Windows 10 yolov5 GPU环境

网上关于yolov5 gpu环境搭建的文章也是一抓一大把,但是实际上好用不好用并不清楚。所以要想按照那些所谓的教程安装配置,很可能会失败。当然按照我的文章进行安装配置也可能会失败。逼乎上有个帖子问新手学习一门语言该不该用ide,还有一大群人建议新手配置各种环境,用sb vim等编辑器,配置各种执行路径,各种源代码路径、库路径。这tm一个ide就解决的问题,非得折腾半天,这是为了让没入门的赶紧放弃?

我简单的说一下我的安装流程:

1.下载cuda安装文件,https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exe_network 安装cuda,默认一路next 即可。

2.安装pytorch-gpu,yolov5的运行环境主要依赖于pytorch。通过官网https://pytorch.org可以找到对应的安装命令:

我使用的就是上面的安装命令:

NOTE: 'conda-forge' channel is required for cudatoolkit 11.1
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c conda-forge

3.安装yolov5,一定要最后安装,前面通过conda安装了pytorch gpu版本之后,通过pip安装yolov5的依赖库就不再需要安装pytorch了。

$ git clone https://github.com/ultralytics/yolov5
$ cd yolov5
$ pip install -r requirements.txt

4.安装完成之后可以通过下面的代码进行测试:

同样,我创建了一个conda环境,可以通过这个链接下载对应的conda环境:https://anaconda.org/obaby/yolov5-gpu

到这里安装就完成了,可以开始训练了。但是很不幸的是,报了下面的错误:

(E:\anaconda_dirs\venvs\yolov5-gpu) C:\Users\obaby>cd /d F:\Pycharm_Projects\yolov5

(E:\anaconda_dirs\venvs\yolov5-gpu) F:\Pycharm_Projects\yolov5>python train_ads.py
train: weights=yolov5s.pt, cfg=, data=data/ads.yaml, hyp=data/hyps/hyp.scratch.yaml, epochs=300, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=True, adam=False, sync_bn=False, workers=8, project=runs/train, entity=None, name=exp, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias=latest, local_rank=-1, freeze=0, patience=30
github: remote: Enumerating objects: 4, done.
remote: Counting objects: 100% (4/4), done.
remote: Compressing objects: 100% (4/4), done.
remote: Total 4 (delta 0), reused 0 (delta 0), pack-reused 0
Unpacking objects: 100% (4/4), 14.05 KiB | 410.00 KiB/s, done.
From https://github.com/ultralytics/yolov5
aa18599..fcb225c master -> origin/master
YOLOv5 is out of date by 26 commits. Use `git pull` or `git clone https://github.com/ultralytics/yolov5` to update.
YOLOv5 v5.0-405-gfad57c2 torch 1.9.0 CUDA:0 (NVIDIA GeForce RTX 3080, 10240.0MB)

hyperparameters: lr0=0.01, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
Weights & Biases: run 'pip install wandb' to automatically track and visualize YOLOv5 runs (RECOMMENDED)
TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/
Overriding model.yaml nc=80 with nc=1

from n params module arguments
0 -1 1 3520 models.common.Focus [3, 32, 3]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 18816 models.common.C3 [64, 64, 1]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 3 156928 models.common.C3 [128, 128, 3]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 3 625152 models.common.C3 [256, 256, 3]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]]
9 -1 1 1182720 models.common.C3 [512, 512, 1, False]
10 -1 1 131584 models.common.Conv [512, 256, 1, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 models.common.Concat [1]
13 -1 1 361984 models.common.C3 [512, 256, 1, False]
14 -1 1 33024 models.common.Conv [256, 128, 1, 1]
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
16 [-1, 4] 1 0 models.common.Concat [1]
17 -1 1 90880 models.common.C3 [256, 128, 1, False]
18 -1 1 147712 models.common.Conv [128, 128, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 1 296448 models.common.C3 [256, 256, 1, False]
21 -1 1 590336 models.common.Conv [256, 256, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 1 1182720 models.common.C3 [512, 512, 1, False]
24 [17, 20, 23] 1 16182 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model Summary: 283 layers, 7063542 parameters, 7063542 gradients, 16.4 GFLOPs

Transferred 356/362 items from yolov5s.pt
Scaled weight_decay = 0.0005
optimizer: SGD with parameter groups 59 weight, 62 weight (no decay), 62 bias
train: Scanning 'data\train.cache' images and labels... 16 found, 0 missing, 0 empty, 0 corrupted: 100%|█| 16/16 [00:00
val: Scanning 'data\val.cache' images and labels... 2 found, 0 missing, 0 empty, 0 corrupted: 100%|█| 2/2 [00:00<?, ?it
Plotting labels...
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\multiprocessing\spawn.py", line 116, in spawn_main
exitcode = _main(fd, parent_sentinel)
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\multiprocessing\spawn.py", line 125, in _main
prepare(preparation_data)
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\multiprocessing\spawn.py", line 236, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path
main_content = runpy.run_path(main_path,
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\runpy.py", line 265, in run_path
return _run_module_code(code, init_globals, run_name,
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "F:\Pycharm_Projects\yolov5\train_ads.py", line 20, in <module>
import torch
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\site-packages\torch\__init__.py", line 124, in <module>
raise err
OSError: [WinError 1455] 页面文件太小,无法完成操作。 Error loading "E:\anaconda_dirs\venvs\yolov5-gpu\lib\site-packages\torch\lib\cudnn_adv_infer64_8.dll" or one of its dependencies.
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\multiprocessing\spawn.py", line 116, in spawn_main
exitcode = _main(fd, parent_sentinel)
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\multiprocessing\spawn.py", line 125, in _main
prepare(preparation_data)
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\multiprocessing\spawn.py", line 236, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path
main_content = runpy.run_path(main_path,
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\runpy.py", line 265, in run_path
return _run_module_code(code, init_globals, run_name,
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "F:\Pycharm_Projects\yolov5\train_ads.py", line 20, in <module>
import torch
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\site-packages\torch\__init__.py", line 124, in <module>
raise err
OSError: [WinError 1455] 页面文件太小,无法完成操作。 Error loading "E:\anaconda_dirs\venvs\yolov5-gpu\lib\site-packages\torch\lib\cudnn_adv_infer64_8.dll" or one of its dependencies.

autoanchor: Analyzing anchors... anchors/target = 4.44, Best Possible Recall (BPR) = 1.0000
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs\train\exp16
Starting training for 300 epochs...

Epoch gpu_mem box obj cls labels img_size
0%| | 0/1 [00:00<?, ?it/s]
Traceback (most recent call last):
File "train_ads.py", line 610, in <module>
main(opt)
File "train_ads.py", line 508, in main
train(opt.hyp, opt, device)
File "train_ads.py", line 286, in train
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\site-packages\tqdm\std.py", line 1185, in __iter__
for obj in iterable:
File "F:\Pycharm_Projects\yolov5\utils\datasets.py", line 139, in __iter__
yield next(self.iterator)
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\site-packages\torch\utils\data\dataloader.py", line 521, in __next__
data = self._next_data()
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\site-packages\torch\utils\data\dataloader.py", line 1203, in _next_data
return self._process_data(data)
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\site-packages\torch\utils\data\dataloader.py", line 1229, in _process_data
data.reraise()
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\site-packages\torch\_utils.py", line 425, in reraise
raise self.exc_type(msg)
cv2.error: Caught error in DataLoader worker process 0.
Original Traceback (most recent call last):
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\site-packages\torch\utils\data\_utils\worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\site-packages\torch\utils\data\_utils\fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "E:\anaconda_dirs\venvs\yolov5-gpu\lib\site-packages\torch\utils\data\_utils\fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "F:\Pycharm_Projects\yolov5\utils\datasets.py", line 536, in __getitem__
img, labels = load_mosaic(self, index)
File "F:\Pycharm_Projects\yolov5\utils\datasets.py", line 666, in load_mosaic
img, _, (h, w) = load_image(self, index)
File "F:\Pycharm_Projects\yolov5\utils\datasets.py", line 645, in load_image
im = cv2.imread(path) # BGR
cv2.error: OpenCV(4.5.3) C:\Users\runneradmin\AppData\Local\Temp\pip-req-build-q3d_8t8e\opencv\modules\core\src\alloc.cpp:73: error: (-4:Insufficient memory) Failed to allocate 7581600 bytes in function 'cv::OutOfMemoryError'

到晚上搜一下这个错误:OSError: [WinError 1455] 页面文件太小,无法完成操作,会发现多数的文章会告诉大家去修改虚拟内存,修改虚拟内存之后:

发现tm没什么鸟用啊。依然报错,网上的另外一个解决方案是修改为”自动管理所有驱动器的分页文件大小”,这个方法我没试,所以不知道有没有用。不过搜索之后还发现另外一个方法就是修改num_workers为0,不知道依据是什么(https://blog.csdn.net/weixin_43817670/article/details/116748349)。

刚看到这个方法不以为然,我觉得32g的内存,不至于这么挫把。worker数量多了就挂了?

不过事实证明,修改worker数量确实是管用的。不过这个系统报错tm有点扯淡,貌似也不是虚拟内存的问题??

def parse_opt(known=False):
    ###
    parser.add_argument('--workers', type=int, default=4, help='maximum number of dataloader workers')

修改上面的参数之后就可以正常运行了,这tm神坑啊。

硬件配置信息:

Processor (CPU)
CPU Name    Intel® Core™ i9-10900K CPU @ 3.70GHz
Threading   1 CPU - 10 Core - 20 Threads
Frequency   4898.82 MHz (49 * 99.98 MHz) - Uncore: 4299 MHz
Multiplier  Current: 49 / Min: 8 / Max: 53
Architecture    Comet Lake / Stepping: Q0/G1 / Technology: 14 nm
CPUID / Ext.    6.5.5 / 6.A5
IA Extensions   MMX, SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, EM64T, VT-x, AES, AVX, AVX2, FMA3
Caches  L1D : 32 KB / L2 : 256 KB / L3 : 20480 KB
Caches Assoc.   L1D : 8-way / L2 : 4-way / L3 : 16-way
Microcode   Rev. 0xCA
TDP / Vcore 125 Watts / 1.226 Volts
Temperature 61 °C / 142 °F
Type    Retail (Stock Frequency : 3700 MHz)
Cores Frequencies   #00: 4898.82 MHz  #01: 4898.82 MHz  #02: 4898.82 MHz  #03: 4898.82 MHz 
#04: 4898.82 MHz  #05: 4898.82 MHz  #06: 4898.82 MHz  #07: 4898.82 MHz 
#08: 4898.82 MHz  #09: 4898.82 MHz 
Motherboard
Model   MSI MPG Z490 GAMING CARBON WIFI (MS-7C73)
Socket  Socket 1200 LGA
North Bridge    Intel Comet Lake rev 05
South Bridge    Intel Z490 rev 00
BIOS    American Megatrends Inc. 1.20 (05/21/2020)
Memory (RAM)
Total Size  32768 MB
Type    Dual Channel (128 bit) DDR4-SDRAM
Frequency   1333.1 MHz (DDR4-2666) - Ratio 1:20
Timings 19-19-19-43-2 (tCAS-tRCD-tRP-tRAS-tCR)
Slot #1 Module  A-Data Technology 16384 MB (DDR4-2662) - XMP 2.0 - P/N: DDR4 3600
Slot #2 Module  A-Data Technology 16384 MB (DDR4-2662) - XMP 2.0 - P/N: DDR4 3600
Graphic Card (GPU)
GPU Type    NVIDIA GeForce RTX 3080 (GA102-200) @ 210 MHz
GPU Brand   Micro-Star International Co. Lt
GPU Specs   GA102-200 / Process: 8nm / Transistors: 28.3B / Die Size: 628 mm² / TDP: 320W
GPU Units   Shader Units: 8704 / Texture Units (TMU): 272 / Render Units (ROP): 96
GPU VRAM    10240 MB GDDR6X / 320-bit Bus @ 405 MHz (Micron)
GPU APIs    DirectX 12.0 (12_2) / OpenGL 4.6 / OpenCL 1.2 / Vulkan 1.2

 

原创文章,转载请注明: 转载自 obaby@mars

本文标题: 《Windows 10 yolov5 GPU环境》

本文链接地址: http://h4ck.org.cn/2021/09/windows-10-yolov5-gpu%e7%8e%af%e5%a2%83/


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