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Dilated convolution import from TensorFlow

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This commit is contained in:
Dmitry Kurtaev 2017-09-12 15:56:51 +03:00
parent 9640bbe76d
commit 58b890b9f7
6 changed files with 59 additions and 16 deletions
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4
modules/dnn/src/layers/convolution_layer.cpp
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@ -81,7 +81,7 @@ public:
Size outSize = Size(outputs[0].size[3], outputs[0].size[2]);
getConvPoolPaddings(Size(input.size[3], input.size[2]), outSize,
kernel, stride, padMode, pad);
kernel, stride, padMode, dilation, pad);
}
bool hasBias() const
@ -183,7 +183,7 @@ public:
}
else
{
getConvPoolOutParams(Size(inpW, inpH), kernel, stride, padMode, out);
getConvPoolOutParams(Size(inpW, inpH), kernel, stride, padMode, dilation, out);
}
int ngroups = inpCn / blobs[0].size[1];

12
modules/dnn/src/layers/layers_common.cpp
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@ -167,12 +167,12 @@ void getConvolutionKernelParams(const LayerParams &params, int &kernelH, int &ke
// we pad more on the right and bottom than on the top and left.
void getConvPoolOutParams(const Size& inp, const Size &kernel,
const Size &stride, const String &padMode,
Size& out)
const Size &dilation, Size& out)
{
if (padMode == "VALID")
{
out.height = (inp.height - kernel.height + stride.height) / stride.height;
out.width = (inp.width- kernel.width + stride.width) / stride.width;
out.height = (inp.height - (dilation.height * (kernel.height - 1) + 1) + stride.height) / stride.height;
out.width = (inp.width - (dilation.width * (kernel.width - 1) + 1) + stride.width) / stride.width;
}
else if (padMode == "SAME")
{
@ -187,7 +187,7 @@ void getConvPoolOutParams(const Size& inp, const Size &kernel,
void getConvPoolPaddings(const Size& inp, const Size& out,
const Size &kernel, const Size &stride,
const String &padMode, Size &pad)
const String &padMode, const Size &dilation, Size &pad)
{
if (padMode == "VALID")
{
@ -195,8 +195,8 @@ void getConvPoolPaddings(const Size& inp, const Size& out,
}
else if (padMode == "SAME")
{
int Ph = std::max(0, (out.height - 1) * stride.height + kernel.height - inp.height);
int Pw = std::max(0, (out.width - 1) * stride.width + kernel.width - inp.width);
int Ph = std::max(0, (out.height - 1) * stride.height + (dilation.height * (kernel.height - 1) + 1) - inp.height);
int Pw = std::max(0, (out.width - 1) * stride.width + (dilation.width * (kernel.width - 1) + 1) - inp.width);
// For odd values of total padding, add more padding at the 'right'
// side of the given dimension.
pad = cv::Size(Pw / 2, Ph / 2);

4
modules/dnn/src/layers/layers_common.hpp
View File

@ -64,11 +64,11 @@ void getPoolingKernelParams(const LayerParams &params, int &kernelH, int &kernel
void getConvPoolOutParams(const Size& inp, const Size &kernel,
const Size &stride, const String &padMode,
Size& out);
const Size &dilation, Size& out);
void getConvPoolPaddings(const Size& inp, const Size& out,
const Size &kernel, const Size &stride,
const String &padMode, Size &pad);
const String &padMode, const Size &dilation, Size &pad);
}
}

5
modules/dnn/src/layers/pooling_layer.cpp
View File

@ -93,7 +93,7 @@ public:
kernel = inp;
}
getConvPoolPaddings(inp, out, kernel, stride, padMode, pad);
getConvPoolPaddings(inp, out, kernel, stride, padMode, Size(1, 1), pad);
}
virtual bool supportBackend(int backendId)
@ -592,8 +592,7 @@ public:
}
else
{
getConvPoolOutParams(in, kernel, stride,
padMode, out);
getConvPoolOutParams(in, kernel, stride, padMode, Size(1, 1), out);
}
outputs.resize(type == MAX ? 2 * inputs.size() : inputs.size());

48
modules/dnn/src/tensorflow/tf_importer.cpp
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@ -88,6 +88,8 @@ static Mat getTensorContent(const tensorflow::TensorProto &tensor)
return Mat(1, content.size() / sizeof(float), CV_32FC1, (void*)content.c_str()).clone();
case tensorflow::DT_DOUBLE:
return Mat(1, content.size() / sizeof(double), CV_64FC1, (void*)content.c_str()).clone();
case tensorflow::DT_INT32:
return Mat(1, content.size() / sizeof(int32_t), CV_32SC1, (void*)content.c_str()).clone();
case tensorflow::DT_HALF:
{
Mat halfs;
@ -563,7 +565,7 @@ void TFImporter::populateNet(Net dstNet)
for (int li = 0; li < layersSize; li++)
{
const tensorflow::NodeDef &layer = net.node(li);
tensorflow::NodeDef layer = net.node(li);
String name = layer.name();
String type = layer.op();
LayerParams layerParams;
@ -571,8 +573,38 @@ void TFImporter::populateNet(Net dstNet)
if(layers_to_ignore.find(li) != layers_to_ignore.end())
continue;
if (type == "Conv2D")
if (type == "Conv2D" || type == "SpaceToBatchND")
{
// The first node of dilated convolution subgraph.
// Extract input node, dilation rate and paddings.
std::string input = layer.input(0);
if (type == "SpaceToBatchND")
{
// op: "SpaceToBatchND"
// input: "input"
// input: "SpaceToBatchND/block_shape"
// input: "SpaceToBatchND/paddings"
CV_Assert(layer.input_size() == 3);
DictValue dilation = parseDims(getConstBlob(layer, value_id, 1));
CV_Assert(dilation.size() == 2 && dilation.get<int>(0) == dilation.get<int>(1));
layerParams.set("dilation", dilation.get<int>(0));
Mat paddings;
parseTensor<int>(getConstBlob(layer, value_id, 2), paddings);
// paddings is a 2x2 matrix: [[top, bot], [left, right]]
layerParams.set("pad_h", paddings.at<float>(0));
layerParams.set("pad_w", paddings.at<float>(2));
StrIntVector next_layers = getNextLayers(net, name, "Conv2D");
CV_Assert(next_layers.size() == 1);
layer = net.node(next_layers[0].second);
layers_to_ignore[next_layers[0].second] = next_layers[0].first;
name = layer.name();
type = layer.op();
}
layerParams.set("bias_term", false);
layerParams.blobs.resize(1);
@ -597,11 +629,21 @@ void TFImporter::populateNet(Net dstNet)
setStrides(layerParams, layer);
setPadding(layerParams, layer);
// The final node of dilated convolution subgraph.
next_layers = getNextLayers(net, name, "BatchToSpaceND");
if (!next_layers.empty())
{
layerParams.set("pad_mode", ""); // We use padding values.
CV_Assert(next_layers.size() == 1);
ExcludeLayer(net, next_layers[0].second, 0, false);
layers_to_ignore[next_layers[0].second] = next_layers[0].first;
}
int id = dstNet.addLayer(name, "Convolution", layerParams);
layer_id[name] = id;
// one input only
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
connect(layer_id, dstNet, parsePin(input), id, 0);
}
else if (type == "BiasAdd" || type == "Add")
{

2
modules/dnn/test/test_tf_importer.cpp
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@ -96,6 +96,8 @@ static void runTensorFlowNet(const std::string& prefix,
TEST(Test_TensorFlow, single_conv)
{
runTensorFlowNet("single_conv");
runTensorFlowNet("atrous_conv2d_valid");
runTensorFlowNet("atrous_conv2d_same");
}
TEST(Test_TensorFlow, padding)