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Moved batchnorm and test_monodepth2_new_format layer to dev

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Signed-off-by: perseusdg <harshvardhan.chandira@gmail.com>
This commit is contained in:
Harshvardhan Chandirasekar
2022-01-24 21:41:59 +05:30
committed by perseusdg
parent 00f06f7bcc
commit afdad8e661
7 changed files with 0 additions and 577 deletions
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include/tkDNN/Layer.h
-78
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@@ -33,7 +33,6 @@ enum layerType_t {
LAYER_REGION,
LAYER_YOLO,
LAYER_PADDING,
LAYER_BATCHNORM
};
#define TKDNN_BN_MIN_EPSILON 1e-5
@@ -90,7 +89,6 @@ public:
case LAYER_REGION: return "Region";
case LAYER_YOLO: return "Yolo";
case LAYER_PADDING: return "Padding";
case LAYER_BATCHNORM: return "BatchNorm";
default: return "unknown";
}
}
@@ -182,65 +180,6 @@ public:
};
class LayerBNWgs : public Layer {
public:
LayerBNWgs(Network* net, int input, int output, std::string fname_weights);
~LayerBNWgs();
int inputs, outputs;
std::string weights_path;
dnnType* bias_h, * bias_d;
dnnType* power_h = nullptr;
dnnType* scales_h = nullptr, * scales_d = nullptr;
dnnType* mean_h = nullptr, * mean_d = nullptr;
dnnType* variance_h = nullptr, * variance_d = nullptr;
__half* bias16_h = nullptr, * bias16_d = nullptr;
__half* power16_h = nullptr, * power16_d = nullptr;
__half* scales16_h = nullptr, * scales16_d = nullptr;
__half* mean16_h = nullptr, * mean16_d = nullptr;
__half* variance16_h = nullptr, * variance16_d = nullptr;
void releaseHost(bool release32 = true, bool release16 = true) {
if (release32) {
if (bias_h != nullptr) { delete[] bias_h; bias_h = nullptr; }
if (scales_h != nullptr) { delete[] scales_h; scales_h = nullptr; }
if (mean_h != nullptr) { delete[] mean_h; mean_h = nullptr; }
if (variance_h != nullptr) { delete[] variance_h; variance_h = nullptr; }
if (power_h != nullptr) { delete[] power_h; power_h = nullptr; }
}
if (net->fp16 && release16) {
if (bias16_h != nullptr) { delete[] bias16_h; bias16_h = nullptr; }
if (scales16_h != nullptr) { delete[] scales16_h; scales16_h = nullptr; }
if (mean16_h != nullptr) { delete[] mean16_h; mean16_h = nullptr; }
if (variance16_h != nullptr) { delete[] variance16_h; variance16_h = nullptr; }
if (power16_h != nullptr) { delete[] power16_h; power16_h = nullptr; }
}
}
void releaseDevice(bool release32 = true, bool release16 = true) {
if (release32) {
if (bias_d != nullptr) { cudaFree(bias_d); bias_d = nullptr; }
if (scales_d != nullptr) { cudaFree(scales_d); scales_d = nullptr; }
if (mean_d != nullptr) { cudaFree(mean_d); mean_d = nullptr; }
if (variance_d != nullptr) { cudaFree(variance_d); variance_d = nullptr; }
}
if (net->fp16 && release16) {
if (bias16_d != nullptr) { cudaFree(bias16_d); bias16_d = nullptr; }
if (scales16_d != nullptr) { cudaFree(scales16_d); scales16_d = nullptr; }
if (mean16_d != nullptr) { cudaFree(mean16_d); mean16_d = nullptr; }
if (variance16_d != nullptr) { cudaFree(variance16_d); variance16_d = nullptr; }
if (power16_d != nullptr) { cudaFree(power16_d); power16_d = nullptr; }
}
}
};
/**
Input layer (it doesn't need weights)
*/
@@ -606,24 +545,7 @@ public:
};
class BatchNorm : public LayerBNWgs {
public:
BatchNorm(Network *net,int output,std::string fname_weights);
virtual ~BatchNorm();
virtual layerType_t getLayerType(){return LAYER_BATCHNORM;};
virtual dnnType* infer(dataDim_t& dim,dnnType* srcData);
std::string weights_bin;
protected:
cudnnFilterDescriptor_t filterDesc;
cudnnConvolutionFwdAlgoPerf_t algo;
cudnnConvolutionBwdDataAlgoPerf_t bwAlgo;
cudnnTensorDescriptor_t biasTensorDesc;
void initCUDNN();
void inferCUDNN(dnnType* srcData);
void* workSpace;
size_t ws_sizeInBytes;
};
/**
Softmax layer
*/
include/tkDNN/NetworkRT.h
-1
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@@ -98,7 +98,6 @@ public:
nvinfer1::IResizeLayer* convert_layer(nvinfer1::ITensor *input, Upsample *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, DeformConv2d *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input,Padding *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor* input,BatchNorm *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor* input,MulAdd *l);
#if NV_TENSORRT_MAJOR > 5 && NV_TENSORRT_MAJOR < 8