lightning/tests/base/eval_model_valid_steps.py

from abc import ABC
from collections import OrderedDict

import torch


class ValidationStepVariations(ABC):
    """
    Houses all variations of validation steps
    """
    def validation_step(self, batch, batch_idx, *args, **kwargs):
        """
        Lightning calls this inside the validation loop
        :param batch:
        :return:
        """
        x, y = batch
        x = x.view(x.size(0), -1)
        y_hat = self(x)

        loss_val = self.loss(y, y_hat)

        # acc
        labels_hat = torch.argmax(y_hat, dim=1)
        val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
        val_acc = torch.tensor(val_acc)

        if self.on_gpu:
            val_acc = val_acc.cuda(loss_val.device.index)

        # in DP mode (default) make sure if result is scalar, there's another dim in the beginning
        if self.trainer.use_dp:
            loss_val = loss_val.unsqueeze(0)
            val_acc = val_acc.unsqueeze(0)

        # alternate possible outputs to test
        if batch_idx % 1 == 0:
            output = OrderedDict({
                'val_loss': loss_val,
                'val_acc': val_acc,
            })
            return output
        if batch_idx % 2 == 0:
            return val_acc

        if batch_idx % 3 == 0:
            output = OrderedDict({
                'val_loss': loss_val,
                'val_acc': val_acc,
                'test_dic': {'val_loss_a': loss_val}
            })
            return output

    def validation_step_multiple_dataloaders(self, batch, batch_idx, dataloader_idx, **kwargs):
        """
        Lightning calls this inside the validation loop
        :param batch:
        :return:
        """
        x, y = batch
        x = x.view(x.size(0), -1)
        y_hat = self(x)

        loss_val = self.loss(y, y_hat)

        # acc
        labels_hat = torch.argmax(y_hat, dim=1)
        val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
        val_acc = torch.tensor(val_acc)

        if self.on_gpu:
            val_acc = val_acc.cuda(loss_val.device.index)

        # in DP mode (default) make sure if result is scalar, there's another dim in the beginning
        if self.trainer.use_dp:
            loss_val = loss_val.unsqueeze(0)
            val_acc = val_acc.unsqueeze(0)

        # alternate possible outputs to test
        if batch_idx % 1 == 0:
            output = OrderedDict({
                'val_loss': loss_val,
                'val_acc': val_acc,
            })
            return output
        if batch_idx % 2 == 0:
            return val_acc

        if batch_idx % 3 == 0:
            output = OrderedDict({
                'val_loss': loss_val,
                'val_acc': val_acc,
                'test_dic': {'val_loss_a': loss_val}
            })
            return output
        if batch_idx % 5 == 0:
            output = OrderedDict({
                f'val_loss_{dataloader_idx}': loss_val,
                f'val_acc_{dataloader_idx}': val_acc,
            })
            return output