Question about prediction a sample

[kobrafarshidi]

Hi Mr. @uakarsh In one of your source code files named "LaTr TextVQA Training with WandB 💥.ipynb" I was worked on it. Now I want to predict samples I use this docs and add this source code but I can't predict samples.

The steps I have done are as follows

1) I add predict_dataloader in DataModule

class DataModule(pl.LightningDataModule):

  def __init__(self, train_dataset, val_dataset,  batch_size = 1):

    super(DataModule, self).__init__()
    self.train_dataset = train_dataset
    self.val_dataset = val_dataset
    self.batch_size = batch_size

    
  ## The parameters for Dataloader have been taken from here: https://docs.mosaicml.com/en/v0.7.1/trainer/dataloaders.html#passing-a-pytorch-dataloader
  def train_dataloader(self):
    return DataLoader(self.train_dataset, batch_size = self.batch_size, 
                      collate_fn = collate_fn, shuffle = True, num_workers = 2, pin_memory = True)
  
  def val_dataloader(self):
    return DataLoader(self.val_dataset, batch_size = self.batch_size,
                    collate_fn = collate_fn, shuffle = False, num_workers = 2, pin_memory = True)
    
  def test_dataloader(self):
    return DataLoader(self.val_dataset, batch_size = self.batch_size,
                    collate_fn = collate_fn, shuffle = False, num_workers = 2, pin_memory = True)
    
  def predict_dataloader(self):
    return DataLoader(self.val_dataset, batch_size = self.batch_size,
                    collate_fn = collate_fn, shuffle = False, num_workers = 2, pin_memory = True)

2) Then I added predict_step as below

def polynomial(base_lr, iter, max_iter = 1e5, power = 1):
    return base_lr * ((1 - float(iter) / max_iter) ** power)

class LaTrForVQA(pl.LightningModule):
  def __init__(self, config , learning_rate = 1e-4, max_steps = 100000//2):
    super(LaTrForVQA, self).__init__()
    
    self.config = config
    self.save_hyperparameters()
    self.latr =  LaTr_for_finetuning(config)
    self.training_losses = []
    self.validation_losses = []
    self.max_steps = max_steps


  def configure_optimizers(self):
    return torch.optim.AdamW(self.parameters(), lr = self.hparams['learning_rate'])


  def forward(self, batch_dict):
    boxes =   batch_dict['boxes']
    img =     batch_dict['img']
    question = batch_dict['question']
    words =   batch_dict['tokenized_words']
    answer_vector = self.latr(lang_vect = words, 
                               spatial_vect = boxes, 
                               img_vect = img, 
                               quest_vect = question
                               )
    return answer_vector

  def calculate_metrics(self, prediction, labels):

      ## Calculate the accuracy score between the prediction and ground label for a batch, with considering the pad sequence
      batch_size = len(prediction)
      ac_score = 0

      for (pred, gt) in zip(prediction, labels):
        ac_score+= calculate_acc_score(pred.detach().cpu(), gt.detach().cpu())
      ac_score = ac_score/batch_size
      return ac_score

  def training_step(self, batch, batch_idx):
    answer_vector = self.forward(batch)

    ## https://discuss.huggingface.co/t/bertformaskedlm-s-loss-and-scores-how-the-loss-is-computed/607/2
    loss = nn.CrossEntropyLoss()(answer_vector.reshape(-1,self.config['classes']), batch['answer'].reshape(-1))
    _, preds = torch.max(answer_vector, dim = -1)

    ## Calculating the accuracy score
    train_acc = self.calculate_metrics(preds, batch['answer'])
    train_acc = torch.tensor(train_acc)

    ## Logging
    self.log('train_ce_loss', loss,prog_bar = True)
    self.log('train_acc', train_acc, prog_bar = True)
    self.training_losses.append(loss.item())

    return loss

  def validation_step(self, batch, batch_idx):
    logits = self.forward(batch)
    loss = nn.CrossEntropyLoss()(logits.reshape(-1,self.config['classes']), batch['answer'].reshape(-1))
    _, preds = torch.max(logits, dim = -1)

    ## Validation Accuracy
    val_acc = self.calculate_metrics(preds.cpu(), batch['answer'].cpu())
    val_acc = torch.tensor(val_acc)

    ## Logging
    self.log('val_ce_loss', loss, prog_bar = True)
    self.log('val_acc', val_acc, prog_bar = True)
    
    return {'val_loss': loss, 'val_acc': val_acc}
  ## For the fine-tuning stage, Warm-up period is set to 1,000 steps and again is linearly decayed to zero, pg. 12, of the paper
  ## Refer here: https://github.com/Lightning-AI/lightning/issues/328#issuecomment-550114178
  
  def optimizer_step(self, epoch_nb, batch_nb, optimizer, optimizer_i, opt_closure = None, on_tpu=False,
    using_native_amp=False, using_lbfgs=False):

        ## Warmup for 1000 steps
        if self.trainer.global_step < 1000:
            lr_scale = min(1., float(self.trainer.global_step + 1) / 1000.)
            for pg in optimizer.param_groups:
                pg['lr'] = lr_scale * self.hparams.learning_rate

        ## Linear Decay
        else:
            for pg in optimizer.param_groups:
                pg['lr'] = polynomial(self.hparams.learning_rate, self.trainer.global_step, max_iter = self.max_steps)

        optimizer.step(opt_closure)
        optimizer.zero_grad()

  def predict_step(self, batch, batch_idx):
    answer_vector = self.forward(batch)
    pred = torch.max(answer_vector, dim = -1)
    return pred
    

  def validation_epoch_end(self, outputs):
        
        
        val_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
        val_acc = torch.stack([x['val_acc'] for x in outputs]).mean()

        self.log('val_loss_epoch_end', val_loss, on_epoch=True, sync_dist=True)
        self.log('val_acc_epoch_end', val_acc, on_epoch=True, sync_dist=True)
        
        self.val_prediction = []

3) In the last step I added predictor as below

preds = trainer.predict(model, datamodule)

preds = torch.concat(preds)

preds = preds.argmax(axis=1)

preds[:5]
tensor([5, 9, 9, 6, 1])

But when I runned it I have error as follow

INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

Predicting DataLoader 0: 100%
79/79 [00:20<00:00, 3.85it/s]

---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

[<ipython-input-61-02e3e8378def>](https://localhost:8080/#) in <module>
      1 preds = trainer.predict(model, datamodule)
      2 
----> 3 preds = torch.concat(preds)
      4 
      5 preds = preds.argmax(axis=1)

### TypeError: expected Tensor as element 0 in argument 0, but got torch.return_types.max

I don't know if my path is right or not and if it is true how to solve this problem.

Would u mind helping me with this case?

[uakarsh]

There is a small mistake in the predict_step in the LatrForVQA class, and that is torch.max returns two values values, and indices, so you can fix the problem in two ways

1. Instead of returning pred, you can return pred.indices, assuming you need the maximum argument
2. Once you got the answer vector, you can return torch.argmax(answer_vector, axis = -1), assuming you need the argument along the last dimension

Hope it helps.

Regards, Akarsh

[kobrafarshidi]

Hi @uakarsh Frist of all I really sorry for delay in reply because in this time I was working in your idea, but unfortunately, I couldn't solve my problem. So, I want to help me again of course I'd be appreciated very much.

1. in part of 1 you mention instead of my source I return pred.indices and in part of 2 you mention I return torch.argmax(answer_vector, axis = -1). Do you mean the same changes that I have made in the code below? if it is true, I encounterred an error and attach that error. I try to solve that, but I couldn't.

class LaTrForVQA(pl.LightningModule):
  def __init__(self, config , learning_rate = 1e-4, max_steps = 100000//2):
    super(LaTrForVQA, self).__init__()
    
    self.config = config
    self.save_hyperparameters()
    self.latr =  LaTr_for_finetuning(config)
    self.training_losses = []
    self.validation_losses = []
    self.max_steps = max_steps

  def configure_optimizers(self):
    return torch.optim.AdamW(self.parameters(), lr = self.hparams['learning_rate'])


  def forward(self, batch_dict):
    boxes =   batch_dict['boxes']
    img =     batch_dict['img']
    question = batch_dict['question']
    words =   batch_dict['tokenized_words']
    answer_vector = self.latr(lang_vect = words, 
                               spatial_vect = boxes, 
                               img_vect = img, 
                               quest_vect = question
                               )
    return answer_vector

  def calculate_metrics(self, prediction, labels):

      ## Calculate the accuracy score between the prediction and ground label for a batch, with considering the pad sequence
      batch_size = len(prediction)
      ac_score = 0

      for (pred, gt) in zip(prediction, labels):
        ac_score+= calculate_acc_score(pred.detach().cpu(), gt.detach().cpu())
      ac_score = ac_score/batch_size
      return ac_score

  def training_step(self, batch, batch_idx):
    answer_vector = self.forward(batch)

    ## https://discuss.huggingface.co/t/bertformaskedlm-s-loss-and-scores-how-the-loss-is-computed/607/2
    loss = nn.CrossEntropyLoss()(answer_vector.reshape(-1,self.config['classes']), batch['answer'].reshape(-1))
    _, preds = torch.max(answer_vector, dim = -1)

    ## Calculating the accuracy score
    train_acc = self.calculate_metrics(preds, batch['answer'])
    train_acc = torch.tensor(train_acc)

    ## Logging
    self.log('train_ce_loss', loss,prog_bar = True)
    self.log('train_acc', train_acc, prog_bar = True)
    self.training_losses.append(loss.item())

    return loss

  def validation_step(self, batch, batch_idx):
    logits = self.forward(batch)
    loss = nn.CrossEntropyLoss()(logits.reshape(-1,self.config['classes']), batch['answer'].reshape(-1))
    _, preds = torch.max(logits, dim = -1)

    ## Validation Accuracy
    val_acc = self.calculate_metrics(preds.cpu(), batch['answer'].cpu())
    val_acc = torch.tensor(val_acc)

    ## Logging
    self.log('val_ce_loss', loss, prog_bar = True)
    self.log('val_acc', val_acc, prog_bar = True)
    
    return {'val_loss': loss, 'val_acc': val_acc}
  ## For the fine-tuning stage, Warm-up period is set to 1,000 steps and again is linearly decayed to zero, pg. 12, of the paper
  ## Refer here: https://github.com/Lightning-AI/lightning/issues/328#issuecomment-550114178
  
  def optimizer_step(self, epoch_nb, batch_nb, optimizer, optimizer_i, opt_closure = None, on_tpu=False,
    using_native_amp=False, using_lbfgs=False):

        ## Warmup for 1000 steps
        if self.trainer.global_step < 1000:
            lr_scale = min(1., float(self.trainer.global_step + 1) / 1000.)
            for pg in optimizer.param_groups:
                pg['lr'] = lr_scale * self.hparams.learning_rate

        ## Linear Decay
        else:
            for pg in optimizer.param_groups:
                pg['lr'] = polynomial(self.hparams.learning_rate, self.trainer.global_step, max_iter = self.max_steps)

        optimizer.step(opt_closure)
        optimizer.zero_grad()

  def predict_step(self, batch, batch_idx):
    answer_vector = self.forward(batch)
    print("answer_vector shape :",answer_vector.shape)
    print("val [0,0,0]",)
    pred,indices = torch.argmax(answer_vector, axis = -1)
    #print(pred.shape)
    #print(pred[0])
    return pred 

when I run preds,indices = trainer.predict(model, datamodule)

I encountered below error

INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
Predicting DataLoader 0: 0%
0/79 [00:00<?, ?it/s]
answer_vector shape : torch.Size([1, 512, 32128])
val [0,0,0]
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
[<ipython-input-38-20865f6cb5d4>](https://localhost:8080/#) in <module>
----> 1 preds = trainer.predict(model, datamodule)

13 frames
[<ipython-input-30-a8120dfbe56d>](https://localhost:8080/#) in predict_step(self, batch, batch_idx)
    108     print("answer_vector shape :",answer_vector.shape)
    109     print("val [0,0,0]",)
--> 110     pred,indices = torch.argmax(answer_vector, axis = -1)
    111     #print(pred.shape)
    112     #print(pred[0])

ValueError: not enough values to unpack (expected 2, got 1)

Is it possible that I did not understand your meaning correctly? Could I please write the correct source code for prediction with a sample?

2. After my problem is solved, my question will be that if I want to make a prediction, if it be from data loader how many predictions of answers it will return ?
3. After my problem is solved, my question will be that, if I must first give a question and an image as input and output will be an answer. is that correct? How do I do this?

Best regards,