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 ...
timm.models._builder.build_model_with_cfg() 转到load_pretrained函数 这里逻辑很清楚,通过_resolve_pretrained_source函数解析权重路径 load_from, pretrained_loc = _resolve_pretrained_source(pretrained_cfg) 解析出来load_from,根据load_from进行参数加载 因为pretrained_cfg中存在hf相关参数,所以解析得到的load_fro...
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more - Major module / path restructure, timm.models.layers -> timm.layers, a
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet,
= state_dict[k].shape: print(f"Removing key {k} from pretrained checkpoint") del checkpoint_model[k] # interpolate position embedding pos_embed_checkpoint = checkpoint_model['pos_embed'] embedding_size = pos_embed_checkpoint.shape[-1] num_patches = model.patch_embed.num_patches num_extr...
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet,
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more - timm/inference.py at main · Xyl-6/timm
importtimmmodel=timm.create_model('mobilenetv3_large_100',pretrained=True)model.eval() 加载图片和预处理: importurllibfromPILimportImagefromtimm.dataimportresolve_data_configfromtimm.data.transforms_factoryimportcreate_transformconfig=resolve_data_config({},model=model)transform=create_transform(**config)ur...
from ._builder import build_model_with_cfg, load_pretrained, load_custom_pretrained, resolve_pretrained_cfg, \ set_pretrained_download_progress, set_pretrained_check_hash from ._factory import create_model, parse_model_name, safe_model_name from ._features import FeatureInfo, FeatureHooks, Feature...
IMAGENET_INCEPTION_STD from .helpers import build_model_with_cfg, resolve_pretrained_cfg from .registry import register_model from .layers import trunc_normal_, create_classifier, Linear def _cfg(url='', **kwargs): return { 'url': url, 'num_classes': 1000, 'input_s...