model.load_state_dict(checkpoint) logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title, resume=True) 1. 2. 3. 4. 5. 6. 7. 8. 9. 1. 首先我注意到model.train()和model.eval(),主要是针对model 在训练时和评价时不同的
TF 2.0检查点通常包含了模型的权重和其他相关信息。我们可以使用tf.train.load_checkpoint函数来加载检查点。下面是相应的代码: importtensorflowastf checkpoint_path="path/to/checkpoint/model.ckpt"reader=tf.compat.v1.train.NewCheckpointReader(checkpoint_path) 1. 2. 3. 4. 在这个代码中,checkpoint_path是检...
若是从 checkpoint 初始化模型,可以向trainer传入参数empty_init=True,这样在读取 checkpoint 之前模型的权重不会占用内存空间,且速度更快。 withtrainer.init_module(empty_init=True): model = MyLightningModule.load_from_checkpoint("my/checkpoint/path.ckpt") trainer.fit(model) 要注意,此时必须保证模型的每个...
model = MyLightingModule.load_from_checkpoint(PATH) 4.加载(Trainer) model = LitModel() trainer = Trainer() # 自动恢复模型 trainer.fit(model, ckpt_path="some/path/to/my_checkpoint.ckpt") 参考 pytorch保存模型方法_Obolicaca的博客-CSDN博客_pytorch保存模型 AIC19:Pytorch 常用操作汇总 Pytorch-LIght...
pathcheckpoint=torch.load(path_checkpoint)start_epoch=checkpoint['epoch']net.load_state_dict(...
.total_training_steps=Noneself.save_hyperparameters(self.cfg)self.dice_metric=Dice()self.f1_metric=F1Score(task='binary')self.val_step_logits=[]self.val_step_masks=[]model=LightningCloudSegNet(cfg=cfg,fold=fold)ckpt_path='saved_models/exp15/exp15_1_last.ckpt'model.load_from_checkpoint...
model = model.to(device) criterion = torch.nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5) # 加载预训练权重 if resume: checkpoint = torch.load(resume, map_location='cpu') model.load_state_dict(checkpoint['model']) optimizer.load_state_dict(ch...
checkpoint file for 'facebook/m2m100_1.2B' at '/cache/transformers/68002fb1a7773d8d8373f1a230588141964ef9f249db6987681f295dbe85356c.ee70663869b89be4f68eed03a21d5c3400b223cb544883f411e469aaea0a25f9'If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=...
run_config=tf.contrib.learn.RunConfig(model_dir=filepath,keep_checkpoint_max=1) 通过这种方式,我们告诉预估者应该从哪个目录中保存或恢复一个检查点,以及要保存多少个检查点。 接下来,我们必须在评估器的初始化中提供这个配置: 代码语言:javascript
We're also already set up to resume from checkpoints in our next experiment run. If the Estimator finds a checkpoint inside the given model folder, it will load from the last checkpoint. Okay, let me try Don't take my word for it - try it out yourself. Here are the steps to run ...