使用lightningmodule.load_from_checkpoint 方法来加载模型权重。 python checkpoint_path = "path/to/your/pretrained_model.ckpt" model = MyLightningModel.load_from_checkpoint(checkpoint_path) (可选)对加载的模型进行测试或评估: 你可以通过打印模型结构或进行前向传播测试来验证模型是否成功加载。 python # 打...
() trainer.fit(model) # automatically auto-loads the best weights from the previous run trainer.test(dataloaders=test_dataloader) # or call with pretrained model model = MyLightningModule.load_from_checkpoint(PATH, arg1=arg1, arg2=arg2) trainer = Trainer() trainer.test(model, dataloaders=...
load('./data/model.pkl') 3.Transformers模型保存 # 保存 model.save_pretrained(model_path) tokenizer.save_pretrained(tokenizer_path) # 加载 model.from_pretrained(model_path) tokenizer.from_pretrained(tokenizer_path) PyTorch-Lightning模型保存与加载 1.自动保存 from pytorch_lightning.callbacks import M...
AutoConfig, AutoTokenizer ) class PythonPredictor: def __init__(self, config): self.device = "cpu" self.tokenizer = AutoTokenizer.from_pretrained("albert-base-v2") self.model = jit.load("model.ts") def predict(self, payload): inputs = self.tokenizer.encode...
classDataModule(pl.LightningDataModule):def__init__(self,model_name="google/bert_uncased_L-2_H-128_A-2",batch_size=32):super().__init__()self.batch_size=batch_size self.tokenizer=AutoTokenizer.from_pretrained(model_name) 这个类在初始化时需要指定模型名称和批量大小,并从 Hugging Face 的...
class DataModule(pl.LightningDataModule): def __init__(self, model_name="google/bert_uncased_L-2_H-128_A-2", batch_size=32): super().__init__() self.batch_size = batch_size self.tokenizer = AutoTokenizer.from_pretrained(model_name) ...
model = models.resnet50(pretrained=True) # 保存地址 save_dir = './models/resnet50.pkl' # 保存整个模型 torch.save(model, save_dir) # 读取整个模型 loaded_model = torch.load(save_dir) save_dir = './models/resnet50_state_dict.pkl' ...
from .model_interface import MInterface 在data_interface中建立一个class DInterface(pl.LightningDataModule):用作所有数据集文件的接口。__init__()函数中import相应Dataset类,setup()进行实例化,并老老实实加入所需要的的train_dataloader, val_dataloader, test_dataloader函数。这些函数往往都是相似的,可以用几...
model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=False, progress=True, num_classes=2, pretrained_backbone=True) model.load_state_dict(torch.load("./mask_rcnn_pedestrian_model.pt")) model.eval() transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()]) ...
class DataModule(pl.LightningDataModule): def __init__(self,model_name="google/bert_uncased_L-2_H-128_A-2",batch_size=32): super().__init__()self.batch_size=batch_size self.tokenizer=AutoTokenizer.from_pretrained(model_name)