260 lines
9.3 KiB
Python
260 lines
9.3 KiB
Python
import os
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import pickle
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import platform
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import re
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from pathlib import Path
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import cloudpickle
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import pytest
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import tests.base.develop_utils as tutils
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import torch
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from pytorch_lightning import Trainer, seed_everything
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from pytorch_lightning.callbacks import ModelCheckpoint
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from pytorch_lightning.loggers import TensorBoardLogger
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from tests.base import EvalModelTemplate
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@pytest.mark.parametrize("save_top_k", [-1, 0, 1, 2])
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def test_model_checkpoint_with_non_string_input(tmpdir, save_top_k):
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"""Test that None in checkpoint callback is valid and that ckpt_path is set correctly"""
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tutils.reset_seed()
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model = EvalModelTemplate()
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checkpoint = ModelCheckpoint(filepath=None, save_top_k=save_top_k)
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trainer = Trainer(
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default_root_dir=tmpdir,
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checkpoint_callback=checkpoint,
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overfit_batches=0.20,
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max_epochs=2,
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)
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trainer.fit(model)
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assert (
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checkpoint.dirpath == tmpdir / trainer.logger.name / "version_0" / "checkpoints"
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)
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@pytest.mark.parametrize(
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"logger_version,expected",
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[(None, "version_0"), (1, "version_1"), ("awesome", "awesome")],
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)
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def test_model_checkpoint_path(tmpdir, logger_version, expected):
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"""Test that "version_" prefix is only added when logger's version is an integer"""
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tutils.reset_seed()
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model = EvalModelTemplate()
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logger = TensorBoardLogger(str(tmpdir), version=logger_version)
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trainer = Trainer(
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default_root_dir=tmpdir, overfit_batches=0.2, max_epochs=2, logger=logger
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)
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trainer.fit(model)
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ckpt_version = Path(trainer.checkpoint_callback.dirpath).parent.name
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assert ckpt_version == expected
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def test_pickling(tmpdir):
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ckpt = ModelCheckpoint(tmpdir)
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ckpt_pickled = pickle.dumps(ckpt)
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ckpt_loaded = pickle.loads(ckpt_pickled)
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assert vars(ckpt) == vars(ckpt_loaded)
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ckpt_pickled = cloudpickle.dumps(ckpt)
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ckpt_loaded = cloudpickle.loads(ckpt_pickled)
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assert vars(ckpt) == vars(ckpt_loaded)
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class ModelCheckpointTestInvocations(ModelCheckpoint):
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# this class has to be defined outside the test function, otherwise we get pickle error
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# due to the way ddp process is launched
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def __init__(self, expected_count, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.count = 0
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self.expected_count = expected_count
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def _save_model(self, filepath, trainer, pl_module):
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# make sure we don't save twice
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assert not os.path.isfile(filepath)
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self.count += 1
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super()._save_model(filepath, trainer, pl_module)
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def on_train_end(self, trainer, pl_module):
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super().on_train_end(trainer, pl_module)
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# on rank 0 we expect the saved files and on all others no saves
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assert (trainer.global_rank == 0 and self.count == self.expected_count) or (
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trainer.global_rank > 0 and self.count == 0
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)
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@pytest.mark.skipif(
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platform.system() == "Windows",
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reason="Distributed training is not supported on Windows",
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)
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def test_model_checkpoint_no_extraneous_invocations(tmpdir):
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"""Test to ensure that the model callback saves the checkpoints only once in distributed mode."""
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model = EvalModelTemplate()
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num_epochs = 4
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model_checkpoint = ModelCheckpointTestInvocations(
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expected_count=num_epochs, save_top_k=-1
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)
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trainer = Trainer(
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distributed_backend="ddp_cpu",
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num_processes=2,
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default_root_dir=tmpdir,
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early_stop_callback=False,
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checkpoint_callback=model_checkpoint,
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max_epochs=num_epochs,
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)
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result = trainer.fit(model)
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assert 1 == result
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def test_model_checkpoint_format_checkpoint_name(tmpdir):
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# empty filename:
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ckpt_name = ModelCheckpoint._format_checkpoint_name('', 3, {})
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assert ckpt_name == 'epoch=3'
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ckpt_name = ModelCheckpoint._format_checkpoint_name(None, 3, {}, prefix='test')
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assert ckpt_name == 'test-epoch=3'
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# no groups case:
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ckpt_name = ModelCheckpoint._format_checkpoint_name('ckpt', 3, {}, prefix='test')
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assert ckpt_name == 'test-ckpt'
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# no prefix
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ckpt_name = ModelCheckpoint._format_checkpoint_name('{epoch:03d}-{acc}', 3, {'acc': 0.03})
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assert ckpt_name == 'epoch=003-acc=0.03'
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# prefix
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char_org = ModelCheckpoint.CHECKPOINT_JOIN_CHAR
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ModelCheckpoint.CHECKPOINT_JOIN_CHAR = '@'
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ckpt_name = ModelCheckpoint._format_checkpoint_name('{epoch},{acc:.5f}', 3, {'acc': 0.03}, prefix='test')
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assert ckpt_name == 'test@epoch=3,acc=0.03000'
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ModelCheckpoint.CHECKPOINT_JOIN_CHAR = char_org
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# no filepath set
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ckpt_name = ModelCheckpoint(filepath=None).format_checkpoint_name(3, {})
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assert ckpt_name == 'epoch=3.ckpt'
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ckpt_name = ModelCheckpoint(filepath='').format_checkpoint_name(5, {})
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assert ckpt_name == 'epoch=5.ckpt'
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# CWD
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ckpt_name = ModelCheckpoint(filepath='.').format_checkpoint_name(3, {})
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assert Path(ckpt_name) == Path('.') / 'epoch=3.ckpt'
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# dir does not exist so it is used as filename
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filepath = tmpdir / 'dir'
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ckpt_name = ModelCheckpoint(filepath=filepath, prefix='test').format_checkpoint_name(3, {})
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assert ckpt_name == tmpdir / 'test-dir.ckpt'
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# now, dir exists
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os.mkdir(filepath)
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ckpt_name = ModelCheckpoint(filepath=filepath, prefix='test').format_checkpoint_name(3, {})
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assert ckpt_name == filepath / 'test-epoch=3.ckpt'
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# with ver
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ckpt_name = ModelCheckpoint(filepath=tmpdir / 'name', prefix='test').format_checkpoint_name(3, {}, ver=3)
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assert ckpt_name == tmpdir / 'test-name-v3.ckpt'
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def test_model_checkpoint_save_last(tmpdir):
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"""Tests that save_last produces only one last checkpoint."""
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model = EvalModelTemplate()
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epochs = 3
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ModelCheckpoint.CHECKPOINT_NAME_LAST = 'last-{epoch}'
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model_checkpoint = ModelCheckpoint(filepath=tmpdir, save_top_k=-1, save_last=True)
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trainer = Trainer(
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default_root_dir=tmpdir,
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early_stop_callback=False,
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checkpoint_callback=model_checkpoint,
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max_epochs=epochs,
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)
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trainer.fit(model)
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last_filename = model_checkpoint._format_checkpoint_name(ModelCheckpoint.CHECKPOINT_NAME_LAST, epochs - 1, {})
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last_filename = last_filename + '.ckpt'
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assert str(tmpdir / last_filename) == model_checkpoint.last_model_path
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assert set(os.listdir(tmpdir)) == set(
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[f'epoch={i}.ckpt' for i in range(epochs)] + [last_filename, 'lightning_logs']
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)
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ModelCheckpoint.CHECKPOINT_NAME_LAST = 'last'
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def test_model_checkpoint_save_last_checkpoint_contents(tmpdir):
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"""Tests that the save_last checkpoint contains the latest information."""
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seed_everything(100)
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model = EvalModelTemplate()
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num_epochs = 3
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model_checkpoint = ModelCheckpoint(filepath=tmpdir, save_top_k=num_epochs, save_last=True)
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trainer = Trainer(
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default_root_dir=tmpdir,
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early_stop_callback=False,
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checkpoint_callback=model_checkpoint,
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max_epochs=num_epochs,
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)
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trainer.fit(model)
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path_last_epoch = model_checkpoint.format_checkpoint_name(num_epochs - 1, {})
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assert path_last_epoch != model_checkpoint.last_model_path
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ckpt_last_epoch = torch.load(path_last_epoch)
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ckpt_last = torch.load(model_checkpoint.last_model_path)
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assert all(ckpt_last_epoch[k] == ckpt_last[k] for k in ("epoch", "global_step"))
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assert all(
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ckpt_last["callbacks"][type(model_checkpoint)][k] == ckpt_last_epoch["callbacks"][type(model_checkpoint)][k]
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for k in ("best_model_score", "best_model_path")
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)
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# it is easier to load the model objects than to iterate over the raw dict of tensors
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model_last_epoch = EvalModelTemplate.load_from_checkpoint(path_last_epoch)
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model_last = EvalModelTemplate.load_from_checkpoint(model_checkpoint.last_model_path)
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for w0, w1 in zip(model_last_epoch.parameters(), model_last.parameters()):
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assert w0.eq(w1).all()
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def test_ckpt_metric_names(tmpdir):
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model = EvalModelTemplate()
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=1,
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gradient_clip_val=1.0,
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overfit_batches=0.20,
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progress_bar_refresh_rate=0,
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limit_train_batches=0.01,
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limit_val_batches=0.01,
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checkpoint_callback=ModelCheckpoint(filepath=tmpdir + "/{val_loss:.2f}"),
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)
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trainer.fit(model)
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# make sure the checkpoint we saved has the metric in the name
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ckpts = os.listdir(tmpdir)
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ckpts = [x for x in ckpts if "val_loss" in x]
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assert len(ckpts) == 1
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val = re.sub("[^0-9.]", "", ckpts[0])
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assert len(val) > 3
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def test_ckpt_metric_names_results(tmpdir):
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model = EvalModelTemplate()
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model.training_step = model.training_step_result_obj
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model.training_step_end = None
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model.training_epoch_end = None
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model.validation_step = model.validation_step_result_obj
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model.validation_step_end = None
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model.validation_epoch_end = None
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=1,
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gradient_clip_val=1.0,
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overfit_batches=0.20,
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progress_bar_refresh_rate=0,
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limit_train_batches=0.01,
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limit_val_batches=0.01,
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checkpoint_callback=ModelCheckpoint(filepath=tmpdir + "/{val_loss:.2f}"),
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)
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trainer.fit(model)
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# make sure the checkpoint we saved has the metric in the name
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ckpts = os.listdir(tmpdir)
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ckpts = [x for x in ckpts if "val_loss" in x]
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assert len(ckpts) == 1
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val = re.sub("[^0-9.]", "", ckpts[0])
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assert len(val) > 3
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