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Fix Darknet models / Small fixes

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This commit is contained in:
Marcos Luciano
2023-06-16 14:56:18 -03:00
parent f8758d0e0e
commit f630b10a80
5 changed files with 27 additions and 15 deletions
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18
docs/YOLONAS.md
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@@ -190,7 +190,7 @@ parse-bbox-func-name=NvDsInferParseYoloE
...
```
**NOTE**: If you are using a custom model, you should edit the `config_infer_primary_yolonas_custom.txt` file.
**NOTE**: If you are using a **custom** model, you should edit the `config_infer_primary_yolonas_custom.txt` file.
**NOTE**: The **YOLO-NAS** resizes the input with left/top padding. To get better accuracy, use
@@ -201,6 +201,22 @@ symmetric-padding=0
...
```
**NOTE**: The **pre-trained YOLO-NAS** uses zero mean normalization on the image preprocess. It is important to change the `net-scale-factor` according to the trained values.
```
...
net-scale-factor=0.0039215697906911373
...
```
**NOTE**: The **custom YOLO-NAS** uses no normalization on the image preprocess. It is important to change the `net-scale-factor` according to the trained values.
```
...
net-scale-factor=1
...
```
**NOTE**: By default, the dynamic batch-size is set. To use implicit batch-size, uncomment the line
```

6
nvdsinfer_custom_impl_Yolo/yoloForward.cu
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@@ -7,7 +7,7 @@
inline __device__ float sigmoidGPU(const float& x) { return 1.0f / (1.0f + __expf(-x)); }
__global__ void gpuYoloLayer(const float* input, float* boxes, float* scores, int* classes, const uint netWidth,
__global__ void gpuYoloLayer(const float* input, float* boxes, float* scores, float* classes, const uint netWidth,
const uint netHeight, const uint gridSizeX, const uint gridSizeY, const uint numOutputClasses, const uint numBBoxes,
const uint64_t lastInputSize, const float scaleXY, const float* anchors, const int* mask)
{
@@ -54,7 +54,7 @@ __global__ void gpuYoloLayer(const float* input, float* boxes, float* scores, in
boxes[count * 4 + 2] = w;
boxes[count * 4 + 3] = h;
scores[count] = maxProb * objectness;
classes[count] = maxIndex;
classes[count] = (float) maxIndex;
}
cudaError_t cudaYoloLayer(const void* input, void* boxes, void* scores, void* classes, const uint& batchSize,
@@ -76,7 +76,7 @@ cudaError_t cudaYoloLayer(const void* input, void* boxes, void* scores, void* cl
reinterpret_cast<const float*> (input) + (batch * inputSize),
reinterpret_cast<float*> (boxes) + (batch * 4 * outputSize),
reinterpret_cast<float*> (scores) + (batch * 1 * outputSize),
reinterpret_cast<int*> (classes) + (batch * 1 * outputSize),
reinterpret_cast<float*> (classes) + (batch * 1 * outputSize),
netWidth, netHeight, gridSizeX, gridSizeY, numOutputClasses, numBBoxes, lastInputSize, scaleXY,
reinterpret_cast<const float*> (anchors), reinterpret_cast<const int*> (mask));
}

6
nvdsinfer_custom_impl_Yolo/yoloForward_nc.cu
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@@ -5,7 +5,7 @@
#include <stdint.h>
__global__ void gpuYoloLayer_nc(const float* input, float* boxes, float* scores, int* classes, const uint netWidth,
__global__ void gpuYoloLayer_nc(const float* input, float* boxes, float* scores, float* classes, const uint netWidth,
const uint netHeight, const uint gridSizeX, const uint gridSizeY, const uint numOutputClasses, const uint numBBoxes,
const uint64_t lastInputSize, const float scaleXY, const float* anchors, const int* mask)
{
@@ -52,7 +52,7 @@ __global__ void gpuYoloLayer_nc(const float* input, float* boxes, float* scores,
boxes[count * 4 + 2] = w;
boxes[count * 4 + 3] = h;
scores[count] = maxProb * objectness;
classes[count] = maxIndex;
classes[count] = (float) maxIndex;
}
cudaError_t cudaYoloLayer_nc(const void* input, void* boxes, void* scores, void* classes, const uint& batchSize,
@@ -74,7 +74,7 @@ cudaError_t cudaYoloLayer_nc(const void* input, void* boxes, void* scores, void*
reinterpret_cast<const float*> (input) + (batch * inputSize),
reinterpret_cast<float*> (boxes) + (batch * 4 * outputSize),
reinterpret_cast<float*> (scores) + (batch * 1 * outputSize),
reinterpret_cast<int*> (classes) + (batch * 1 * outputSize),
reinterpret_cast<float*> (classes) + (batch * 1 * outputSize),
netWidth, netHeight, gridSizeX, gridSizeY, numOutputClasses, numBBoxes, lastInputSize, scaleXY,
reinterpret_cast<const float*> (anchors), reinterpret_cast<const int*> (mask));
}

6
nvdsinfer_custom_impl_Yolo/yoloForward_v2.cu
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@@ -27,7 +27,7 @@ __device__ void softmaxGPU(const float* input, const int bbindex, const int numG
}
}
__global__ void gpuRegionLayer(const float* input, float* softmax, float* boxes, float* scores, int* classes,
__global__ void gpuRegionLayer(const float* input, float* softmax, float* boxes, float* scores, float* classes,
const uint netWidth, const uint netHeight, const uint gridSizeX, const uint gridSizeY, const uint numOutputClasses,
const uint numBBoxes, const uint64_t lastInputSize, const float* anchors)
{
@@ -73,7 +73,7 @@ __global__ void gpuRegionLayer(const float* input, float* softmax, float* boxes,
boxes[count * 4 + 2] = w;
boxes[count * 4 + 3] = h;
scores[count] = maxProb * objectness;
classes[count] = maxIndex;
classes[count] = (float) maxIndex;
}
cudaError_t cudaRegionLayer(const void* input, void* softmax, void* boxes, void* scores, void* classes,
@@ -96,7 +96,7 @@ cudaError_t cudaRegionLayer(const void* input, void* softmax, void* boxes, void*
reinterpret_cast<float*> (softmax) + (batch * inputSize),
reinterpret_cast<float*> (boxes) + (batch * 4 * outputSize),
reinterpret_cast<float*> (scores) + (batch * 1 * outputSize),
reinterpret_cast<int*> (classes) + (batch * 1 * outputSize),
reinterpret_cast<float*> (classes) + (batch * 1 * outputSize),
netWidth, netHeight, gridSizeX, gridSizeY, numOutputClasses, numBBoxes, lastInputSize,
reinterpret_cast<const float*> (anchors));
}

6
nvdsinfer_custom_impl_Yolo/yoloPlugins.cpp
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@@ -181,17 +181,13 @@ YoloLayer::getOutputDimensions(INT index, const nvinfer1::DimsExprs* inputs, INT
bool
YoloLayer::supportsFormatCombination(INT pos, const nvinfer1::PluginTensorDesc* inOut, INT nbInputs, INT nbOutputs) noexcept
{
return inOut[pos].format == nvinfer1::TensorFormat::kLINEAR && (inOut[pos].type == nvinfer1::DataType::kFLOAT ||
inOut[pos].type == nvinfer1::DataType::kINT32);
return inOut[pos].format == nvinfer1::TensorFormat::kLINEAR && inOut[pos].type == nvinfer1::DataType::kFLOAT;
}
nvinfer1::DataType
YoloLayer::getOutputDataType(INT index, const nvinfer1::DataType* inputTypes, INT nbInputs) const noexcept
{
assert(index < 3);
if (index == 2) {
return nvinfer1::DataType::kINT32;
}
return nvinfer1::DataType::kFLOAT;
}