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Import and convert FP16 weights from TensorFlow

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pull/9576/head
Dmitry Kurtaev 7 years ago
parent 1caca2112b
commit ce41a15437
2 changed files with 82 additions and 11 deletions
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  1. 73
      modules/dnn/src/tensorflow/tf_importer.cpp
  2. 20
      modules/dnn/test/test_tf_importer.cpp

73
modules/dnn/src/tensorflow/tf_importer.cpp
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@ -63,10 +63,15 @@ void blobShapeFromTensor(const tensorflow::TensorProto &tensor, MatShape& shape)
{
const tensorflow::TensorShapeProto &_shape = tensor.tensor_shape();
int i, n = _shape.dim_size();
shape.resize(n);
if (n)
{
shape.resize(n);
for (i = 0; i < n; i++)
shape[i] = (int)_shape.dim(i).size();
for (i = 0; i < n; i++)
shape[i] = (int)_shape.dim(i).size();
}
else
shape.resize(1, 1); // Scalar.
}
else
{
@ -74,6 +79,43 @@ void blobShapeFromTensor(const tensorflow::TensorProto &tensor, MatShape& shape)
}
}
static Mat getTensorContent(const tensorflow::TensorProto &tensor)
{
std::string content = tensor.tensor_content();
switch (tensor.dtype())
{
case tensorflow::DT_FLOAT:
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_HALF:
{
Mat halfs;
if (!content.empty())
{
static const int kHalfSize = 2;
halfs = Mat(1, content.size() / kHalfSize, CV_16UC1, (void*)content.c_str());
}
else
{
const RepeatedField<int32_t>& field = tensor.half_val();
CV_Assert(!field.empty());
Mat ints(1, field.size(), CV_32SC1, (void*)field.data());
ints.convertTo(halfs, CV_16UC1);
}
// Reinterpret as a signed shorts just for a convertFp16 call.
Mat halfsSigned(halfs.size(), CV_16SC1, halfs.data);
Mat floats(halfs.size(), CV_32FC1);
convertFp16(halfsSigned, floats);
return floats;
}
default:
CV_Error(Error::StsError, "Tensor's data type is not supported");
break;
}
return Mat();
}
template <typename T>
void parseTensor(const tensorflow::TensorProto &tensor, Mat &dstBlob)
{
@ -90,11 +132,12 @@ void parseTensor(const tensorflow::TensorProto &tensor, Mat &dstBlob)
dstBlob.create(shape, CV_32F);
int size = tensor.tensor_content().size() / sizeof(T);
Mat tensorContent = getTensorContent(tensor);
int size = tensorContent.total();
CV_Assert(size == (int)dstBlob.total());
float *dstData = dstBlob.ptr<float>();
const T *data = reinterpret_cast<const T*>(tensor.tensor_content().c_str());
const T *data = reinterpret_cast<const T*>(tensorContent.data);
if (dims == 4)
{
@ -125,6 +168,7 @@ void blobFromTensor(const tensorflow::TensorProto &tensor, Mat &dstBlob)
{
switch (tensor.dtype()) {
case tensorflow::DT_FLOAT:
case tensorflow::DT_HALF:
parseTensor<float>(tensor, dstBlob);
break;
case tensorflow::DT_DOUBLE:
@ -406,7 +450,8 @@ void TFImporter::kernelFromTensor(const tensorflow::TensorProto &tensor, Mat &ds
int dims = (int)shape.size();
// TODO: other blob types
CV_Assert(tensor.dtype() == tensorflow::DT_FLOAT);
CV_Assert(tensor.dtype() == tensorflow::DT_FLOAT ||
tensor.dtype() == tensorflow::DT_HALF);
CV_Assert(dims == 4);
// REORDER kernel HWIO to OIHW
@ -416,11 +461,12 @@ void TFImporter::kernelFromTensor(const tensorflow::TensorProto &tensor, Mat &ds
dstBlob.create(shape, CV_32F);
int size = tensor.tensor_content().size() / sizeof(float);
Mat tensorContent = getTensorContent(tensor);
int size = tensorContent.total();
CV_Assert(size == (int)dstBlob.total());
float *dstData = dstBlob.ptr<float>();
const float *data = reinterpret_cast<const float*>(tensor.tensor_content().c_str());
const float *data = reinterpret_cast<const float*>(tensorContent.data);
int out_c = shape[0], input_c = shape[1], height = shape[2], width = shape[3];
int total = out_c*input_c*height*width;
@ -753,7 +799,16 @@ void TFImporter::populateNet(Net dstNet)
// Multiplication by constant.
CV_Assert(layer.input_size() == 2);
float scale = getConstBlob(layer, value_id).float_val()[0];
float scale;
if (!getConstBlob(layer, value_id).float_val().empty())
scale = getConstBlob(layer, value_id).float_val()[0];
else
{
Mat scaleMat;
blobFromTensor(getConstBlob(layer, value_id), scaleMat);
CV_Assert(scaleMat.total() == 1 && scaleMat.type() == CV_32FC1);
scale = scaleMat.at<float>(0, 0);
}
layerParams.set("scale", scale);
int id = dstNet.addLayer(name, "Power", layerParams);

20
modules/dnn/test/test_tf_importer.cpp
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@ -76,7 +76,8 @@ static std::string path(const std::string& file)
return findDataFile("dnn/tensorflow/" + file, false);
}
static void runTensorFlowNet(const std::string& prefix)
static void runTensorFlowNet(const std::string& prefix,
double l1 = 1e-5, double lInf = 1e-4)
{
std::string netPath = path(prefix + "_net.pb");
std::string inpPath = path(prefix + "_in.npy");
@ -89,7 +90,7 @@ static void runTensorFlowNet(const std::string& prefix)
net.setInput(input);
cv::Mat output = net.forward();
normAssert(target, output);
normAssert(target, output, "", l1, lInf);
}
TEST(Test_TensorFlow, single_conv)
@ -130,4 +131,19 @@ TEST(Test_TensorFlow, deconvolution)
runTensorFlowNet("deconvolution");
}
TEST(Test_TensorFlow, fp16)
{
const float l1 = 1e-3;
const float lInf = 1e-2;
runTensorFlowNet("fp16_single_conv", l1, lInf);
runTensorFlowNet("fp16_deconvolution", l1, lInf);
runTensorFlowNet("fp16_max_pool_odd_same", l1, lInf);
runTensorFlowNet("fp16_padding_valid", l1, lInf);
runTensorFlowNet("fp16_eltwise_add_mul", l1, lInf);
runTensorFlowNet("fp16_max_pool_odd_valid", l1, lInf);
runTensorFlowNet("fp16_pad_and_concat", l1, lInf);
runTensorFlowNet("fp16_max_pool_even", l1, lInf);
runTensorFlowNet("fp16_padding_same", l1, lInf);
}
}

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