784 lines
27 KiB
Python
784 lines
27 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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"""
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Tests to ensure that the training loop works with a dict (1.0)
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"""
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from copy import deepcopy
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from typing import Any, Callable
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from unittest import mock
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import pytest
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import torch
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from torch.utils.data import DataLoader
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from torchmetrics import Accuracy, AveragePrecision
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from pytorch_lightning import LightningModule
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from pytorch_lightning.callbacks.base import Callback
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from pytorch_lightning.trainer import Trainer
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from pytorch_lightning.trainer.connectors.logger_connector.fx_validator import FxValidator
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from pytorch_lightning.trainer.connectors.logger_connector.metrics_holder import MetricsHolder
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from pytorch_lightning.trainer.connectors.logger_connector.result import Result
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from pytorch_lightning.trainer.connectors.logger_connector.result_new import MetricSource, ResultCollection
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.helpers.boring_model import BoringModel, RandomDataset
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from tests.helpers.runif import RunIf
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def decorator_with_arguments(fx_name: str = '', hook_fx_name: str = None) -> Callable:
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def decorator(func: Callable) -> Callable:
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def wrapper(self, *args, **kwargs) -> Any:
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# Set information
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self._current_fx_name = fx_name
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self._current_hook_fx_name = hook_fx_name
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self._results = Result()
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result = func(self, *args, **kwargs)
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# cache metrics
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self.trainer.logger_connector.cache_logged_metrics()
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return result
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return wrapper
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return decorator
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def test__logger_connector__epoch_result_store__train(tmpdir):
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"""
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Tests that LoggerConnector will properly capture logged information
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and reduce them
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"""
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class TestModel(BoringModel):
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train_losses = []
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@decorator_with_arguments(fx_name="training_step")
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def training_step(self, batch, batch_idx):
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output = self.layer(batch)
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loss = self.loss(batch, output)
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self.train_losses.append(loss)
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self.log("train_loss", loss, on_step=True, on_epoch=True)
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return {"loss": loss}
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def training_step_end(self, *_):
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self.train_results = deepcopy(self.trainer.logger_connector.cached_results)
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model = TestModel()
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model.training_epoch_end = None
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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=4,
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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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train_results = model.train_results
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assert len(train_results(fx_name="training_step", dl_idx=0, opt_idx=0)) == 2
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generated = train_results(fx_name="training_step", dl_idx=0, opt_idx=0, batch_idx=0, split_idx=0)["train_loss"]
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assert generated == model.train_losses[0]
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generated = train_results(fx_name="training_step", dl_idx=0, opt_idx=0, batch_idx=1, split_idx=0)["train_loss"]
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assert generated == model.train_losses[1]
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assert train_results.has_reduced is not True
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train_results.has_batch_loop_finished = True
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assert train_results.has_reduced is True
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generated = train_results(fx_name="training_step", dl_idx=0, opt_idx=0, reduced=True)['train_loss_epoch'].item()
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expected = torch.stack(model.train_losses).mean().item()
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assert generated == expected
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def test__logger_connector__epoch_result_store__train__tbptt(tmpdir):
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"""
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Tests that LoggerConnector will properly capture logged information with ttbt
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and reduce them
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"""
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truncated_bptt_steps = 2
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sequence_size = 30
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batch_size = 30
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x_seq = torch.rand(batch_size, sequence_size, 1)
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y_seq_list = torch.rand(batch_size, sequence_size, 1).tolist()
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class MockSeq2SeqDataset(torch.utils.data.Dataset):
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def __getitem__(self, i):
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return x_seq, y_seq_list
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def __len__(self):
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return 1
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class TestModel(BoringModel):
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train_losses = []
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def __init__(self):
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super().__init__()
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self.test_hidden = None
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self.layer = torch.nn.Linear(2, 2)
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@decorator_with_arguments(fx_name="training_step")
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def training_step(self, batch, batch_idx, hiddens):
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assert hiddens == self.test_hidden, "Hidden state not persistent between tbptt steps"
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self.test_hidden = torch.rand(1)
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x_tensor, y_list = batch
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assert x_tensor.shape[1] == truncated_bptt_steps, "tbptt split Tensor failed"
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y_tensor = torch.tensor(y_list, dtype=x_tensor.dtype)
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assert y_tensor.shape[1] == truncated_bptt_steps, "tbptt split list failed"
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pred = self(x_tensor.view(batch_size, truncated_bptt_steps))
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loss = torch.nn.functional.mse_loss(pred, y_tensor.view(batch_size, truncated_bptt_steps))
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self.train_losses.append(loss)
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self.log('a', loss, on_epoch=True)
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return {'loss': loss, 'hiddens': self.test_hidden}
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def on_train_epoch_start(self) -> None:
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self.test_hidden = None
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def train_dataloader(self):
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return torch.utils.data.DataLoader(
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dataset=MockSeq2SeqDataset(),
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batch_size=batch_size,
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shuffle=False,
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sampler=None,
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)
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def training_step_end(self, training_step_output):
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self.train_results = deepcopy(self.trainer.logger_connector.cached_results)
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# must return
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return training_step_output
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model = TestModel()
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model.training_epoch_end = None
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model.example_input_array = torch.randn(5, truncated_bptt_steps)
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=10,
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limit_val_batches=0,
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truncated_bptt_steps=truncated_bptt_steps,
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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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train_results = model.train_results
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generated = train_results(fx_name="training_step", dl_idx=0, opt_idx=0, batch_idx=0)
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assert len(generated) == len(model.train_losses)
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# assert reduction didn't happen yet
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assert train_results.has_reduced is False
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# Launch reduction
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train_results.has_batch_loop_finished = True
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# assert reduction did happen
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assert train_results.has_reduced is True
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generated = train_results(fx_name="training_step", dl_idx=0, opt_idx=0, reduced=True)['a_epoch'].item()
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assert generated == torch.stack(model.train_losses).mean().item()
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@pytest.mark.parametrize('num_dataloaders', [1, 2])
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def test__logger_connector__epoch_result_store__test_multi_dataloaders(tmpdir, num_dataloaders):
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"""
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Tests that LoggerConnector will properly capture logged information in multi dataloaders scenario
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"""
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class TestModel(BoringModel):
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test_losses = {dl_idx: [] for dl_idx in range(num_dataloaders)}
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@decorator_with_arguments(fx_name="test_step")
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def test_step(self, batch, batch_idx, dl_idx=0):
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output = self.layer(batch)
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loss = self.loss(batch, output)
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self.test_losses[dl_idx].append(loss)
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self.log("test_loss", loss, on_step=True, on_epoch=True)
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return {"test_loss": loss}
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def on_test_batch_end(self, *args, **kwargs):
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# save objects as it will be reset at the end of epoch.
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self.batch_results = deepcopy(self.trainer.logger_connector.cached_results)
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def on_test_epoch_end(self):
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# save objects as it will be reset at the end of epoch.
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self.reduce_results = deepcopy(self.trainer.logger_connector.cached_results)
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def test_dataloader(self):
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return [super().test_dataloader()] * num_dataloaders
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model = TestModel()
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model.test_epoch_end = None
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limit_test_batches = 4
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=0,
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limit_val_batches=0,
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limit_test_batches=limit_test_batches,
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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.test(model)
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test_results = model.batch_results
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generated = test_results(fx_name="test_step")
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assert len(generated) == num_dataloaders
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for dl_idx in range(num_dataloaders):
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generated = test_results(fx_name="test_step", dl_idx=dl_idx)
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assert len(generated) == limit_test_batches
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test_results = model.reduce_results
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for dl_idx in range(num_dataloaders):
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expected = torch.stack(model.test_losses[dl_idx]).mean()
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generated = test_results(fx_name="test_step", dl_idx=dl_idx, reduced=True)["test_loss_epoch"]
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torch.testing.assert_allclose(generated, expected)
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def test_fx_validator(tmpdir):
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funcs_name = sorted([f for f in dir(Callback) if not f.startswith('_')])
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callbacks_func = [
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'on_after_backward',
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'on_batch_end',
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'on_batch_start',
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'on_before_accelerator_backend_setup',
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'on_before_zero_grad',
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'on_epoch_end',
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'on_epoch_start',
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'on_fit_end',
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'on_configure_sharded_model',
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'on_fit_start',
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'on_init_end',
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'on_init_start',
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'on_keyboard_interrupt',
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'on_load_checkpoint',
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'on_pretrain_routine_end',
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'on_pretrain_routine_start',
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'on_sanity_check_end',
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'on_sanity_check_start',
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'on_save_checkpoint',
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'on_test_batch_end',
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'on_test_batch_start',
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'on_test_end',
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'on_test_epoch_end',
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'on_test_epoch_start',
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'on_test_start',
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'on_train_batch_end',
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'on_train_batch_start',
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'on_train_end',
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'on_train_epoch_end',
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'on_train_epoch_start',
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'on_train_start',
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'on_validation_batch_end',
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'on_validation_batch_start',
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'on_validation_end',
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'on_validation_epoch_end',
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'on_validation_epoch_start',
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'on_validation_start',
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"on_predict_batch_end",
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"on_predict_batch_start",
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"on_predict_end",
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"on_predict_epoch_end",
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"on_predict_epoch_start",
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"on_predict_start",
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'setup',
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'teardown',
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]
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not_supported = [
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"on_before_accelerator_backend_setup",
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"on_fit_end",
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"on_fit_start",
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"on_configure_sharded_model",
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"on_init_end",
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"on_init_start",
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"on_keyboard_interrupt",
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"on_load_checkpoint",
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"on_pretrain_routine_end",
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"on_pretrain_routine_start",
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"on_sanity_check_end",
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"on_sanity_check_start",
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"on_predict_batch_end",
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"on_predict_batch_start",
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"on_predict_end",
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"on_predict_epoch_end",
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"on_predict_epoch_start",
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"on_predict_start",
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"on_save_checkpoint",
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"on_test_end",
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"on_train_end",
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"on_validation_end",
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"setup",
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"teardown",
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]
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assert funcs_name == sorted(callbacks_func), (
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"Detected new callback function. Need to add its logging"
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" permission to FxValidator and update this test"
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)
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validator = FxValidator()
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for func_name in funcs_name:
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# This summarizes where and what is currently possible to log using `self.log`
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is_stage = "train" in func_name or "test" in func_name or "validation" in func_name
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is_start = "start" in func_name or "batch" in func_name
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is_epoch = "epoch" in func_name
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on_step = is_stage and not is_start and not is_epoch
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on_epoch = True
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# creating allowed condition
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allowed = (
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is_stage or "batch" in func_name or "epoch" in func_name or "grad" in func_name or "backward" in func_name
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)
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allowed = (
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allowed and "pretrain" not in func_name and "predict" not in func_name
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and func_name not in ["on_train_end", "on_test_end", "on_validation_end"]
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)
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if allowed:
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validator.check_logging(fx_name=func_name, on_step=on_step, on_epoch=on_epoch)
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if not is_start and is_stage:
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with pytest.raises(MisconfigurationException, match="You can't"):
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validator.check_logging(fx_name=func_name, on_step=True, on_epoch=on_epoch)
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else:
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assert func_name in not_supported
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with pytest.raises(MisconfigurationException, match="function doesn't support"):
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validator.check_logging(fx_name=func_name, on_step=on_step, on_epoch=on_epoch)
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with pytest.raises(RuntimeError, match="`foo` but it is not implemented"):
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validator.check_logging("foo", False, False)
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@RunIf(min_gpus=2)
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def test_epoch_results_cache_dp(tmpdir):
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root_device = torch.device("cuda", 0)
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class TestModel(BoringModel):
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def training_step(self, *args, **kwargs):
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result = super().training_step(*args, **kwargs)
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self.log("train_loss_epoch", result["loss"], on_step=False, on_epoch=True)
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return result
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def training_step_end(self, training_step_outputs): # required for dp
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loss = training_step_outputs["loss"].mean()
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return loss
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def training_epoch_end(self, outputs):
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assert all(out["loss"].device == root_device for out in outputs)
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assert self.trainer.callback_metrics["train_loss_epoch"].device == root_device
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def validation_step(self, *args, **kwargs):
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val_loss = torch.rand(1, device=torch.device("cuda", 1))
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self.log("val_loss_epoch", val_loss, on_step=False, on_epoch=True)
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return val_loss
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def validation_epoch_end(self, outputs):
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assert all(loss.device == root_device for loss in outputs)
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assert self.trainer.callback_metrics["val_loss_epoch"].device == root_device
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def test_step(self, *args, **kwargs):
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test_loss = torch.rand(1, device=torch.device("cuda", 1))
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self.log("test_loss_epoch", test_loss, on_step=False, on_epoch=True)
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return test_loss
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def test_epoch_end(self, outputs):
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assert all(loss.device == root_device for loss in outputs)
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assert self.trainer.callback_metrics["test_loss_epoch"].device == root_device
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def train_dataloader(self):
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return DataLoader(RandomDataset(32, 64), batch_size=4)
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def val_dataloader(self):
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return DataLoader(RandomDataset(32, 64), batch_size=4)
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def test_dataloader(self):
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return DataLoader(RandomDataset(32, 64), batch_size=4)
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model = TestModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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accelerator="dp",
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gpus=2,
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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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)
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trainer.fit(model)
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trainer.test(model, ckpt_path=None)
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@pytest.mark.parametrize('to_float', [False, True])
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def test_metrics_holder(to_float, tmpdir):
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device = "cuda" if torch.cuda.is_available() else "cpu"
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preds = torch.tensor([[0.9, 0.1]], device=device)
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def is_float(value: Any) -> bool:
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return isinstance(value, float)
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expected_function = is_float if to_float else torch.is_tensor
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targets = torch.tensor([1], device=device)
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acc = Accuracy().to(device)
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metric_holder = MetricsHolder(to_float=to_float)
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metric_holder.update({
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"x": 1,
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"y": torch.tensor(2),
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"z": acc(preds, targets),
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})
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metric_holder.convert(device)
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metrics = metric_holder.metrics
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assert expected_function(metrics["x"])
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assert expected_function(metrics["y"])
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assert expected_function(metrics["z"])
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def test_metric_holder_raises(tmpdir):
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"""Check that an error is raised when trying to convert non-scalar tensors"""
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class TestModel(BoringModel):
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def validation_step(self, batch, *args, **kwargs):
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output = self(batch)
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self.log('test', output)
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def test_step(self, *args, **kwargs):
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return self.validation_step(*args, **kwargs)
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model = TestModel()
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model.validation_epoch_end = None
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model.test_epoch_end = None
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trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
|
|
|
|
match = "The metric `.*` does not contain a single element"
|
|
with pytest.raises(MisconfigurationException, match=match):
|
|
trainer.validate(model)
|
|
with pytest.raises(MisconfigurationException, match=match):
|
|
trainer.test(model)
|
|
|
|
|
|
def test_can_return_tensor_with_more_than_one_element(tmpdir):
|
|
"""Ensure {validation,test}_step return values are not included as callback metrics. #6623"""
|
|
|
|
class TestModel(BoringModel):
|
|
|
|
def validation_step(self, batch, *args, **kwargs):
|
|
return {"val": torch.tensor([0, 1])}
|
|
|
|
def validation_epoch_end(self, outputs):
|
|
# ensure validation step returns still appear here
|
|
assert len(outputs) == 2
|
|
assert all(list(d) == ["val"] for d in outputs) # check keys
|
|
assert all(torch.equal(d["val"], torch.tensor([0, 1])) for d in outputs) # check values
|
|
|
|
def test_step(self, batch, *args, **kwargs):
|
|
return {"test": torch.tensor([0, 1])}
|
|
|
|
def test_epoch_end(self, outputs):
|
|
assert len(outputs) == 2
|
|
assert all(list(d) == ["test"] for d in outputs) # check keys
|
|
assert all(torch.equal(d["test"], torch.tensor([0, 1])) for d in outputs) # check values
|
|
|
|
model = TestModel()
|
|
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=2, progress_bar_refresh_rate=0)
|
|
trainer.fit(model)
|
|
trainer.validate(model)
|
|
trainer.test(model)
|
|
|
|
|
|
def test_logging_to_progress_bar_with_reserved_key(tmpdir):
|
|
""" Test that logging a metric with a reserved name to the progress bar raises a warning. """
|
|
|
|
class TestModel(BoringModel):
|
|
|
|
def training_step(self, *args, **kwargs):
|
|
output = super().training_step(*args, **kwargs)
|
|
self.log("loss", output["loss"], prog_bar=True)
|
|
return output
|
|
|
|
model = TestModel()
|
|
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
|
|
with pytest.warns(UserWarning, match="The progress bar already tracks a metric with the .* 'loss'"):
|
|
trainer.fit(model)
|
|
|
|
|
|
@pytest.mark.parametrize("add_dataloader_idx", [False, True])
|
|
def test_auto_add_dataloader_idx(tmpdir, add_dataloader_idx):
|
|
""" test that auto_add_dataloader_idx argument works """
|
|
|
|
class TestModel(BoringModel):
|
|
|
|
def val_dataloader(self):
|
|
dl = super().val_dataloader()
|
|
return [dl, dl]
|
|
|
|
def validation_step(self, *args, **kwargs):
|
|
output = super().validation_step(*args[:-1], **kwargs)
|
|
if add_dataloader_idx:
|
|
name = "val_loss"
|
|
else:
|
|
name = f"val_loss_custom_naming_{args[-1]}"
|
|
|
|
self.log(name, output["x"], add_dataloader_idx=add_dataloader_idx)
|
|
return output
|
|
|
|
model = TestModel()
|
|
model.validation_epoch_end = None
|
|
|
|
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=2)
|
|
trainer.fit(model)
|
|
logged = trainer.logged_metrics
|
|
|
|
# Check that the correct keys exist
|
|
if add_dataloader_idx:
|
|
assert 'val_loss/dataloader_idx_0' in logged
|
|
assert 'val_loss/dataloader_idx_1' in logged
|
|
else:
|
|
assert 'val_loss_custom_naming_0' in logged
|
|
assert 'val_loss_custom_naming_1' in logged
|
|
|
|
|
|
def test_metrics_reset(tmpdir):
|
|
"""Tests that metrics are reset correctly after the end of the train/val/test epoch."""
|
|
|
|
class TestModel(LightningModule):
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.layer = torch.nn.Linear(32, 1)
|
|
|
|
for stage in ['train', 'val', 'test']:
|
|
acc = Accuracy()
|
|
acc.reset = mock.Mock(side_effect=acc.reset)
|
|
ap = AveragePrecision(num_classes=1, pos_label=1)
|
|
ap.reset = mock.Mock(side_effect=ap.reset)
|
|
self.add_module(f"acc_{stage}", acc)
|
|
self.add_module(f"ap_{stage}", ap)
|
|
|
|
def forward(self, x):
|
|
return self.layer(x)
|
|
|
|
def _step(self, stage, batch):
|
|
labels = (batch.detach().sum(1) > 0).float() # Fake some targets
|
|
logits = self.forward(batch)
|
|
loss = torch.nn.functional.binary_cross_entropy_with_logits(logits, labels.unsqueeze(1))
|
|
probs = torch.sigmoid(logits.detach())
|
|
self.log(f"loss/{stage}", loss)
|
|
|
|
acc = self._modules[f"acc_{stage}"]
|
|
ap = self._modules[f"ap_{stage}"]
|
|
|
|
labels_int = labels.to(torch.long)
|
|
acc(probs.flatten(), labels_int)
|
|
ap(probs.flatten(), labels_int)
|
|
|
|
# Metric.forward calls reset so reset the mocks here
|
|
acc.reset.reset_mock()
|
|
ap.reset.reset_mock()
|
|
|
|
self.log(f"{stage}/accuracy", acc)
|
|
self.log(f"{stage}/ap", ap)
|
|
|
|
return loss
|
|
|
|
def training_step(self, batch, batch_idx, *args, **kwargs):
|
|
return self._step('train', batch)
|
|
|
|
def validation_step(self, batch, batch_idx, *args, **kwargs):
|
|
return self._step('val', batch)
|
|
|
|
def test_step(self, batch, batch_idx, *args, **kwargs):
|
|
return self._step('test', batch)
|
|
|
|
def configure_optimizers(self):
|
|
optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
|
|
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
|
|
return [optimizer], [lr_scheduler]
|
|
|
|
def train_dataloader(self):
|
|
return DataLoader(RandomDataset(32, 64))
|
|
|
|
def val_dataloader(self):
|
|
return DataLoader(RandomDataset(32, 64))
|
|
|
|
def test_dataloader(self):
|
|
return DataLoader(RandomDataset(32, 64))
|
|
|
|
def _assert_epoch_end(self, stage):
|
|
acc = self._modules[f"acc_{stage}"]
|
|
ap = self._modules[f"ap_{stage}"]
|
|
|
|
acc.reset.asset_not_called()
|
|
ap.reset.assert_not_called()
|
|
|
|
def on_train_epoch_end(self):
|
|
self._assert_epoch_end('train')
|
|
|
|
def on_validation_epoch_end(self):
|
|
self._assert_epoch_end('val')
|
|
|
|
def on_test_epoch_end(self):
|
|
self._assert_epoch_end('test')
|
|
|
|
def _assert_called(model, stage):
|
|
acc = model._modules[f"acc_{stage}"]
|
|
ap = model._modules[f"ap_{stage}"]
|
|
|
|
acc.reset.assert_called_once()
|
|
acc.reset.reset_mock()
|
|
|
|
ap.reset.assert_called_once()
|
|
ap.reset.reset_mock()
|
|
|
|
model = TestModel()
|
|
trainer = Trainer(
|
|
default_root_dir=tmpdir,
|
|
limit_train_batches=2,
|
|
limit_val_batches=2,
|
|
limit_test_batches=2,
|
|
max_epochs=1,
|
|
progress_bar_refresh_rate=0,
|
|
)
|
|
|
|
trainer.fit(model)
|
|
_assert_called(model, 'train')
|
|
_assert_called(model, 'val')
|
|
|
|
trainer.validate(model)
|
|
_assert_called(model, 'val')
|
|
|
|
trainer.test(model)
|
|
_assert_called(model, 'test')
|
|
|
|
|
|
def test_result_collection_on_tensor_with_mean_reduction():
|
|
result_collection = ResultCollection(True, torch.device("cpu"))
|
|
product = [(True, True), (False, True), (True, False), (False, False)]
|
|
values = torch.arange(1, 10).float() # need to convert to float() due to precision issues using torch 1.4
|
|
batches = values * values
|
|
|
|
for i, v in enumerate(values):
|
|
for prog_bar in [False, True]:
|
|
for logger in [False, True]:
|
|
for on_step, on_epoch in product:
|
|
name = "loss"
|
|
if on_step:
|
|
name += "_on_step"
|
|
if on_epoch:
|
|
name += "_on_epoch"
|
|
if prog_bar:
|
|
name += "_prog_bar"
|
|
if logger:
|
|
name += "_logger"
|
|
result_collection.log(
|
|
"training_step",
|
|
name,
|
|
v,
|
|
on_step=on_step,
|
|
on_epoch=on_epoch,
|
|
batch_size=batches[i],
|
|
prog_bar=prog_bar,
|
|
logger=logger,
|
|
)
|
|
|
|
total_value = sum(values * batches)
|
|
total_batches = sum(batches)
|
|
assert result_collection["training_step.loss_on_step_on_epoch"].value == total_value
|
|
assert result_collection["training_step.loss_on_step_on_epoch"].cumulated_batch_size == total_batches
|
|
|
|
batch_metrics = result_collection.metrics(True)
|
|
max_ = max(values)
|
|
assert batch_metrics[MetricSource.PBAR] == {
|
|
'loss_on_step_on_epoch_prog_bar_step': max_,
|
|
'loss_on_step_on_epoch_prog_bar_logger_step': max_,
|
|
'loss_on_step_prog_bar': max_,
|
|
'loss_on_step_prog_bar_logger': max_,
|
|
}
|
|
assert batch_metrics[MetricSource.LOG] == {
|
|
'loss_on_step_on_epoch_logger_step': max_,
|
|
'loss_on_step_logger': max_,
|
|
'loss_on_step_on_epoch_prog_bar_logger_step': max_,
|
|
'loss_on_step_prog_bar_logger': max_,
|
|
}
|
|
assert batch_metrics[MetricSource.CALLBACK] == {
|
|
'loss_on_step': max_,
|
|
'loss_on_step_logger': max_,
|
|
'loss_on_step_on_epoch': max_,
|
|
'loss_on_step_on_epoch_logger': max_,
|
|
'loss_on_step_on_epoch_logger_step': max_,
|
|
'loss_on_step_on_epoch_prog_bar': max_,
|
|
'loss_on_step_on_epoch_prog_bar_logger': max_,
|
|
'loss_on_step_on_epoch_prog_bar_logger_step': max_,
|
|
'loss_on_step_on_epoch_prog_bar_step': max_,
|
|
'loss_on_step_on_epoch_step': max_,
|
|
'loss_on_step_prog_bar': max_,
|
|
'loss_on_step_prog_bar_logger': max_,
|
|
}
|
|
|
|
epoch_metrics = result_collection.metrics(False)
|
|
mean = total_value / total_batches
|
|
assert epoch_metrics[MetricSource.PBAR] == {
|
|
'loss_on_epoch_prog_bar': mean,
|
|
'loss_on_epoch_prog_bar_logger': mean,
|
|
'loss_on_step_on_epoch_prog_bar_epoch': mean,
|
|
'loss_on_step_on_epoch_prog_bar_logger_epoch': mean,
|
|
}
|
|
assert epoch_metrics[MetricSource.LOG] == {
|
|
'loss_on_epoch_logger': mean,
|
|
'loss_on_epoch_prog_bar_logger': mean,
|
|
'loss_on_step_on_epoch_logger_epoch': mean,
|
|
'loss_on_step_on_epoch_prog_bar_logger_epoch': mean
|
|
}
|
|
assert epoch_metrics[MetricSource.CALLBACK] == {
|
|
'loss_on_epoch': mean,
|
|
'loss_on_epoch_logger': mean,
|
|
'loss_on_epoch_prog_bar': mean,
|
|
'loss_on_epoch_prog_bar_logger': mean,
|
|
'loss_on_step_on_epoch': mean,
|
|
'loss_on_step_on_epoch_epoch': mean,
|
|
'loss_on_step_on_epoch_logger': mean,
|
|
'loss_on_step_on_epoch_logger_epoch': mean,
|
|
'loss_on_step_on_epoch_prog_bar': mean,
|
|
'loss_on_step_on_epoch_prog_bar_epoch': mean,
|
|
'loss_on_step_on_epoch_prog_bar_logger': mean,
|
|
'loss_on_step_on_epoch_prog_bar_logger_epoch': mean
|
|
}
|