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...
runner = Runner( model=ResNet18(), work_dir='./work_dir', train_dataloader=train_dataloader_cfg, optim_wrapper=dict(optimizer=dict(type='SGD', lr=0.001, momentum=0.9)), train_cfg=dict(by_epoch=True, max_epochs=3), load_from='./work_dir/epoch_2.pth', resume=True, ) runner.train...
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.get('test_cfg'),auto_scale_lr=cfg.get('auto_scale_lr'),optim_wrapper=cfg.get('...
optim_wrapper=dict(optimizer=dict(type=SGD, lr=0.001, momentum=0.9)), # set some training configs like epochs train_cfg=dict(by_epoch=True, max_epochs=5, val_interval=1), val_dataloader=val_dataloader, val_cfg=dict(), val_evaluator=dict(type=Accuracy), ) Launch Training runner.train(...
train_cfg=dict(by_epoch=True, max_epochs=1), optim_wrapper=dict(optimizer=dict(type='SGD', lr=0.01), accumulative_counts=4) ) runner.train() ``` ## Large Model Training `FSDP` is officially supported from PyTorch 1.11. The config can be written in this way: ```python # located in...
# a wrapper to execute back propagation and gradient update, etc. optim_wrapper=dict(optimizer=dict(type=SGD, lr=0.001, momentum=0.9)), # set some training configs like epochs train_cfg=dict(by_epoch=True, max_epochs=5, val_interval=1), val_dataloader=val_dataloader, val_cfg=dict(), ...
用于模型优化,并提供 AMP、梯度累积等附加功能 optim_wrapper=dict(optimizer=dict(type=SGD, lr=0.001, momentum=0.9)), # 训练配置,例如 epoch 等 train_cfg=dict(by_epoch=True, max_epochs=5, val_interval=1), val_dataloader=val_dataloader, val_cfg=dict(), val_evaluator=dict(type=Accuracy), )...
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...
=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()...
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 runner = Runner.from_cfg(cfg) runner.train() # 用于训练 runner.test() # 用于测试 接下来分步骤具体看下整个 Runner 调用流程。 1、整体流程构建 Runner 初始化 跟进from_cfg(cfg) ...