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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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31
pina/data/base_dataset.py
View File

@@ -59,9 +59,11 @@ class BaseDataset(Dataset):
keys = list(data.keys())
if set(self.__slots__) == set(keys):
self._populate_init_list(data)
idx = [key for key, val in
self.problem.collector.conditions_name.items() if
val == name]
idx = [
key for key, val in
self.problem.collector.conditions_name.items()
if val == name
]
self.conditions_idx.append(idx)
self.initialize()
@@ -89,15 +91,16 @@ class BaseDataset(Dataset):
if isinstance(slot_data, (LabelTensor, torch.Tensor)):
dims = len(slot_data.size())
slot_data = slot_data.permute(
[batching_dim] + [dim for dim in range(dims) if
dim != batching_dim])
[batching_dim] +
[dim for dim in range(dims) if dim != batching_dim])
if current_cond_num_el is None:
current_cond_num_el = len(slot_data)
elif current_cond_num_el != len(slot_data):
raise ValueError('Different dimension in same condition')
current_list = getattr(self, slot)
current_list += [slot_data] if not (
isinstance(slot_data, list)) else slot_data
current_list += [
slot_data
] if not (isinstance(slot_data, list)) else slot_data
self.num_el_per_condition.append(current_cond_num_el)
def initialize(self):
@@ -108,14 +111,12 @@ class BaseDataset(Dataset):
logging.debug(f'Initialize dataset {self.__class__.__name__}')
if self.num_el_per_condition:
self.condition_indices = torch.cat(
[
torch.tensor([i] * self.num_el_per_condition[i],
dtype=torch.uint8)
for i in range(len(self.num_el_per_condition))
],
dim=0
)
self.condition_indices = torch.cat([
torch.tensor([i] * self.num_el_per_condition[i],
dtype=torch.uint8)
for i in range(len(self.num_el_per_condition))
],
dim=0)
for slot in self.__slots__:
current_attribute = getattr(self, slot)
if all(isinstance(a, LabelTensor) for a in current_attribute):

35
pina/data/data_module.py
View File

@@ -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)

9
pina/data/pina_batch.py
View File

@@ -37,14 +37,11 @@ class Batch:
if item in super().__getattribute__('attributes'):
dataset = super().__getattribute__(item)
index = super().__getattribute__(item + '_idx')
return PinaSubset(
dataset.dataset,
dataset.indices[index])
else:
return super().__getattribute__(item)
return PinaSubset(dataset.dataset, dataset.indices[index])
return super().__getattribute__(item)
def __getattr__(self, item):
if item == 'data' and len(self.attributes) == 1:
item = self.attributes[0]
return super().__getattribute__(item)
raise AttributeError(f"'Batch' object has no attribute '{item}'")
raise AttributeError(f"'Batch' object has no attribute '{item}'")

10
pina/data/sample_dataset.py
View File

@@ -19,15 +19,17 @@ class SamplePointDataset(BaseDataset):
data_dict.pop('equation')
super().add_points(data_dict, condition_idx)
def _init_from_problem(self, collector_dict, batching_dim=0):
def _init_from_problem(self, collector_dict):
for name, data in collector_dict.items():
keys = list(data.keys())
if set(self.__slots__) == set(keys):
data = deepcopy(data)
data.pop('equation')
self._populate_init_list(data)
idx = [key for key, val in
self.problem.collector.conditions_name.items() if
val == name]
idx = [
key for key, val in
self.problem.collector.conditions_name.items()
if val == name
]
self.conditions_idx.append(idx)
self.initialize()