288 lines
8.1 KiB
Python
288 lines
8.1 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 re import escape
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import pytest
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import torch
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from torch import nn
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from torchmetrics import Metric as TMetric
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from pytorch_lightning import Trainer
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from pytorch_lightning.metrics import Metric as PLMetric
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from pytorch_lightning.metrics import MetricCollection
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.helpers.boring_model import BoringModel
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class SumMetric(TMetric):
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def __init__(self):
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super().__init__()
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self.add_state("x", torch.tensor(0.0), dist_reduce_fx="sum")
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def update(self, x):
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self.x += x
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def compute(self):
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return self.x
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class DiffMetric(PLMetric):
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def __init__(self):
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super().__init__()
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self.add_state("x", torch.tensor(0.0), dist_reduce_fx="sum")
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def update(self, x):
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self.x -= x
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def compute(self):
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return self.x
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def test_metric_lightning(tmpdir):
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class TestModel(BoringModel):
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def __init__(self):
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super().__init__()
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self.metric = SumMetric()
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self.sum = 0.0
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def training_step(self, batch, batch_idx):
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x = batch
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self.metric(x.sum())
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self.sum += x.sum()
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return self.step(x)
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def training_epoch_end(self, outs):
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assert torch.allclose(self.sum, self.metric.compute())
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self.sum = 0.0
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self.metric.reset()
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model = TestModel()
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model.val_dataloader = None
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=2,
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limit_val_batches=2,
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max_epochs=2,
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log_every_n_steps=1,
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weights_summary=None,
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)
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trainer.fit(model)
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def test_metric_lightning_log(tmpdir):
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""" Test logging a metric object and that the metric state gets reset after each epoch."""
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class TestModel(BoringModel):
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def __init__(self):
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super().__init__()
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self.metric_step = SumMetric()
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self.metric_epoch = SumMetric()
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self.sum = 0.0
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self.total_sum = 0.0
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def on_epoch_start(self):
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self.sum = 0.0
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def training_step(self, batch, batch_idx):
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x = batch
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self.metric_step(x.sum())
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self.sum += x.sum()
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self.log("sum_step", self.metric_step, on_epoch=True, on_step=False)
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return {'loss': self.step(x), 'data': x}
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def training_epoch_end(self, outs):
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total = torch.stack([o['data'] for o in outs]).sum()
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self.metric_epoch(total)
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self.log("sum_epoch", self.metric_epoch)
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self.total_sum = total
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model = TestModel()
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model.val_dataloader = None
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=2,
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limit_val_batches=2,
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max_epochs=2,
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log_every_n_steps=1,
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weights_summary=None,
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)
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trainer.fit(model)
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logged = trainer.logged_metrics
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assert torch.allclose(torch.tensor(logged["sum_step"]), model.sum)
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assert torch.allclose(torch.tensor(logged["sum_epoch"]), model.total_sum)
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def test_scriptable(tmpdir):
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class TestModel(BoringModel):
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def __init__(self):
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super().__init__()
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# the metric is not used in the module's `forward`
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# so the module should be exportable to TorchScript
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self.metric = SumMetric()
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self.sum = 0.0
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def training_step(self, batch, batch_idx):
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x = batch
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self.metric(x.sum())
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self.sum += x.sum()
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self.log("sum", self.metric, on_epoch=True, on_step=False)
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return self.step(x)
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model = TestModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=2,
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limit_val_batches=2,
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max_epochs=1,
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log_every_n_steps=1,
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weights_summary=None,
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logger=False,
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checkpoint_callback=False,
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)
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trainer.fit(model)
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rand_input = torch.randn(10, 32)
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script_model = model.to_torchscript()
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# test that we can still do inference
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output = model(rand_input)
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script_output = script_model(rand_input)
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assert torch.allclose(output, script_output)
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def test_metric_collection_lightning_log(tmpdir):
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class TestModel(BoringModel):
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def __init__(self):
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super().__init__()
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self.metric = MetricCollection([SumMetric(), DiffMetric()])
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self.sum = 0.0
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self.diff = 0.0
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def training_step(self, batch, batch_idx):
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x = batch
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metric_vals = self.metric(x.sum())
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self.sum += x.sum()
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self.diff -= x.sum()
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self.log_dict({f'{k}_step': v for k, v in metric_vals.items()})
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return self.step(x)
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def training_epoch_end(self, outputs):
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metric_vals = self.metric.compute()
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self.log_dict({f'{k}_epoch': v for k, v in metric_vals.items()})
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model = TestModel()
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model.val_dataloader = None
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=2,
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limit_val_batches=2,
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max_epochs=1,
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log_every_n_steps=1,
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weights_summary=None,
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)
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trainer.fit(model)
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logged = trainer.logged_metrics
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assert torch.allclose(torch.tensor(logged["SumMetric_epoch"]), model.sum)
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assert torch.allclose(torch.tensor(logged["DiffMetric_epoch"]), model.diff)
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def test_log_metric_no_attributes_raises(tmpdir):
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class TestModel(BoringModel):
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def training_step(self, *args):
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metric = SumMetric()
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self.log("foo", metric)
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trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=1)
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model = TestModel()
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with pytest.raises(MisconfigurationException, match="Could not find the `LightningModule` attribute"):
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trainer.fit(model)
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def test_log_metrics_wrong_attributes_raises(tmpdir):
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class TestModel(BoringModel):
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def __init__(self):
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super().__init__()
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self.a_metric = SumMetric()
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def training_step(self, *args):
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metric = SumMetric()
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self.log("foo", metric)
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trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=1)
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model = TestModel()
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with pytest.raises(MisconfigurationException, match=escape("where `name` is one of ['a_metric']")):
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trainer.fit(model)
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def test_log_metric_dict(tmpdir):
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class TestModel(BoringModel):
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def __init__(self):
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super().__init__()
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self.metrics = nn.ModuleDict({'sum': SumMetric(), 'diff': DiffMetric()})
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self.sum = 0.0
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self.diff = 0.0
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def training_step(self, batch, batch_idx):
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x = batch
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self.metrics['sum'](x.sum())
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self.metrics['diff'](x.sum())
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self.sum += x.sum()
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self.diff -= x.sum()
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self.log_dict({f'{k}_step': v for k, v in self.metrics.items()})
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return self.step(x)
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def training_epoch_end(self, outputs):
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self.metrics['sum'].compute()
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self.metrics['diff'].compute()
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self.log_dict({f'{k}_epoch': v for k, v in self.metrics.items()})
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model = TestModel()
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model.val_dataloader = None
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=2,
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limit_val_batches=2,
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max_epochs=1,
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log_every_n_steps=1,
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weights_summary=None,
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)
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trainer.fit(model)
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logged = trainer.logged_metrics
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assert torch.allclose(torch.tensor(logged["sum_epoch"]), model.sum)
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assert torch.allclose(torch.tensor(logged["diff_epoch"]), model.diff)
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