clean logic, fix problems for tutorial1

pina/data/data_module.py

@@ -7,7 +7,7 @@ from torch.utils.data import DataLoader, BatchSampler, SequentialSampler, \
     RandomSampler
 from torch.utils.data.distributed import DistributedSampler
 from .dataset import PinaDatasetFactory
-
+from ..collector import Collector
 
 class DummyDataloader:
     def __init__(self, dataset, device):
@@ -87,7 +87,7 @@ class PinaDataModule(LightningDataModule):
     """
 
     def __init__(self,
-                 collector,
+                 problem,
                  train_size=.7,
                  test_size=.2,
                  val_size=.1,
@@ -99,7 +99,6 @@ class PinaDataModule(LightningDataModule):
                  ):
         """
         Initialize the object, creating dataset based on input problem
-        :param Collector collector: PINA problem
         :param train_size: number/percentage of elements in train split
         :param test_size: number/percentage of elements in test split
         :param val_size: number/percentage of elements in evaluation split
@@ -135,6 +134,10 @@ class PinaDataModule(LightningDataModule):
             self.predict_dataset = None
         else:
             self.predict_dataloader = super().predict_dataloader
+
+        collector = Collector(problem)
+        collector.store_fixed_data()
+        collector.store_sample_domains()
         self.collector_splits = self._create_splits(collector, splits_dict)
         self.transfer_batch_to_device = self._transfer_batch_to_device
 

pina/data/dataset.py

@@ -58,6 +58,7 @@ class PinaTensorDataset(PinaDataset):
     def __init__(self, conditions_dict, max_conditions_lengths,
                  automatic_batching):
         super().__init__(conditions_dict, max_conditions_lengths)
+
         if automatic_batching:
             self._getitem_func = self._getitem_int
         else: