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Save / Load Hyperparameters with checkpoint (#415)

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* Save and load hparams from checkpoints

* Update docs

* Add warning when not saving hparams

* Missing import

* Update .run_local_tests.sh

* Update lm_test_module_mixins.py

* Update lightning_module_template.py
This commit is contained in:
Nic Eggert
2019-10-23 04:48:24 -04:00
committed by William Falcon
parent 0db422777c
commit 05cea3ff8b
7 changed files with 104 additions and 6 deletions
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docs/LightningModule/methods.md
+19 -2
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@@ -10,8 +10,25 @@ model.freeze()
---
### load_from_metrics
This is the easiest/fastest way which uses the meta_tags.csv file from test-tube to rebuild the model.
The meta_tags.csv file can be found in the test-tube experiment save_dir.
This is the easiest/fastest way which loads hyperparameters and weights from a checkpoint,
such as the one saved by the `ModelCheckpoint` callback
```{.python}
pretrained_model = MyLightningModule.load_from_checkpoint(
checkpoint_path='/path/to/pytorch_checkpoint.ckpt'
)
# predict
pretrained_model.eval()
pretrained_model.freeze()
y_hat = pretrained_model(x)
```
---
### load_from_metrics
If you're using test tube, there is an alternate method which uses the meta_tags.csv
file from test-tube to rebuild the model. The meta_tags.csv file can be found in the
test-tube experiment save_dir.
```{.python}
pretrained_model = MyLightningModule.load_from_metrics(