294 lines
9.5 KiB
Python
294 lines
9.5 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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import os
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from argparse import Namespace
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from distutils.version import LooseVersion
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from unittest import mock
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import pytest
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import torch
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import yaml
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from omegaconf import OmegaConf
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from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
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from pytorch_lightning import Trainer
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from pytorch_lightning.loggers import TensorBoardLogger
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from tests.helpers import BoringModel
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@pytest.mark.skipif(
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LooseVersion(torch.__version__) < LooseVersion("1.5.0"),
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reason="Minimal PT version is set to 1.5",
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)
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def test_tensorboard_hparams_reload(tmpdir):
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class CustomModel(BoringModel):
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def __init__(self, b1=0.5, b2=0.999):
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super().__init__()
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self.save_hyperparameters()
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trainer = Trainer(max_steps=1, default_root_dir=tmpdir)
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model = CustomModel()
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assert trainer.log_dir == trainer.logger.log_dir
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trainer.fit(model)
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assert trainer.log_dir == trainer.logger.log_dir
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folder_path = trainer.log_dir
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# make sure yaml is there
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with open(os.path.join(folder_path, "hparams.yaml")) as file:
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# The FullLoader parameter handles the conversion from YAML
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# scalar values to Python the dictionary format
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yaml_params = yaml.safe_load(file)
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assert yaml_params["b1"] == 0.5
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assert yaml_params["b2"] == 0.999
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assert len(yaml_params.keys()) == 2
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# verify artifacts
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assert len(os.listdir(os.path.join(folder_path, "checkpoints"))) == 1
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# verify tb logs
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event_acc = EventAccumulator(folder_path)
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event_acc.Reload()
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data_pt_1_5 = b'\x12\x1b"\x04\n\x02b1"\x04\n\x02b2*\r\n\x0b\x12\thp_metric'
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data_pt_1_6 = b'\x12\x1f"\x06\n\x02b1 \x03"\x06\n\x02b2 \x03*\r\n\x0b\x12\thp_metric'
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hparams_data = data_pt_1_6 if LooseVersion(torch.__version__) >= LooseVersion("1.6.0") else data_pt_1_5
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assert event_acc.summary_metadata['_hparams_/experiment'].plugin_data.plugin_name == 'hparams'
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assert event_acc.summary_metadata['_hparams_/experiment'].plugin_data.content == hparams_data
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def test_tensorboard_automatic_versioning(tmpdir):
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"""Verify that automatic versioning works"""
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root_dir = tmpdir / "tb_versioning"
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root_dir.mkdir()
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(root_dir / "version_0").mkdir()
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(root_dir / "version_1").mkdir()
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logger = TensorBoardLogger(save_dir=tmpdir, name="tb_versioning")
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assert logger.version == 2
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def test_tensorboard_manual_versioning(tmpdir):
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"""Verify that manual versioning works"""
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root_dir = tmpdir / "tb_versioning"
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root_dir.mkdir()
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(root_dir / "version_0").mkdir()
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(root_dir / "version_1").mkdir()
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(root_dir / "version_2").mkdir()
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logger = TensorBoardLogger(save_dir=tmpdir, name="tb_versioning", version=1)
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assert logger.version == 1
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def test_tensorboard_named_version(tmpdir):
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"""Verify that manual versioning works for string versions, e.g. '2020-02-05-162402' """
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name = "tb_versioning"
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(tmpdir / name).mkdir()
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expected_version = "2020-02-05-162402"
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logger = TensorBoardLogger(save_dir=tmpdir, name=name, version=expected_version)
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logger.log_hyperparams({"a": 1, "b": 2, 123: 3, 3.5: 4, 5j: 5}) # Force data to be written
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assert logger.version == expected_version
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assert os.listdir(tmpdir / name) == [expected_version]
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assert os.listdir(tmpdir / name / expected_version)
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@pytest.mark.parametrize("name", ["", None])
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def test_tensorboard_no_name(tmpdir, name):
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"""Verify that None or empty name works"""
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logger = TensorBoardLogger(save_dir=tmpdir, name=name)
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logger.log_hyperparams({"a": 1, "b": 2, 123: 3, 3.5: 4, 5j: 5}) # Force data to be written
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assert logger.root_dir == tmpdir
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assert os.listdir(tmpdir / "version_0")
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@pytest.mark.parametrize("step_idx", [10, None])
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def test_tensorboard_log_metrics(tmpdir, step_idx):
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logger = TensorBoardLogger(tmpdir)
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metrics = {
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"float": 0.3,
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"int": 1,
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"FloatTensor": torch.tensor(0.1),
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"IntTensor": torch.tensor(1),
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}
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logger.log_metrics(metrics, step_idx)
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def test_tensorboard_log_hyperparams(tmpdir):
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logger = TensorBoardLogger(tmpdir)
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hparams = {
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"float": 0.3,
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"int": 1,
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"string": "abc",
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"bool": True,
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"dict": {
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"a": {
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"b": "c"
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}
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},
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"list": [1, 2, 3],
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"namespace": Namespace(foo=Namespace(bar="buzz")),
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"layer": torch.nn.BatchNorm1d,
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}
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logger.log_hyperparams(hparams)
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def test_tensorboard_log_hparams_and_metrics(tmpdir):
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logger = TensorBoardLogger(tmpdir, default_hp_metric=False)
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hparams = {
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"float": 0.3,
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"int": 1,
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"string": "abc",
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"bool": True,
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"dict": {
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"a": {
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"b": "c"
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}
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},
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"list": [1, 2, 3],
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"namespace": Namespace(foo=Namespace(bar="buzz")),
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"layer": torch.nn.BatchNorm1d,
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}
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metrics = {"abc": torch.tensor([0.54])}
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logger.log_hyperparams(hparams, metrics)
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def test_tensorboard_log_omegaconf_hparams_and_metrics(tmpdir):
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logger = TensorBoardLogger(tmpdir, default_hp_metric=False)
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hparams = {
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"float": 0.3,
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"int": 1,
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"string": "abc",
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"bool": True,
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"dict": {
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"a": {
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"b": "c"
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}
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},
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"list": [1, 2, 3],
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# "namespace": Namespace(foo=Namespace(bar="buzz")),
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# "layer": torch.nn.BatchNorm1d,
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}
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hparams = OmegaConf.create(hparams)
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metrics = {"abc": torch.tensor([0.54])}
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logger.log_hyperparams(hparams, metrics)
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@pytest.mark.parametrize("example_input_array", [None, torch.rand(2, 32)])
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def test_tensorboard_log_graph(tmpdir, example_input_array):
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""" test that log graph works with both model.example_input_array and
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if array is passed externaly
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"""
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model = BoringModel()
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if example_input_array is not None:
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model.example_input_array = None
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logger = TensorBoardLogger(tmpdir, log_graph=True)
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logger.log_graph(model, example_input_array)
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def test_tensorboard_log_graph_warning_no_example_input_array(tmpdir):
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""" test that log graph throws warning if model.example_input_array is None """
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model = BoringModel()
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model.example_input_array = None
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logger = TensorBoardLogger(tmpdir, log_graph=True)
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with pytest.warns(
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UserWarning,
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match='Could not log computational graph since the `model.example_input_array`'
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' attribute is not set or `input_array` was not given'
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):
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logger.log_graph(model)
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@mock.patch('pytorch_lightning.loggers.TensorBoardLogger.log_metrics')
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@pytest.mark.parametrize('expected', [
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([5, 11, 17]),
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])
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def test_tensorboard_with_accummulated_gradients(mock_log_metrics, expected, tmpdir):
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"""
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Tests to ensure that tensorboard log properly when accumulated_gradients > 1
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"""
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class TestModel(BoringModel):
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def __init__(self):
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super().__init__()
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self._count = 0
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self._indexes = []
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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.log('count', self._count, on_step=True, on_epoch=True)
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self.log('loss', loss, on_step=True, on_epoch=True)
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if not self.trainer.train_loop.should_accumulate():
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if self.trainer.logger_connector.should_update_logs:
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self._indexes.append(self.trainer.global_step)
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return loss
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def validation_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.log('val_loss', loss, on_step=True, on_epoch=True)
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return loss
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def configure_optimizers(self):
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optimizer = torch.optim.SGD(self.layer.parameters(), lr=.001)
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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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model = TestModel()
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model.training_epoch_end = None
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model.validation_epoch_end = None
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logger_0 = TensorBoardLogger(tmpdir, default_hp_metric=False)
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=12,
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limit_val_batches=0,
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max_epochs=3,
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gpus=0,
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accumulate_grad_batches=2,
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logger=[logger_0],
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log_every_n_steps=3,
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)
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trainer.fit(model)
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mock_count_epochs = [m[2]["step"] for m in mock_log_metrics.mock_calls if "count_epoch" in m[2]["metrics"]]
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assert mock_count_epochs == expected
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mock_count_steps = [m[2]["step"] for m in mock_log_metrics.mock_calls if "count_step" in m[2]["metrics"]]
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assert model._indexes == mock_count_steps
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@mock.patch('pytorch_lightning.loggers.tensorboard.SummaryWriter')
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def test_tensorboard_finalize(summary_writer, tmpdir):
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""" Test that the SummaryWriter closes in finalize. """
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logger = TensorBoardLogger(save_dir=tmpdir)
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logger.finalize("any")
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summary_writer().flush.assert_called()
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summary_writer().close.assert_called()
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