schedules配置(imagenet_bs256.py) 运行配置(default_runtime.py) 3、编写配置文件(resnet18_finetune.py) 复制模型、数据集、运行配置文件的内容至resnet18_finetune.py 修改配置文件 模型: 加init_cfg=dict(type="Pretrained",checkpoint='https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_...
class CLIP(BaseModel): def __init__(self, vision_backbone: dict, projection: dict, text_backbone: dict, tokenizer: dict, vocab_size: int, transformer_width: int, proj_dim: int, context_length: int = 77, data_preprocessor: Optional[dict] = None, init_cfg: Optional[dict] = None): ...
MMPreTrain是一款基于PyTorch的开源深度学习预工具箱,是OpenMMLab项目的成员之一 MMPreTrain的主要特性有: 支持多元化的主干网络与预训练模型 支持多种训练策略(有监督学习,无监督学习,多模态学习等) 提供多种训练技巧 大量的训练配置文件 高效率和高可扩展性 功能强大的工具...
( type='LinearClsHead', num_classes=2,#注意修改为自己数据集的类别 in_channels=2048, loss=dict(type='CrossEntropyLoss', loss_weight=1.0), topk=(1, ), )) # - # 浼樺寲绛栫暐鍦ㄤ箣鍓嶅熀纭€涓婅 缁冩洿澶氫釜 epoch train_cfg = dict(max_epochs=100, val_interval=1) # 璁粌300...
mmpretrain projects requirements resources tests tools .coveragerc .gitattributes .gitignore .pre-commit-config.yaml .readthedocs.yml CITATION.cff CONTRIBUTING.md LICENSE MANIFEST.in README.md README_zh-CN.md dataset-index.yml model-index.yml requirements.txt setup.cfg setup.pyBreadcrumbs...
dist_cfg: {'backend': 'nccl'} seed: 476397440 Distributed launcher: none Distributed training: False GPU number: 1 01/18 20:10:41 - mmengine - INFO - Config: auto_scale_lr = dict(base_batch_size=16, enable=False) backend_args = None ...
# 'by_epoch=True' 默认使用 `EpochBaseLoop`, 'by_epoch=False' 默认使用 `IterBaseLoop` train_cfg = dict(by_epoch=True, max_epochs=100, val_interval=1) # 使用默认的验证循环控制器 val_cfg = dict() # 使用默认的测试循环控制器 test_cfg = dict() # 通过默认策略自动缩放学习率,此策略适用...
train_cfg = dict(by_epoch=True, max_epochs=100, val_interval=1) val_cfg = dict() test_cfg = dict() # NOTE: `auto_scale_lr` is for automatically scaling LR, # based on the actual training batch size. auto_scale_lr = dict(base_batch_size=256) 最后是'../_base_/default_runtime...
train_cfg/val_cfg/test_cfg:训练/测试/验证的流程设置,要主要时对epoch大小进行设置,以及规定多少个epoch做一次验证,为空时表示使用默认设置 auto_scaler_lr:在训练当中如果batchsize变了,则根据此处设置自动缩放学习率 default_hook:运行参数,一般都不需要去修改这里的参数,常用的有两个 logger:设置每多少个epoch...
train_cfg = dict(by_epoch=True, max_epochs=100, val_interval=1) val_cfg = dict() test_cfg = dict() # NOTE: `auto_scale_lr` is for automatically scaling LR, # based on the actual training batch size. auto_scale_lr = dict(base_batch_size=256) ...