191 lines
6.4 KiB
Python
191 lines
6.4 KiB
Python
import os
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from argparse import Namespace
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from distutils.version import LooseVersion
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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.base import EvalModelTemplate
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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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model = EvalModelTemplate()
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trainer = Trainer(max_epochs=1, default_root_dir=tmpdir)
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trainer.fit(model)
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folder_path = trainer.logger.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 len(yaml_params.keys()) == 10
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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\x93\x01"\x0b\n\tdrop_prob"\x0c\n\nbatch_size"\r\n\x0bin_features"\x0f\n\rlearning_rate"' \
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b'\x10\n\x0eoptimizer_name"\x0b\n\tdata_root"\x0e\n\x0cout_features"\x0c\n\nhidden_dim"' \
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b'\x04\n\x02b1"\x04\n\x02b2*\r\n\x0b\x12\thp_metric'
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data_pt_1_6 = b'\x12\xa7\x01"\r\n\tdrop_prob \x03"\x0e\n\nbatch_size \x03"\x0f\n\x0bin_features \x03"' \
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b'\x11\n\rlearning_rate \x03"\x12\n\x0eoptimizer_name \x01"\r\n\tdata_root \x01"' \
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b'\x10\n\x0cout_features \x03"\x0e\n\nhidden_dim \x03"\x06\n\x02b1 \x03"' \
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b'\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}) # 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}) # 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": {"a": {"b": "c"}},
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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": {"a": {"b": "c"}},
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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": {"a": {"b": "c"}},
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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, 28 * 28)])
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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 = EvalModelTemplate()
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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 = EvalModelTemplate()
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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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