##参数设置MODEL:BACKBONE:NAME:"build_resnet_fpn_backbone"RESNETS:OUT_FEATURES:["res2","res3","res4","res5"]FPN:IN_FEATURES:["res2","res3","res4","res5"] (相同道理,全局搜索build_resnet_fpn_backbone,如果有能运行起来demo的话,那就单步跟进最方便) 改环节主要包含两个部分: 创建基网络...
2.1Registry机制与build_model Trainer中初始化模型调用的接口是build_model函数,通过modeling/__init__.py可以知道它是在modeling/meta_arch/build.py中定义的。但是在阅读build.py的过程中,我们发现它使用了一个叫做Registry的东西——那么什么是Registry呢? Registry机制来自于FaceBook计算机视觉研究组的常用函数库fvcor...
在detectron2模型(及其子模型)是由功能,如内置build_model,build_backbone,build_roi_heads: fromdetectron2.modeling import build_model model= build_model(cfg) # returns a torch.nn.Module 1.1、加载/保存检查点 fromdetectron2.checkpoint import DetectionCheckpointer DetectionCheckpointer(model).load(file_pat...
detectron2中的模型(及其子模型)由函数构成,例如build_model,build_backbone,build_roi_heads等函数: from detectron2.modeling import build_modelmodel = build_model(cfg) #返回torch.nn.Module 1. 2. 注意,build_model仅构建模型结构,并用随机参数填充它。如果要将现有检查点加载到模型,请使用 DetectionCheckpoi...
模型的构建接口就是build_model,这一部分的实现在modeling子模块,在detectron2中,当要加入一个model,就需要注册一个meta_arch(注册指的是在model zoo中加入模型,背后就是维护一个模型字典),比如加入RetinaNet模型: @META_ARCH_REGISTRY.register()classRetinaNet(nn.Module):pass ...
在detectron2模型(及其子模型)是由功能,如内置build_model,build_backbone,build_roi_heads: from detectron2.modeling import build_model model = build_model(cfg) # returns a torch.nn.Module 1. 2. 1.1、加载/保存检查点 from detectron2.checkpoint import DetectionCheckpointer ...
from detectron2.modeling import build_model class DefaultTrainer(SimpleTrainer): def __init__(self, cfg): """ Args: cfg (CfgNode): """ # Assume these objects must be constructed in this order. model = self.build_model(cfg) optimizer = self.build_optimizer(cfg, model) data_loader = ...
from detectron2.modeling import build_model class DefaultTrainer(SimpleTrainer): def __init__(self, cfg): """ Args: cfg (CfgNode): """ # Assume these objects must be constructed in this order. model = self.build_model(cfg) optimizer = self.build_optimizer(cfg, model) ...
defbuild_train_loader(cls,cfg):"""Returns:iterable"""returnbuild_detection_train_loader(cfg) 函数调用关系如下图: 结合前面两篇文章的内容可以看到detectron2在构建model,optimizer和data_loader的时候都是在对应的build.py文件里实现的。我们看一下build_detection_train_loader是如何定义的(对应上图中紫色方框...
the COCO Evaluator function and pass the Validation Datasetevaluator = COCOEvaluator("boardetect_val", cfg, False, output_dir="/output/")val_loader = build_detection_test_loader(cfg, "boardetect_val")#Use the created predicted model in the previous stepinference_on_dataset(predictor.model, ...