mirror of
https://github.com/labmlai/annotated_deep_learning_paper_implementations.git
synced 2025-10-31 18:58:43 +08:00
323 lines
10 KiB
Python
323 lines
10 KiB
Python
import random
|
|
from pathlib import PurePath, Path
|
|
from typing import List, Callable, Dict, Optional
|
|
|
|
from torchvision import datasets, transforms
|
|
|
|
import torch
|
|
from labml import lab
|
|
from labml import monit
|
|
from labml.configs import BaseConfigs
|
|
from labml.configs import aggregate, option
|
|
from labml.utils.download import download_file
|
|
from torch.utils.data import DataLoader
|
|
from torch.utils.data import IterableDataset, Dataset
|
|
|
|
|
|
def _mnist_dataset(is_train, transform):
|
|
return datasets.MNIST(str(lab.get_data_path()),
|
|
train=is_train,
|
|
download=True,
|
|
transform=transform)
|
|
|
|
|
|
class MNISTConfigs(BaseConfigs):
|
|
"""
|
|
Configurable MNIST data set.
|
|
|
|
Arguments:
|
|
dataset_name (str): name of the data set, ``MNIST``
|
|
dataset_transforms (torchvision.transforms.Compose): image transformations
|
|
train_dataset (torchvision.datasets.MNIST): training dataset
|
|
valid_dataset (torchvision.datasets.MNIST): validation dataset
|
|
|
|
train_loader (torch.utils.data.DataLoader): training data loader
|
|
valid_loader (torch.utils.data.DataLoader): validation data loader
|
|
|
|
train_batch_size (int): training batch size
|
|
valid_batch_size (int): validation batch size
|
|
|
|
train_loader_shuffle (bool): whether to shuffle training data
|
|
valid_loader_shuffle (bool): whether to shuffle validation data
|
|
"""
|
|
|
|
dataset_name: str = 'MNIST'
|
|
dataset_transforms: transforms.Compose
|
|
train_dataset: datasets.MNIST
|
|
valid_dataset: datasets.MNIST
|
|
|
|
train_loader: DataLoader
|
|
valid_loader: DataLoader
|
|
|
|
train_batch_size: int = 64
|
|
valid_batch_size: int = 1024
|
|
|
|
train_loader_shuffle: bool = True
|
|
valid_loader_shuffle: bool = False
|
|
|
|
|
|
@option(MNISTConfigs.dataset_transforms)
|
|
def mnist_transforms():
|
|
return transforms.Compose([
|
|
transforms.ToTensor(),
|
|
transforms.Normalize((0.1307,), (0.3081,))
|
|
])
|
|
|
|
|
|
@option(MNISTConfigs.train_dataset)
|
|
def mnist_train_dataset(c: MNISTConfigs):
|
|
return _mnist_dataset(True, c.dataset_transforms)
|
|
|
|
|
|
@option(MNISTConfigs.valid_dataset)
|
|
def mnist_valid_dataset(c: MNISTConfigs):
|
|
return _mnist_dataset(False, c.dataset_transforms)
|
|
|
|
|
|
@option(MNISTConfigs.train_loader)
|
|
def mnist_train_loader(c: MNISTConfigs):
|
|
return DataLoader(c.train_dataset,
|
|
batch_size=c.train_batch_size,
|
|
shuffle=c.train_loader_shuffle)
|
|
|
|
|
|
@option(MNISTConfigs.valid_loader)
|
|
def mnist_valid_loader(c: MNISTConfigs):
|
|
return DataLoader(c.valid_dataset,
|
|
batch_size=c.valid_batch_size,
|
|
shuffle=c.valid_loader_shuffle)
|
|
|
|
|
|
aggregate(MNISTConfigs.dataset_name, 'MNIST',
|
|
(MNISTConfigs.dataset_transforms, 'mnist_transforms'),
|
|
(MNISTConfigs.train_dataset, 'mnist_train_dataset'),
|
|
(MNISTConfigs.valid_dataset, 'mnist_valid_dataset'),
|
|
(MNISTConfigs.train_loader, 'mnist_train_loader'),
|
|
(MNISTConfigs.valid_loader, 'mnist_valid_loader'))
|
|
|
|
|
|
def _cifar_dataset(is_train, transform):
|
|
return datasets.CIFAR10(str(lab.get_data_path()),
|
|
train=is_train,
|
|
download=True,
|
|
transform=transform)
|
|
|
|
|
|
class CIFAR10Configs(BaseConfigs):
|
|
"""
|
|
Configurable CIFAR 10 data set.
|
|
|
|
Arguments:
|
|
dataset_name (str): name of the data set, ``CIFAR10``
|
|
dataset_transforms (torchvision.transforms.Compose): image transformations
|
|
train_dataset (torchvision.datasets.CIFAR10): training dataset
|
|
valid_dataset (torchvision.datasets.CIFAR10): validation dataset
|
|
|
|
train_loader (torch.utils.data.DataLoader): training data loader
|
|
valid_loader (torch.utils.data.DataLoader): validation data loader
|
|
|
|
train_batch_size (int): training batch size
|
|
valid_batch_size (int): validation batch size
|
|
|
|
train_loader_shuffle (bool): whether to shuffle training data
|
|
valid_loader_shuffle (bool): whether to shuffle validation data
|
|
"""
|
|
dataset_name: str = 'CIFAR10'
|
|
dataset_transforms: transforms.Compose
|
|
train_dataset: datasets.CIFAR10
|
|
valid_dataset: datasets.CIFAR10
|
|
|
|
train_loader: DataLoader
|
|
valid_loader: DataLoader
|
|
|
|
train_batch_size: int = 64
|
|
valid_batch_size: int = 1024
|
|
|
|
train_loader_shuffle: bool = True
|
|
valid_loader_shuffle: bool = False
|
|
|
|
|
|
@CIFAR10Configs.calc(CIFAR10Configs.dataset_transforms)
|
|
def cifar10_transforms():
|
|
return transforms.Compose([
|
|
transforms.ToTensor(),
|
|
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
|
|
])
|
|
|
|
|
|
@CIFAR10Configs.calc(CIFAR10Configs.train_dataset)
|
|
def cifar10_train_dataset(c: CIFAR10Configs):
|
|
return _cifar_dataset(True, c.dataset_transforms)
|
|
|
|
|
|
@CIFAR10Configs.calc(CIFAR10Configs.valid_dataset)
|
|
def cifar10_valid_dataset(c: CIFAR10Configs):
|
|
return _cifar_dataset(False, c.dataset_transforms)
|
|
|
|
|
|
@CIFAR10Configs.calc(CIFAR10Configs.train_loader)
|
|
def cifar10_train_loader(c: CIFAR10Configs):
|
|
return DataLoader(c.train_dataset,
|
|
batch_size=c.train_batch_size,
|
|
shuffle=c.train_loader_shuffle)
|
|
|
|
|
|
@CIFAR10Configs.calc(CIFAR10Configs.valid_loader)
|
|
def cifar10_valid_loader(c: CIFAR10Configs):
|
|
return DataLoader(c.valid_dataset,
|
|
batch_size=c.valid_batch_size,
|
|
shuffle=c.valid_loader_shuffle)
|
|
|
|
|
|
CIFAR10Configs.aggregate(CIFAR10Configs.dataset_name, 'CIFAR10',
|
|
(CIFAR10Configs.dataset_transforms, 'cifar10_transforms'),
|
|
(CIFAR10Configs.train_dataset, 'cifar10_train_dataset'),
|
|
(CIFAR10Configs.valid_dataset, 'cifar10_valid_dataset'),
|
|
(CIFAR10Configs.train_loader, 'cifar10_train_loader'),
|
|
(CIFAR10Configs.valid_loader, 'cifar10_valid_loader'))
|
|
|
|
|
|
class TextDataset:
|
|
itos: List[str]
|
|
stoi: Dict[str, int]
|
|
n_tokens: int
|
|
train: str
|
|
valid: str
|
|
standard_tokens: List[str] = []
|
|
|
|
@staticmethod
|
|
def load(path: PurePath):
|
|
with open(str(path), 'r') as f:
|
|
return f.read()
|
|
|
|
def __init__(self, path: PurePath, tokenizer: Callable, train: str, valid: str, test: str, *,
|
|
n_tokens: Optional[int] = None,
|
|
stoi: Optional[Dict[str, int]] = None,
|
|
itos: Optional[List[str]] = None):
|
|
self.test = test
|
|
self.valid = valid
|
|
self.train = train
|
|
self.tokenizer = tokenizer
|
|
self.path = path
|
|
|
|
if n_tokens or stoi or itos:
|
|
assert stoi and itos and n_tokens
|
|
self.n_tokens = n_tokens
|
|
self.stoi = stoi
|
|
self.itos = itos
|
|
else:
|
|
self.n_tokens = len(self.standard_tokens)
|
|
self.stoi = {t: i for i, t in enumerate(self.standard_tokens)}
|
|
|
|
with monit.section("Tokenize"):
|
|
tokens = self.tokenizer(self.train) + self.tokenizer(self.valid)
|
|
tokens = sorted(list(set(tokens)))
|
|
|
|
for t in monit.iterate("Build vocabulary", tokens):
|
|
self.stoi[t] = self.n_tokens
|
|
self.n_tokens += 1
|
|
|
|
self.itos = [''] * self.n_tokens
|
|
for t, n in self.stoi.items():
|
|
self.itos[n] = t
|
|
|
|
def text_to_i(self, text: str) -> torch.Tensor:
|
|
tokens = self.tokenizer(text)
|
|
return torch.tensor([self.stoi[s] for s in tokens if s in self.stoi], dtype=torch.long)
|
|
|
|
def __repr__(self):
|
|
return f'{len(self.train) / 1_000_000 :,.2f}M, {len(self.valid) / 1_000_000 :,.2f}M - {str(self.path)}'
|
|
|
|
|
|
class SequentialDataLoader(IterableDataset):
|
|
def __init__(self, *, text: str, dataset: TextDataset,
|
|
batch_size: int, seq_len: int):
|
|
self.seq_len = seq_len
|
|
data = dataset.text_to_i(text)
|
|
n_batch = data.shape[0] // batch_size
|
|
data = data.narrow(0, 0, n_batch * batch_size)
|
|
data = data.view(batch_size, -1).t().contiguous()
|
|
self.data = data
|
|
|
|
def __len__(self):
|
|
return self.data.shape[0] // self.seq_len
|
|
|
|
def __iter__(self):
|
|
self.idx = 0
|
|
return self
|
|
|
|
def __next__(self):
|
|
if self.idx >= self.data.shape[0] - 1:
|
|
raise StopIteration()
|
|
|
|
seq_len = min(self.seq_len, self.data.shape[0] - 1 - self.idx)
|
|
i = self.idx + seq_len
|
|
data = self.data[self.idx: i]
|
|
target = self.data[self.idx + 1: i + 1]
|
|
self.idx = i
|
|
return data, target
|
|
|
|
def __getitem__(self, idx):
|
|
seq_len = min(self.seq_len, self.data.shape[0] - 1 - idx)
|
|
i = idx + seq_len
|
|
data = self.data[idx: i]
|
|
target = self.data[idx + 1: i + 1]
|
|
return data, target
|
|
|
|
|
|
class SequentialUnBatchedDataset(Dataset):
|
|
def __init__(self, *, text: str, dataset: TextDataset,
|
|
seq_len: int,
|
|
is_random_offset: bool = True):
|
|
self.is_random_offset = is_random_offset
|
|
self.seq_len = seq_len
|
|
self.data = dataset.text_to_i(text)
|
|
|
|
def __len__(self):
|
|
return (self.data.shape[0] - 1) // self.seq_len
|
|
|
|
def __getitem__(self, idx):
|
|
start = idx * self.seq_len
|
|
assert start + self.seq_len + 1 <= self.data.shape[0]
|
|
if self.is_random_offset:
|
|
start += random.randint(0, min(self.seq_len - 1, self.data.shape[0] - (start + self.seq_len + 1)))
|
|
|
|
end = start + self.seq_len
|
|
data = self.data[start: end]
|
|
target = self.data[start + 1: end + 1]
|
|
return data, target
|
|
|
|
|
|
class TextFileDataset(TextDataset):
|
|
standard_tokens = []
|
|
|
|
def __init__(self, path: PurePath, tokenizer: Callable, *,
|
|
url: Optional[str] = None,
|
|
filter_subset: Optional[int] = None):
|
|
path = Path(path)
|
|
if not path.exists():
|
|
if not url:
|
|
raise FileNotFoundError(str(path))
|
|
else:
|
|
download_file(url, path)
|
|
|
|
with monit.section("Load data"):
|
|
text = self.load(path)
|
|
if filter_subset:
|
|
text = text[:filter_subset]
|
|
split = int(len(text) * .9)
|
|
train = text[:split]
|
|
valid = text[split:]
|
|
|
|
super().__init__(path, tokenizer, train, valid, '')
|
|
|
|
|
|
def _test_tiny_shakespeare():
|
|
from labml import lab
|
|
_ = TextFileDataset(lab.get_data_path() / 'tiny_shakespeare.txt', lambda x: list(x),
|
|
url='https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
_test_tiny_shakespeare()
|