adds tensorboard hparams logging test

tests/loggers/test_tensorboard.py

@@ -1,9 +1,48 @@
+import os
 from argparse import Namespace
+from packaging import version
 
 import pytest
 import torch
+import yaml
+from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
 
+from pytorch_lightning import Trainer
 from pytorch_lightning.loggers import TensorBoardLogger
+from tests.base import EvalModelTemplate
+
+
+@pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.5.0'),
+                    reason='Minimal PT version is set to 1.5')
+def test_tensorboard_hparams_reload(tmpdir):
+    model = EvalModelTemplate()
+
+    trainer = Trainer(max_epochs=1, default_root_dir=tmpdir)
+    trainer.fit(model)
+
+    folder_path = trainer.logger.log_dir
+
+    # make sure yaml is there
+    with open(os.path.join(folder_path, 'hparams.yaml')) as file:
+        # The FullLoader parameter handles the conversion from YAML
+        # scalar values to Python the dictionary format
+        yaml_params = yaml.load(file, Loader=yaml.FullLoader)
+        assert yaml_params['b1'] == 0.5
+        assert len(yaml_params.keys()) == 10
+
+    # verify artifacts
+    assert len(os.listdir(os.path.join(folder_path, 'checkpoints'))) == 1
+
+    # verify tb logs
+    event_acc = EventAccumulator(folder_path)
+    event_acc.Reload()
+
+    hparams_data = b'\x12\x84\x01"\x0b\n\tdrop_prob"\x0c\n\nbatch_size"\r\n\x0bin_features"' \
+                   b'\x0f\n\rlearning_rate"\x10\n\x0eoptimizer_name"\x0b\n\tdata_root"\x0e\n' \
+                   b'\x0cout_features"\x0c\n\nhidden_dim"\x04\n\x02b1"\x04\n\x02b2'
+
+    assert event_acc.summary_metadata['_hparams_/experiment'].plugin_data.plugin_name == 'hparams'
+    assert event_acc.summary_metadata['_hparams_/experiment'].plugin_data.content == hparams_data
 
 
 def test_tensorboard_automatic_versioning(tmpdir):