修改yolov5的detect层，提高Triton推理服务的性能

1. 测试方法
2. 修改前的性能指标
3. 具体步骤
- 3.1 clone ultralytics yolov5 repo
- 3.2 改造detect层
- 3.3 导出onnx
- 3.4 导出tensorrt engine
4. 修改后的性能
REFERENCES

Infer模式下, yolov5 默认的detect层输出的数据是一个形状为[batches, 25200, 85]的张量。如果部署在Nvidia Triton中，输出层的张量大小过大，处理输出的时间会变大，造成队列积压。特别是在Triton Server和Client不在同一台机器，无法使用shared memory的情况下，通过网络将数据传输到client的时间还会变大，影响推理服务的性能。相关代码链接

1. 测试方法

将模型转换为tensorrt engine, 并部署在Triton Inference Server，instance group数量为1，类型为GPU，在其他机器上通过Triton提供的perf_analyzer工具进行性能测试。

将yolov5s.pt转换为onnx格式

将onnx转换为tensorrt engine

/usr/src/tensorrt/bin/trtexec  \
--onnx=yolov5s.onnx \
--minShapes=images:1x3x640x640 \
--optShapes=images:8x3x640x640 \
--maxShapes=images:32x3x640x640 \
--workspace=4096 \
--saveEngine=yolov5s.engine \
--shapes=images:1x3x640x640 \
--verbose \
--fp16 \
> result-FP16.txt

部署在Triton Inference Server

模型上传到Triton server 设置的model repository路径，编写模型服务配置

生成真实数据

python generate_input.py --input_images <image_path> ----output_file <real_data>.json

利用真实数据进行性能测试

perf_analyzer  -m <triton_model_name>  -b 1  --input-data <real_data>.json  --concurrency-range 1:10  --measurement-interval 10000  -u <triton server endpoint> -i gRPC  -f <triton_model_name>.csv

2. 修改前的性能指标

如下为使用默认detect层的yolov5 trt engine, 部署在triton的性能测试结果，可以看到，使用默认的detect层，大量时间消耗在队列积压(Server Queue)和输出数据的处理(Server Compute Output)，吞吐量甚至达不到 1 infer/sec

除了吞吐，其余指标的单位均为us, 其中Client Send和Client Recv分别为gRPC序列化、反序列化数据的时间

Concurrency	Inferences/Second	Client Send	Network+Server Send/Recv	Server Queue	Server Compute Input	Server Compute Infer	Server Compute Output	p90 latency
1	0.7	1683	1517232	466	8003	4412	9311	1592936
2	0.8	1464	1514475	393	10659	4616	956736	2583025
3	0.7	2613	1485868	1013992	7370	4396	1268070	3879331
4	0.7	2268	1463386	2230040	9933	5734	1250245	4983687
5	0.6	2064	1540583	3512025	11057	4843	1226058	6512305
6	0.6	2819	1573869	4802885	10134	4320	1234644	7888080
7	0.5	1664	1507386	6007235	11197	4899	1244482	8854777

因此，改造的一个方案就是将数据层进行精简，在送入nms之前根据conf对bbox进行粗略的筛选, 最后参考tensorrtx中对detect层的处理，将输出改造成形状为[batches, num_bboxes, 6]的向量, 其中num_bboxes=1000

6 = [cx,cy,w,h,conf,cls_id], 其中conf = obj_conf * cls_prob

3. 具体步骤

3.1 clone ultralytics yolov5 repo

git clone -b v6.1 https://github.com/ultralytics/yolov5.git

3.2 改造detect层

将detect的forward函数修改为

def forward(self, x):z = []  # inference outputfor i in range(self.nl):x[i] = self.m[i](x[i])  # convbs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()if not self.training:  # inferenceif self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)y = x[i].sigmoid()if self.inplace:y[..., 0:2] = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i]  # xyy[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # whelse:  # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953xy = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i]  # xywh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # why = torch.cat((xy, wh, y[..., 4:]), -1)z.append(y.view(bs, -1, self.no))# custom output >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>># [bs, 25200, 85]origin_output = torch.cat(z, 1)output_bboxes_nums = 1000# operator argsort to ONNX opset version 12 is not supported.# top_conf_index = origin_output[..., 4].argsort(descending=True)[:,:output_bboxes_nums]# [bs, 1000]top_conf_index =origin_output[..., 4].topk(k=output_bboxes_nums)[1]# torch.Size([bs, 1000, 85])filter_output = origin_output.gather(1, top_conf_index.unsqueeze(-1).expand(-1, -1, 85))filter_output[...,5:] *= filter_output[..., 4].unsqueeze(-1)  # conf = obj_conf * cls_confbboxes =  filter_output[..., :4]conf, cls_id = filter_output[..., 5:].max(2, keepdim=True)# [bs, 1000, 6]filter_output = torch.cat((bboxes, conf, cls_id.float()), 2)return x if self.training else filter_output# custom output >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>># return x if self.training else (torch.cat(z, 1), x)

3.3 导出onnx

onnx simplify的时候，必须注释掉下面的代码，否则导出的onnx模型仍然为static shape

model_onnx, check = onnxsim.simplify(model_onnx,dynamic_input_shape=dynamic# 必须注释#input_shapes={'images': list(im.shape)} if dynamic else None)

运行python export.py --weight yolov5s.pt --dynamic --simplify --include onnx导出onnx模型，导出的onnx结构如下:

3.4 导出tensorrt engine

见上文

4. 修改后的性能

batch size = 1

吞吐量提升了25倍以上，Server Queue和Server Compute Output的时间也显著降低

Concurrency	Inferences/Second	Client Send	Network+Server Send/Recv	Server Queue	Server Compute Input	Server Compute Infer	Server Compute Output	Client Recv	p90 latency
1	11.9	1245	69472	286	7359	5022	340	3	93457
2	19.2	1376	89804	341	7538	4997	161	3	118114
3	20.2	1406	131265	1500	8240	4881	500	3	171370
4	20	1382	180621	2769	9051	5184	496	3	235043
5	20.5	1362	226046	2404	8112	5068	622	3	286810
6	20.8	1487	271714	2034	8331	5076	506	3	406248
7	20.1	1535	328144	2626	8444	5122	405	3	430850
8	19.9	1512	384690	3511	8168	5018	581	5	465658
9	20.2	1433	420893	3499	9034	5180	389	3	522285
10	20.5	1476	469029	3369	8280	5165	442	3	622745

batch size = 8

相对 batch size = 1, Server Compute Input、Server Compute Infer, Server Compute Output速度分别提升了约1.4倍、2倍、4倍，代价是随着batch增大，数据传输的耗时增大

Concurrency	Inferences/Second	Client Send	Network+Server Send/Recv	Server Queue	Server Compute Input	Server Compute Infer	Server Compute Output	Client Recv	p90 latency
1	15.2	11202	527075	360	5386	2488	43	5	570189
2	18.4	10424	829927	124	5780	2491	33	4	901743
3	20	10203	1178111	2290	5640	2570	20	4	1267145
4	20	10097	1595614	4843	5998	2454	104	5	1716309
5	19.2	9117	1971608	2397	5376	2480	203	4	2518530
6	20	8728	2338066	2914	6304	2496	96	4	2706257
7	20	14785	2708292	6581	5556	2489	160	5	3170047
8	20	13035	3052707	5067	6353	2492	62	4	3235293
9	17.6	10870	3535601	7037	6307	2480	136	5	3856391
10	18.4	9357	3953830	8044	5629	2520	64	3	4531638

REFERENCES

Ultralytics Yolov5
Perf Analyzer