by_epoch=False,interval=500))runner=Runner(model=MMResNet50(),work_dir='./work_dir',train_dataloader=train_dataloader,optim_wrapper=dict(optimizer=dict(type=SGD,lr=0.001,momentum=0.9)),train_cfg=dict(by_epoch=False,max_iters=10000,val_interval...
model配置文件: model_cfg.py ## 1.optimizer_cfg.pyoptimizer=dict(type='SGD',lr=0.02,momentum=0.9,weight_decay=0.0001)## 2.model_cfg.py_base_=['optimizer_cfg.py']model=dict(type='ResNet',depth=50)'''## learn.pycfg = Config.fromfile('model_cfg.py')print(cfg)'optimizer': {'type...
cfg=copy.deepcopy(cfg)runner=cls(model=cfg['model'],work_dir=cfg['work_dir'],train_dataloader=cfg.get('train_dataloader'),val_dataloader=cfg.get('val_dataloader'),test_dataloader=cfg.get('test_dataloader'),train_cfg=cfg.get('train_cfg'),val_cfg=cfg.get('val_cfg'),test_cfg=cfg.ge...
train_cfg=dict(by_epoch=True,max_epochs=220)runner=Runner(model,work_dir='runs/gan/',train_dataloader=train_dataloader,train_cfg=train_cfg,optim_wrapper=opt_wrapper_dict)runner.train() 到这里,我们就完成了一个 GAN 的训练,通过下面的代码可以查看刚才训练的 GAN 生成的结果。
Normalize(**norm_cfg) ]))) val_dataloader = DataLoader(batch_size=32, shuffle=False, dataset=torchvision.datasets.CIFAR10( 'data/cifar10', train=False, download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize(**norm_cfg) ]))) Build Metrics To validate and ...
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import torchvision.transforms as transforms from torch.utils.data import DataLoader norm_cfg = dict(mean=[0.491, 0.482, 0.447], std=[0.202, 0.199, 0.201]) train_dataloader = DataLoader(batch_size=32, shuffle=True, dataset=torchvision.datasets.CIFAR10( 'data/cifar10', train=True, download=True...
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 @zhouzaida in https://github.com/open-mmlab/mmengine/pull/576 Delete the error comment in registry.md by @vansin in https:...
只需要在训练脚本中加入配置--cfg-options efficient_conv_bn_eval="[backbone]";具体怎么加呢? 2023-08-17· 广东 回复喜欢 游凯超 作者 比如说mmdetection的文档 Train predefined models on standard datasets,[optional arguments]部分加上--cfg-options efficient_conv_bn_eval="[backbone]" 2023-...
=train_dataloader_cfg,optim_wrapper=dict(type='AmpOptimWrapper',# 如果你想要使用 BF16,请取消下面一行的代码注释# dtype='bfloat16', # 可用值: ('float16', 'bfloat16', None)optimizer=dict(type='SGD',lr=0.001,momentum=0.9)),train_cfg=dict(by_epoch=True,max_epochs=3),)runner.train()...