deftraining_step(self,batch,batch_idx):x,y=batchy_hat=self.model(x)loss=F.cross_entropy(y_hat,y)pred=...return{"loss":loss,"pred":pred}deftraining_step_end(self,batch_parts):# 从每个GUP计算到的predictionspredictions=
6,自动搜索最大batch_size(auto_scale_batch_size="power") 四,训练涨分技巧 1,SWA 随机权重平均 (pl.callbacks.stochastic_weight_avg.StochasticWeightAveraging) 2,CyclicLR学习率调度策略(torch.optim.lr_scheduler.CyclicLR) 3, 最优学习率搜索(auto_lr_find=True) pytorch-lightning 是建立在pytorch之上的...
# enable 16-bit on the model and the optimizermodel, optimizers = amp.initialize(model, optimizers, opt_level= O2 )# when doing .backward, let amp do it so it can scale the losswith amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward()amp包会处理好大部分事情。如果...
callbacks.EarlyStopping(monitor = 'val_loss', patience=3, mode = 'min') trainer = pl.Trainer.from_argparse_args( hparams, max_epochs=10, callbacks = [ckpt_callback,early_stopping] ) if hparams.auto_scale_batch_size is not None: #搜索不发生OOM的最大batch_size max_batch_size = trainer...
Tensor - The loss tensor dict - A dictionary. Can include any keys, but must include the key 'loss' None - Training will skip to the next batch 返回值无论如何也需要有一个loss量。如果是字典,要有这个key。没loss这个batch就被跳过了。例: ...
最后,第三部分提供了一个我总结出来的易用于大型项目、容易迁移、易于复用的模板,有兴趣的可以去GitHub—https://github.com/miracleyoo/pytorch-lightning-template试用。 核心 Pytorch-Lighting 的一大特点是把模型和系统分开来看。模型是像Resnet18, RNN之类的纯模型, 而系统定...
model,optimizers=amp.initialize(model,optimizers,opt_level='O2')# 当进行.backward()时,让amp处理以便它可以对损失进行缩放withamp.scale_loss(loss,optimizer)asscaled_loss:scaled_loss.backward() 1. 2. 3. 4. 5. 6. apex库会处理大部分工作,包括梯度缩放,以防止梯度爆炸或接近零。
loss = some_loss(out,y)loss.backward()scaled_loss += loss.item()# update weights after 8 steps. effective batch = 8*16 optimizer.step()# loss is now scaled up by the number of accumulated batches actual_loss = scaled_loss / 16 而在Lightning中，这些已经自动执行了。trainer = Trainer(...
( monitor='val_loss', save_top_k=1, mode='min' ) early_stopping = pl.callbacks.EarlyStopping(monitor = 'val_loss', patience=3, mode = 'min') trainer = pl.Trainer.from_argparse_args( hparams, max_epochs=10, callbacks = [ckpt_callback,early_stopping] ) if hparams.auto_scale_batch...
loss_normal = (tgt_normal-tgt_pseudo_normal).abs().mean() 1. 2、掩码排序损失 loss_mask_ranking = loss_functions.mask_ranking_loss(self.outputs[("depth", 0, scale)], self.inputs[("tgt_pseudo_depth",scale)], dyna_mask) 1.