102 lines
2.9 KiB
Python
102 lines
2.9 KiB
Python
from abc import ABC
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from collections import OrderedDict
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import torch
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class ValidationStepVariations(ABC):
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"""
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Houses all variations of validation steps
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"""
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def validation_step(self, batch, batch_idx, *args, **kwargs):
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"""
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Lightning calls this inside the validation loop
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:param batch:
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:return:
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"""
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x, y = batch
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x = x.view(x.size(0), -1)
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y_hat = self(x)
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loss_val = self.loss(y, y_hat)
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# acc
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labels_hat = torch.argmax(y_hat, dim=1)
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val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
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val_acc = torch.tensor(val_acc)
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if self.on_gpu:
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val_acc = val_acc.cuda(loss_val.device.index)
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# in DP mode (default) make sure if result is scalar, there's another dim in the beginning
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if self.trainer.use_dp:
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loss_val = loss_val.unsqueeze(0)
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val_acc = val_acc.unsqueeze(0)
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# alternate possible outputs to test
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if batch_idx % 1 == 0:
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output = OrderedDict({
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'val_loss': loss_val,
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'val_acc': val_acc,
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})
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return output
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if batch_idx % 2 == 0:
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return val_acc
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if batch_idx % 3 == 0:
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output = OrderedDict({
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'val_loss': loss_val,
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'val_acc': val_acc,
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'test_dic': {'val_loss_a': loss_val}
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})
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return output
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def validation_step_multiple_dataloaders(self, batch, batch_idx, dataloader_idx, **kwargs):
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"""
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Lightning calls this inside the validation loop
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:param batch:
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:return:
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"""
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x, y = batch
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x = x.view(x.size(0), -1)
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y_hat = self(x)
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loss_val = self.loss(y, y_hat)
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# acc
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labels_hat = torch.argmax(y_hat, dim=1)
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val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
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val_acc = torch.tensor(val_acc)
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if self.on_gpu:
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val_acc = val_acc.cuda(loss_val.device.index)
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# in DP mode (default) make sure if result is scalar, there's another dim in the beginning
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if self.trainer.use_dp:
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loss_val = loss_val.unsqueeze(0)
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val_acc = val_acc.unsqueeze(0)
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# alternate possible outputs to test
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if batch_idx % 1 == 0:
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output = OrderedDict({
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'val_loss': loss_val,
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'val_acc': val_acc,
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})
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return output
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if batch_idx % 2 == 0:
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return val_acc
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if batch_idx % 3 == 0:
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output = OrderedDict({
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'val_loss': loss_val,
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'val_acc': val_acc,
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'test_dic': {'val_loss_a': loss_val}
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})
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return output
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if batch_idx % 5 == 0:
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output = OrderedDict({
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f'val_loss_{dataloader_idx}': loss_val,
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f'val_acc_{dataloader_idx}': val_acc,
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})
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return output
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