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import torch
from pathlib import Path
import os
from typing import Callable, Optional, TypeVar, Dict, Tuple, List, Union
DEFAULT_CACHE_DIR_ROOT = Path('./cache_dir/')
DataLoader = TypeVar('DataLoader')
InputType = [str, Optional[int], Optional[int]]
ReturnType = Tuple[DataLoader, DataLoader, DataLoader, Dict, int, int, int, int]
# Custom loading functions must therefore have the template.
dataset_fn = Callable[[str, Optional[int], Optional[int]], ReturnType]
# Example interface for making a loader.
def custom_loader(cache_dir: str,
bsz: int = 50,
seed: int = 42) -> ReturnType:
...
def make_data_loader(dset,
dobj,
seed: int,
batch_size: int=128,
shuffle: bool=True,
drop_last: bool=True,
collate_fn: callable=None):
"""
:param dset: (PT dset): PyTorch dataset object.
:param dobj (=None): (AG data): Dataset object, as returned by A.G.s dataloader.
:param seed: (int): Int for seeding shuffle.
:param batch_size: (int): Batch size for batches.
:param shuffle: (bool): Shuffle the data loader?
:param drop_last: (bool): Drop ragged final batch (particularly for training).
:return:
"""
# Create a generator for seeding random number draws.
if seed is not None:
rng = torch.Generator()
rng.manual_seed(seed)
else:
rng = None
if dobj is not None:
assert collate_fn is None
collate_fn = dobj._collate_fn
# Generate the dataloaders.
return torch.utils.data.DataLoader(dataset=dset, collate_fn=collate_fn, batch_size=batch_size, shuffle=shuffle,
drop_last=drop_last, generator=rng)
def create_lra_imdb_classification_dataset(cache_dir: Union[str, Path] = DEFAULT_CACHE_DIR_ROOT,
bsz: int = 50,
seed: int = 42) -> ReturnType:
"""
:param cache_dir: (str): Not currently used.
:param bsz: (int): Batch size.
:param seed: (int) Seed for shuffling data.
:return:
"""
print("[*] Generating LRA-text (IMDB) Classification Dataset")
from s5.dataloaders.lra import IMDB
name = 'imdb'
dataset_obj = IMDB('imdb', )
dataset_obj.cache_dir = Path(cache_dir) / name
dataset_obj.setup()
trainloader = make_data_loader(dataset_obj.dataset_train, dataset_obj, seed=seed, batch_size=bsz)
testloader = make_data_loader(dataset_obj.dataset_test, dataset_obj, seed=seed, batch_size=bsz, drop_last=False, shuffle=False)
valloader = None
N_CLASSES = dataset_obj.d_output
SEQ_LENGTH = dataset_obj.l_max
IN_DIM = 135 # We should probably stop this from being hard-coded.
TRAIN_SIZE = len(dataset_obj.dataset_train)
aux_loaders = {}
return trainloader, valloader, testloader, aux_loaders, N_CLASSES, SEQ_LENGTH, IN_DIM, TRAIN_SIZE
def create_lra_listops_classification_dataset(cache_dir: Union[str, Path] = DEFAULT_CACHE_DIR_ROOT,
bsz: int = 50,
seed: int = 42) -> ReturnType:
"""
See abstract template.
"""
print("[*] Generating LRA-listops Classification Dataset")
from s5.dataloaders.lra import ListOps
name = 'listops'
dir_name = './raw_datasets/lra_release/lra_release/listops-1000'
dataset_obj = ListOps(name, data_dir=dir_name)
dataset_obj.cache_dir = Path(cache_dir) / name
dataset_obj.setup()
trn_loader = make_data_loader(dataset_obj.dataset_train, dataset_obj, seed=seed, batch_size=bsz)
val_loader = make_data_loader(dataset_obj.dataset_val, dataset_obj, seed=seed, batch_size=bsz, drop_last=False, shuffle=False)
tst_loader = make_data_loader(dataset_obj.dataset_test, dataset_obj, seed=seed, batch_size=bsz, drop_last=False, shuffle=False)
N_CLASSES = dataset_obj.d_output
SEQ_LENGTH = dataset_obj.l_max
IN_DIM = 20
TRAIN_SIZE = len(dataset_obj.dataset_train)
aux_loaders = {}
return trn_loader, val_loader, tst_loader, aux_loaders, N_CLASSES, SEQ_LENGTH, IN_DIM, TRAIN_SIZE
def create_lra_path32_classification_dataset(cache_dir: Union[str, Path] = DEFAULT_CACHE_DIR_ROOT,
bsz: int = 50,
seed: int = 42) -> ReturnType:
"""
See abstract template.
"""
print("[*] Generating LRA-Pathfinder32 Classification Dataset")
from s5.dataloaders.lra import PathFinder
name = 'pathfinder'
resolution = 32
dir_name = f'./raw_datasets/lra_release/lra_release/pathfinder{resolution}'
dataset_obj = PathFinder(name, data_dir=dir_name, resolution=resolution)
dataset_obj.cache_dir = Path(cache_dir) / name
dataset_obj.setup()
trn_loader = make_data_loader(dataset_obj.dataset_train, dataset_obj, seed=seed, batch_size=bsz)
val_loader = make_data_loader(dataset_obj.dataset_val, dataset_obj, seed=seed, batch_size=bsz, drop_last=False, shuffle=False)
tst_loader = make_data_loader(dataset_obj.dataset_test, dataset_obj, seed=seed, batch_size=bsz, drop_last=False, shuffle=False)
N_CLASSES = dataset_obj.d_output
SEQ_LENGTH = dataset_obj.dataset_train.tensors[0].shape[1]
IN_DIM = dataset_obj.d_input
TRAIN_SIZE = dataset_obj.dataset_train.tensors[0].shape[0]
aux_loaders = {}
return trn_loader, val_loader, tst_loader, aux_loaders, N_CLASSES, SEQ_LENGTH, IN_DIM, TRAIN_SIZE
def create_lra_pathx_classification_dataset(cache_dir: Union[str, Path] = DEFAULT_CACHE_DIR_ROOT,
bsz: int = 50,
seed: int = 42) -> ReturnType:
"""
See abstract template.
"""
print("[*] Generating LRA-PathX Classification Dataset")
from s5.dataloaders.lra import PathFinder
name = 'pathfinder'
resolution = 128
dir_name = f'./raw_datasets/lra_release/lra_release/pathfinder{resolution}'
dataset_obj = PathFinder(name, data_dir=dir_name, resolution=resolution)
dataset_obj.cache_dir = Path(cache_dir) / name
dataset_obj.setup()
trn_loader = make_data_loader(dataset_obj.dataset_train, dataset_obj, seed=seed, batch_size=bsz)
val_loader = make_data_loader(dataset_obj.dataset_val, dataset_obj, seed=seed, batch_size=bsz, drop_last=False, shuffle=False)
tst_loader = make_data_loader(dataset_obj.dataset_test, dataset_obj, seed=seed, batch_size=bsz, drop_last=False, shuffle=False)
N_CLASSES = dataset_obj.d_output
SEQ_LENGTH = dataset_obj.dataset_train.tensors[0].shape[1]
IN_DIM = dataset_obj.d_input
TRAIN_SIZE = dataset_obj.dataset_train.tensors[0].shape[0]
aux_loaders = {}
return trn_loader, val_loader, tst_loader, aux_loaders, N_CLASSES, SEQ_LENGTH, IN_DIM, TRAIN_SIZE
def create_lra_image_classification_dataset(cache_dir: Union[str, Path] = DEFAULT_CACHE_DIR_ROOT,
seed: int = 42,
bsz: int=128) -> ReturnType:
"""
See abstract template.
Cifar is quick to download and is automatically cached.
"""
print("[*] Generating LRA-listops Classification Dataset")
from s5.dataloaders.basic import CIFAR10
name = 'cifar'
kwargs = {
'grayscale': True, # LRA uses a grayscale CIFAR image.
}
dataset_obj = CIFAR10(name, data_dir=cache_dir, **kwargs) # TODO - double check what the dir here does.
dataset_obj.setup()
trn_loader = make_data_loader(dataset_obj.dataset_train, dataset_obj, seed=seed, batch_size=bsz)
val_loader = make_data_loader(dataset_obj.dataset_val, dataset_obj, seed=seed, batch_size=bsz, drop_last=False, shuffle=False)
tst_loader = make_data_loader(dataset_obj.dataset_test, dataset_obj, seed=seed, batch_size=bsz, drop_last=False, shuffle=False)
N_CLASSES = dataset_obj.d_output
SEQ_LENGTH = 32 * 32
IN_DIM = 1
TRAIN_SIZE = len(dataset_obj.dataset_train)
aux_loaders = {}
return trn_loader, val_loader, tst_loader, aux_loaders, N_CLASSES, SEQ_LENGTH, IN_DIM, TRAIN_SIZE
def create_lra_aan_classification_dataset(cache_dir: Union[str, Path] = DEFAULT_CACHE_DIR_ROOT,
bsz: int = 50,
seed: int = 42, ) -> ReturnType:
"""
See abstract template.
"""
print("[*] Generating LRA-AAN Classification Dataset")
from s5.dataloaders.lra import AAN
name = 'aan'
dir_name = './raw_datasets/lra_release/lra_release/tsv_data'
kwargs = {
'n_workers': 1, # Multiple workers seems to break AAN.
}
dataset_obj = AAN(name, data_dir=dir_name, **kwargs)
dataset_obj.cache_dir = Path(cache_dir) / name
dataset_obj.setup()
trn_loader = make_data_loader(dataset_obj.dataset_train, dataset_obj, seed=seed, batch_size=bsz)
val_loader = make_data_loader(dataset_obj.dataset_val, dataset_obj, seed=seed, batch_size=bsz, drop_last=False, shuffle=False)
tst_loader = make_data_loader(dataset_obj.dataset_test, dataset_obj, seed=seed, batch_size=bsz, drop_last=False, shuffle=False)
N_CLASSES = dataset_obj.d_output
SEQ_LENGTH = dataset_obj.l_max
IN_DIM = len(dataset_obj.vocab)
TRAIN_SIZE = len(dataset_obj.dataset_train)
aux_loaders = {}
return trn_loader, val_loader, tst_loader, aux_loaders, N_CLASSES, SEQ_LENGTH, IN_DIM, TRAIN_SIZE
def create_speechcommands35_classification_dataset(cache_dir: Union[str, Path] = DEFAULT_CACHE_DIR_ROOT,
bsz: int = 50,
seed: int = 42) -> ReturnType:
"""
AG inexplicably moved away from using a cache dir... Grumble.
The `cache_dir` will effectively be ./raw_datasets/speech_commands/0.0.2 .
See abstract template.
"""
print("[*] Generating SpeechCommands35 Classification Dataset")
from s5.dataloaders.basic import SpeechCommands
name = 'sc'
dir_name = f'./raw_datasets/speech_commands/0.0.2/'
os.makedirs(dir_name, exist_ok=True)
kwargs = {
'all_classes': True,
'sr': 1 # Set the subsampling rate.
}
dataset_obj = SpeechCommands(name, data_dir=dir_name, **kwargs)
dataset_obj.setup()
trn_loader = make_data_loader(dataset_obj.dataset_train, dataset_obj, seed=seed, batch_size=bsz)
val_loader = make_data_loader(dataset_obj.dataset_val, dataset_obj, seed=seed, batch_size=bsz, drop_last=False, shuffle=False)
tst_loader = make_data_loader(dataset_obj.dataset_test, dataset_obj, seed=seed, batch_size=bsz, drop_last=False, shuffle=False)
N_CLASSES = dataset_obj.d_output
SEQ_LENGTH = dataset_obj.dataset_train.tensors[0].shape[1]
IN_DIM = 1
TRAIN_SIZE = dataset_obj.dataset_train.tensors[0].shape[0]
# Also make the half resolution dataloader.
kwargs['sr'] = 2
dataset_obj = SpeechCommands(name, data_dir=dir_name, **kwargs)
dataset_obj.setup()
val_loader_2 = make_data_loader(dataset_obj.dataset_val, dataset_obj, seed=seed, batch_size=bsz, drop_last=False, shuffle=False)
tst_loader_2 = make_data_loader(dataset_obj.dataset_test, dataset_obj, seed=seed, batch_size=bsz, drop_last=False, shuffle=False)
aux_loaders = {
'valloader2': val_loader_2,
'testloader2': tst_loader_2,
}
return trn_loader, val_loader, tst_loader, aux_loaders, N_CLASSES, SEQ_LENGTH, IN_DIM, TRAIN_SIZE
def create_cifar_classification_dataset(cache_dir: Union[str, Path] = DEFAULT_CACHE_DIR_ROOT,
seed: int = 42,
bsz: int=128) -> ReturnType:
"""
See abstract template.
Cifar is quick to download and is automatically cached.
"""
print("[*] Generating CIFAR (color) Classification Dataset")
from s5.dataloaders.basic import CIFAR10
name = 'cifar'
kwargs = {
'grayscale': False, # LRA uses a grayscale CIFAR image.
}
dataset_obj = CIFAR10(name, data_dir=cache_dir, **kwargs)
dataset_obj.setup()
trn_loader = make_data_loader(dataset_obj.dataset_train, dataset_obj, seed=seed, batch_size=bsz)
val_loader = make_data_loader(dataset_obj.dataset_val, dataset_obj, seed=seed, batch_size=bsz, drop_last=False, shuffle=False)
tst_loader = make_data_loader(dataset_obj.dataset_test, dataset_obj, seed=seed, batch_size=bsz, drop_last=False, shuffle=False)
N_CLASSES = dataset_obj.d_output
SEQ_LENGTH = 32 * 32
IN_DIM = 3
TRAIN_SIZE = len(dataset_obj.dataset_train)
aux_loaders = {}
return trn_loader, val_loader, tst_loader, aux_loaders, N_CLASSES, SEQ_LENGTH, IN_DIM, TRAIN_SIZE
def create_mnist_classification_dataset(cache_dir: Union[str, Path] = DEFAULT_CACHE_DIR_ROOT,
seed: int = 42,
bsz: int=128) -> ReturnType:
"""
See abstract template.
Cifar is quick to download and is automatically cached.
"""
print("[*] Generating MNIST Classification Dataset")
from s5.dataloaders.basic import MNIST
name = 'mnist'
kwargs = {
'permute': False
}
dataset_obj = MNIST(name, data_dir=cache_dir, **kwargs)
dataset_obj.setup()
trn_loader = make_data_loader(dataset_obj.dataset_train, dataset_obj, seed=seed, batch_size=bsz)
val_loader = make_data_loader(dataset_obj.dataset_val, dataset_obj, seed=seed, batch_size=bsz, drop_last=False, shuffle=False)
tst_loader = make_data_loader(dataset_obj.dataset_test, dataset_obj, seed=seed, batch_size=bsz, drop_last=False, shuffle=False)
N_CLASSES = dataset_obj.d_output
SEQ_LENGTH = 28 * 28
IN_DIM = 1
TRAIN_SIZE = len(dataset_obj.dataset_train)
aux_loaders = {}
return trn_loader, val_loader, tst_loader, aux_loaders, N_CLASSES, SEQ_LENGTH, IN_DIM, TRAIN_SIZE
def create_pmnist_classification_dataset(cache_dir: Union[str, Path] = DEFAULT_CACHE_DIR_ROOT,
seed: int = 42,
bsz: int=128) -> ReturnType:
"""
See abstract template.
Cifar is quick to download and is automatically cached.
"""
print("[*] Generating permuted-MNIST Classification Dataset")
from s5.dataloaders.basic import MNIST
name = 'mnist'
kwargs = {
'permute': True
}
dataset_obj = MNIST(name, data_dir=cache_dir, **kwargs)
dataset_obj.setup()
trn_loader = make_data_loader(dataset_obj.dataset_train, dataset_obj, seed=seed, batch_size=bsz)
val_loader = make_data_loader(dataset_obj.dataset_val, dataset_obj, seed=seed, batch_size=bsz, drop_last=False, shuffle=False)
tst_loader = make_data_loader(dataset_obj.dataset_test, dataset_obj, seed=seed, batch_size=bsz, drop_last=False, shuffle=False)
N_CLASSES = dataset_obj.d_output
SEQ_LENGTH = 28 * 28
IN_DIM = 1
TRAIN_SIZE = len(dataset_obj.dataset_train)
aux_loaders = {}
return trn_loader, val_loader, tst_loader, aux_loaders, N_CLASSES, SEQ_LENGTH, IN_DIM, TRAIN_SIZE
Datasets = {
# Other loaders.
"mnist-classification": create_mnist_classification_dataset,
"pmnist-classification": create_pmnist_classification_dataset,
"cifar-classification": create_cifar_classification_dataset,
# LRA.
"imdb-classification": create_lra_imdb_classification_dataset,
"listops-classification": create_lra_listops_classification_dataset,
"aan-classification": create_lra_aan_classification_dataset,
"lra-cifar-classification": create_lra_image_classification_dataset,
"pathfinder-classification": create_lra_path32_classification_dataset,
"pathx-classification": create_lra_pathx_classification_dataset,
# Speech.
"speech35-classification": create_speechcommands35_classification_dataset,
}
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