为了查看模型的结构和参数,首先需要加载模型。在MMDetection中,模型通常是通过配置文件(config file)来定义的。以下是一个加载模型的示例代码: python from mmdet.apis import init_detector # 指定配置文件的路径 config_file = 'path/to/your/config_file.py' # 指定预训练模型权重文件的路径 checkpoint_file = ...
init_detector(config_file, checkpoint_file, device='cuda:0') # 测试单张图片并展示结果 img = os.path.join(base_dir, r'demo\demo.jpg') # 或者 img = mmcv.imread(img),这样图片仅会被读一次 result = inference_detector(model, img) # 在一个新的窗口中将结果可视化 model.show_result(img, ...
device = 'cuda:0' model = init_detector(config_file, 'fcos_r50_caffe_fpn_gn-head_4x4_1x_coco_20200229-4c4fc3ad.pth', device=device) 加载图像并进行预测: img = mmcv.imread('https://github.com/open-mmlab/mmdetection/blob/master/demo/demo.jpg') result = inference_detector(model, img)...
下面是一个简单的MMDetection代码示例,用于加载模型并进行目标检测: importtorchfrommmdet.apisimportinit_detector,inference_detector,show_result_pyplot config_file='configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'checkpoint_file='checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'# 初...
apis import init_detector, inference_detector, show_result_pyplot model = init_detector('configs/my_config.py', 'checkpoints/my_model.pth', device='cuda:0') # 初始化模型 img = 'path/to/your/image.jpg' # 输入图像的路径 result = inference_detector(model, img) # 推理 show_result_pyplot...
gt_bboxes, # gt_bboxes (list[Tensor]): 框的GT,shape:[tl_x, tl_y, br_x, br_y] (t代表上,l代表左,b代表下,r代表右) gt_labels, # gt_labels (list[Tensor]): Class indices corresponding to each box gt_bboxes_ignore=None): super(SingleStageDetector, self).forward_train(img, ...
『记录』简单调试mmdet3d的训练流程 调试流程 主要流程:train.py,train_model函数,train_detector函数,runner.run 在train.py主流程,和进入的train_detector中管理了外部事务。进入所构建的runner.run(),开始训练。 在mm
File"/home/mmxsrt/sg-pcr-client/pr_handler_new.py", line4,in<module>from mmdet.apis import init_detector, inference_detector, show_result File"/mmdetection/mmdet/__init__.py", line1,in<module>from .version import __version__, short_version ...
定义的模型继承自Base3DDetector,Base3DDetector继承自BaseDetector,BaseDetector继承自BaseModel,BaseModel中有train_step,val_step,而_train_loop是build_train_loop(train_cfg)的返回值 train_step定义如下: def train_step(self, data: Union[dict, tuple, list], optim_wrapper: OptimWrapper) -> Dict[str, ...
importtorchfrommmdet.apisimportinit_detector,inference_detector# Load the model from config fileconfig_file='configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'checkpoint_file='checkpoints/faster_rcnn_r50_fpn_1x_coco.pth'model=init_detector(config_file,checkpoint_file,device='cuda:0') ...