3. 自定义多个dataset 不论是直接build和自定义的ACDC都不行,因为两者都不能相互访问各自的注册类,太奇怪了(因为每次都要编译,所以我没有编译mmsegentation)。后面发现直接用runner能成功
本部分内容学习mmengine中的dataset目录 dataset目录包含的模块如下: 'BaseDataset', 'Compose', 'force_full_init', 'ClassBalancedDataset', 'C… 阅读全文 MMEngine目录学习-sturcture 本部分内容学习mmengine中的structure目录 runner目录下的方法列表如下: 'BaseDataElement', 'InstanceData', 'LabelData',...
Build Models First, we need to define a model which 1) inherits from BaseModel and 2) accepts an additional argument mode in the forward method, in addition to those arguments related to the dataset. During training, the value of mode is "loss", and the forward method should return a ...
.runnerimportRunnerNUM_WORKERS=int(os.cpu_count()/2)BATCH_SIZE=256iftorch.cuda.is_available()else64train_dataloader=dict(batch_size=BATCH_SIZE,num_workers=NUM_WORKERS,persistent_workers=True,sampler=dict(type='DefaultSampler',shuffle=True),dataset=dataset)train_dataloader=Runner.build_dataloader(...
Build Models First, we need to define amodelwhich 1) inherits fromBaseModeland 2) accepts an additional argumentmodein theforwardmethod, in addition to those arguments related to the dataset. During training, the value ofmodeis “loss”, and theforwardmethod should return adictcontaining the ke...
runner=RUNNERS.build(cfg)# start training runner.train() 测试部分 tools/test.py 主要差异就是调用 test() 方法,综合来看, 与 Runner 相关最重要的就是以下几行命令: 代码语言:javascript 复制 runner=Runner.from_cfg(cfg)runner.train()# 用于训练 ...
Support comparing NumPy array dataset meta in Runner.resume by @HAOCHENYE in https://github.com/open-mmlab/mmengine/pull/511 Use get instead of pop to dump runner_type in build_runner_from_cfg by @nijkah in https://github.com/open-mmlab/mmengine/pull/549 Upgrade pre-commit hooks by @...
myrunner = MYRUNNERS.build(dict(type='MYRunner', peseduo_param=1)) print(myrunner) # <__main__.RUNNER_CONSTRUCTORS_learn.<locals>.MYRunner object at 0x000002034B80A130> HOOKS_learn def HOOKS_learn(): """ HOOKS为mmengine中定义的另一系列的注册器,其主要作用于Runner执行器进行模型训练过...
importtorchvision.transformsastvtfrommmengine.registryimportDATASETS,TRANSFORMSfrommmengine.dataset.base_datasetimportCompose# 注册 torchvision 的 CIFAR10 数据集# 数据预处理也需要在此一起构建@DATASETS.register_module(name='Cifar10',force=False)defbuild_torchvision_cifar10(transform=None,**kwargs):ifisinsta...
build_evaluator(evaluator) if hasattr(self.dataloader.dataset, 'metainfo'): self.evaluator.dataset_meta = self.dataloader.dataset.metainfo self.runner.visualizer.dataset_meta = self.dataloader.dataset.metainfo self.fp16 = fp16 def run(self) -> dict: self.runner.call_hook('before_val') self...