展厅大屏1号主机配置Anaconda Pytorch环境
1、创建环境
conda create -n wind_2022 python==3.7
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(base) D:\> (base) D:\> (base) D:\> (base) D:\>conda create -n wind_2022 python==3.7 Collecting package metadata (current_repodata.json): done Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source. Collecting package metadata (repodata.json): done Solving environment: done ==> WARNING: A newer version of conda exists. <== current version: 4.12.0 latest version: 4.13.0 Please update conda by running $ conda update -n base -c defaults conda ## Package Plan ## environment location: C:\Users\y\.conda\envs\wind_2022 added / updated specs: - python==3.7 The following packages will be downloaded: package | build ---------------------------|----------------- certifi-2022.6.15 | py37haa95532_0 153 KB pip-22.1.2 | py37haa95532_0 2.5 MB python-3.7.0 | hea74fb7_0 16.6 MB setuptools-61.2.0 | py37haa95532_0 1.0 MB wincertstore-0.2 | py37haa95532_2 15 KB ------------------------------------------------------------ Total: 20.3 MB The following NEW packages will be INSTALLED: certifi pkgs/main/win-64::certifi-2022.6.15-py37haa95532_0 pip pkgs/main/win-64::pip-22.1.2-py37haa95532_0 python pkgs/main/win-64::python-3.7.0-hea74fb7_0 setuptools pkgs/main/win-64::setuptools-61.2.0-py37haa95532_0 vc pkgs/main/win-64::vc-14.2-h21ff451_1 vs2015_runtime pkgs/main/win-64::vs2015_runtime-14.27.29016-h5e58377_2 wheel pkgs/main/noarch::wheel-0.37.1-pyhd3eb1b0_0 wincertstore pkgs/main/win-64::wincertstore-0.2-py37haa95532_2 Proceed ([y]/n)?y
创建完成:
To activate this environment, use
#
# $ conda activate wind_2022
#
# To deactivate an active environment, use
#
# $ conda deactivate
(base) D:\>
(base) D:\>
(base) D:\>
2、配置pytorch
激活环境
(base) D:\>
(base) D:\>
(base) D:\>conda activate wind_2022
(wind_2022) D:\>
#pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113
pip install opencv-python
pip install pyrealsense2
pip install tqdm
pip install pyyaml
pip install matplotlib
pip install pandas
pip install seaborn
pip install argparse
3、测试yolov5
(wind_2022) E:\PyTorch_Project\yolov5-master>
(wind_2022) E:\PyTorch_Project\yolov5-master>python detect.py --weights models/yolov5s.pt --source data/images
detect: weights=['models/yolov5s.pt'], source=data/images, data=data\coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs\detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False
YOLOv5 2022-7-13 Python-3.7.0 torch-1.11.0+cu113 CUDA:0 (NVIDIA GeForce RTX 3060, 12288MiB)
Fusing layers...
YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients
image 1/2 E:\PyTorch_Project\yolov5-master\data\images\bus.jpg: 640x480 4 persons, 1 bus, Done. (0.000s)
image 2/2 E:\PyTorch_Project\yolov5-master\data\images\zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.000s)
Speed: 0.0ms pre-process, 0.0ms inference, 7.8ms NMS per image at shape (1, 3, 640, 640)
Results saved to runs\detect\exp
(wind_2022) E:\PyTorch_Project\yolov5-master>
(wind_2022) E:\PyTorch_Project\yolov5-master>
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