world_size = world_size """YOLO World v8头部。""" def loss(self, img_feats: Tuple[Tensor], txt_feats: Tensor, batch_data_samples: Union[list, dict]) -> dict: """对上游网络的特征执行前向传播和损失计算""" outs = self(img_feats, txt_feats) # 快速版本 loss_inputs = outs + (...
此时的world_size=1,随后进入do_train方法中,该方法位于\ultralytics\engine\trainer.py中,这也是最终执行训练的地方。 def _do_train(self, world_size=1): """Train completed, evaluate and plot if specified by arguments.""" if world_size > 1: self._setup_ddp(world_size) self._setup_train(w...
args.training_script = r"yolov5-master/train.py"#修改成你要训练的train.py的绝对路径 # world size in terms of number of processes dist_world_size = args.nproc_per_node * args.nnodes # set PyTorch distributed related environmental variables current_env = os.environ.copy() current_env["MAST...
paramwise_cfg:Optional[dict] =None) ->None:# 调用父类的初始化函数super().__init__(optim_wrapper_cfg, paramwise_cfg)# 从参数配置中弹出'base_total_batch_size',默认值为64self.base_total_batch_size = self.paramwise_cfg.pop('base_total_batch_size',64)# 定义一个方法,用于为模型创建优化...
简介: yolo-world 源码解析(五) .\YOLO-World\yolo_world\datasets\transformers\mm_transforms.py# 导入所需的库 import json import random from typing import Tuple import numpy as np from mmyolo.registry import TRANSFORMS # 注册 RandomLoadText 类为 TRANSFORMS 模块 ...
batch_size: 前向传播的批次大小,运行val.py传入默认32 。运行train.py则传入batch_size // WORLD_SIZE * 2 imgsz: 输入图像的大小,默认为640x640 conf_thres: 置信度阈值,默认为0.001 iou_thres: 非极大值抑制的iou阈值,默认为0.6 task: 设置测试的类型 有train, val, test, speed or study几种,默认va...
https://hf-mirror.com/wondervictor/YOLO-World/blob/main/yolo_world_v2_s_obj365v1_goldg_pretrain_1280ft-fc4ff4f7.pth # 为了少输几个字符,可以重命名或者软连接:ln -sf yolo_world_v2_s_obj365v1_goldg_pretrain_1280ft-fc4ff4f7.pth yolo_world_v2_s.pth ...
[4, 8, _base_.last_stage_out_channels // 2 // 32] base_lr = 2e-4 weight_decay = 0.05 train_batch_size_per_gpu = 8 load_from = 'pretrained_models/yolo_world_m_clip_base_dual_vlpan_2e-3adamw_32xb16_100e_o365_goldg_train_pretrained-2b7bd1be.pth' persistent_workers = False...
last_stage_out_channels // 2 // 32] base_lr = 2e-3 weight_decay = 0.05 / 2 train_batch_size_per_gpu = 16 # 模型设置 model = dict( type='YOLOWorldDetector', mm_neck=True, num_train_classes=num_training_classes, num_test_classes=num_classes, data_preprocessor=dict(type='YOLOWDet...
在预训练阶段,采用AdamW优化器,初始学习率为0.002,权重衰减为0.05。在32个NVIDIA V100 GPU上进行预训练,batch size大小为512。数据增强包括颜色增强、随机仿射、随机翻转和mosaic。文本编码器在预训练时被冻结。 6.2 预训练 简要总结: (1)YOLO-World在Objects365、GQA、Flickr、CC3M数据集上进行预训练。