folivo/PINA
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Improve conditions and refactor dataset classes (#475)

Browse Source 
* Reimplement conditions

* Refactor datasets and implement LabelBatch

---------

Co-authored-by: Dario Coscia <dariocos99@gmail.com>
This commit is contained in:
Filippo Olivo
2025-03-07 11:24:09 +01:00
committed by Nicola Demo
parent bdad144461
commit a0cbf1c44a
40 changed files with 943 additions and 550 deletions
Download Patch File Download Diff File Expand all files Collapse all files

16
pina/data/data_module.py
View File

@@ -285,7 +285,7 @@ class PinaDataModule(LightningDataModule):
@staticmethod
def _split_condition(condition_dict, splits_dict):
len_condition = len(condition_dict["input_points"])
len_condition = len(condition_dict["input"])
lengths = [
int(len_condition * length) for length in splits_dict.values()
@@ -343,7 +343,7 @@ class PinaDataModule(LightningDataModule):
condition_name,
condition_dict,
) in collector.data_collections.items():
len_data = len(condition_dict["input_points"])
len_data = len(condition_dict["input"])
if self.shuffle:
_apply_shuffle(condition_dict, len_data)
for key, data in self._split_condition(
@@ -390,12 +390,12 @@ class PinaDataModule(LightningDataModule):
max_conditions_lengths = {}
for k, v in self.collector_splits[split].items():
if self.batch_size is None:
max_conditions_lengths[k] = len(v["input_points"])
max_conditions_lengths[k] = len(v["input"])
elif self.repeat:
max_conditions_lengths[k] = self.batch_size
else:
max_conditions_lengths[k] = min(
len(v["input_points"]), self.batch_size
len(v["input"]), self.batch_size
)
return max_conditions_lengths
@@ -455,15 +455,15 @@ class PinaDataModule(LightningDataModule):
raise ValueError("The sum of the splits must be 1")
@property
def input_points(self):
def input(self):
"""
# TODO
"""
to_return = {}
if hasattr(self, "train_dataset") and self.train_dataset is not None:
to_return["train"] = self.train_dataset.input_points
to_return["train"] = self.train_dataset.input
if hasattr(self, "val_dataset") and self.val_dataset is not None:
to_return["val"] = self.val_dataset.input_points
to_return["val"] = self.val_dataset.input
if hasattr(self, "test_dataset") and self.test_dataset is not None:
to_return = self.test_dataset.input_points
to_return = self.test_dataset.input
return to_return