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remove labml_helpers dep
This commit is contained in:
Varuna Jayasiri
322
labml_nn/helpers/datasets.py
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322
labml_nn/helpers/datasets.py
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import random
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from pathlib import PurePath, Path
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from typing import List, Callable, Dict, Optional
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from torchvision import datasets, transforms
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import torch
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from labml import lab
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from labml import monit
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from labml.configs import BaseConfigs
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from labml.configs import aggregate, option
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from labml.utils.download import download_file
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from torch.utils.data import DataLoader
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from torch.utils.data import IterableDataset, Dataset
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def _dataset(is_train, transform):
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return datasets.MNIST(str(lab.get_data_path()),
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train=is_train,
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download=True,
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transform=transform)
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class MNISTConfigs(BaseConfigs):
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"""
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Configurable MNIST data set.
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Arguments:
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dataset_name (str): name of the data set, ``MNIST``
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dataset_transforms (torchvision.transforms.Compose): image transformations
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train_dataset (torchvision.datasets.MNIST): training dataset
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valid_dataset (torchvision.datasets.MNIST): validation dataset
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train_loader (torch.utils.data.DataLoader): training data loader
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valid_loader (torch.utils.data.DataLoader): validation data loader
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train_batch_size (int): training batch size
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valid_batch_size (int): validation batch size
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train_loader_shuffle (bool): whether to shuffle training data
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valid_loader_shuffle (bool): whether to shuffle validation data
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"""
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dataset_name: str = 'MNIST'
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dataset_transforms: transforms.Compose
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train_dataset: datasets.MNIST
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valid_dataset: datasets.MNIST
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train_loader: DataLoader
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valid_loader: DataLoader
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train_batch_size: int = 64
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valid_batch_size: int = 1024
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train_loader_shuffle: bool = True
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valid_loader_shuffle: bool = False
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@option(MNISTConfigs.dataset_transforms)
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def mnist_transforms():
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return transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.1307,), (0.3081,))
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])
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@option(MNISTConfigs.train_dataset)
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def mnist_train_dataset(c: MNISTConfigs):
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return _dataset(True, c.dataset_transforms)
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@option(MNISTConfigs.valid_dataset)
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def mnist_valid_dataset(c: MNISTConfigs):
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return _dataset(False, c.dataset_transforms)
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@option(MNISTConfigs.train_loader)
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def mnist_train_loader(c: MNISTConfigs):
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return DataLoader(c.train_dataset,
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batch_size=c.train_batch_size,
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shuffle=c.train_loader_shuffle)
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@option(MNISTConfigs.valid_loader)
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def mnist_valid_loader(c: MNISTConfigs):
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return DataLoader(c.valid_dataset,
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batch_size=c.valid_batch_size,
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shuffle=c.valid_loader_shuffle)
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aggregate(MNISTConfigs.dataset_name, 'MNIST',
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(MNISTConfigs.dataset_transforms, 'mnist_transforms'),
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(MNISTConfigs.train_dataset, 'mnist_train_dataset'),
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(MNISTConfigs.valid_dataset, 'mnist_valid_dataset'),
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(MNISTConfigs.train_loader, 'mnist_train_loader'),
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(MNISTConfigs.valid_loader, 'mnist_valid_loader'))
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def _dataset(is_train, transform):
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return datasets.CIFAR10(str(lab.get_data_path()),
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train=is_train,
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download=True,
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transform=transform)
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class CIFAR10Configs(BaseConfigs):
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"""
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Configurable CIFAR 10 data set.
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Arguments:
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dataset_name (str): name of the data set, ``CIFAR10``
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dataset_transforms (torchvision.transforms.Compose): image transformations
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train_dataset (torchvision.datasets.CIFAR10): training dataset
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valid_dataset (torchvision.datasets.CIFAR10): validation dataset
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train_loader (torch.utils.data.DataLoader): training data loader
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valid_loader (torch.utils.data.DataLoader): validation data loader
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train_batch_size (int): training batch size
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valid_batch_size (int): validation batch size
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train_loader_shuffle (bool): whether to shuffle training data
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valid_loader_shuffle (bool): whether to shuffle validation data
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"""
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dataset_name: str = 'CIFAR10'
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dataset_transforms: transforms.Compose
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train_dataset: datasets.CIFAR10
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valid_dataset: datasets.CIFAR10
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train_loader: DataLoader
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valid_loader: DataLoader
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train_batch_size: int = 64
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valid_batch_size: int = 1024
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train_loader_shuffle: bool = True
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valid_loader_shuffle: bool = False
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@CIFAR10Configs.calc(CIFAR10Configs.dataset_transforms)
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def cifar10_transforms():
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return transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
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])
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@CIFAR10Configs.calc(CIFAR10Configs.train_dataset)
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def cifar10_train_dataset(c: CIFAR10Configs):
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return _dataset(True, c.dataset_transforms)
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@CIFAR10Configs.calc(CIFAR10Configs.valid_dataset)
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def cifar10_valid_dataset(c: CIFAR10Configs):
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return _dataset(False, c.dataset_transforms)
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@CIFAR10Configs.calc(CIFAR10Configs.train_loader)
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def cifar10_train_loader(c: CIFAR10Configs):
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return DataLoader(c.train_dataset,
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batch_size=c.train_batch_size,
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shuffle=c.train_loader_shuffle)
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@CIFAR10Configs.calc(CIFAR10Configs.valid_loader)
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def cifar10_valid_loader(c: CIFAR10Configs):
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return DataLoader(c.valid_dataset,
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batch_size=c.valid_batch_size,
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shuffle=c.valid_loader_shuffle)
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CIFAR10Configs.aggregate(CIFAR10Configs.dataset_name, 'CIFAR10',
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(CIFAR10Configs.dataset_transforms, 'cifar10_transforms'),
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(CIFAR10Configs.train_dataset, 'cifar10_train_dataset'),
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(CIFAR10Configs.valid_dataset, 'cifar10_valid_dataset'),
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(CIFAR10Configs.train_loader, 'cifar10_train_loader'),
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(CIFAR10Configs.valid_loader, 'cifar10_valid_loader'))
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class TextDataset:
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itos: List[str]
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stoi: Dict[str, int]
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n_tokens: int
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train: str
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valid: str
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standard_tokens: List[str] = []
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@staticmethod
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def load(path: PurePath):
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with open(str(path), 'r') as f:
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return f.read()
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def __init__(self, path: PurePath, tokenizer: Callable, train: str, valid: str, test: str, *,
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n_tokens: Optional[int] = None,
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stoi: Optional[Dict[str, int]] = None,
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itos: Optional[List[str]] = None):
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self.test = test
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self.valid = valid
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self.train = train
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self.tokenizer = tokenizer
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self.path = path
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if n_tokens or stoi or itos:
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assert stoi and itos and n_tokens
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self.n_tokens = n_tokens
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self.stoi = stoi
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self.itos = itos
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else:
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self.n_tokens = len(self.standard_tokens)
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self.stoi = {t: i for i, t in enumerate(self.standard_tokens)}
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with monit.section("Tokenize"):
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tokens = self.tokenizer(self.train) + self.tokenizer(self.valid)
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tokens = sorted(list(set(tokens)))
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for t in monit.iterate("Build vocabulary", tokens):
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self.stoi[t] = self.n_tokens
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self.n_tokens += 1
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self.itos = [''] * self.n_tokens
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for t, n in self.stoi.items():
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self.itos[n] = t
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def text_to_i(self, text: str) -> torch.Tensor:
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tokens = self.tokenizer(text)
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return torch.tensor([self.stoi[s] for s in tokens if s in self.stoi], dtype=torch.long)
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def __repr__(self):
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return f'{len(self.train) / 1_000_000 :,.2f}M, {len(self.valid) / 1_000_000 :,.2f}M - {str(self.path)}'
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class SequentialDataLoader(IterableDataset):
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def __init__(self, *, text: str, dataset: TextDataset,
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batch_size: int, seq_len: int):
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self.seq_len = seq_len
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data = dataset.text_to_i(text)
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n_batch = data.shape[0] // batch_size
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data = data.narrow(0, 0, n_batch * batch_size)
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data = data.view(batch_size, -1).t().contiguous()
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self.data = data
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def __len__(self):
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return self.data.shape[0] // self.seq_len
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def __iter__(self):
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self.idx = 0
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return self
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def __next__(self):
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if self.idx >= self.data.shape[0] - 1:
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raise StopIteration()
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seq_len = min(self.seq_len, self.data.shape[0] - 1 - self.idx)
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i = self.idx + seq_len
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data = self.data[self.idx: i]
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target = self.data[self.idx + 1: i + 1]
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self.idx = i
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return data, target
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def __getitem__(self, idx):
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seq_len = min(self.seq_len, self.data.shape[0] - 1 - idx)
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i = idx + seq_len
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data = self.data[idx: i]
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target = self.data[idx + 1: i + 1]
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return data, target
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class SequentialUnBatchedDataset(Dataset):
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def __init__(self, *, text: str, dataset: TextDataset,
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seq_len: int,
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is_random_offset: bool = True):
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self.is_random_offset = is_random_offset
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self.seq_len = seq_len
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self.data = dataset.text_to_i(text)
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def __len__(self):
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return (self.data.shape[0] - 1) // self.seq_len
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def __getitem__(self, idx):
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start = idx * self.seq_len
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assert start + self.seq_len + 1 <= self.data.shape[0]
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if self.is_random_offset:
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start += random.randint(0, min(self.seq_len - 1, self.data.shape[0] - (start + self.seq_len + 1)))
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end = start + self.seq_len
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data = self.data[start: end]
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target = self.data[start + 1: end + 1]
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return data, target
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class TextFileDataset(TextDataset):
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standard_tokens = []
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def __init__(self, path: PurePath, tokenizer: Callable, *,
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url: Optional[str] = None,
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filter_subset: Optional[int] = None):
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path = Path(path)
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if not path.exists():
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if not url:
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raise FileNotFoundError(str(path))
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else:
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download_file(url, path)
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with monit.section("Load data"):
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text = self.load(path)
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if filter_subset:
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text = text[:filter_subset]
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split = int(len(text) * .9)
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train = text[:split]
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valid = text[split:]
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super().__init__(path, tokenizer, train, valid, '')
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def _test_tiny_shakespeare():
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from labml import lab
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_ = TextFileDataset(lab.get_data_path() / 'tiny_shakespeare.txt', lambda x: list(x),
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url='https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt')
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if __name__ == '__main__':
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_test_tiny_shakespeare()
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