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Support for MobileNetV3-SSD from TensorFlow

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This commit is contained in:
Dmitry Kurtaev 2020-03-08 20:15:18 +03:00
parent 969cc3dd95
commit b927ce18b2
3 changed files with 66 additions and 4 deletions
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57
modules/dnn/src/tensorflow/tf_importer.cpp
View File

@ -458,6 +458,7 @@ private:
tensorflow::GraphDef netTxt;
std::vector<String> netInputsNames;
std::vector<MatShape> netInputShapes;
};
TFImporter::TFImporter(const char *model, const char *config)
@ -1401,6 +1402,27 @@ void TFImporter::populateNet(Net dstNet)
netInputsNames.push_back(name);
layer_id[name] = 0;
}
if (hasLayerAttr(layer, "shape"))
{
const tensorflow::TensorShapeProto& shape = getLayerAttr(layer, "shape").shape();
MatShape dims(shape.dim_size());
for (int i = 0; i < dims.size(); ++i)
dims[i] = shape.dim(i).size();
if (dims.size() == 4 && predictedLayout == DATA_LAYOUT_NHWC)
{
std::swap(dims[1], dims[3]); // NHWC->NCWH
std::swap(dims[2], dims[3]); // NCWH->NCHW
if (dims[0] == -1) // It's OK to have undetermined batch size
dims[0] = 1;
}
bool hasNeg = false;
for (int i = 0; i < dims.size() && !hasNeg; ++i)
{
hasNeg = dims[i] < 0;
}
if (!hasNeg)
netInputShapes.push_back(dims);
}
}
else if (type == "Split") {
// TODO: determining axis index remapping by input dimensions order of input blob
@ -1579,9 +1601,42 @@ void TFImporter::populateNet(Net dstNet)
connect(layer_id, dstNet, parsePin(layer.input(1)), id, 0);
}
else
{
// Check if all the inputs have the same shape.
bool equalInpShapes = true;
MatShape outShape0;
for (int ii = 0; ii < layer.input_size() && !netInputShapes.empty(); ii++)
{
Pin pin = parsePin(layer.input(ii));
int inpId = layer_id.find(pin.name)->second;
// Get input shape
MatShape outShape;
std::vector<MatShape> inpShapes, outShapes;
dstNet.getLayerShapes(netInputShapes, inpId, inpShapes, outShapes);
CV_CheckGT(static_cast<int>(outShapes.size()), pin.blobIndex, "");
outShape = outShapes[pin.blobIndex];
if (ii == 0)
{
outShape0 = outShape;
}
else if (outShape != outShape0)
{
equalInpShapes = false;
break;
}
}
int id;
if (equalInpShapes || netInputShapes.empty())
{
layerParams.set("operation", "prod");
int id = dstNet.addLayer(name, "Eltwise", layerParams);
id = dstNet.addLayer(name, "Eltwise", layerParams);
}
else
id = dstNet.addLayer(name, "Scale", layerParams);
layer_id[name] = id;
for (int ii = 0; ii < layer.input_size(); ii++)

7
modules/dnn/test/test_tf_importer.cpp
View File

@ -181,6 +181,13 @@ TEST_P(Test_TensorFlow_layers, eltwise)
runTensorFlowNet("eltwise_sub");
}
TEST_P(Test_TensorFlow_layers, channel_broadcast)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
runTensorFlowNet("channel_broadcast");
}
TEST_P(Test_TensorFlow_layers, pad_and_concat)
{
runTensorFlowNet("pad_and_concat");

4
samples/dnn/tf_text_graph_ssd.py
View File

@ -64,7 +64,7 @@ def createSSDGraph(modelPath, configPath, outputPath):
# Nodes that should be kept.
keepOps = ['Conv2D', 'BiasAdd', 'Add', 'AddV2', 'Relu', 'Relu6', 'Placeholder', 'FusedBatchNorm',
'DepthwiseConv2dNative', 'ConcatV2', 'Mul', 'MaxPool', 'AvgPool', 'Identity',
'Sub', 'ResizeNearestNeighbor', 'Pad', 'FusedBatchNormV3']
'Sub', 'ResizeNearestNeighbor', 'Pad', 'FusedBatchNormV3', 'Mean']
# Node with which prefixes should be removed
prefixesToRemove = ('MultipleGridAnchorGenerator/', 'Concatenate/', 'Postprocessor/', 'Preprocessor/map')
@ -235,7 +235,7 @@ def createSSDGraph(modelPath, configPath, outputPath):
# Connect input node to the first layer
assert(graph_def.node[0].op == 'Placeholder')
# assert(graph_def.node[1].op == 'Conv2D')
weights = graph_def.node[1].input[0]
weights = graph_def.node[1].input[-1]
for i in range(len(graph_def.node[1].input)):
graph_def.node[1].input.pop()
graph_def.node[1].input.append(graph_def.node[0].name)