] = inpShape[axis];
}
layerParams.set("kernel_size", DictValue::arrayInt(&kernel_size[0], kernel_size.size()));
LayerParams poolLp = layerParams;
poolLp.name = layerParams.name + "/avg";
CV_Assert(layer_id.find(poolLp.name) == layer_id.end());
poolLp.set("kernel_size", DictValue::arrayInt(&kernel_size[0], kernel_size.size()));
node_proto.set_output(0, poolLp.name);
addLayer(dstNet, poolLp, node_proto, layer_id, outShapes);
}
layerParams.type = "Reshape";
layerParams.set("dim", DictValue::arrayInt(&targetShape[0], targetShape.size()));
node_proto.set_input(0, node_proto.output(0));
node_proto.set_output(0, layerParams.name);
}
}
else if (layer_type == "Slice")
@ -1001,15 +1021,10 @@ void ONNXImporter::populateNet(Net dstNet)
{
Mat inp0 = getBlob(node_proto, constBlobs, 0);
Mat inp1 = getBlob(node_proto, constBlobs, 1);
if (inp0.size != inp1.size)
if (inp0.size != inp1.size && inp1.total() != 1)
CV_Error(Error::StsNotImplemented, "Constant multiply with different shapes");
Mat out;
if (isDiv)
divide(inp0, inp1, out);
else
multiply(inp0, inp1, out);
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, constBlobs, outShapes);
@ -1180,9 +1195,45 @@ void ONNXImporter::populateNet(Net dstNet)
Mat newShapeMat = getBlob(node_proto, constBlobs, 1);
MatShape targetShape(newShapeMat.ptr<int>(), newShapeMat.ptr<int>() + newShapeMat.total());
shapeIt = outShapes.find(node_proto.input(0));
CV_Assert(shapeIt != outShapes.end());
MatShape inpShape = shapeIt->second;
MatShape inpShape;
bool haveVariables = constBlobs.find(node_proto.input(0)) == constBlobs.end();
if (haveVariables)
{
shapeIt = outShapes.find(node_proto.input(0));
CV_Assert(shapeIt != outShapes.end());
inpShape = shapeIt->second;
}
else
{
inpShape = shape(getBlob(node_proto, constBlobs, 0));
}
String srcName = node_proto.input(0);
// Unsqueeze and repeat along new axis
if (targetShape.size() == inpShape.size() + 1)
{
for (int i = 0; i < targetShape.size(); i++)
{
if (targetShape[i] == -1 && i < inpShape.size())
targetShape[i] = inpShape[i];
else if (i < inpShape.size() && targetShape[i] != inpShape[i])
inpShape.insert(inpShape.begin() + i, 1);
}
if (haveVariables)
{
LayerParams reshapeLp;
reshapeLp.name = layerParams.name + "/reshape";
reshapeLp.type = "Reshape";
CV_Assert(layer_id.find(reshapeLp.name) == layer_id.end());
reshapeLp.set("dim", DictValue::arrayInt(&inpShape[0], inpShape.size()));
opencv_onnx::NodeProto proto;
proto.add_input(node_proto.input(0));
proto.add_output(reshapeLp.name);
addLayer(dstNet, reshapeLp, proto, layer_id, outShapes);
srcName = reshapeLp.name;
}
}
CV_CheckEQ(inpShape.size(), targetShape.size(), "Unsupported Expand op with different dims");
std::vector<int> broadcast_axes;
@ -1197,6 +1248,19 @@ void ONNXImporter::populateNet(Net dstNet)
}
}
if (!haveVariables)
{
if (broadcast_axes.size() != 1)
CV_Error(Error::StsNotImplemented, "Expand op doesn't support multiple axes for constant input");
Mat input = getBlob(node_proto, constBlobs, 0);
input = input.reshape(0, total(inpShape, 0, broadcast_axes[0]));
Mat output = cv::repeat(input, 1, targetShape[broadcast_axes[0]]);
output = output.reshape(0, targetShape);
addConstant(layerParams.name, output, constBlobs, outShapes);
continue;
}
if (broadcast_axes.size() == 2 &&
broadcast_axes[0] == broadcast_axes[1] - 1 && broadcast_axes[1] == inpShape.size() - 1)
{
@ -1231,6 +1295,7 @@ void ONNXImporter::populateNet(Net dstNet)
CV_Assert(layer_id.find(copyLP.name) == layer_id.end());
input_names.push_back(copyLP.name);
node_proto.set_input(0, srcName);
node_proto.set_output(0, copyLP.name);
addLayer(dstNet, copyLP, node_proto, layer_id, outShapes);
}
@ -1241,6 +1306,7 @@ void ONNXImporter::populateNet(Net dstNet)
}
layerParams.set("axis", broadcast_axes[0]);
layerParams.type = "Concat";
node_proto.set_output(0, layerParams.name);
}
else
CV_Error(Error::StsNotImplemented, "Unsupported Expand op");

6
modules/dnn/test/test_onnx_importer.cpp
Unescape View File

@ -257,6 +257,11 @@ TEST_P(Test_ONNX_layers, ReduceMean)
testONNXModels("reduce_mean_axis2");
}
TEST_P(Test_ONNX_layers, ReduceSum)
{
testONNXModels("reduce_sum");
}
TEST_P(Test_ONNX_layers, ReduceMean3D)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
@ -417,6 +422,7 @@ TEST_P(Test_ONNX_layers, Expand)
{
testONNXModels("expand_batch");
testONNXModels("expand_channels");
testONNXModels("expand_neg_batch");
}
TEST_P(Test_ONNX_layers, ExpandHW)

Write Preview
Loading…
Cancel
Save