audiosr/clap/training/data.py

import json
import logging
import os
import random
from dataclasses import dataclass
import numpy as np
import pandas as pd
import torch
import torchvision.datasets as datasets
from PIL import Image
from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torch.utils.data.distributed import DistributedSampler
import soundfile as sf
import io
from pathlib import Path

# import wget

from audiosr.clap.open_clip.utils import get_tar_path_from_dataset_name
from audiosr.clap.open_clip.utils import load_class_label

try:
    import horovod.torch as hvd
except ImportError:
    hvd = None

try:
    import torchaudio
except ImportError:
    torchaudio = None

from audiosr.clap.open_clip import tokenize


def tokenizer(text):
    return tokenize(text).squeeze(0)


from transformers import RobertaTokenizer

tokenize = RobertaTokenizer.from_pretrained("roberta-base")


def tokenizer(text):
    result = tokenize(
        text,
        padding="max_length",
        truncation=True,
        max_length=77,
        return_tensors="pt",
    )
    return {k: v.squeeze(0) for k, v in result.items()}


# initizlied the audioset map
_AUDIOSET_MAP_PATH = os.path.join(Path(__file__).parent, "audioset_textmap.npy")
_AUDIOSET_MAP = np.load(_AUDIOSET_MAP_PATH, allow_pickle=True)


def int16_to_float32(x):
    return (x / 32767.0).astype(np.float32)


def float32_to_int16(x):
    x = np.clip(x, a_min=-1.0, a_max=1.0)
    return (x * 32767.0).astype(np.int16)


# For Toy Dataset
# class ToyDataset(Dataset):
#     def __init__(self, index_path, ipc, config, eval_mode=False):
#         """Toy Dataset for testing the audioset input with text labels
#         Parameters
#         ----------
#             index_path: str
#                 the link to the h5 file of each audio
#             idc: str
#                 the link to the npy file, the number of samples in each class
#             config: dict
#                 the audio cfg file
#            eval_model (bool): to indicate if the dataset is a testing dataset
#         """
#         self.audio_cfg = config["audio_cfg"]
#         self.text_cfg = config["text_cfg"]
#         self.fp = h5py.File(index_path, "r")
#         self.ipc = np.load(ipc, allow_pickle=True)
#         self.total_size = len(self.fp["audio_name"])
#         self.classes_num = self.audio_cfg["class_num"]
#         self.eval_mode = eval_mode

#         if not eval_mode:
#             self.generate_queue()
#         else:
#             self.queue = []
#             for i in range(self.total_size):
#                 target = self.fp["target"][i]
#                 if np.sum(target) > 0:
#                     self.queue.append(i)
#             self.total_size = len(self.queue)
#         logging.info("total dataset size: %d" % (self.total_size))
#         logging.info("class num: %d" % (self.classes_num))

#     def time_shifting(self, x):
#         frame_num = len(x)
#         shift_len = random.randint(0, frame_num - 1)
#         new_sample = np.concatenate([x[shift_len:], x[:shift_len]], axis=0)
#         return new_sample

#     def generate_queue(self):
#         self.queue = []
#         while len(self.queue) < self.total_size:
#             class_set = [*range(self.classes_num)]
#             random.shuffle(class_set)
#             self.queue += [
#                 self.ipc[d][random.randint(0, len(self.ipc[d]) - 1)] for d in class_set
#             ]
#         self.queue = self.queue[: self.total_size]

#         logging.info("queue regenerated:%s" % (self.queue[-5:]))

#     def crop_wav(self, x):
#         crop_size = self.audio_cfg["crop_size"]
#         crop_pos = random.randint(0, len(x) - crop_size - 1)
#         return x[crop_pos : crop_pos + crop_size]

#     def prompt_text(self, target):
#         events = _AUDIOSET_MAP[np.where(target > 0)]
#         event_text = "The sounds of " + ", ".join(events[:-1]) + " and " + events[-1]
#         text = tokenize(event_text)[0]
#         return text

#     def __getitem__(self, index):
#         """Load waveform, text, and target of an audio clip

#         Parameters
#         ----------
#             index: int
#                 the index number
#         Return
#         ------
#             output: dict {
#                 "hdf5_path": str,
#                 "index_in_hdf5": int,
#                 "audio_name": str,
#                 "waveform": list (audio_length,),
#                 "target": list (class_num, ),
#                 "text": torch.tensor (context_length,)
#             }
#                 the output dictionary
#         """
#         s_index = self.queue[index]

#         audio_name = self.fp["audio_name"][s_index].decode()
#         # Hardcode here CHANGE
#         hdf5_path = (
#             self.fp["hdf5_path"][s_index]
#             .decode()
#             .replace(
#                 "../workspace",
#                 "/home/la/kechen/Research/ke_zsasp/workspace",
#             )
#         )
#         r_idx = self.fp["index_in_hdf5"][s_index]
#         target = self.fp["target"][s_index].astype(np.float32)
#         text = self.prompt_text(target)
#         with h5py.File(hdf5_path, "r") as f:
#             waveform = int16_to_float32(f["waveform"][r_idx])[
#                 : self.audio_cfg["clip_samples"]
#             ]
#         assert (
#             len(waveform) == self.audio_cfg["clip_samples"]
#         ), "The sample length is not match"
#         # Time shift
#         # if (self.config.enable_time_shift) and (not self.eval_mode):
#         #     waveform = self.time_shifting(waveform)
#         # # Label Enhance
#         # if (self.config.crop_size is not None) and (not self.eval_mode):
#         #     waveform = self.crop_wav(waveform)
#         # # the label enhance rate is fixed 0.5
#         # if (self.config.enable_label_enhance) and (not self.eval_mode) and random.random() < 0.5:
#         #     kidx = np.where(target)[0]
#         #     for k in kidx:
#         #         for add_key in self.class_map[k][1]:
#         #             target[add_key] = 1.0
#         #         if len(self.class_map[k][2]) > 0:
#         #             add_key = random.choice(self.class_map[k][2])
#         #             target[add_key] = 1.0

#         # missing the text input
#         mel_spec = get_mel(torch.from_numpy(waveform), self.audio_cfg)[None, :, :]
#         mel_spec = (
#             torch.cat(
#                 [mel_spec, mel_spec.clone(), mel_spec.clone(), mel_spec.clone()], dim=0
#             )
#             .cpu()
#             .numpy()
#         )
#         longer = random.choice([True, False])
#         if longer == False:
#             mel_spec[1:, :, :] = 0.0
#         data_dict = {
#             "hdf5_path": hdf5_path,
#             "index_in_hdf5": r_idx,
#             "audio_name": audio_name,
#             "waveform": waveform,
#             "class_label": target,
#             "text": text,
#             "longer": longer,
#             "mel_fusion": mel_spec,
#         }
#         return data_dict

#     def __len__(self):
#         return self.total_size


class CsvDataset(Dataset):
    def __init__(self, input_filename, transforms, img_key, caption_key, sep="\t"):
        logging.debug(f"Loading csv data from {input_filename}.")
        df = pd.read_csv(input_filename, sep=sep)

        self.images = df[img_key].tolist()
        self.captions = df[caption_key].tolist()
        self.transforms = transforms
        logging.debug("Done loading data.")

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

    def __getitem__(self, idx):
        images = self.transforms(Image.open(str(self.images[idx])))
        texts = tokenize([str(self.captions[idx])])[0]
        return images, texts


@dataclass
class DataInfo:
    dataloader: DataLoader
    sampler: DistributedSampler


def preprocess_txt(text):
    return tokenize([str(text)])[0]


# def get_dataset_size(shards, sizefilepath_=None, is_local=True):
#     if isinstance(shards, list):
#         size_list = []
#         for s in shards:
#             size_list.append(
#                 get_dataset_size(s, sizefilepath_=sizefilepath_, is_local=is_local)[0]
#             )
#     else:
#         if not is_local:
#             for n in dataset_split.keys():
#                 if n in shards.split("/"):
#                     break
#             for s in dataset_split[n]:
#                 if s in shards.split("/"):
#                     break
#             sizefilepath_ = f"./json_files/{n}/{s}/sizes.json"
#         shards_list = list(braceexpand.braceexpand(shards))
#         dir_path = os.path.dirname(shards)
#         if sizefilepath_ is not None:
#             sizes = json.load(open(sizefilepath_, "r"))
#             total_size = sum(
#                 [
#                     int(sizes[os.path.basename(shard.replace(".tar -", ".tar"))])
#                     for shard in shards_list
#                 ]
#             )
#         else:
#             sizes_filename = os.path.join(dir_path, "sizes.json")
#             len_filename = os.path.join(dir_path, "__len__")
#             if os.path.exists(sizes_filename):
#                 sizes = json.load(open(sizes_filename, "r"))
#                 total_size = sum(
#                     [int(sizes[os.path.basename(shard)]) for shard in shards_list]
#                 )
#             elif os.path.exists(len_filename):
#                 # FIXME this used to be eval(open(...)) but that seemed rather unsafe
#                 total_size = ast.literal_eval(open(len_filename, "r").read())
#             else:
#                 raise Exception(
#                     "Cannot find sizes file for dataset. Please specify the path to the file."
#                 )
#                 # total_size = None  # num samples undefined
#                 # some common dataset sizes (at time of authors last download)
#                 # cc3m-train: 2905954
#                 # cc12m: 10968539
#                 # LAION-400m: 407332084
#         num_shards = len(shards_list)
#     if isinstance(shards, list):
#         return sum(size_list), len(shards)
#     else:
#         return total_size, num_shards


def get_imagenet(args, preprocess_fns, split):
    assert split in ["train", "val", "v2"]
    is_train = split == "train"
    preprocess_train, preprocess_val = preprocess_fns

    if split == "v2":
        from imagenetv2_pytorch import ImageNetV2Dataset

        dataset = ImageNetV2Dataset(location=args.imagenet_v2, transform=preprocess_val)
    else:
        if is_train:
            data_path = args.imagenet_train
            preprocess_fn = preprocess_train
        else:
            data_path = args.imagenet_val
            preprocess_fn = preprocess_val
        assert data_path

        dataset = datasets.ImageFolder(data_path, transform=preprocess_fn)

    if is_train:
        idxs = np.zeros(len(dataset.targets))
        target_array = np.array(dataset.targets)
        k = 50
        for c in range(1000):
            m = target_array == c
            n = len(idxs[m])
            arr = np.zeros(n)
            arr[:k] = 1
            np.random.shuffle(arr)
            idxs[m] = arr

        idxs = idxs.astype("int")
        sampler = SubsetRandomSampler(np.where(idxs)[0])
    else:
        sampler = None

    dataloader = torch.utils.data.DataLoader(
        dataset,
        batch_size=args.batch_size,
        num_workers=args.workers,
        sampler=sampler,
    )

    return DataInfo(dataloader, sampler)


def count_samples(dataloader):
    os.environ["WDS_EPOCH"] = "0"
    n_elements, n_batches = 0, 0
    for images, texts in dataloader:
        n_batches += 1
        n_elements += len(images)
        assert len(images) == len(texts)
    return n_elements, n_batches


def filter_no_caption(sample):
    return "txt" in sample


def log_and_continue(exn):
    """Call in an exception handler to ignore any exception, isssue a warning, and continue."""
    logging.warning(f"Handling webdataset error ({repr(exn)}). Ignoring.")
    return True


_SHARD_SHUFFLE_SIZE = 2000
_SHARD_SHUFFLE_INITIAL = 500
_SAMPLE_SHUFFLE_SIZE = 5000
_SAMPLE_SHUFFLE_INITIAL = 1000


# def sample_prop(sizefile, inputs, proportion, is_local=True):
#     """
#     Sample a proportion of the data.
#     """
#     file_path_dict = {
#         os.path.split(inputs[i])[1]: os.path.split(inputs[i])[0]
#         for i in range(len(inputs))
#     }
#     sampled_filepath_dict = {}
#     sampled_size_dict = {}
#     if not is_local:
#         if os.path.exists("sizes.json"):
#             os.remove("sizes.json")
#         wget.download(sizefile, "sizes.json")
#         sizefile = "sizes.json"
#     with open(sizefile, "r", encoding="UTF-8") as f:
#         load_dict = json.load(f)
#     L = int(len(file_path_dict) * proportion)
#     subkeys = random.sample(file_path_dict.keys(), L)
#     for k in subkeys:
#         sampled_size_dict[k] = load_dict[k]
#         sampled_filepath_dict[k] = file_path_dict[k]
#     return (
#         sum(sampled_size_dict.values()),
#         L,
#         [os.path.join(v, k) for k, v in sampled_filepath_dict.items()],
#         sampled_size_dict,
#     )


def get_mel(audio_data, audio_cfg):
    # mel shape: (n_mels, T)
    mel = torchaudio.transforms.MelSpectrogram(
        sample_rate=audio_cfg["sample_rate"],
        n_fft=audio_cfg["window_size"],
        win_length=audio_cfg["window_size"],
        hop_length=audio_cfg["hop_size"],
        center=True,
        pad_mode="reflect",
        power=2.0,
        norm=None,
        onesided=True,
        n_mels=64,
        f_min=audio_cfg["fmin"],
        f_max=audio_cfg["fmax"],
    ).to(audio_data.device)
    mel = mel(audio_data)
    # we use log mel spectrogram as input
    mel = torchaudio.transforms.AmplitudeToDB(top_db=None)(mel)
    return mel.T  # (T, n_mels)


def get_audio_features(
    audio_data, mel, max_len, data_truncating, data_filling, audio_cfg
):
    """
    Calculate and add audio features to sample.
    Sample: a dict containing all the data of current sample.
    audio_data: a tensor of shape (T) containing audio data.
    max_len: the maximum length of audio data.
    data_truncating: the method of truncating data.
    data_filling: the method of filling data.
    audio_cfg: a dict containing audio configuration. Comes from model_cfg['audio_cfg'].
    """
    sample = {}

    # assert audio_data.size(-1) <= max_len, str(audio_data.size())

    # split to three parts
    chunk_frames = (
        max_len // audio_cfg["hop_size"] + 1
    )  # the +1 related to how the spectrogram is computed
    mel = mel[:chunk_frames]

    audio_data = audio_data[..., :max_len]
    sample["mel_fusion"] = mel
    longer = torch.tensor([True])

    sample["longer"] = longer
    sample["waveform"] = audio_data

    return sample


def preprocess(
    sample,
    audio_ext,
    text_ext,
    max_len,
    audio_cfg,
    class_index_dict=None,
    data_filling="pad",
    data_truncating="rand_trunc",
    text_augment_selection=None,
):
    """
    Preprocess a single sample for wdsdataloader.
    """
    audio_data, orig_sr = sf.read(io.BytesIO(sample[audio_ext]))
    audio_data = int16_to_float32(float32_to_int16(audio_data))
    audio_data = torch.tensor(audio_data).float()

    # TODO: (yusong) to be include in the future
    # # if torchaudio not installed, use soundfile to load audio
    # if torchaudio is None:
    #     audio_data, orig_sr = sf.read(io.BytesIO(sample[audio_ext]))
    #     audio_data = torch.tensor(audio_data).float()
    # else:
    #     # https://github.com/webdataset/webdataset/blob/main/webdataset/autodecode.py
    #     with tempfile.TemporaryDirectory() as dirname:
    #         os.makedirs(dirname, exist_ok=True)
    #         fname = os.path.join(dirname, f"file.flac")
    #         with open(fname, "wb") as stream:
    #             stream.write(sample[audio_ext])
    #         audio_data, orig_sr = torchaudio.load(fname)
    #         audio_data = audio_data[0, :].float()

    sample = get_audio_features(
        sample, audio_data, max_len, data_truncating, data_filling, audio_cfg
    )
    del sample[audio_ext]

    try:
        json_dict_raw = json.loads(sample[text_ext].decode("utf-8"))
    except:
        print("sample[__url__]:", sample["__url__"])

    # For selecting augmented text from dataset
    if text_augment_selection is None or text_augment_selection == "none":
        texts = json_dict_raw["text"]
    elif text_augment_selection == "all":
        if "text_augment_all" in json_dict_raw.keys():
            texts = json_dict_raw["text_augment_all"]
        else:
            texts = json_dict_raw["text"]
    elif text_augment_selection == "augment_only":
        if "text_augment_all" in json_dict_raw.keys():
            if json_dict_raw["text_augment_t5"] is None:
                texts = json_dict_raw["text"]
            else:
                texts = json_dict_raw["text_augment_t5"]
        else:
            texts = json_dict_raw["text"]
    else:
        raise NotImplementedError(
            f"text_augment_selection {text_augment_selection} not implemented"
        )
    sample["full_text"] = texts

    if isinstance(texts, list) and isinstance(texts[0], str) and len(texts) > 1:
        texts = random.choice(texts)
    sample["raw_text"] = texts
    sample["text"] = tokenizer(texts)  # text shape: [num_token]
    if class_index_dict is not None:
        # https://stackoverflow.com/questions/48004243/how-to-share-large-read-only-dictionary-list-across-processes-in-multiprocessing
        # https://stackoverflow.com/questions/45693949/storing-strings-in-a-multiprocessing-sharedctypes-array
        # key, val = class_index_dict
        # key = key[:].split('\n')
        # _dict = {k: v for k, v in zip(key, val)}
        sample["class_label"] = np.zeros(len(class_index_dict.keys()))
        for x in json_dict_raw["tag"]:
            sample["class_label"][class_index_dict[x]] = 1
        sample["class_label"] = torch.tensor(sample["class_label"]).float()
    del sample[text_ext]
    sample["audio_name"] = sample["__key__"].split("/")[-1] + "." + audio_ext
    sample["text_name"] = sample["__key__"].split("/")[-1] + "." + text_ext
    sample["audio_orig_sr"] = orig_sr
    return sample


def collate_fn(batch):
    """
    Collate function for wdsdataloader.
    batch: a list of dict, each dict is a sample
    """
    # concatenate values in each dictionary. if it is a tensor, concatenate. if it is a list, extend.
    batch_dict = {}
    for k in batch[0].keys():
        if isinstance(batch[0][k], dict):  # dealwith bert tokenizer output
            batch_dict[k] = {}
            for kk in batch[0][k].keys():
                tmp = []
                for i in range(len(batch)):
                    tmp.append(batch[i][k][kk])
                batch_dict[k][kk] = torch.vstack(tmp)
        elif isinstance(batch[0][k], torch.Tensor):
            batch_dict[k] = torch.stack([sample[k] for sample in batch])
        elif isinstance(batch[0][k], np.ndarray):
            batch_dict[k] = torch.tensor(np.stack([sample[k] for sample in batch]))
        else:
            batch_dict[k] = [sample[k] for sample in batch]
    return batch_dict


# def get_wds_dataset(
#     args,
#     model_cfg,
#     is_train,
#     audio_ext="flac",
#     text_ext="json",
#     max_len=480000,
#     proportion=1.0,
#     sizefilepath_=None,
#     is_local=None,
# ):
#     """
#     Get a dataset for wdsdataloader.
#     """
#     if is_local is None and (not args.remotedata is None):
#         is_local = not args.remotedata

#     input_shards = args.train_data if is_train else args.val_data
#     assert input_shards is not None

#     if not sizefilepath_ is None:
#         sizefilepath = sizefilepath_
#     else:
#         sizefilepath = os.path.join(os.path.dirname(input_shards[0]), "sizes.json")

#     if proportion != 1.0:
#         num_samples, num_shards, input_shards, _ = sample_prop(
#             sizefilepath, input_shards, proportion, is_local=is_local
#         )
#     else:
#         num_samples, num_shards = get_dataset_size(
#             input_shards, sizefilepath_=sizefilepath_, is_local=is_local
#         )

#     if not num_samples:
#         if is_train:
#             num_samples = args.train_num_samples
#             if not num_samples:
#                 raise RuntimeError(
#                     "Currently, number of dataset samples must be specified for training dataset. "
#                     "Please specify via `--train-num-samples` if no dataset length info present."
#                 )
#         else:
#             num_samples = (
#                 args.val_num_samples or 0
#             )  # eval will just exhaust the iterator if not specified

#     pipeline = [wds.SimpleShardList(input_shards)]
#     # at this point we have an iterator over all the shards
#     # TODO: (yusong): add a if statement of distributed. If not, we don't need to split_by_node
#     if is_train or args.parallel_eval:
#         pipeline.extend(
#             [
#                 wds.detshuffle(
#                     bufsize=_SHARD_SHUFFLE_SIZE,
#                     initial=_SHARD_SHUFFLE_INITIAL,
#                     seed=args.seed,
#                 ),
#                 wds.split_by_node,
#                 wds.split_by_worker,
#                 # at this point, we have an iterator over the shards assigned to each worker at each node
#                 wds.tarfile_to_samples(handler=log_and_continue),
#                 wds.shuffle(
#                     bufsize=_SAMPLE_SHUFFLE_SIZE,
#                     initial=_SAMPLE_SHUFFLE_INITIAL,
#                     rng=random.Random(args.seed),
#                 ),
#                 # wds.repeatedly,  # FIXME determine if this is beneficial
#             ]
#         )
#     else:
#         pipeline.extend(
#             [
#                 wds.split_by_worker,
#                 # at this point, we have an iterator over the shards assigned to each worker
#                 wds.tarfile_to_samples(handler=log_and_continue),
#             ]
#         )
#     pipeline.append(
#         wds.map(
#             partial(
#                 preprocess,
#                 audio_ext=audio_ext,
#                 text_ext=text_ext,
#                 max_len=max_len,
#                 audio_cfg=model_cfg["audio_cfg"],
#                 class_index_dict=copy.deepcopy(args.class_index_dict),
#                 data_filling=args.data_filling,
#                 data_truncating=args.data_truncating,
#                 text_augment_selection=args.text_augment_selection,
#             )
#         ),
#     )

#     pipeline.append(
#         wds.batched(
#             args.batch_size,
#             partial=not (is_train or args.parallel_eval),
#             collation_fn=collate_fn,
#         )
#     )

#     dataset = wds.DataPipeline(*pipeline)
#     if is_train or args.parallel_eval:
#         # (yusong): Currently parallel evaluation will be not precise as we are repeat the last few samples.
#         # (yusong): See comments below.
#         # roll over and repeat a few samples to get same number of full batches on each node
#         global_batch_size = args.batch_size * args.world_size
#         num_batches = math.ceil(num_samples / global_batch_size)
#         num_workers = max(1, args.workers)
#         num_worker_batches = math.ceil(
#             num_batches / num_workers
#         )  # per dataloader worker
#         num_batches = num_worker_batches * num_workers
#         num_samples = num_batches * global_batch_size
#         dataset = dataset.with_epoch(
#             num_worker_batches
#         )  # each worker is iterating over this
#     else:
#         # last batches are partial, eval is done on single (master) node
#         num_batches = math.ceil(num_samples / args.batch_size)

#     kwargs = {}
#     if args.horovod:  # multi-node training on summit
#         kwargs["multiprocessing_context"] = "forkserver"

#     dataloader = wds.WebLoader(
#         dataset, batch_size=None, shuffle=False, num_workers=args.workers, **kwargs
#     )

#     # FIXME not clear which approach is better, with_epoch before vs after dataloader?
#     # hoping to resolve via https://github.com/webdataset/webdataset/issues/169
#     # if is_train:
#     #     # roll over and repeat a few samples to get same number of full batches on each node
#     #     global_batch_size = args.batch_size * args.world_size
#     #     num_batches = math.ceil(num_samples / global_batch_size)
#     #     num_workers = max(1, args.workers)
#     #     num_batches = math.ceil(num_batches / num_workers) * num_workers
#     #     num_samples = num_batches * global_batch_size
#     #     dataloader = dataloader.with_epoch(num_batches)
#     # else:
#     #     # last batches are partial, eval is done on single (master) node
#     #     num_batches = math.ceil(num_samples / args.batch_size)

#     # add meta-data to dataloader instance for convenience
#     dataloader.num_batches = num_batches
#     dataloader.num_samples = num_samples

#     return DataInfo(dataloader, None)


def wds_batch_list2dict(
    batch,
    keys=[
        "__url__",
        "__key__",
        "waveform",
        "text",
        "raw_text",
        "audio_name",
        "text_name",
        "audio_orig_sr",
    ],
):
    """
    Return a dictionary of the batch, with keys as the names of the fields.
    """
    assert len(keys) == len(
        batch
    ), "batch must have same number of keys as keys argument"
    return {keys[i]: batch[i] for i in range(len(batch))}


def get_csv_dataset(args, preprocess_fn, is_train):
    input_filename = args.train_data if is_train else args.val_data
    assert input_filename
    dataset = CsvDataset(
        input_filename,
        preprocess_fn,
        img_key=args.csv_img_key,
        caption_key=args.csv_caption_key,
        sep=args.csv_separator,
    )
    num_samples = len(dataset)
    sampler = DistributedSampler(dataset) if args.distributed and is_train else None
    shuffle = is_train and sampler is None

    dataloader = DataLoader(
        dataset,
        batch_size=args.batch_size,
        shuffle=shuffle,
        num_workers=args.workers,
        pin_memory=True,
        sampler=sampler,
        drop_last=is_train,
    )
    dataloader.num_samples = num_samples
    dataloader.num_batches = len(dataloader)

    return DataInfo(dataloader, sampler)


def get_toy_dataset(args, model_cfg, is_train):
    index_path = args.train_data if is_train else args.val_data
    ipc_path = args.train_ipc if is_train else args.val_ipc
    assert index_path and ipc_path
    eval_mode = not is_train
    dataset = ToyDataset(index_path, ipc_path, model_cfg, eval_mode=eval_mode)

    num_samples = len(dataset)
    sampler = (
        DistributedSampler(dataset, shuffle=False)
        if args.distributed and is_train
        else None
    )

    dataloader = DataLoader(
        dataset,
        batch_size=args.batch_size,
        shuffle=False,
        num_workers=args.workers,
        sampler=sampler,
        drop_last=is_train,
    )
    dataloader.num_samples = num_samples
    dataloader.num_batches = len(dataloader)

    return DataInfo(dataloader, sampler)


def get_dataset_fn(data_path, dataset_type):
    if dataset_type == "webdataset":
        return get_wds_dataset
    elif dataset_type == "csv":
        return get_csv_dataset
    elif dataset_type == "auto":
        ext = data_path.split(".")[-1]
        if ext in ["csv", "tsv"]:
            return get_csv_dataset
        elif ext in ["tar"]:
            return get_wds_dataset
        else:
            raise ValueError(
                f"Tried to figure out dataset type, but failed for extension {ext}."
            )
    elif dataset_type == "toy":
        return get_toy_dataset
    else:
        raise ValueError(f"Unsupported dataset type: {dataset_type}")


def get_data(args, model_cfg):
    data = {}

    args.class_index_dict = load_class_label(args.class_label_path)

    if args.datasetinfos is None:
        args.datasetinfos = ["train", "unbalanced_train", "balanced_train"]
    if args.dataset_type == "webdataset":
        args.train_data = get_tar_path_from_dataset_name(
            args.datasetnames,
            args.datasetinfos,
            islocal=not args.remotedata,
            proportion=args.dataset_proportion,
            dataset_path=args.datasetpath,
            full_dataset=args.full_train_dataset,
        )

        if args.full_train_dataset is None:
            args.full_train_dataset = []
        if args.exclude_eval_dataset is None:
            args.exclude_eval_dataset = []
        excluded_eval_datasets = args.full_train_dataset + args.exclude_eval_dataset

        val_dataset_names = (
            [n for n in args.datasetnames if n not in excluded_eval_datasets]
            if excluded_eval_datasets
            else args.datasetnames
        )
        args.val_dataset_names = val_dataset_names
        args.val_data = get_tar_path_from_dataset_name(
            val_dataset_names,
            ["valid", "test", "eval"],
            islocal=not args.remotedata,
            proportion=1,
            dataset_path=args.datasetpath,
            full_dataset=None,
        )

    if args.train_data:
        data["train"] = get_dataset_fn(args.train_data, args.dataset_type)(
            args, model_cfg, is_train=True
        )

    if args.val_data:
        data["val"] = get_dataset_fn(args.val_data, args.dataset_type)(
            args, model_cfg, is_train=False
        )

    return data