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Merge pull request #10402 from dkurt:dnn_tf_quantized

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This commit is contained in:
Alexander Alekhin 2017-12-22 15:58:56 +00:00
commit 019b7c5a66
2 changed files with 57 additions and 2 deletions
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54
modules/dnn/src/tensorflow/tf_importer.cpp
View File

@ -146,6 +146,11 @@ static Mat getTensorContent(const tensorflow::TensorProto &tensor)
convertFp16(halfsSigned, floats);
return floats;
}
case tensorflow::DT_QUINT8:
{
CV_Assert(!content.empty());
return Mat(1, content.size(), CV_8UC1, (void*)content.c_str()).clone();
}
default:
CV_Error(Error::StsError, "Tensor's data type is not supported");
break;
@ -596,7 +601,7 @@ const tensorflow::TensorProto& TFImporter::getConstBlob(const tensorflow::NodeDe
}
}
static void addConstNodes(const tensorflow::GraphDef& net, std::map<String, int>& const_layers,
static void addConstNodes(tensorflow::GraphDef& net, std::map<String, int>& const_layers,
std::set<String>& layers_to_ignore)
{
for (int li = 0; li < net.node_size(); li++)
@ -605,7 +610,52 @@ static void addConstNodes(const tensorflow::GraphDef& net, std::map<String, int>
String name = layer.name();
String type = layer.op();
if (type != "Const")
if (type == "Dequantize")
{
// Example of Dequantize node:
// name: "conv2d_1/bias"
// op: "Dequantize"
// input: "conv2d_1/bias_quantized_const" (tensor of dtype DT_QUINT8)
// input: "conv2d_1/bias_quantized_min"
// input: "conv2d_1/bias_quantized_max"
// attr { key: "T" value { type: DT_QUINT8 } } (quantized type)
// attr { key: "mode" value { s: "MIN_FIRST" } } (quantization technique)
CV_Assert(layer.input_size() == 3);
for (int i = 0; i < 3; ++i)
CV_Assert(const_layers.find(layer.input(i)) != const_layers.end());
CV_Assert(hasLayerAttr(layer, "mode") &&
getLayerAttr(layer, "mode").s() == "MIN_FIRST");
int tensorId = const_layers[layer.input(0)];
int minId = const_layers[layer.input(1)];
int maxId = const_layers[layer.input(2)];
tensorflow::TensorProto* tensor = net.mutable_node(tensorId)
->mutable_attr()->at("value")
.mutable_tensor();
CV_Assert(tensor->dtype() == tensorflow::DT_QUINT8);
Mat qMin = getTensorContent(net.node(minId).attr().at("value").tensor());
Mat qMax = getTensorContent(net.node(maxId).attr().at("value").tensor());
CV_Assert(qMin.total() == 1, qMin.type() == CV_32FC1,
qMax.total() == 1, qMax.type() == CV_32FC1);
Mat content = getTensorContent(*tensor);
float minVal = qMin.at<float>(0);
float rangeScale = (qMax.at<float>(0) - minVal) / 255;
CV_Assert(rangeScale >= 0);
content.convertTo(content, CV_32FC1, rangeScale,
rangeScale * cvRound(minVal / rangeScale));
tensor->set_dtype(tensorflow::DT_FLOAT);
tensor->set_tensor_content(content.data, content.total() * content.elemSize1());
ExcludeLayer(net, li, 0, false);
layers_to_ignore.insert(name);
continue;
}
else if (type != "Const")
continue; // only Const parameters are supported
if (layer.attr().find("value") != layer.attr().end())

5
modules/dnn/test/test_tf_importer.cpp
View File

@ -190,6 +190,11 @@ TEST(Test_TensorFlow, fp16)
runTensorFlowNet("fp16_padding_same", false, l1, lInf);
}
TEST(Test_TensorFlow, quantized)
{
runTensorFlowNet("uint8_single_conv");
}
TEST(Test_TensorFlow, MobileNet_SSD)
{
std::string netPath = findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false);