495 lines
18 KiB
Python
495 lines
18 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 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.result import MetricSource, ResultCollection
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from pytorch_lightning.trainer.states import TrainerFn
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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 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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def test_can_return_tensor_with_more_than_one_element(tmpdir):
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"""Ensure {validation,test}_step return values are not included as callback metrics. #6623"""
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class TestModel(BoringModel):
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def validation_step(self, batch, *args, **kwargs):
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return {"val": torch.tensor([0, 1])}
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def validation_epoch_end(self, outputs):
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# ensure validation step returns still appear here
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assert len(outputs) == 2
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assert all(list(d) == ["val"] for d in outputs) # check keys
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assert all(torch.equal(d["val"], torch.tensor([0, 1])) for d in outputs) # check values
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def test_step(self, batch, *args, **kwargs):
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return {"test": torch.tensor([0, 1])}
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def test_epoch_end(self, outputs):
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assert len(outputs) == 2
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assert all(list(d) == ["test"] for d in outputs) # check keys
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assert all(torch.equal(d["test"], torch.tensor([0, 1])) for d in outputs) # check values
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model = TestModel()
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trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=2, progress_bar_refresh_rate=0)
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trainer.fit(model)
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trainer.validate(model)
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trainer.test(model)
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def test_logging_to_progress_bar_with_reserved_key(tmpdir):
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""" Test that logging a metric with a reserved name to the progress bar raises a warning. """
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class TestModel(BoringModel):
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def training_step(self, *args, **kwargs):
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output = super().training_step(*args, **kwargs)
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self.log("loss", output["loss"], prog_bar=True)
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return output
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model = TestModel()
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trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
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with pytest.warns(UserWarning, match="The progress bar already tracks a metric with the .* 'loss'"):
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trainer.fit(model)
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@pytest.mark.parametrize("add_dataloader_idx", [False, True])
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def test_auto_add_dataloader_idx(tmpdir, add_dataloader_idx):
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""" test that auto_add_dataloader_idx argument works """
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class TestModel(BoringModel):
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def val_dataloader(self):
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dl = super().val_dataloader()
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return [dl, dl]
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def validation_step(self, *args, **kwargs):
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output = super().validation_step(*args[:-1], **kwargs)
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if add_dataloader_idx:
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name = "val_loss"
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else:
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name = f"val_loss_custom_naming_{args[-1]}"
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self.log(name, output["x"], add_dataloader_idx=add_dataloader_idx)
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return output
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model = TestModel()
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model.validation_epoch_end = None
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trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=2)
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trainer.fit(model)
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logged = trainer.logged_metrics
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# Check that the correct keys exist
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if add_dataloader_idx:
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assert 'val_loss/dataloader_idx_0' in logged
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assert 'val_loss/dataloader_idx_1' in logged
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else:
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assert 'val_loss_custom_naming_0' in logged
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assert 'val_loss_custom_naming_1' in logged
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def test_metrics_reset(tmpdir):
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"""Tests that metrics are reset correctly after the end of the train/val/test epoch."""
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class TestModel(LightningModule):
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def __init__(self):
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super().__init__()
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self.layer = torch.nn.Linear(32, 1)
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for stage in ['train', 'val', 'test']:
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acc = Accuracy()
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acc.reset = mock.Mock(side_effect=acc.reset)
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ap = AveragePrecision(num_classes=1, pos_label=1)
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ap.reset = mock.Mock(side_effect=ap.reset)
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self.add_module(f"acc_{stage}", acc)
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self.add_module(f"ap_{stage}", ap)
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def forward(self, x):
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return self.layer(x)
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def _step(self, stage, batch):
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labels = (batch.detach().sum(1) > 0).float() # Fake some targets
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logits = self.forward(batch)
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loss = torch.nn.functional.binary_cross_entropy_with_logits(logits, labels.unsqueeze(1))
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probs = torch.sigmoid(logits.detach())
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self.log(f"loss/{stage}", loss)
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acc = self._modules[f"acc_{stage}"]
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ap = self._modules[f"ap_{stage}"]
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labels_int = labels.to(torch.long)
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acc(probs.flatten(), labels_int)
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ap(probs.flatten(), labels_int)
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# Metric.forward calls reset so reset the mocks here
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acc.reset.reset_mock()
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ap.reset.reset_mock()
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self.log(f"{stage}/accuracy", acc)
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self.log(f"{stage}/ap", ap)
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return loss
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def training_step(self, batch, batch_idx, *args, **kwargs):
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return self._step('train', batch)
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def validation_step(self, batch, batch_idx, *args, **kwargs):
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return self._step('val', batch)
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def test_step(self, batch, batch_idx, *args, **kwargs):
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return self._step('test', batch)
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def configure_optimizers(self):
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optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
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lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
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return [optimizer], [lr_scheduler]
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def train_dataloader(self):
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return DataLoader(RandomDataset(32, 64))
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def val_dataloader(self):
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return DataLoader(RandomDataset(32, 64))
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def test_dataloader(self):
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return DataLoader(RandomDataset(32, 64))
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def _assert_epoch_end(self, stage):
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acc = self._modules[f"acc_{stage}"]
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ap = self._modules[f"ap_{stage}"]
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acc.reset.assert_called_once()
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ap.reset.assert_called_once()
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def teardown(self, stage):
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if stage == TrainerFn.FITTING:
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self._assert_epoch_end('train')
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self._assert_epoch_end('val')
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elif stage == TrainerFn.VALIDATING:
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self._assert_epoch_end('val')
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elif stage == TrainerFn.TESTING:
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self._assert_epoch_end('test')
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def _assert_called(model, stage):
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acc = model._modules[f"acc_{stage}"]
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ap = model._modules[f"ap_{stage}"]
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assert acc.reset.call_count == 1
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acc.reset.reset_mock()
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assert ap.reset.call_count == 1
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ap.reset.reset_mock()
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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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limit_test_batches=2,
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max_epochs=1,
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progress_bar_refresh_rate=0,
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num_sanity_val_steps=2,
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)
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trainer.fit(model)
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_assert_called(model, 'train')
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_assert_called(model, 'val')
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trainer.validate(model)
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_assert_called(model, 'val')
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trainer.test(model)
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_assert_called(model, 'test')
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def test_result_collection_on_tensor_with_mean_reduction():
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result_collection = ResultCollection(True, torch.device("cpu"))
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product = [(True, True), (False, True), (True, False), (False, False)]
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values = torch.arange(1, 10).float() # need to convert to float() due to precision issues using torch 1.4
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batches = values * values
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for i, v in enumerate(values):
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for prog_bar in [False, True]:
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for logger in [False, True]:
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for on_step, on_epoch in product:
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name = "loss"
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if on_step:
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name += "_on_step"
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if on_epoch:
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name += "_on_epoch"
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if prog_bar:
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name += "_prog_bar"
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if logger:
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name += "_logger"
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result_collection.log(
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"training_step",
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name,
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v,
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on_step=on_step,
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on_epoch=on_epoch,
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batch_size=batches[i],
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prog_bar=prog_bar,
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logger=logger,
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)
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total_value = sum(values * batches)
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total_batches = sum(batches)
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assert result_collection["training_step.loss_on_step_on_epoch"].value == total_value
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assert result_collection["training_step.loss_on_step_on_epoch"].cumulated_batch_size == total_batches
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batch_metrics = result_collection.metrics(True)
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max_ = max(values)
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assert batch_metrics[MetricSource.PBAR] == {
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'loss_on_step_on_epoch_prog_bar_step': max_,
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'loss_on_step_on_epoch_prog_bar_logger_step': max_,
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'loss_on_step_prog_bar': max_,
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'loss_on_step_prog_bar_logger': max_,
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}
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assert batch_metrics[MetricSource.LOG] == {
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'loss_on_step_on_epoch_logger_step': max_,
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'loss_on_step_logger': max_,
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'loss_on_step_on_epoch_prog_bar_logger_step': max_,
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'loss_on_step_prog_bar_logger': max_,
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}
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assert batch_metrics[MetricSource.CALLBACK] == {
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'loss_on_step': max_,
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'loss_on_step_logger': max_,
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'loss_on_step_on_epoch': max_,
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'loss_on_step_on_epoch_logger': max_,
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'loss_on_step_on_epoch_logger_step': max_,
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'loss_on_step_on_epoch_prog_bar': max_,
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'loss_on_step_on_epoch_prog_bar_logger': max_,
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'loss_on_step_on_epoch_prog_bar_logger_step': max_,
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'loss_on_step_on_epoch_prog_bar_step': max_,
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'loss_on_step_on_epoch_step': max_,
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'loss_on_step_prog_bar': max_,
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'loss_on_step_prog_bar_logger': max_,
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}
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epoch_metrics = result_collection.metrics(False)
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mean = total_value / total_batches
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assert epoch_metrics[MetricSource.PBAR] == {
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'loss_on_epoch_prog_bar': mean,
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'loss_on_epoch_prog_bar_logger': mean,
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'loss_on_step_on_epoch_prog_bar_epoch': mean,
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'loss_on_step_on_epoch_prog_bar_logger_epoch': mean,
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}
|
|
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
|
|
}
|