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...
指定预训练模型保存的路径。这个路径通常是 .ckpt 文件(PyTorch Lightning 默认保存的模型格式)。 python checkpoint_path = "path/to/your/pretrained_model.ckpt" 使用PyTorch Lightning的加载函数加载模型: PyTorch Lightning 提供了 LightningModule.load_from_checkpoint 方法来加载模型。你需要定义一个 LightningModul...
import pytorch_lightning as pl import torch import torch.nn as nn frompytorch_lightning.loggers import TensorBoardLogger from torchvision.models import resnet50 import torch.optim as optim from pytorch_lightning.callbacks import ModelCheckpoint from torchvision import transforms import torch.utils.data as...
classLitModel(pl.LightningModule):def__init__(...):defforward(...):deftraining_step(...)deftraining_step_end(...)deftraining_epoch_end(...)defvalidation_step(...)defvalidation_step_end(...)defvalidation_epoch_end(...)deftest_step(...)deftest_step_end(...)deftest_epoch_end(.....
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' ...
model = torchvision.models.resnet18(pretrained=False) # pth_file = 'resnet18-5c106cde.pth' # model.load_state_dict(torch.load(pth_file, map_location=device)) model.to(device) # data type n*c*h*w dummy_input = torch.randn(1, 3, 256, 256) ...
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)
summary(pretrainedmodel_vgg, (3, 224, 224)) Output: In the following output, we can see that the PyTorch bert model summary is printed on the screen. PyTorch bert model summary Read:PyTorch Batch Normalization PyTorch lightning model summary ...
AutoModelForSequenceClassification, AutoConfig, AutoTokenizer ) classPythonPredictor: def__init__(self, config): self.device ="cpu" self.tokenizer = AutoTokenizer.from_pretrained("albert-base-v2") self.model = MyModel.load_from_checkpoint(check...
data和modle两个文件夹中放入__init__.py文件,做成包。这样方便导入。两个init文件分别是:from .data_interface import DInterface和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。__...