from torch.utils.data import Dataset
import h5py
from torchvision import transforms
import glob
from skimage import io
import os
import numpy as np
import torch
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True


class Physics(Dataset):

    def __init__(self, root, mode):
        assert mode in ['train', 'val', 'test']
        self.root = root
        self.file = os.path.join(self.root, f'{mode}.hdf5')
        assert os.path.exists(self.file), 'Path {} does not exist'.format(self
            .file)

    def __getitem__(self, index):
        with h5py.File(self.file, 'r') as f:
            imgs = f['X']
            positions = f['y']
            img = imgs[index]
            position = positions[index]
        img = torch.from_numpy(img).permute(0, 3, 1, 2)
        img = img.float()
        position = torch.Tensor(position)
        return img, position

    def __len__(self):
        with h5py.File(self.file, 'r') as f:
            imgs = f['X']
            return len(imgs)

a9078eb94a7d2e4fde81d02a573596c8b286c49f
<mask token>


class Physics(Dataset):
    <mask token>

    def __getitem__(self, index):
        with h5py.File(self.file, 'r') as f:
            imgs = f['X']
            positions = f['y']
            img = imgs[index]
            position = positions[index]
        img = torch.from_numpy(img).permute(0, 3, 1, 2)
        img = img.float()
        position = torch.Tensor(position)
        return img, position

    def __len__(self):
        with h5py.File(self.file, 'r') as f:
            imgs = f['X']
            return len(imgs)

<mask token>


class Physics(Dataset):

    def __init__(self, root, mode):
        assert mode in ['train', 'val', 'test']
        self.root = root
        self.file = os.path.join(self.root, f'{mode}.hdf5')
        assert os.path.exists(self.file), 'Path {} does not exist'.format(self
            .file)

    def __getitem__(self, index):
        with h5py.File(self.file, 'r') as f:
            imgs = f['X']
            positions = f['y']
            img = imgs[index]
            position = positions[index]
        img = torch.from_numpy(img).permute(0, 3, 1, 2)
        img = img.float()
        position = torch.Tensor(position)
        return img, position

    def __len__(self):
        with h5py.File(self.file, 'r') as f:
            imgs = f['X']
            return len(imgs)

<mask token>
ImageFile.LOAD_TRUNCATED_IMAGES = True


class Physics(Dataset):

    def __init__(self, root, mode):
        assert mode in ['train', 'val', 'test']
        self.root = root
        self.file = os.path.join(self.root, f'{mode}.hdf5')
        assert os.path.exists(self.file), 'Path {} does not exist'.format(self
            .file)

    def __getitem__(self, index):
        with h5py.File(self.file, 'r') as f:
            imgs = f['X']
            positions = f['y']
            img = imgs[index]
            position = positions[index]
        img = torch.from_numpy(img).permute(0, 3, 1, 2)
        img = img.float()
        position = torch.Tensor(position)
        return img, position

    def __len__(self):
        with h5py.File(self.file, 'r') as f:
            imgs = f['X']
            return len(imgs)

from torch.utils.data import Dataset
import h5py
from torchvision import transforms
import glob
from skimage import io
import os
import numpy as np
import torch
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True


class Physics(Dataset):

    def __init__(self, root, mode):
        assert mode in ['train', 'val', 'test']
        self.root = root
        self.file = os.path.join(self.root, f'{mode}.hdf5')
        assert os.path.exists(self.file), 'Path {} does not exist'.format(self
            .file)

    def __getitem__(self, index):
        with h5py.File(self.file, 'r') as f:
            imgs = f['X']
            positions = f['y']
            img = imgs[index]
            position = positions[index]
        img = torch.from_numpy(img).permute(0, 3, 1, 2)
        img = img.float()
        position = torch.Tensor(position)
        return img, position

    def __len__(self):
        with h5py.File(self.file, 'r') as f:
            imgs = f['X']
            return len(imgs)
