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import torch
from torch.utils.data import ConcatDataset, DataLoader, Dataset, random_split
from lightning import LightningDataModule
from hydra.utils import instantiate
class BatchTensorConverter:
"""Callable to convert an unprocessed (labels + strings) batch to a
processed (labels + tensor) batch.
"""
def __init__(self, target_keys: Optional[List] = None):
self.target_keys = target_keys
def __call__(self, raw_batch: Sequence[Dict[str, object]]):
B = len(raw_batch)
# Only do for Tensor
target_keys = self.target_keys \
if self.target_keys is not None else [k for k,v in raw_batch[0].items() if torch.is_tensor(v)]
# Non-array, for example string, int
non_array_keys = [k for k in raw_batch[0] if k not in target_keys]
collated_batch = dict()
for k in target_keys:
collated_batch[k] = self.collate_dense_tensors([d[k] for d in raw_batch], pad_v=0.0)
for k in non_array_keys: # return non-array keys as is
collated_batch[k] = [d[k] for d in raw_batch]
return collated_batch
@staticmethod
def collate_dense_tensors(samples: Sequence, pad_v: float = 0.0):
"""
Takes a list of tensors with the following dimensions:
[(d_11, ..., d_1K),
(d_21, ..., d_2K),
...,
(d_N1, ..., d_NK)]
and stack + pads them into a single tensor of:
(N, max_i=1,N { d_i1 }, ..., max_i=1,N {diK})
"""
if len(samples) == 0:
return torch.Tensor()
if len(set(x.dim() for x in samples)) != 1:
raise RuntimeError(
f"Samples has varying dimensions: {[x.dim() for x in samples]}"
)
(device,) = tuple(set(x.device for x in samples)) # assumes all on same device
max_shape = [max(lst) for lst in zip(*[x.shape for x in samples])]
result = torch.empty(
len(samples), *max_shape, dtype=samples[0].dtype, device=device
)
result.fill_(pad_v)
for i in range(len(samples)):
result_i = result[i]
t = samples[i]
result_i[tuple(slice(0, k) for k in t.shape)] = t
return result
class ProteinDataModule(LightningDataModule):
"""`LightningDataModule` for a single protein dataset,
for pretrain or finetune purpose.
### To be revised.###
The MNIST database of handwritten digits has a training set of 60,000 examples, and a test set of 10,000 examples.
It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a
fixed-size image. The original black and white images from NIST were size normalized to fit in a 20x20 pixel box
while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing
technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of
mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.
A `LightningDataModule` implements 7 key methods:
```python
def prepare_data(self):
# Things to do on 1 GPU/TPU (not on every GPU/TPU in DDP).
# Download data, pre-process, split, save to disk, etc...
def setup(self, stage):
# Things to do on every process in DDP.
# Load data, set variables, etc...
def train_dataloader(self):
# return train dataloader
def val_dataloader(self):
# return validation dataloader
def test_dataloader(self):
# return test dataloader
def predict_dataloader(self):
# return predict dataloader
def teardown(self, stage):
# Called on every process in DDP.
# Clean up after fit or test.
```
This allows you to share a full dataset without explaining how to download,
split, transform and process the data.
Read the docs:
https://lightning.ai/docs/pytorch/latest/data/datamodule.html
"""
def __init__(
self,
dataset: torch.utils.data.Dataset,
batch_size: int = 64,
generator_seed: int = 42,
train_val_split: Tuple[float, float] = (0.95, 0.05),
num_workers: int = 0,
pin_memory: bool = False,
shuffle: bool = False,
) -> None:
"""Initialize a `MNISTDataModule`.
:param data_dir: The data directory. Defaults to `"data/"`.
:param train_val_test_split: The train, validation and test split. Defaults to `(55_000, 5_000, 10_000)`.
:param batch_size: The batch size. Defaults to `64`.
:param num_workers: The number of workers. Defaults to `0`.
:param pin_memory: Whether to pin memory. Defaults to `False`.
"""
super().__init__()
# this line allows to access init params with 'self.hparams' attribute
# also ensures init params will be stored in ckpt
self.save_hyperparameters(logger=False)
self.dataset = dataset
self.data_train: Optional[Dataset] = None
self.data_val: Optional[Dataset] = None
self.data_test: Optional[Dataset] = None
self.batch_size_per_device = batch_size
def prepare_data(self) -> None:
"""Download data if needed. Lightning ensures that `self.prepare_data()` is called only
within a single process on CPU, so you can safely add your downloading logic within. In
case of multi-node training, the execution of this hook depends upon
`self.prepare_data_per_node()`.
Do not use it to assign state (self.x = y).
"""
pass
def setup(self, stage: Optional[str] = None) -> None:
"""Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`.
This method is called by Lightning before `trainer.fit()`, `trainer.validate()`, `trainer.test()`, and
`trainer.predict()`, so be careful not to execute things like random split twice! Also, it is called after
`self.prepare_data()` and there is a barrier in between which ensures that all the processes proceed to
`self.setup()` once the data is prepared and available for use.
:param stage: The stage to setup. Either `"fit"`, `"validate"`, `"test"`, or `"predict"`. Defaults to ``None``.
"""
# Divide batch size by the number of devices.
if self.trainer is not None:
if self.hparams.batch_size % self.trainer.world_size != 0:
raise RuntimeError(
f"Batch size ({self.hparams.batch_size}) is not divisible by the number of devices ({self.trainer.world_size})."
)
self.batch_size_per_device = self.hparams.batch_size // self.trainer.world_size
# load and split datasets only if not loaded already
if stage == 'fit' and not self.data_train and not self.data_val:
# dataset = ConcatDataset(datasets=[trainset, testset])
self.data_train, self.data_val = random_split(
dataset=self.dataset,
lengths=self.hparams.train_val_split,
generator=torch.Generator().manual_seed(self.hparams.generator_seed),
)
elif stage in ('predict', 'test'):
self.data_test = self.dataset
else:
raise NotImplementedError(f"Stage {stage} not implemented.")
def _dataloader_template(self, dataset: Dataset[Any]) -> DataLoader[Any]:
"""Create a dataloader from a dataset.
:param dataset: The dataset.
:return: The dataloader.
"""
batch_collator = BatchTensorConverter() # list of dicts -> dict of tensors
return DataLoader(
dataset=dataset,
collate_fn=batch_collator,
batch_size=self.batch_size_per_device,
num_workers=self.hparams.num_workers,
pin_memory=self.hparams.pin_memory,
shuffle=self.hparams.shuffle,
)
def train_dataloader(self) -> DataLoader[Any]:
"""Create and return the train dataloader.
:return: The train dataloader.
"""
return self._dataloader_template(self.data_train)
def val_dataloader(self) -> DataLoader[Any]:
"""Create and return the validation dataloader.
:return: The validation dataloader.
"""
return self._dataloader_template(self.data_val)
def test_dataloader(self) -> DataLoader[Any]:
"""Create and return the test dataloader.
:return: The test dataloader.
"""
return self._dataloader_template(self.data_test)
def teardown(self, stage: Optional[str] = None) -> None:
"""Lightning hook for cleaning up after `trainer.fit()`, `trainer.validate()`,
`trainer.test()`, and `trainer.predict()`.
:param stage: The stage being torn down. Either `"fit"`, `"validate"`, `"test"`, or `"predict"`.
Defaults to ``None``.
"""
pass
def state_dict(self) -> Dict[Any, Any]:
"""Called when saving a checkpoint. Implement to generate and save the datamodule state.
:return: A dictionary containing the datamodule state that you want to save.
"""
return {}
def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
"""Called when loading a checkpoint. Implement to reload datamodule state given datamodule
`state_dict()`.
:param state_dict: The datamodule state returned by `self.state_dict()`.
"""
pass
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