92e0e4920b
* device training --------- Co-authored-by: Dario Coscia <dcoscia@lovelace.maths.sissa.it> Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local>
80 lines
2.2 KiB
Python
80 lines
2.2 KiB
Python
""" """
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from torch.utils.data import Dataset, DataLoader
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import functools
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class PinaDataset():
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def __init__(self, pinn) -> None:
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self.pinn = pinn
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@property
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def dataloader(self):
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return self._create_dataloader()
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@property
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def dataset(self):
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return [self.SampleDataset(key, val)
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for key, val in self.input_pts.items()]
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def _create_dataloader(self):
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"""Private method for creating dataloader
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:return: dataloader
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:rtype: torch.utils.data.DataLoader
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"""
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if self.pinn.batch_size is None:
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return {key: [{key: val}] for key, val in self.pinn.input_pts.items()}
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def custom_collate(batch):
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# extracting pts labels
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_, pts = list(batch[0].items())[0]
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labels = pts.labels
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# calling default torch collate
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collate_res = default_collate(batch)
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# save collate result in dict
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res = {}
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for key, val in collate_res.items():
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val.labels = labels
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res[key] = val
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def __getitem__(self, index):
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tensor = self._tensor.select(0, index)
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return {self._location: tensor}
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def __len__(self):
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return self._len
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# TODO: working also for datapoints
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class DummyLoader:
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def __init__(self, data, device) -> None:
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# TODO: We need to make a dataset somehow
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# and the PINADataset needs to have a method
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# to send points to device
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# now we simply do it here
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# send data to device
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def convert_tensors(pts, device):
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pts = pts.to(device)
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pts.requires_grad_(True)
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pts.retain_grad()
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return pts
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for location, pts in data.items():
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if isinstance(pts, (tuple, list)):
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pts = tuple(map(functools.partial(convert_tensors, device=device),pts))
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else:
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pts = pts.to(device)
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pts = pts.requires_grad_(True)
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pts.retain_grad()
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data[location] = pts
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# iterator
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self.data = [data]
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def __iter__(self):
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return iter(self.data)
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