refactor(docs): simplify resume.py

docs/source/examples/resume.py

@@ -15,67 +15,15 @@
 #         * Training was performed with :doc:`AIdsorb CLI <../cli>` or :ref:`AIdsorb +
 #           PyTorch Lightning <aidsorb_with_pytorch_and_lightning>`.
 
-import yaml
 import torch
 import lightning as L
-from lightning.pytorch.cli import LightningArgumentParser
-from aidsorb.datamodules import PCDDataModule
 from aidsorb.litmodels import PCDLit
+from aidsorb.datamodules import PCDDataModule
 
-# %%
-# The following snipper let us instantiate:
-# 
-# * :class:`~lightning.pytorch.trainer.trainer.Trainer`
-# * :class:`~lightning.pytorch.core.LightningModule`
-# * :class:`~lightning.pytorch.core.LightningDataModule`
-# 
-# with the same settings as in the ``.yaml`` configuration file. For more
-# information 👉 `here
-# <https://github.com/Lightning-AI/pytorch-lightning/discussions/10363#discussioncomment-2326235>`_.
-
-# %%
-# .. note::
-#     You are responsible for restoring the model's state (the weights of the model).
-
-filename = 'path/to/config.yaml'
-with open(filename, 'r') as f:
-    config_dict = yaml.safe_load(f)
-
-parser = LightningArgumentParser()
-parser.add_lightning_class_args(PCDLit, 'model')
-parser.add_lightning_class_args(PCDDataModule, 'data')
-parser.add_class_arguments(L.Trainer, 'trainer')
-
-# Any other key present in the config file must also be added.
-# parser.add_argument(--<keyname>, ...)
-# For more information 👉 https://jsonargparse.readthedocs.io/en/stable/#parsers
-parser.add_argument('--seed_everything')
-parser.add_argument('--ckpt_path')
-parser.add_argument('--optimizer')
-parser.add_argument('--lr_scheduler')
-
-config = parser.parse_object(config_dict)
-objects = parser.instantiate_classes(config)
-
-# %%
-
-trainer, litmodel, dm = objects.trainer, objects.model, objects.data
-
-# %%  The remaining part is to restore the model's state, i.e. load back the trained weights.
-
-# %%
-# Restoring model's state 
-# -----------------------
-
-# Load the the checkpoint.
-ckpt = torch.load('path/to/checkpoints/checkpoint.ckpt')
-
-# %%
-
-# Load back the weights.
-litmodel.load_state_dict(ckpt['state_dict'])
-
-# %%
+# Restore lightning modules from checkpoint.
+ckpt_path = 'path/to/checkpoint.ckpt'
+litmodel = PCDLit.load_from_checkpoint(ckpt_path)
+dm = PCDDataModule.load_from_checkpoint(ckpt_path)
 
 # Set the model for inference (disable grads & enable eval mode).
 litmodel.freeze()
@@ -89,6 +37,9 @@
 # Measure performance
 # -------------------
 
+# Instantiate a trainer object.
+trainer = L.Trainer(...)
+
 # Measure performance on test set.
 trainer.test(litmodel, dm)