自动调用 .eval(), enabling/disabling grads 权重加载 保存日志到tensorboard 支持多-GPU、TPU、AMP PL的训练验证测试过程 训练、验证和测试的过程是一样的,就是对三个函数进行重写。 training_step(self, batch, batch_idx) validation_step(self, batch, batch_idx) test_step(self, batch, batch_idx) 除以...
def validation_step(self, batch, batch_idx): self._shared_eval(batch, batch_idx, "val") def test_step(self, batch, batch_idx): self._shared_eval(batch, batch_idx, "test") def _shared_eval(self, batch, batch_idx, prefix): x, _ = batch representation = self.encoder(x) x_hat ...
deffit(...):on_fit_start()ifglobal_rank==0:# prepare data is called on GLOBAL_ZERO onlyprepare_data()forgpu/tpuingpu/tpus:train_on_device(model.copy())on_fit_end()deftrain_on_device(model):# setup is called PER DEVICEsetup()configure_optimizers()on_pretrain_routine_start()forepoch...
self.model = ColaModel.load_from_checkpoint(model_path) self.model.eval() self.model.freeze() Predict() 方法接受文本输入,使用分词器对其进行处理,并返回模型的预测: def predict(self, text): inference_sample = {"sentence": text} processed = self.processor.tokenize_data(inference_sample) logits ...
data,label = next(iter(data_module.test_dataloader())) model.eval() prediction = model(data) print(prediction) 1. 2. 3. 4. tensor([[-13.0112, -2.8257, -1.8588, -3.6137, -0.3307, -5.4953, -19.7282, 15.9651, -8.0379, -2.2925], [ -6.0261, -2.5480, 13.4140, -5.5701, -10.2049, -...
eval() # 将模型设置为评估模式 # 预处理输入数据 def preprocess_input(input_data): # 这里添加你的预处理逻辑 # 例如,如果输入数据是图像,你可能需要进行归一化、调整大小等操作 return torch.tensor(input_data, dtype=torch.float32) 3. 使用PyTorch Lightning的推理功能对输入数据进行预测 在PyTorch ...
model.eval() result = torch.cat([model.forward(t[0].to(model.device)) for t in dl]) return(result.data) result = predict(model,dl_valid) 1. 2. 3. 4. 5. 6. result 1. tensor([[9.8850e-01], [2.3642e-03], [1.2128e-04], ...
pass#model.eval() and torch.no_grad() are called automatically16defvalidation_step_end(self, *args, **kwargs):pass#接受validation_step的返回值17defvalidation_epoch_end(self, outputs)18deftest_step(self, batch, batch_idx, dataloader_idx):pass#model.eval() and torch.no_grad() are called...
decoder_model.eval()# Option 2: Forward# using the AE to extract embeddingsclassLitAutoEncoder(LightningModule):def__init__(self):super().__init__() self.encoder = nn.Sequential(nn.Linear(28*28,64))defforward(self, x): embedding = self.encoder(x)returnembedding ...
我们deprecate 了 EvalResult 和 TrainResult,这有利于简化数据流,并在训练和验证循环中将日志与数据解耦。每个循环(训练、验证、测试)都有三个可以实现的钩子(hooks): x_step x_step_end x_epoch_end 为了说明数据是如何流动的,我们将使用训练循环(即: x = training) ...