utils_data.py

from utils.utils import instantiate_from_config
import os
import sys
from functools import partial

import numpy as np
import pytorch_lightning as pl
import torch
from torch.utils.data import DataLoader, Dataset

os.chdir(sys.path[0])
sys.path.append("..")


def t_range(name, tensor):
    print(
        f"{name}: shape={tensor.shape}, max={torch.max(tensor)}, min={torch.min(tensor)}."
    )


def worker_init_fn(_):
    worker_info = torch.utils.data.get_worker_info()
    worker_id = worker_info.id
    return np.random.seed(np.random.get_state()[1][0] + worker_id)


class WrappedDataset(Dataset):
    """Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset"""

    def __init__(self, dataset):
        self.data = dataset

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        return self.data[idx]


class DataModuleFromConfig(pl.LightningDataModule):
    def __init__(
        self,
        batch_size,
        train=None,
        validation=None,
        test=None,
        predict=None,
        train_img=None,
        wrap=False,
        num_workers=None,
        shuffle_test_loader=False,
        use_worker_init_fn=False,
        shuffle_val_dataloader=False,
        test_max_n_samples=None,
        **kwargs,
    ):
        super().__init__()
        self.batch_size = batch_size
        self.dataset_configs = dict()
        self.num_workers = num_workers if num_workers is not None else batch_size * 2
        self.use_worker_init_fn = use_worker_init_fn
        if train is not None:
            self.dataset_configs["train"] = train
            self.train_dataloader = self._train_dataloader
        if validation is not None:
            self.dataset_configs["validation"] = validation
            self.val_dataloader = partial(
                self._val_dataloader, shuffle=shuffle_val_dataloader
            )
        if test is not None:
            self.dataset_configs["test"] = test
            self.test_dataloader = partial(
                self._test_dataloader, shuffle=shuffle_test_loader
            )
        if predict is not None:
            self.dataset_configs["predict"] = predict
            self.predict_dataloader = self._predict_dataloader
        # train image dataset
        if train_img is not None:
            img_data = instantiate_from_config(train_img)
            self.img_loader = img_data.train_dataloader()
        else:
            self.img_loader = None
        self.wrap = wrap
        self.test_max_n_samples = test_max_n_samples
        self.collate_fn = None

    def prepare_data(self):
        # for data_cfg in self.dataset_configs.values():
        #     instantiate_from_config(data_cfg)
        pass

    def setup(self, stage=None):
        self.datasets = dict(
            (k, instantiate_from_config(self.dataset_configs[k]))
            for k in self.dataset_configs
        )
        if self.wrap:
            for k in self.datasets:
                self.datasets[k] = WrappedDataset(self.datasets[k])

    def _train_dataloader(self):
        is_iterable_dataset = False
        if is_iterable_dataset or self.use_worker_init_fn:
            init_fn = worker_init_fn
        else:
            init_fn = None
        loader = DataLoader(
            self.datasets["train"],
            batch_size=self.batch_size,
            num_workers=self.num_workers,
            shuffle=False if is_iterable_dataset else True,
            worker_init_fn=init_fn,
            collate_fn=self.collate_fn,
        )
        if self.img_loader is not None:
            return {"loader_video": loader, "loader_img": self.img_loader}
        else:
            return loader

    def _val_dataloader(self, shuffle=False):
        init_fn = None
        return DataLoader(
            self.datasets["validation"],
            batch_size=self.batch_size,
            num_workers=self.num_workers,
            worker_init_fn=init_fn,
            shuffle=shuffle,
            collate_fn=self.collate_fn,
        )

    def _test_dataloader(self, shuffle=False):
        is_iterable_dataset = False
        if is_iterable_dataset or self.use_worker_init_fn:
            init_fn = worker_init_fn
        else:
            init_fn = None

        # do not shuffle dataloader for iterable dataset
        shuffle = shuffle and (not is_iterable_dataset)
        if self.test_max_n_samples is not None:
            dataset = torch.utils.data.Subset(
                self.datasets["test"], list(range(self.test_max_n_samples))
            )
        else:
            dataset = self.datasets["test"]
        return DataLoader(
            dataset,
            batch_size=self.batch_size,
            num_workers=self.num_workers,
            worker_init_fn=init_fn,
            shuffle=shuffle,
            collate_fn=self.collate_fn,
        )

    def _predict_dataloader(self, shuffle=False):
        init_fn = None
        return DataLoader(
            self.datasets["predict"],
            batch_size=self.batch_size,
            num_workers=self.num_workers,
            worker_init_fn=init_fn,
            collate_fn=self.collate_fn,
        )