Source code for torchvision.datasets.moving_mnist

import os.path
from pathlib import Path
from typing import Callable, Optional, Union

import numpy as np
import torch
from torchvision.datasets.utils import download_url, verify_str_arg
from import VisionDataset

[docs]class MovingMNIST(VisionDataset): """`MovingMNIST <>`_ Dataset. Args: root (str or ``pathlib.Path``): Root directory of dataset where ``MovingMNIST/mnist_test_seq.npy`` exists. split (string, optional): The dataset split, supports ``None`` (default), ``"train"`` and ``"test"``. If ``split=None``, the full data is returned. split_ratio (int, optional): The split ratio of number of frames. If ``split="train"``, the first split frames ``data[:, :split_ratio]`` is returned. If ``split="test"``, the last split frames ``data[:, split_ratio:]`` is returned. If ``split=None``, this parameter is ignored and the all frames data is returned. transform (callable, optional): A function/transform that takes in a torch Tensor and returns a transformed version. E.g, ``transforms.RandomCrop`` download (bool, optional): If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again. """ _URL = "" def __init__( self, root: Union[str, Path], split: Optional[str] = None, split_ratio: int = 10, download: bool = False, transform: Optional[Callable] = None, ) -> None: super().__init__(root, transform=transform) self._base_folder = os.path.join(self.root, self.__class__.__name__) self._filename = self._URL.split("/")[-1] if split is not None: verify_str_arg(split, "split", ("train", "test")) self.split = split if not isinstance(split_ratio, int): raise TypeError(f"`split_ratio` should be an integer, but got {type(split_ratio)}") elif not (1 <= split_ratio <= 19): raise ValueError(f"`split_ratio` should be `1 <= split_ratio <= 19`, but got {split_ratio} instead.") self.split_ratio = split_ratio if download: if not self._check_exists(): raise RuntimeError("Dataset not found. You can use download=True to download it.") data = torch.from_numpy(np.load(os.path.join(self._base_folder, self._filename))) if self.split == "train": data = data[: self.split_ratio] elif self.split == "test": data = data[self.split_ratio :] = data.transpose(0, 1).unsqueeze(2).contiguous()
[docs] def __getitem__(self, idx: int) -> torch.Tensor: """ Args: index (int): Index Returns: torch.Tensor: Video frames (torch Tensor[T, C, H, W]). The `T` is the number of frames. """ data =[idx] if self.transform is not None: data = self.transform(data) return data
def __len__(self) -> int: return len( def _check_exists(self) -> bool: return os.path.exists(os.path.join(self._base_folder, self._filename)) def download(self) -> None: if self._check_exists(): return download_url( url=self._URL, root=self._base_folder, filename=self._filename, md5="be083ec986bfe91a449d63653c411eb2", )


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources