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Manage TensorFlow's NHWC data layout is smoother

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pull/10364/head
Dmitry Kurtaev 7 years ago
parent dcdd6af5a8
commit 7e48fa58eb
2 changed files with 139 additions and 10 deletions
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  1. 147
      modules/dnn/src/tensorflow/tf_importer.cpp
  2. 2
      modules/dnn/test/test_tf_importer.cpp

147
modules/dnn/src/tensorflow/tf_importer.cpp
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@ -42,6 +42,14 @@ namespace
static int toNCHW[] = {0, 2, 3, 1};
// This values are used to indicate layer output's data layout where it's possible.
enum DataLayout
{
DATA_LAYOUT_NHWC,
DATA_LAYOUT_NCHW,
DATA_LAYOUT_UNKNOWN
};
typedef std::vector<std::pair<String, int> > StrIntVector;
struct Pin
@ -608,6 +616,31 @@ static void addConstNodes(const tensorflow::GraphDef& net, std::map<String, int>
}
}
// If all inputs of specific layer have the same data layout we can say that
// this layer's output has this data layout too. Returns DATA_LAYOUT_UNKNOWN otherwise.
static int predictOutputDataLayout(const tensorflow::NodeDef& layer, const std::map<String, int>& data_layouts)
{
int layout = DATA_LAYOUT_UNKNOWN;
std::map<String, int>::const_iterator it;
for (int i = 0, n = layer.input_size(); i < n; ++i)
{
it = data_layouts.find(layer.input(i));
if (it != data_layouts.end())
{
if (it->second == DATA_LAYOUT_UNKNOWN)
return DATA_LAYOUT_UNKNOWN;
else if (it->second != layout)
{
if (layout == DATA_LAYOUT_UNKNOWN)
layout = it->second;
else
return DATA_LAYOUT_UNKNOWN;
}
}
}
return layout;
}
void TFImporter::populateNet(Net dstNet)
{
RemoveIdentityOps(netBin);
@ -619,6 +652,8 @@ void TFImporter::populateNet(Net dstNet)
int layersSize = net.node_size();
std::map<String, int> data_layouts;
// find all Const layers for params
std::map<String, int> value_id;
addConstNodes(netBin, value_id, layers_to_ignore);
@ -636,6 +671,8 @@ void TFImporter::populateNet(Net dstNet)
if(layers_to_ignore.find(name) != layers_to_ignore.end())
continue;
data_layouts[name] = predictOutputDataLayout(layer, data_layouts);
if (type == "Conv2D" || type == "SpaceToBatchND" || type == "DepthwiseConv2dNative")
{
// The first node of dilated convolution subgraph.
@ -731,6 +768,19 @@ void TFImporter::populateNet(Net dstNet)
// one input only
connect(layer_id, dstNet, parsePin(input), id, 0);
if (hasLayerAttr(layer, "data_format"))
{
std::string format = getLayerAttr(layer, "data_format").s();
if (format == "NHWC")
data_layouts[name] = DATA_LAYOUT_NHWC;
else if (format == "NCHW")
data_layouts[name] = DATA_LAYOUT_NCHW;
else
CV_Error(Error::StsParseError, "Unknown data_format value: " + format);
}
else
data_layouts[name] = DATA_LAYOUT_NHWC;
}
else if (type == "BiasAdd" || type == "Add")
{
@ -806,22 +856,55 @@ void TFImporter::populateNet(Net dstNet)
// one input only
int input_blob_index = kernel_blob_index == 0 ? 1 : 0;
connect(layer_id, dstNet, parsePin(layer.input(input_blob_index)), id, 0);
data_layouts[name] = DATA_LAYOUT_UNKNOWN;
}
else if (type == "Reshape")
{
layerParams.set("dim", parseDims(getConstBlob(layer, value_id, 1)));
Pin inpId = parsePin(layer.input(0));
DictValue newShape = parseDims(getConstBlob(layer, value_id, 1));
if (newShape.size() != 4 && data_layouts[layer.input(0)] == DATA_LAYOUT_NHWC)
{
LayerParams permLP;
int order[] = {0, 2, 3, 1}; // From OpenCV's NCHW to NHWC.
permLP.set("order", DictValue::arrayInt<int*>(order, 4));
std::string permName = name + "/nchw";
CV_Assert(layer_id.find(permName) == layer_id.end());
int permId = dstNet.addLayer(permName, "Permute", permLP);
layer_id[permName] = permId;
connect(layer_id, dstNet, inpId, permId, 0);
inpId = Pin(permName);
}
layerParams.set("dim", newShape);
int id = dstNet.addLayer(name, "Reshape", layerParams);
layer_id[name] = id;
// one input only
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
connect(layer_id, dstNet, inpId, id, 0);
data_layouts[name] = DATA_LAYOUT_UNKNOWN;
}
else if (type == "Flatten")
{
Pin inpId = parsePin(layer.input(0));
if (data_layouts[layer.input(0)] == DATA_LAYOUT_NHWC)
{
LayerParams permLP;
int order[] = {0, 2, 3, 1}; // From OpenCV's NCHW to NHWC.
permLP.set("order", DictValue::arrayInt<int*>(order, 4));
std::string permName = name + "/nchw";
CV_Assert(layer_id.find(permName) == layer_id.end());
int permId = dstNet.addLayer(permName, "Permute", permLP);
layer_id[permName] = permId;
connect(layer_id, dstNet, inpId, permId, 0);
inpId = Pin(permName);
}
int id = dstNet.addLayer(name, "Flatten", layerParams);
layer_id[name] = id;
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
connect(layer_id, dstNet, inpId, id, 0);
data_layouts[name] = DATA_LAYOUT_UNKNOWN;
}
else if (type == "Transpose")
{
@ -830,16 +913,57 @@ void TFImporter::populateNet(Net dstNet)
int* permData = (int*)perm.data;
if (perm.total() == 4)
{
for (int i = 0; i < 4; ++i)
permData[i] = toNCHW[permData[i]];
// Only NHWC <-> NCHW permutations are allowed. OpenCV is always
// keep NCHW layout this way.
if (data_layouts[layer.input(0)] == DATA_LAYOUT_NHWC)
{
if (permData[0] == 0 && permData[1] == 3 && permData[2] == 1 && permData[3] == 2)
{
// in TensorFlow: NHWC->NCHW
// in OpenCV: NCHW->NCHW
data_layouts[name] = DATA_LAYOUT_NCHW;
}
else if (permData[0] == 0 && permData[1] == 1 && permData[2] == 2 && permData[3] == 3)
{
// in TensorFlow: NHWC->NHWC
// in OpenCV: NCHW->NCHW
data_layouts[name] = DATA_LAYOUT_NHWC;
}
else
CV_Assert(Error::StsParseError, "Only NHWC <-> NCHW permutations are allowed.");
}
else if (data_layouts[layer.input(0)] == DATA_LAYOUT_NCHW)
{
if (permData[0] == 0 && permData[1] == 2 && permData[2] == 3 && permData[3] == 1)
{
// in TensorFlow: NCHW->NHWC
// in OpenCV: NCHW->NCHW
data_layouts[name] = DATA_LAYOUT_NHWC;
}
else if (permData[0] == 0 && permData[1] == 1 && permData[2] == 2 && permData[3] == 3)
{
// in TensorFlow: NCHW->NCHW
// in OpenCV: NCHW->NCHW
data_layouts[name] = DATA_LAYOUT_NCHW;
}
else
CV_Assert(Error::StsParseError, "Only NHWC <-> NCHW permutations are allowed.");
}
int id = dstNet.addLayer(name, "Identity", layerParams);
layer_id[name] = id;
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
}
layerParams.set("order", DictValue::arrayInt<int*>(permData, perm.total()));
else
{
layerParams.set("order", DictValue::arrayInt<int*>(permData, perm.total()));
int id = dstNet.addLayer(name, "Permute", layerParams);
layer_id[name] = id;
int id = dstNet.addLayer(name, "Permute", layerParams);
layer_id[name] = id;
// one input only
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
// one input only
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
data_layouts[name] = DATA_LAYOUT_UNKNOWN;
}
}
else if (type == "Const")
{
@ -1207,6 +1331,7 @@ void TFImporter::populateNet(Net dstNet)
// one input only
connect(layer_id, dstNet, parsePin(layer.input(1)), id, 0);
data_layouts[name] = DATA_LAYOUT_UNKNOWN;
}
else if (type == "ResizeNearestNeighbor")
{
@ -1258,6 +1383,7 @@ void TFImporter::populateNet(Net dstNet)
layer_id[name] = id;
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
connect(layer_id, dstNet, parsePin(layer.input(1)), id, 1);
data_layouts[name] = DATA_LAYOUT_UNKNOWN;
}
else if (type == "DetectionOutput")
{
@ -1288,6 +1414,7 @@ void TFImporter::populateNet(Net dstNet)
layer_id[name] = id;
for (int i = 0; i < 3; ++i)
connect(layer_id, dstNet, parsePin(layer.input(i)), id, i);
data_layouts[name] = DATA_LAYOUT_UNKNOWN;
}
else if (type == "Abs" || type == "Tanh" || type == "Sigmoid" ||
type == "Relu" || type == "Elu" || type == "Softmax" ||

2
modules/dnn/test/test_tf_importer.cpp
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@ -159,6 +159,8 @@ TEST(Test_TensorFlow, deconvolution)
TEST(Test_TensorFlow, matmul)
{
runTensorFlowNet("matmul");
runTensorFlowNet("nhwc_reshape_matmul");
runTensorFlowNet("nhwc_transpose_reshape_matmul");
}
TEST(Test_TensorFlow, defun)

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