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Merge pull request #16715 from l-bat:slice_onnx

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* Support Slice layer with multiple inputs

* Add test

* Supported Resize from PyTorch

* Rewrite test

* Remove Cast layer (supported in #16735)

* Support ConstantOfShape

* Fix tests

* Fix coments

* Remove useless condition

* Fixed failed tests
This commit is contained in:
Liubov Batanina 2020-03-17 17:31:01 +03:00 committed by GitHub
parent 1d7bfcc958
commit 718d7e4b04
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2 changed files with 174 additions and 43 deletions
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193
modules/dnn/src/onnx/onnx_importer.cpp
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@ -367,45 +367,97 @@ void ONNXImporter::populateNet(Net dstNet)
}
else if (layer_type == "Slice")
{
if (layerParams.has("steps")) {
DictValue steps = layerParams.get("steps");
for (int i = 0; i < steps.size(); ++i) {
if (steps.get<int>(i) != 1)
CV_Error(Error::StsNotImplemented,
"Slice layer only supports steps = 1");
}
}
int axis = 0;
if (layerParams.has("axes")) {
DictValue axes = layerParams.get("axes");
for (int i = 1; i < axes.size(); ++i) {
CV_Assert(axes.get<int>(i - 1) == axes.get<int>(i) - 1);
}
axis = axes.get<int>(0);
}
layerParams.set("axis", axis);
DictValue starts = layerParams.get("starts");
DictValue ends = layerParams.get("ends");
CV_Assert(starts.size() == ends.size());
std::vector<int> begin;
std::vector<int> end;
if (axis > 0) {
begin.resize(axis, 0);
end.resize(axis, -1);
}
int inp_size = node_proto.input_size();
for (int i = 0; i < starts.size(); ++i)
if (inp_size == 1)
{
begin.push_back(starts.get<int>(i));
int finish = ends.get<int>(i);
end.push_back((finish < 0) ? --finish : finish); // numpy doesn't include last dim
if (layerParams.has("steps"))
{
DictValue steps = layerParams.get("steps");
for (int i = 0; i < steps.size(); ++i)
{
if (steps.get<int>(i) != 1)
CV_Error(Error::StsNotImplemented,
"Slice layer only supports steps = 1");
}
}
if (layerParams.has("axes")) {
DictValue axes = layerParams.get("axes");
for (int i = 1; i < axes.size(); ++i) {
CV_Assert(axes.get<int>(i - 1) == axes.get<int>(i) - 1);
}
axis = axes.get<int>(0);
}
DictValue starts = layerParams.get("starts");
DictValue ends = layerParams.get("ends");
CV_Assert(starts.size() == ends.size());
if (axis > 0) {
begin.resize(axis, 0);
end.resize(axis, -1);
}
for (int i = 0; i < starts.size(); ++i)
{
begin.push_back(starts.get<int>(i));
int finish = ends.get<int>(i);
end.push_back((finish < 0) ? --finish : finish); // numpy doesn't include last dim
}
} else {
CV_Assert(inp_size >= 3);
for (int i = 1; i < inp_size; i++) {
CV_Assert(constBlobs.find(node_proto.input(i)) != constBlobs.end());
}
Mat start_blob = getBlob(node_proto, constBlobs, 1);
Mat end_blob = getBlob(node_proto, constBlobs, 2);
CV_Assert(start_blob.total() == end_blob.total());
if (inp_size > 3) {
Mat axes_blob = getBlob(node_proto, constBlobs, 3);
const int* axes = (int*)axes_blob.data;
for (int i = 1; i < axes_blob.total(); ++i) {
CV_Assert(axes[i - 1] == axes[i] - 1);
}
axis = axes[0];
}
const int* starts = start_blob.ptr<int>();
const int* ends = end_blob.ptr<int>();
if (axis > 0) {
begin.resize(axis, 0);
end.resize(axis, -1);
}
std::copy(starts, starts + start_blob.total(), std::back_inserter(begin));
for (int i = 0; i < end_blob.total(); ++i)
{
int finish = ends[i];
end.push_back((finish < 0) ? --finish : finish); // numpy doesn't include last dim
}
if (inp_size == 5) {
CV_Assert(constBlobs.find(node_proto.input(4)) != constBlobs.end());
Mat step_blob = getBlob(node_proto, constBlobs, 4);
CV_CheckEQ(countNonZero(step_blob != 1), 0, "Slice layer only supports steps = 1");
}
}
layerParams.set("begin", DictValue::arrayInt(&begin[0], begin.size()));
layerParams.set("end", DictValue::arrayInt(&end[0], end.size()));
}
layerParams.set("axis", axis);
if (constBlobs.find(node_proto.input(0)) != constBlobs.end())
{
Mat inp = getBlob(node_proto, constBlobs, 0);
std::vector<Mat> inputs, sliced;
inputs.push_back(inp);
runLayer(layerParams, inputs, sliced);
CV_Assert(sliced.size() == 1);
constBlobs.insert(std::make_pair(layerParams.name, sliced[0]));
continue;
}
}
else if (layer_type == "Split")
{
if (layerParams.has("split"))
@ -444,16 +496,35 @@ void ONNXImporter::populateNet(Net dstNet)
}
else if (is_const_0 || is_const_1)
{
Mat blob = getBlob(node_proto, constBlobs, is_const_0 ? 0 : 1);
blob = blob.reshape(1, 1);
if (blob.total() == 1) {
int const_blob_id = is_const_0 ? 0 : 1;
Mat blob = getBlob(node_proto, constBlobs, const_blob_id);
int blob_total = blob.total();
if (blob_total == 1) {
layerParams.type = "Power";
layerParams.set("shift", (isSub ? -1 : 1) * blob.at<float>(0));
}
else {
layerParams.type = "Scale";
layerParams.set("bias_term", true);
layerParams.blobs.push_back((isSub ? -1 : 1) * blob);
MatShape inpShape = outShapes[node_proto.input(1 - const_blob_id)];
if (shape(blob) == inpShape)
{
LayerParams constParams;
constParams.name = layerParams.name + "/const";
constParams.type = "Const";
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";
node_proto.set_input(const_blob_id, constParams.name);
}
else
{
layerParams.type = "Scale";
layerParams.set("bias_term", true);
blob = blob.reshape(1, 1);
layerParams.blobs.push_back((isSub ? -1 : 1) * blob);
}
}
}
else if (outShapes[node_proto.input(0)] == outShapes[node_proto.input(1)])
@ -947,6 +1018,17 @@ void ONNXImporter::populateNet(Net dstNet)
else
layerParams.type = "Identity";
}
else if (layer_type == "ConstantOfShape")
{
float fill_value = layerParams.blobs.empty() ? 0 : layerParams.blobs[0].at<float>(0, 0);
MatShape inpShape = getBlob(node_proto, constBlobs, 0);
for (int i = 0; i < inpShape.size(); i++)
CV_CheckGT(inpShape[i], 0, "");
Mat tensor(inpShape.size(), &inpShape[0], CV_32F, Scalar(fill_value));
constBlobs.insert(std::make_pair(layerParams.name, tensor));
outShapes[node_proto.output(0)] = shape(tensor);
continue;
}
else if (layer_type == "Gather")
{
CV_Assert(node_proto.input_size() == 2);
@ -990,6 +1072,39 @@ void ONNXImporter::populateNet(Net dstNet)
continue;
}
}
else if (layer_type == "Resize")
{
for (int i = 1; i < node_proto.input_size(); i++)
CV_Assert(layer_id.find(node_proto.input(i)) == layer_id.end());
String interp_mode = layerParams.get<String>("coordinate_transformation_mode");
CV_Assert_N(interp_mode != "tf_crop_and_resize", interp_mode != "asymmetric",
interp_mode != "tf_half_pixel_for_nn");
layerParams.set("align_corners", interp_mode == "align_corners");
Mat shapes = getBlob(node_proto, constBlobs, node_proto.input_size() - 1);
CV_CheckEQ(shapes.size[0], 4, "");
CV_CheckEQ(shapes.size[1], 1, "");
CV_CheckTypeEQ(shapes.depth(), CV_32S, "");
int height = shapes.at<int>(2);
int width = shapes.at<int>(3);
if (node_proto.input_size() == 3)
{
shapeIt = outShapes.find(node_proto.input(0));
CV_Assert(shapeIt != outShapes.end());
MatShape scales = shapeIt->second;
height *= scales[2];
width *= scales[3];
}
layerParams.set("width", width);
layerParams.set("height", height);
if (layerParams.get<String>("mode") == "linear") {
layerParams.set("mode", interp_mode == "pytorch_half_pixel" ?
"opencv_linear" : "bilinear");
}
replaceLayerParam(layerParams, "mode", "interpolation");
}
else if (layer_type == "Upsample")
{
layerParams.type = "Resize";
@ -1038,10 +1153,12 @@ void ONNXImporter::populateNet(Net dstNet)
}
std::vector<MatShape> layerInpShapes, layerOutShapes, layerInternalShapes;
int inpNum = 0;
for (int j = 0; j < node_proto.input_size(); j++) {
layerId = layer_id.find(node_proto.input(j));
if (layerId != layer_id.end()) {
dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, j);
dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, inpNum);
++inpNum;
// Collect input shapes.
shapeIt = outShapes.find(node_proto.input(j));
CV_Assert(shapeIt != outShapes.end());

24
modules/dnn/test/test_onnx_importer.cpp
View File

@ -57,8 +57,13 @@ public:
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
std::vector<String> inputNames;
for (int i = 0; i < numInps; ++i)
net.setInput(inps[i], numInps > 1 ? format("%d", i) : "");
inputNames.push_back(format("%d", i));
net.setInputsNames(inputNames);
for (int i = 0; i < numInps; ++i)
net.setInput(inps[i], inputNames[i]);
Mat out = net.forward("");
if (useSoftmax)
@ -166,6 +171,11 @@ TEST_P(Test_ONNX_layers, Clip)
testONNXModels("clip", npy);
}
TEST_P(Test_ONNX_layers, Shape)
{
testONNXModels("shape_of_constant");
}
TEST_P(Test_ONNX_layers, ReduceMean)
{
testONNXModels("reduce_mean");
@ -347,6 +357,11 @@ TEST_P(Test_ONNX_layers, Broadcast)
testONNXModels("channel_broadcast", npy, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, DynamicResize)
{
testONNXModels("dynamic_resize", npy, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, Div)
{
const String model = _tf("models/div.onnx");
@ -375,10 +390,8 @@ TEST_P(Test_ONNX_layers, Div)
TEST_P(Test_ONNX_layers, DynamicReshape)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
testONNXModels("dynamic_reshape");
testONNXModels("dynamic_reshape_opset_11");
testONNXModels("flatten_by_prod");
@ -418,6 +431,7 @@ TEST_P(Test_ONNX_layers, Slice)
testONNXModels("slice", npy, 0, 0, false, false);
#else
testONNXModels("slice");
testONNXModels("slice_opset_11");
#endif
}