97 lines
2.9 KiB
Python
97 lines
2.9 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 TestStepVariations(ABC):
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"""
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Houses all variations of test steps
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"""
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def test_step(self, batch, batch_idx, *args, **kwargs):
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"""
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Default, baseline test_step
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:param batch:
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:return:
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"""
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self.test_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_test = 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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test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
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test_acc = torch.tensor(test_acc)
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test_acc = test_acc.type_as(x)
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# alternate possible outputs to test
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if batch_idx % 1 == 0:
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output = OrderedDict({'test_loss': loss_test, 'test_acc': test_acc})
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return output
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if batch_idx % 2 == 0:
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return test_acc
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if batch_idx % 3 == 0:
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output = OrderedDict({
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'test_loss': loss_test,
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'test_acc': test_acc,
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'test_dic': dict(test_loss_a=loss_test),
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})
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return output
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def test_step__multiple_dataloaders(self, batch, batch_idx, dataloader_idx, **kwargs):
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"""
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Default, baseline test_step
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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_test = 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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test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
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test_acc = torch.tensor(test_acc)
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test_acc = test_acc.type_as(x)
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# alternate possible outputs to test
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if batch_idx % 1 == 0:
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output = OrderedDict({'test_loss': loss_test, 'test_acc': test_acc})
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return output
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if batch_idx % 2 == 0:
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return test_acc
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if batch_idx % 3 == 0:
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output = OrderedDict({
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'test_loss': loss_test,
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'test_acc': test_acc,
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'test_dic': dict(test_loss_a=loss_test),
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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({f'test_loss_{dataloader_idx}': loss_test, f'test_acc_{dataloader_idx}': test_acc})
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return output
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