defparse_model(d, ch):# model_dict, input_channels(3)logger.info('\n%3s%18s%3s%10s %-40s%-30s'% ('','from','n','params','module','arguments')) anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] na = (len(anchors[0]) //2)...
opt=parser.parse_args() opt.img_size*= 2iflen(opt.img_size) == 1else1#expandprint(opt) set_logging() t=time.time()#Load PyTorch modeldevice =select_device(opt.device) model= attempt_load(opt.weights, map_location=device)#load FP32 modellabels =model.names#Checksgs = int(max(model...
parser.add_argument('--weights', type=str, default='/home/ubuntu/conda/yolov7/weights/yolov7_training.pt', help='initial weights path') parser.add_argument('--cfg', type=str, default='/home/ubuntu/conda/yolov7/cfg/training/yolov7.yaml', help='model.yaml path') parser.add_argument...
三、在yolo.pyparse_model模块修改增加自己的模块 if m in { Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x, C3STR}: # add a C3STR at the end of the...
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist self.names = [str(i) for i in range(self.yaml['nc'])] # default names # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))]) ...
from yolov7 import val # 初始化YOLOv7模型 model_path = 'runs/train/ir_security/weights/best.pt' # 评估模型 results = val.run( data='data.yaml', weights=model_path, imgsz=640, task='val' ) # 打印评估结果 print(results) infer.py import sys import cv2 import numpy as np import ra...
opt = parser.parse_args() opt.img_size *=2iflen(opt.img_size) ==1else1# expandopt.dynamic = opt.dynamicandnotopt.end2end opt.dynamic =Falseifopt.dynamic_batchelseopt.dynamic print(opt) set_logging() t = time.time()# Load PyTorch modeldevice = select_device(opt.device) ...
opt = parser.parse_args() 四、训练模型 上面操作全部做好之后,直接运行train.py就可以开始训练,显卡估计就开始爆炸了,程序出现下面这样子,就是在炼丹状态了,等着就ok了。 autoanchor: Analyzing anchors... anchors/target = 4.13, Best Possible Recall (BPR) = 1.0000 ...
将上面的模块封装好后,就可以在yolo.py的parse_model函数中增加模块的参数配置逻辑了,代码如下: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 if m in [nn.Conv2d, Conv, RobustConv, RobustConv2, E_ELAN, E_ELAN_H, DWConv, GhostConv, RepConv, RepConv_OREPA, DownC, SPP, SPPF, SPPCSPC...
parser.add_argument('--no-trace', action='store_true', help='don`t trace model')+ parser.add_argument('--cfg', type=str, default='yolor-csp-c.yaml', help='model.yaml') opt = parser.parse_args() print(opt) #check_requirements(exclude=('pycocotools', 'thop')) 模型转换和验证 ...