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Codacy correction

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This commit is contained in:
FilippoOlivo
2024-10-31 09:50:19 +01:00
committed by Nicola Demo
parent ea3d1924e7
commit dd43c8304c
23 changed files with 246 additions and 214 deletions
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35
pina/data/data_module.py
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@@ -44,8 +44,9 @@ class PinaDataModule(LightningDataModule):
super().__init__()
self.problem = problem
self.device = device
self.dataset_classes = [SupervisedDataset, UnsupervisedDataset,
SamplePointDataset]
self.dataset_classes = [
SupervisedDataset, UnsupervisedDataset, SamplePointDataset
]
if datasets is None:
self.datasets = None
else:
@@ -71,15 +72,12 @@ class PinaDataModule(LightningDataModule):
self.split_length.append(val_size)
self.split_names.append('val')
self.loader_functions['val_dataloader'] = lambda: PinaDataLoader(
self.splits['val'], self.batch_size,
self.condition_names)
self.splits['val'], self.batch_size, self.condition_names)
if predict_size > 0:
self.split_length.append(predict_size)
self.split_names.append('predict')
self.loader_functions[
'predict_dataloader'] = lambda: PinaDataLoader(
self.splits['predict'], self.batch_size,
self.condition_names)
self.loader_functions['predict_dataloader'] = lambda: PinaDataLoader(
self.splits['predict'], self.batch_size, self.condition_names)
self.splits = {k: {} for k in self.split_names}
self.shuffle = shuffle
@@ -104,8 +102,8 @@ class PinaDataModule(LightningDataModule):
self.split_length,
shuffle=self.shuffle)
for i in range(len(self.split_length)):
self.splits[
self.split_names[i]][dataset.data_type] = splits[i]
self.splits[self.split_names[i]][
dataset.data_type] = splits[i]
elif stage == 'test':
raise NotImplementedError("Testing pipeline not implemented yet")
else:
@@ -137,14 +135,12 @@ class PinaDataModule(LightningDataModule):
if seed is not None:
generator = torch.Generator()
generator.manual_seed(seed)
indices = torch.randperm(sum(lengths),
generator=generator)
indices = torch.randperm(sum(lengths), generator=generator)
else:
indices = torch.randperm(sum(lengths))
dataset.apply_shuffle(indices)
indices = torch.arange(0, sum(lengths), 1,
dtype=torch.uint8).tolist()
indices = torch.arange(0, sum(lengths), 1, dtype=torch.uint8).tolist()
offsets = [
sum(lengths[:i]) if i > 0 else 0 for i in range(len(lengths))
]
@@ -161,13 +157,16 @@ class PinaDataModule(LightningDataModule):
collector = self.problem.collector
batching_dim = self.problem.batching_dimension
datasets_slots = [i.__slots__ for i in self.dataset_classes]
self.datasets = [dataset(device=self.device) for dataset in
self.dataset_classes]
self.datasets = [
dataset(device=self.device) for dataset in self.dataset_classes
]
logging.debug('Filling datasets in PinaDataModule obj')
for name, data in collector.data_collections.items():
keys = list(data.keys())
idx = [key for key, val in collector.conditions_name.items() if
val == name]
idx = [
key for key, val in collector.conditions_name.items()
if val == name
]
for i, slot in enumerate(datasets_slots):
if slot == keys:
self.datasets[i].add_points(data, idx[0], batching_dim)