I am trying to train a Convolutional Neural Network to classify Cat and Dog Images and it is throwing an error that I can’t seem to solve.
Here is the whole error:
  File "train.py", line 70, in <module>
    main(config, args.resume)
  File "train.py", line 44, in main
    trainer.train()
  File "...\Coding\ConvNet\base\base_trainer.py", line 85, in train
    result = self._train_epoch(epoch)
  File "...Coding\ConvNet\trainer\trainer.py", line 52, in _train_epoch
    loss = self.loss(output, target)
  File "...\Coding\ConvNet\model\loss.py", line 4, in ces_loss
    return nn.CrossEntropyLoss(output,target)
  File "...\Anaconda3\envs\ConvNet\lib\site-packages\torch\nn\modules\loss.py", line 898, in __init__
    super(CrossEntropyLoss, self).__init__(weight, size_average, reduce, reduction)
  File "...\Anaconda3\envs\ConvNet\lib\site-packages\torch\nn\modules\loss.py", line 23, in __init__
    super(_WeightedLoss, self).__init__(size_average, reduce, reduction)
  File "...\Anaconda3\envs\ConvNet\lib\site-packages\torch\nn\modules\loss.py", line 16, in __init__
    self.reduction = _Reduction.legacy_get_string(size_average, reduce)
  File "...\Anaconda3\envs\ConvNet\lib\site-packages\torch\nn\_reduction.py", line 42, in legacy_get_string
    if size_average and reduce:
RuntimeError: bool value of Tensor with more than one value is ambiguous


I am relatively new to Pytorch so I am using the format found in the Github Repo listed below.  I changed the train loader so that it uses ImageFolder.

  
      
      GitHub
  
  
    

victoresque/pytorch-template 3

PyTorch deep learning projects made easy. Contribute to victoresque/pytorch-template development by creating an account on GitHub.


  
  
    
    
  
  


From what ive seen previously I would assume that I need to put in a:
if not None

but to no avail.
Here is my DataLoader Script:
from torchvision import datasets, transforms
from base import BaseDataLoader


class CatDogDataLoader(BaseDataLoader):
    """
    Cat and Dog data loading demo using BaseDataLoader
    """
    def __init__(self, data_dir, batch_size, shuffle, validation_split, num_workers, training=True, IMG_SIZE=50):
        self.IMG_SIZE = IMG_SIZE
        trsfm = transforms.Compose([
            transforms.Resize((self.IMG_SIZE,self.IMG_SIZE)),
            transforms.Grayscale(1),
            transforms.ToTensor()

            ])
        self.data_dir = data_dir
        self.dataset = datasets.ImageFolder(self.data_dir, transform=trsfm)
        super(CatDogDataLoader, self).__init__(self.dataset, batch_size, shuffle, validation_split, num_workers)
        

and here is my TrainLoader:
import numpy as np
import torch
from torchvision.utils import make_grid
from base import BaseTrainer


class Trainer(BaseTrainer):
    """
    Trainer class
    Note:
        Inherited from BaseTrainer.
    """
    def __init__(self, model, loss, metrics, optimizer, resume, config,
                 data_loader, valid_data_loader=None, lr_scheduler=None, train_logger=None):
        super(Trainer, self).__init__(model, loss, metrics, optimizer, resume, config, train_logger)
        self.config = config
        self.data_loader = data_loader
        self.valid_data_loader = valid_data_loader
        self.do_validation = self.valid_data_loader is not None
        self.lr_scheduler = lr_scheduler
        self.log_step = int(np.sqrt(data_loader.batch_size))

    def _eval_metrics(self, output, target):
        acc_metrics = np.zeros(len(self.metrics))
        for i, metric in enumerate(self.metrics):
            acc_metrics[i] += metric(output, target)
            self.writer.add_scalar(f'{metric.__name__}', acc_metrics[i])
        return acc_metrics

    def _train_epoch(self, epoch):
        """
        Training logic for an epoch
        :param epoch: Current training epoch.
        :return: A log that contains all information you want to save.
        Note:
            If you have additional information to record, for example:
                > additional_log = {"x": x, "y": y}
            merge it with log before return. i.e.
                > log = {**log, **additional_log}
                > return log
            The metrics in log must have the key 'metrics'.
        """
        self.model.train()
    
        total_loss = 0
        total_metrics = np.zeros(len(self.metrics))
        for batch_idx, (data, target) in enumerate(self.data_loader):
            data, target = data.to(self.device), target.to(self.device)

            self.optimizer.zero_grad()
            output = self.model(data)
            loss = self.loss(output, target)
            loss.backward()
            self.optimizer.step()

            self.writer.set_step((epoch - 1) * len(self.data_loader) + batch_idx)
            self.writer.add_scalar('loss', loss.item())
            total_loss += loss.item()
            total_metrics += self._eval_metrics(output, target)

            if self.verbosity >= 2 and batch_idx % self.log_step == 0:
                self.logger.info('Train Epoch: {} [{}/{} ({:.0f}%)] Loss: {:.6f}'.format(
                    epoch,
                    batch_idx * self.data_loader.batch_size,
                    self.data_loader.n_samples,
                    100.0 * batch_idx / len(self.data_loader),
                    loss.item()))
                self.writer.add_image('input', make_grid(data.cpu(), nrow=8, normalize=True))

        log = {
            'loss': total_loss / len(self.data_loader),
            'metrics': (total_metrics / len(self.data_loader)).tolist()
        }

        if self.do_validation:
            val_log = self._valid_epoch(epoch)
            log = {**log, **val_log}

        if self.lr_scheduler is not None:
            self.lr_scheduler.step()

        return log

    def _valid_epoch(self, epoch):
        """
        Validate after training an epoch
        :return: A log that contains information about validation
        Note:
            The validation metrics in log must have the key 'val_metrics'.
        """
        self.model.eval()
        total_val_loss = 0
        total_val_metrics = np.zeros(len(self.metrics))
        with torch.no_grad():
            for batch_idx, (data, target) in enumerate(self.valid_data_loader):
                data, target = data.to(self.device), target.to(self.device)

                output = self.model(data)
                loss = self.loss(output, target)

                self.writer.set_step((epoch - 1) * len(self.valid_data_loader) + batch_idx, 'valid')
                self.writer.add_scalar('loss', loss.item())
                total_val_loss += loss.item()
                total_val_metrics += self._eval_metrics(output, target)
                self.writer.add_image('input', make_grid(data.cpu(), nrow=8, normalize=True))

        return {
            'val_loss': total_val_loss / len(self.valid_data_loader),
            'val_metrics': (total_val_metrics / len(self.valid_data_loader)).tolist()
        }

I can include some more code if needed.
Thanks.