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Merge pull request #14315 from dkurt:tf_squeeze_and_slim_softmax_v2

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This commit is contained in:
Alexander Alekhin 2019-04-18 16:01:24 +00:00
commit 4f764b812e
4 changed files with 98 additions and 7 deletions
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10
modules/dnn/src/layers/flatten_layer.cpp
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@ -105,6 +105,16 @@ public:
return true;
}
void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays) CV_OVERRIDE
{
std::vector<Mat> inputs;
inputs_arr.getMatVector(inputs);
int numAxes = inputs[0].dims;
_startAxis = clamp(_startAxis, numAxes);
_endAxis = clamp(_endAxis, numAxes);
}
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
{

25
modules/dnn/src/tensorflow/tf_graph_simplifier.cpp
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@ -646,6 +646,30 @@ public:
}
};
class SoftMaxSlimV2Subgraph : public Subgraph
{
public:
SoftMaxSlimV2Subgraph()
{
int input = addNodeToMatch("");
int shape = addNodeToMatch("Shape", input);
int shape_2 = addNodeToMatch("Shape", input);
int rank = addNodeToMatch("Const");
int y = addNodeToMatch("Const");
int sub = addNodeToMatch("Sub", rank, y);
int begin = addNodeToMatch("Pack", sub);
int size = addNodeToMatch("Const");
int slice = addNodeToMatch("Slice", shape, begin, size);
int values = addNodeToMatch("Const");
int axis = addNodeToMatch("Const");
int concat = addNodeToMatch("ConcatV2", values, slice, axis);
int reshape = addNodeToMatch("Reshape", input, concat);
int softmax = addNodeToMatch("Softmax", reshape);
addNodeToMatch("Reshape", softmax, shape_2);
setFusedNode("Softmax", input);
}
};
void simplifySubgraphs(tensorflow::GraphDef& net)
{
std::vector<Ptr<Subgraph> > subgraphs;
@ -663,6 +687,7 @@ void simplifySubgraphs(tensorflow::GraphDef& net)
subgraphs.push_back(Ptr<Subgraph>(new UpsamplingKerasSubgraph()));
subgraphs.push_back(Ptr<Subgraph>(new ReshapeAsShapeSubgraph()));
subgraphs.push_back(Ptr<Subgraph>(new SoftMaxSlimSubgraph()));
subgraphs.push_back(Ptr<Subgraph>(new SoftMaxSlimV2Subgraph()));
int numNodes = net.node_size();
std::vector<int> matchedNodesIds;

21
modules/dnn/src/tensorflow/tf_importer.cpp
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@ -1125,18 +1125,25 @@ void TFImporter::populateNet(Net dstNet)
{
CV_Assert(hasLayerAttr(layer, "squeeze_dims"));
const tensorflow::AttrValue& dims = getLayerAttr(layer, "squeeze_dims");
if (inpLayout == DATA_LAYOUT_NHWC)
std::vector<int> dimsVector(dims.list().i_size());
for (int i = 0; i < dimsVector.size(); ++i)
dimsVector[i] = dims.list().i(i);
// Flatten layer can squeeze dimensions range into one.
std::sort(dimsVector.begin(), dimsVector.end());
for (int i = 1; i < dimsVector.size(); ++i)
{
if (dims.list().i_size() != 2 || dims.list().i(0) != 1 || dims.list().i(1) != 2)
if (dimsVector[i] != dimsVector[i - 1] + 1)
CV_Error(Error::StsNotImplemented, "Unsupported squeeze configuration");
}
else if (inpLayout == DATA_LAYOUT_NCHW)
int start = dimsVector.front() - 1, end = dimsVector.back();
if (start == -1 && end == 0) // squeeze 0th dimension
{
if (dims.list().i_size() != 2 || dims.list().i(0) != 2 || dims.list().i(1) != 3)
CV_Error(Error::StsNotImplemented, "Unsupported squeeze configuration");
start = 0;
end = 1;
}
else
CV_Error(Error::StsNotImplemented, "Unsupported squeeze configuration");
layerParams.set("axis", start);
layerParams.set("end_axis", end);
}
if (inpLayout == DATA_LAYOUT_NHWC)
{

49
modules/dnn/test/test_tf_importer.cpp
View File

@ -637,6 +637,17 @@ TEST_P(Test_TensorFlow_layers, softmax)
runTensorFlowNet("slim_softmax");
}
TEST_P(Test_TensorFlow_layers, slim_softmax_v2)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD &&
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2
)
throw SkipTestException("Test is disabled for Myriad2");
#endif
runTensorFlowNet("slim_softmax_v2");
}
TEST_P(Test_TensorFlow_layers, relu6)
{
runTensorFlowNet("keras_relu6");
@ -654,6 +665,44 @@ TEST_P(Test_TensorFlow_layers, resize_bilinear)
runTensorFlowNet("resize_bilinear_factor");
}
TEST_P(Test_TensorFlow_layers, squeeze)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2
)
throw SkipTestException("Test is disabled for Myriad2");
#endif
int inpShapes[][4] = {{1, 3, 4, 2}, {1, 3, 1, 2}, {1, 3, 4, 1}, {1, 3, 4, 1}}; // TensorFlow's shape (NHWC)
int outShapes[][3] = {{3, 4, 2}, {1, 3, 2}, {1, 3, 4}, {1, 3, 4}};
int squeeze_dims[] = {0, 2, 3, -1};
for (int i = 0; i < 4; ++i)
{
SCOPED_TRACE(format("i=%d", i));
std::string pbtxt =
"node { name: \"input\" op: \"Placeholder\""
"attr { key: \"data_format\" value { s: \"NHWC\" } } }"
"node { name: \"squeeze\" op: \"Squeeze\" input: \"input\""
"attr { key: \"squeeze_dims\" value { list { i:" + format("%d", squeeze_dims[i]) + "}}}}";
Net net = readNetFromTensorflow(0, 0, pbtxt.c_str(), pbtxt.size());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat tfInp(4, &inpShapes[i][0], CV_32F);
randu(tfInp, -1, 1);
// NHWC to NCHW
CV_Assert(inpShapes[i][0] == 1);
std::swap(inpShapes[i][2], inpShapes[i][3]);
std::swap(inpShapes[i][1], inpShapes[i][2]);
Mat cvInp = tfInp.reshape(1, tfInp.total() / inpShapes[i][1]).t();
cvInp = cvInp.reshape(1, 4, &inpShapes[i][0]);
net.setInput(cvInp);
Mat out = net.forward();
normAssert(tfInp.reshape(1, 3, &outShapes[i][0]), out, "", default_l1, default_lInf);
}
}
INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_layers, dnnBackendsAndTargets());
TEST(Test_TensorFlow, two_inputs)