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update README (#673)

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add latency

improve nms
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JumpPandaer 2021-08-16 10:59:57 +08:00 committed by GitHub
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commit ccdf0e6316
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3 changed files with 56 additions and 49 deletions
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48
rcnn/BatchedNms.cu
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@ -16,34 +16,32 @@
namespace nvinfer1 {
__global__ void batched_nms_kernel(
const int num_per_thread, const float threshold, const int num_detections,
const float threshold, const int num_detections,
const int *indices, float *scores, const float *classes, const float4 *boxes) {
// Go through detections by descending score
for (int m = 0; m < num_detections; m++) {
for (int n = 0; n < num_per_thread; n++) {
int i = threadIdx.x * num_per_thread + n;
if (i < num_detections && m < i && scores[m] > 0.0f) {
int idx = indices[i];
int max_idx = indices[m];
int icls = classes[idx];
int mcls = classes[max_idx];
if (mcls == icls) {
float4 ibox = boxes[idx];
float4 mbox = boxes[max_idx];
float x1 = max(ibox.x, mbox.x);
float y1 = max(ibox.y, mbox.y);
float x2 = min(ibox.z, mbox.z);
float y2 = min(ibox.w, mbox.w);
float w = max(0.0f, x2 - x1);
float h = max(0.0f, y2 - y1);
float iarea = (ibox.z - ibox.x) * (ibox.w - ibox.y);
float marea = (mbox.z - mbox.x) * (mbox.w - mbox.y);
float inter = w * h;
float overlap = inter / (iarea + marea - inter);
if (overlap > threshold) {
scores[i] = 0.0f;
}
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < num_detections && m < i && scores[m] > 0.0f) {
int idx = indices[i];
int max_idx = indices[m];
int icls = classes[idx];
int mcls = classes[max_idx];
if (mcls == icls) {
float4 ibox = boxes[idx];
float4 mbox = boxes[max_idx];
float x1 = max(ibox.x, mbox.x);
float y1 = max(ibox.y, mbox.y);
float x2 = min(ibox.z, mbox.z);
float y2 = min(ibox.w, mbox.w);
float w = max(0.0f, x2 - x1);
float h = max(0.0f, y2 - y1);
float iarea = (ibox.z - ibox.x) * (ibox.w - ibox.y);
float marea = (mbox.z - mbox.x) * (mbox.w - mbox.y);
float inter = w * h;
float overlap = inter / (iarea + marea - inter);
if (overlap > threshold) {
scores[i] = 0.0f;
}
}
}
@ -104,7 +102,7 @@ int batchedNms(int batch_size,
// TODO: different device has differnet max threads
const int max_threads = 1024;
int num_per_thread = ceil(static_cast<float>(num_detections) / max_threads);
batched_nms_kernel << <1, max_threads, 0, stream >> > (num_per_thread, nms_thresh, num_detections,
batched_nms_kernel << <num_per_thread, max_threads, 0, stream >> > (nms_thresh, num_detections,
indices_sorted, scores_sorted, in_classes, in_boxes);
// Re-sort with updated scores

15
rcnn/README.md
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@ -101,7 +101,7 @@ sudo ./rcnn -d faster.engine ../samples
R101-faster: ./configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml
R50-mask: ./configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml
R101-mask: ./configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml
3.set BACKBONE_RESNETTYPE = R50(R101) rcnn.cpp line 13
3.set BACKBONE_RESNETTYPE = R50(R101) rcnn.cpp line 14
4.set STRIDE_IN_1X1=true in backbone.hpp
5.follow how to run
```
@ -130,7 +130,18 @@ sudo ./rcnn -d faster.engine ../samples
1. quantizationType:fp32,fp16,int8. see BuildRcnnModel(rcnn.cpp line 345) for detail.
2. the usage of int8 is same with [tensorrtx/yolov5](../yolov5/README.md), but it has no improvement comparing to fp16.
2. the usage of int8 is same with [tensorrtx/yolov5](../yolov5/README.md).
## Latency
average cost of doInference(in rcnn.cpp) from second time with batch=1 under the ubuntu environment above, input size: 640(w)*480(h)
| | fp32 | fp16 | int8 |
| ------------- | ----- | ---- | ---- |
| Faster-R50C4 | 138ms | 36ms | 30ms |
| Faster-R101C4 | 146ms | 38ms | 32ms |
| Mask-R50C4 | 153ms | 44ms | 33ms |
| Mask-R101C4 | 168ms | 45ms | 35ms |
## Plugins

42
rcnn/RpnNms.cu
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@ -16,31 +16,29 @@
namespace nvinfer1 {
__global__ void rpn_nms_kernel(
const int num_per_thread, const float threshold, const int num_detections,
const float threshold, const int num_detections,
const int *indices, float *scores, const float4 *boxes) {
// Go through detections by descending score
for (int m = 0; m < num_detections; m++) {
for (int n = 0; n < num_per_thread; n++) {
int i = threadIdx.x * num_per_thread + n;
if (i < num_detections && m < i && scores[m] > -FLT_MAX) {
int idx = indices[i];
int max_idx = indices[m];
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < num_detections && m < i && scores[m] > -FLT_MAX) {
int idx = indices[i];
int max_idx = indices[m];
float4 ibox = boxes[idx];
float4 mbox = boxes[max_idx];
float x1 = max(ibox.x, mbox.x);
float y1 = max(ibox.y, mbox.y);
float x2 = min(ibox.z, mbox.z);
float y2 = min(ibox.w, mbox.w);
float w = max(0.0f, x2 - x1);
float h = max(0.0f, y2 - y1);
float iarea = (ibox.z - ibox.x) * (ibox.w - ibox.y);
float marea = (mbox.z - mbox.x) * (mbox.w - mbox.y);
float inter = w * h;
float overlap = inter / (iarea + marea - inter);
if (overlap > threshold) {
scores[i] = -FLT_MAX;
}
float4 ibox = boxes[idx];
float4 mbox = boxes[max_idx];
float x1 = max(ibox.x, mbox.x);
float y1 = max(ibox.y, mbox.y);
float x2 = min(ibox.z, mbox.z);
float y2 = min(ibox.w, mbox.w);
float w = max(0.0f, x2 - x1);
float h = max(0.0f, y2 - y1);
float iarea = (ibox.z - ibox.x) * (ibox.w - ibox.y);
float marea = (mbox.z - mbox.x) * (mbox.w - mbox.y);
float inter = w * h;
float overlap = inter / (iarea + marea - inter);
if (overlap > threshold) {
scores[i] = -FLT_MAX;
}
}
@ -98,7 +96,7 @@ namespace nvinfer1 {
// TODO: different device has differnet max threads
const int max_threads = 1024;
int num_per_thread = ceil(static_cast<float>(num_detections) / max_threads);
rpn_nms_kernel << <1, max_threads, 0, stream >> > (num_per_thread, nms_thresh, num_detections,
rpn_nms_kernel << <num_per_thread, max_threads, 0, stream >> > (nms_thresh, num_detections,
indices_sorted, scores_sorted, in_boxes);
// Re-sort with updated scores