YOLOv5 - 游戏本 GTX1070 和 RTX3070 的 AI 性能对比

目录

一、YOLOv5四个模型效果

二、GTX1070 和 RTX3070显卡参数对比

三、GTX1070，HP OMEN Laptop 17-an0xx

1. weights yolov5s.pt

2. 从摄像头采集图像

3. weights yolov5m.pt

4. Train性能

四、RTX3070，ASUS 天选 AIR

1. weights yolov5s.pt

2. weights yolov5m.pt

3. 查看显卡信息

4. 安装CUDA11.2后查看显卡信息

5. Train性能

6. RTX3070性能显示

五、小结

---

游戏本是做AI学习、训练的一个不错的选择，既可以名正言顺的买来作为生产力工具，也可以顺便玩玩游戏。RTX30系列的显卡一度抢购中，被誉为空气卡，笑称：看得见，买不着！

比起动辄上万的显卡和一万好几的游戏本，华硕天选系列是一款性价比不错的游戏本。春节的时候抢购了一台。

既然作为生产力工具，就要测试一下AI的性能，拿目前比较火的YOLOv5为例，顺便拿几年前的HP OMEN 17游戏本做一个参照。

一、YOLOv5四个模型效果

yolov5按大小分为四个模型yolov5s、yolov5m、yolov5l、yolov5x，这四个模型的表现见下图：

上图为基于5000张COCO val2017图像进行推理时，每张图像的平均端到端时间，batch size = 32, GPU：Tesla V100（计算能力7.0，拥有 640个Tensor Cores 和 5120个CUDA核心，16GB HBM2以及15TFLOPS的单精度性能），这个时间包括图像预处理，FP16推理，后处理和NMS（非极大值抑制）。 EfficientDet的数据是从 google/automl 仓库得到的（batch size = 8）。

Model	size	APval	APtest	AP50	SpeedV100	FPSV100	params	GFLOPS
YOLOv5s	640	36.8	36.8	55.6	2.2ms	455	7.3M	17.0
YOLOv5m	640	44.5	44.5	63.1	2.9ms	345	21.4M	51.3
YOLOv5l	640	48.1	48.1	66.4	3.8ms	264	47.0M	115.4
YOLOv5x	640	50.1	50.1	68.7	6.0ms	167	87.7M	218.8
YOLOv5x + TTA	832	51.9	51.9	69.6	24.9ms	40	87.7M	1005.3

二、GTX1070 和 RTX3070显卡参数对比

RTX3070的计算能力是8.6，使用目前最新版的GPU-Z 2.37.0版本，才能正确识别RTX3070的Laptop GPU。

从CUDA核心数来看，ASUS 天选 air 的 RTX3070 Laptop GPU是5120个CUDA核，HP OMEN 17 的 GTX1070并不是Laptop 版本，应该是标准版，是2048核心，RTX3070采用新一代 Ampere 构架，RTX3070的性能应该至少在GTX1070的两倍以上，但是，考虑到GPU的运行频率、所用显存的频率，以及厂家对散热的处理和功率的限制等，性能会有较大出入。

Intel 11代酷睿的集成 Iris Xe 显卡有96个EU，全新的EU集成了8-wide FP/INT ALU8和2-wide extended math ALU。相当于96 * 8=768 个CUDA核心? 从核心数目上来说，跟GeForce MX 350 的 768 个CUDA 核心一样多， 比GeForce MX 450 的 896 个CUDA 核心少。（感觉GPU-Z的数据有点低，不确定是不是新出的GPU还不认识？）

Tflops理论峰值

单精度理论峰值＝ GPU芯片数量＊GPU Boost主频＊核心数量＊单个时钟周期内能处理的浮点计算次数，

单精度理论峰值 ＝ FP32 cores ＊ GPU Boost Clock ＊ 2

Intel 11代酷睿 Iris Xe: 96 * 8 ＊ 1.35GHz ＊ 2 ＝  2.07 TFlops ？

GeForce MX 450:     896 ＊ 1.575GHz ＊ 2 ＝  2.8 TFlops

GeForce GTX 1070: 2048 ＊ 1.645GHz ＊ 2 ＝  6.7 TFlops（惠普OMEN）

GeForce RTX 3060: 3840 ＊ 1.525GHz ＊ 2 ＝  11.7 TFlops（ASUS天选air，8499元）

GeForce RTX 3070: 5120 ＊ 1.390GHz ＊ 2 ＝  14.2 TFlops（ASUS天选air，9999元）

详细一点的对比，请参看链接：

RTX30系列游戏本与台式机、云服务器显卡AI计算力对比

三、GTX1070，HP OMEN Laptop 17-an0xx，17寸，3.5Kg，

1. weights yolov5s.pt

(yolov5) C:\yolo\yolov5>python detect.py --source data/images --weights weights/yolov5s.pt --conf 0.25
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='data/images', update=False, view_img=False, weights=['weights/yolov5s.pt'])
Using torch 1.7.0+cu101 CUDA:0 (GeForce GTX 1070, 8192.0MB)

Fusing layers...
Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPS
image 1/3 C:\yolo\yolov5\data\images\bus.jpg: 640x480 4 persons, 1 buss, Done. (0.014s)
image 2/3 C:\yolo\yolov5\data\images\gj.jpg: 480x640 Done. (0.010s)
image 3/3 C:\yolo\yolov5\data\images\zidane.jpg: 384x640 2 persons, 1 ties, Done. (0.010s)
Results saved to runs\detect\exp4
Done. (0.345s)

图1： bus.jpg

图2：zidane.jpg

2. 从摄像头采集图像

(yolov5) C:\yolo\yolov5>python detect.py --source 0 --weights weights/yolov5s.pt --conf 0.25
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='0', update=False, view_img=False, weights=['weights/yolov5s.pt'])
Using torch 1.7.0+cu101 CUDA:0 (GeForce GTX 1070, 8192.0MB)

Fusing layers...
Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPS
1/1: 0...  success (640x480 at 30.00 FPS).

0: 480x640 1 persons, Done. (0.585s)
0: 480x640 1 persons, Done. (0.016s)
0: 480x640 1 persons, Done. (0.010s)

3. weights yolov5m.pt

(yolov5) C:\yolo\yolov5>python detect.py --source data/images --weights weights/yolov5m.pt --conf 0.25
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='data/images', update=False, view_img=False, weights=['weights/yolov5m.pt'])
Using torch 1.7.0+cu101 CUDA:0 (GeForce GTX 1070, 8192.0MB)

Fusing layers...
Model Summary: 308 layers, 21356877 parameters, 0 gradients, 51.3 GFLOPS
image 1/3 C:\yolo\yolov5\data\images\bus.jpg: 640x480 4 persons, 1 buss, Done. (0.023s)
image 2/3 C:\yolo\yolov5\data\images\gj.jpg: 480x640 Done. (0.019s)
image 3/3 C:\yolo\yolov5\data\images\zidane.jpg: 384x640 2 persons, 1 ties, Done. (0.017s)
Results saved to runs\detect\exp7
Done. (0.375s)

4. Train性能

(yolov5) C:\yolo\yolov5>python train.py --img 640 --batch 1 --epochs 16 --data data/coco128.yaml --weights weights/yolov5s.pt --nosave --cache
Using torch 1.7.0+cu101 CUDA:0 (GeForce GTX 1070, 8192.0MB)

Optimizer stripped from runs\train\exp13\weights\last.pt, 14.8MB
Optimizer stripped from runs\train\exp13\weights\best.pt, 14.8MB
16 epochs completed in 0.088 hours.

(yolov5) C:\yolo\yolov5>python train.py --img 640 --batch 2 --epochs 16 --data data/coco128.yaml --weights weights/yolov5s.pt --nosave --cache
Using torch 1.7.0+cu101 CUDA:0 (GeForce GTX 1070, 8192.0MB)

Optimizer stripped from runs\train\exp15\weights\last.pt, 14.8MB
16 epochs completed in 0.050 hours.

--batch 3 以上会报错（显存不够？），所以只能测试--batch 2 以下。

四、RTX3070，ASUS 天选 AIR，15.6寸，2.1Kg

1. weights yolov5s.pt

(yolov5) C:\yolo\yolov5>python detect.py --source data/images --weights weights/yolov5s.pt --conf 0.25
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='data/images', update=False, view_img=False, weights=['weights/yolov5s.pt'])
YOLOv5  torch 1.7.1 CUDA:0 (GeForce RTX 3070 Laptop GPU, 8192.0MB)

Fusing layers...
Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPS
image 1/3 C:\yolo\yolov5\data\images\bus.jpg: 640x480 4 persons, 1 bus, Done. (0.036s)
image 2/3 C:\yolo\yolov5\data\images\gj.jpg: 480x640 Done. (0.018s)
image 3/3 C:\yolo\yolov5\data\images\zidane.jpg: 384x640 2 persons, 1 tie, Done. (0.014s)
Results saved to runs\detect\exp24
Done. (0.291s)

2. weights yolov5m.pt

(yolov5) C:\yolo\yolov5>python detect.py --source data/images --weights weights/yolov5m.pt --conf 0.25
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='data/images', update=False, view_img=False, weights=['weights/yolov5m.pt'])
YOLOv5  torch 1.7.1 CUDA:0 (GeForce RTX 3070 Laptop GPU, 8192.0MB)

Fusing layers...
Model Summary: 308 layers, 21356877 parameters, 0 gradients, 51.3 GFLOPS
image 1/3 C:\yolo\yolov5\data\images\bus.jpg: 640x480 4 persons, 1 bus, Done. (0.031s)
image 2/3 C:\yolo\yolov5\data\images\gj.jpg: 480x640 Done. (0.016s)
image 3/3 C:\yolo\yolov5\data\images\zidane.jpg: 384x640 2 persons, 1 tie, Done. (0.016s)
Results saved to runs\detect\exp25
Done. (0.234s)

3. 查看显卡信息

C:\ProgramData\NVIDIA Corporation\CUDA Samples\v11.0\bin\win64\Release>deviceQuery.exe
deviceQuery.exe Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "GeForce RTX 3070 Laptop GPU"
  CUDA Driver Version / Runtime Version          11.2 / 11.0
  CUDA Capability Major/Minor version number:    8.6
  Total amount of global memory:                 8192 MBytes (8589934592 bytes)
MapSMtoCores for SM 8.6 is undefined.  Default to use 64 Cores/SM
MapSMtoCores for SM 8.6 is undefined.  Default to use 64 Cores/SM
  (40) Multiprocessors, ( 64) CUDA Cores/MP:     2560 CUDA Cores
  GPU Max Clock rate:                            1290 MHz (1.29 GHz)
  Memory Clock rate:                             6001 Mhz
  Memory Bus Width:                              256-bit
  L2 Cache Size:                                 4194304 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       49152 bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  1536
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             512 bytes
  Concurrent copy and kernel execution:          Yes with 5 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Disabled
  CUDA Device Driver Mode (TCC or WDDM):         WDDM (Windows Display Driver Model)
  Device supports Unified Addressing (UVA):      Yes
  Device supports Managed Memory:                Yes
  Device supports Compute Preemption:            Yes
  Supports Cooperative Kernel Launch:            Yes
  Supports MultiDevice Co-op Kernel Launch:      No
  Device PCI Domain ID / Bus ID / location ID:   0 / 1 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.2, CUDA Runtime Version = 11.0, NumDevs = 1
Result = PASS

C:\ProgramData\NVIDIA Corporation\CUDA Samples\v11.0\bin\win64\Release>bandwidthTest.exe
[CUDA Bandwidth Test] - Starting...
Running on...

 Device 0: GeForce RTX 3070 Laptop GPU
 Quick Mode

 Host to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)        Bandwidth(GB/s)
   32000000                     5.9

 Device to Host Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)        Bandwidth(GB/s)
   32000000                     6.6

 Device to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)        Bandwidth(GB/s)
   32000000                     356.8

Result = PASS

NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.

4. 安装CUDA11.2后查看显卡信息

C:\ProgramData\NVIDIA Corporation\CUDA Samples\v11.2\bin\win64\Release>deviceQuery.exe

deviceQuery.exe Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "GeForce RTX 3070 Laptop GPU"
  CUDA Driver Version / Runtime Version          11.2 / 11.2
  CUDA Capability Major/Minor version number:    8.6
  Total amount of global memory:                 8192 MBytes (8589934592 bytes)
  (40) Multiprocessors, (128) CUDA Cores/MP:     5120 CUDA Cores
  GPU Max Clock rate:                            1290 MHz (1.29 GHz)
  Memory Clock rate:                             6001 Mhz
  Memory Bus Width:                              256-bit
  L2 Cache Size:                                 4194304 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       49152 bytes
  Total shared memory per multiprocessor:        102400 bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  1536
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             512 bytes
  Concurrent copy and kernel execution:          Yes with 5 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Disabled
  CUDA Device Driver Mode (TCC or WDDM):         WDDM (Windows Display Driver Model)
  Device supports Unified Addressing (UVA):      Yes
  Device supports Managed Memory:                Yes
  Device supports Compute Preemption:            Yes
  Supports Cooperative Kernel Launch:            Yes
  Supports MultiDevice Co-op Kernel Launch:      No
  Device PCI Domain ID / Bus ID / location ID:   0 / 1 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.2, CUDA Runtime Version = 11.2, NumDevs = 1
Result = PASS

5. Train性能

(yolov5) C:\yolo\yolov5>python train.py --img 640 --batch 1 --epochs 16 --data data/coco128.yaml --weights weights/yolov5s.pt --nosave --cache
github: skipping check (not a git repository)
YOLOv5  torch 1.7.1 CUDA:0 (GeForce RTX 3070 Laptop GPU, 8192.0MB)

Optimizer stripped from runs\train\exp24\weights\last.pt, 14.8MB
Optimizer stripped from runs\train\exp24\weights\best.pt, 14.8MB

16 epochs completed in 0.050 hours.

(yolov5) C:\yolo\yolov5>python train.py --img 640 --batch 2 --epochs 16 --data data/coco128.yaml --weights weights/yolov5s.pt --nosave --cache
github: skipping check (not a git repository)
YOLOv5  torch 1.7.1 CUDA:0 (GeForce RTX 3070 Laptop GPU, 8192.0MB)

Optimizer stripped from runs\train\exp27\weights\last.pt, 14.8MB
Optimizer stripped from runs\train\exp27\weights\best.pt, 14.8MB
16 epochs completed in 0.029 hours.

(yolov5) C:\yolo\yolov5>python train.py --img 640 --batch 6 --epochs 16 --data data/coco128.yaml --weights weights/yolov5s.pt --nosave --cache
github: skipping check (not a git repository)
YOLOv5  torch 1.7.1 CUDA:0 (GeForce RTX 3070 Laptop GPU, 8192.0MB)

Optimizer stripped from runs\train\exp26\weights\last.pt, 14.8MB
16 epochs completed in 0.025 hours.

--batch 7 以上会报错（显存不够？），所以只能测试--batch 6 以下。

6. RTX3070性能显示

将显卡的性能调整为“增强”模式，以期发挥RTX的最大性能。可以看到，ASUS 天选 air 的GPU频率与使用率随时在变化，导致深度学习算法的计算时间也不是固定的，计时结果仅供参考：

五、小结

开始仅从Detect 的结果来看，RTX3070好像干不过GTX1070，仔细检查发现，CUDA11.0版本不能够正确识别RTX3070。

重新安装CUDA11.2，并测试。安装过程请见：YOLOv5 - Win10 + RTX3070 的 PyTorch 安装指南（ASUS 天选 air）

从Train的结果来看：

对于 --batch 1参数，GTX 1070 用时：0.088h，RTX 3070 用时：0.050h，RTX 3070 用时是 GTX 1070 的57%左右。

对于 --batch 2参数，GTX 1070 用时：0.050h，RTX 3070 用时：0.029h，RTX 3070 用时是 GTX 1070 的58%左右。

对于 --batch 6参数，GTX 1070 报错：测不出，RTX 3070 用时：0.025h。

所以，就测试的两台电脑而言，RTX 3070 的 AI 训练能力大约是 GTX 1070 的 1.7 倍。

 

                                                                                                                                                         老徐  2021/2/19