model = create_fn(pretrained=pretrained, **kwargs) if checkpoint_path: load_checkpoint(model, checkpoint_path) return model #首先通过split_model_name函数将模型名字分割成两部分,一部分是模型的来源,一部分是模型的名字 #然后通过is_model函数判断模型是否存在于(是否已经注册进_model_entrypoints字典) #如果...
create_model函数和register_model修饰器 首先,对输入的model_name做parse(如:vit_base_patch16_224)并运行split_model_name_tag(model_name),得到model_name和相关的配置参数 接着,会运行is_model(model_name) 从而判断该model是否已经注册在timm的_model_entrypoints字典中(这里乍一看可能看不懂,等我解释完registe...
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。 报错信息: File"/home/lingdu/zyt/works/PD_6/get_model.py",line10,in<module> model = timm.create_model(File"/home/lingdu/.conda/envs/codiff/lib/python3.8/site-packages/timm/models/_factory.py",line117,increate...
models import create_model, apply_test_time_pool, load_checkpoint from timm.data import create_dataset, create_loader, resolve_data_config from timm.layers import apply_test_time_pool from timm.models import create_model from timm.utils import AverageMeter, setup_default_logging, set_jit_fuser ...
These can be passed to timm.models.load_checkpoint(..., filter_fn=timm.models.swin_transformer_v2.checkpoint_filter_fn) to remap your existing checkpoint. The Hugging Face Hub (https://huggingface.co/timm) is now the primary source for timm weights. Model cards include link to papers, ...
import timmm = timm.create_model('mobilenetv3_large_100', pretrained=True)m.eval()MobileNetV3((conv_stem): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)...
python inference.py /imagenet/validation/ --model mobilenetv3_large_100 --checkpoint ./output/train/model_best.pth.tar 1. 2. 这里给个例子: EfficientNet-B2 with RandAugment - ->80.4 top-1, 95.1 top-5 These params are for dual Titan RTX cards with NVIDIA Apex installed: ...
checkpoint = torch.load(checkpoint_path, map_location=device) File "/home/lingdu/.conda/envs/codiff/lib/python3.8/site-packages/torch/serialization.py", line 1025, in load return _load(opened_zipfile, File "/home/lingdu/.conda/envs/codiff/lib/python3.8/site-packages/torch/serialization.py...
checkpoint = torch.load(checkpoint_path, map_location=device) File "/home/lingdu/.conda/envs/codiff/lib/python3.8/site-packages/torch/serialization.py", line 1025, in load return _load(opened_zipfile, File "/home/lingdu/.conda/envs/codiff/lib/python3.8/site-packages/torch/serialization.py...
直接将url对应的链接输入到浏览器,即可下载。 2. 将预训练的权重加载到模型: model = timm.create_model( model_name, pretrained=True, num_classes=0, global_pool="avg", pretrained_cfg_overlay=dict(file=CFG.checkpoint_path) ) # pretrained_cfg_overlay对应为下载的预训练权重。