83 lines
2.6 KiB
Python
83 lines
2.6 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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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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self.validation_step_called = True
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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).type_as(x)
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output = OrderedDict({"val_loss": loss_val, "val_acc": val_acc, "test_dic": dict(val_loss_a=loss_val)})
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return output
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def validation_step__dp(self, batch, batch_idx, *args, **kwargs):
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self.validation_step_called = True
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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.to(self.device))
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y = y.to(y_hat.device)
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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).type_as(x)
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self.log("val_loss", loss_val)
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self.log("val_acc", val_acc)
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return loss_val
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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).type_as(x)
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output = OrderedDict({f"val_loss_{dataloader_idx}": loss_val, f"val_acc_{dataloader_idx}": val_acc})
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return output
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