获得权重文件后,再使用timm.create_model方法,通过将pretrained_cfg_overlay参数指定为权重文件,来创建模型,这样就是本地创建了: backbone_name = 'resnet50' ckpt_path = './ckpt/resnet50_a1_0-14fe96d1.pth' model = timm.create_model(backbone_name, pretrained=True, pretrained_cfg_overlay=dict(file...
url是模型pth地址,如果下载不下来可以手动下载,注意通过url下载和通过hf-hub下载下来之后存储的路径是不一样的。timm的下载机制目前通过load_from, pretrained_loc = _resolve_pretrained_source(pretrained_cfg)来区分(位于timm\models\_builder.py) model结构 可以直接打印出整个模型的架构: print(model) model特征 ...
獲得權重檔案後,再使用timm.create_model方法,透過將pretrained_cfg_overlay引數指定為權重檔案,來建立模型,這樣就是本地建立了: backbone_name = 'resnet50' ckpt_path = './ckpt/resnet50_a1_0-14fe96d1.pth' model = timm.create_model(backbone_name, pretrained=True, pretrained_cfg_overlay=dict(file...
pretrained_cfg_overlay: Optional[Dict] = None, model_cfg: Optional[Any] = None, feature_cfg: Optional[Dict] = None, pretrained_strict: bool = True, pretrained_filter_fn: Optional[Callable] = None, kwargs_filter: Optional[Tuple[str]] = None, **kwargs, ): """ Build model with specif...
获得权重文件后,再使用timm.create_model方法,通过将pretrained_cfg_overlay参数指定为权重文件,来创建模型,这样就是本地创建了: backbone_name='resnet50'ckpt_path='./ckpt/resnet50_a1_0-14fe96d1.pth'model= timm.create_model(backbone_name,pretrained=True,pretrained_cfg_overlay=dict(file=ckpt_path))...
下载pytorch_model.bin 文件,改名后,直接放到工程中,然后在代码中增加一个参数pretrained_cfg_overlay,指定此权重文件。重新执行,此时不再需要连接huggingface.co。 net=timm.create_model(name,pretrained=True,pretrained_cfg_overlay=dict(file="../pretrained/vit_tiny_patch16_224.bin")) ...
pretrained_cfg_overlay =dict(file='/home/lingdu/zyt/works/pretrained_models/deit_small_patch16_224-cd65a155.pth')) torch.save(model,'timm_models/deit_small.pth') 目的是想通过本地的权重文件,通过timm库来创建一个deit_small_patch16_224模型。
pretrained_cfg_overlay = dict(file='/home/lingdu/zyt/works/pretrained_models/deit_small_patch16_224-cd65a155.pth')) torch.save(model, 'timm_models/deit_small.pth') 目的是想通过本地的权重文件,通过timm库来创建一个deit_small_patch16_224模型。
pretrained_cfg_overlay = dict(file='/home/lingdu/zyt/works/pretrained_models/deit_small_patch16_224-cd65a155.pth')) torch.save(model, 'timm_models/deit_small.pth') 目的是想通过本地的权重文件,通过timm库来创建一个deit_small_patch16_224模型。
Swin, MaxViT, CoAtNet, and BEiT models support resizing of image/window size on creation with adaptation of pretrained weights Example validation cmd to test w/ non-square resize python validate.py /imagenet --model swin_base_patch4_window7_224.ms_in22k_ft_in1k --amp --amp-dtype bfloat...