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Fix the issue of int8 quantization compilation errors in YOLOv8 (#1393)

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* Fix the issue of int8 quantization compilation errors in YOLOv8

Fix the issue of int8 quantization compilation errors in YOLOv8

* Update model.cpp

* Update model.cpp

---------

Co-authored-by: Wang Xinyu <shaywxy@gmail.com>
This commit is contained in:
lindsayshuo 2023-10-30 15:26:53 +08:00 committed by GitHub
parent 120121891d
commit 2fafde1af6
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GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 6 additions and 6 deletions
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12
yolov8/src/model.cpp
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@ -18,14 +18,14 @@ static int get_depth(int x, float gd) {
return std::max<int>(r, 1);
}
static nvinfer1::IElementWiseLayer* Proto(nvinfer1::INetworkDefinition* network, std::map<std::string, nvinfer1::Weights>& weightMap,
static nvinfer1::IElementWiseLayer* Proto(nvinfer1::INetworkDefinition* network, std::map<std::string, nvinfer1::Weights>& weightMap,
nvinfer1::ITensor& input, std::string lname, float gw, int max_channels) {
int mid_channel = get_width(256, gw, max_channels);
auto cv1 = convBnSiLU(network, weightMap, input, mid_channel, 3, 1, 1, "model.22.proto.cv1");
float* convTranpsose_bais = (float*)weightMap["model.22.proto.upsample.bias"].values;
int convTranpsose_bais_len = weightMap["model.22.proto.upsample.bias"].count;
nvinfer1::Weights bias{nvinfer1::DataType::kFLOAT, convTranpsose_bais, convTranpsose_bais_len};
auto convTranpsose = network->addDeconvolutionNd(*cv1->getOutput(0), mid_channel, nvinfer1::DimsHW{2,2}, weightMap["model.22.proto.upsample.weight"], bias);
auto convTranpsose = network->addDeconvolutionNd(*cv1->getOutput(0), mid_channel, nvinfer1::DimsHW{2,2}, weightMap["model.22.proto.upsample.weight"], bias);
assert(convTranpsose);
convTranpsose->setStrideNd(nvinfer1::DimsHW{2, 2});
auto cv2 = convBnSiLU(network,weightMap,*convTranpsose->getOutput(0), mid_channel, 3, 1, 1, "model.22.proto.cv2");
@ -34,9 +34,9 @@ static nvinfer1::IElementWiseLayer* Proto(nvinfer1::INetworkDefinition* network,
return cv3;
}
static nvinfer1::IShuffleLayer* ProtoCoef(nvinfer1::INetworkDefinition* network, std::map<std::string, nvinfer1::Weights>& weightMap,
static nvinfer1::IShuffleLayer* ProtoCoef(nvinfer1::INetworkDefinition* network, std::map<std::string, nvinfer1::Weights>& weightMap,
nvinfer1::ITensor& input, std::string lname, int grid_shape, float gw) {
int mid_channle = 0;
if(gw == 0.25 || gw== 0.5) {
mid_channle = 32;
@ -205,7 +205,7 @@ nvinfer1::IHostMemory* buildEngineYolov8Det(nvinfer1::IBuilder* builder,
std::cout << "Your platform support int8: " << (builder->platformHasFastInt8() ? "true" : "false") << std::endl;
assert(builder->platformHasFastInt8());
config->setFlag(nvinfer1::BuilderFlag::kINT8);
nvinfer1::IInt8EntropyCalibrator2* calibrator = new Calibrator(1, kInputW, kInputH, "../calibrator/", "int8calib.table", kInputTensorName);
auto* calibrator = new Int8EntropyCalibrator2(1, kInputW, kInputH, "../coco_calib/", "int8calib.table", kInputTensorName);
config->setInt8Calibrator(calibrator);
#endif
@ -377,7 +377,7 @@ nvinfer1::IHostMemory* buildEngineYolov8Seg(nvinfer1::IBuilder* builder,
std::cout << "Your platform support int8: " << (builder->platformHasFastInt8() ? "true" : "false") << std::endl;
assert(builder->platformHasFastInt8());
config->setFlag(nvinfer1::BuilderFlag::kINT8);
nvinfer1::IInt8EntropyCalibrator2* calibrator = new Calibrator(1, kInputW, kInputH, "../calibrator/", "int8calib.table", kInputTensorName);
auto* calibrator = new Int8EntropyCalibrator2(1, kInputW, kInputH, "../coco_calib/", "int8calib.table", kInputTensorName);
config->setInt8Calibrator(calibrator);
#endif