【pytorch系列 00】torch基础

torch.tensor(data, dtype=None, device=0, requires_grad=False)

torch.tensor()作为一个函数被调用，返回的是Tensor类型，传入的data可以是list、tuple、scalar、np.array等
究竟是LongTensor、FloatTensor、DoubleTensor根据传入的data来

>>> b = torch.tensor([1,2])
>>> b.dtype
torch.int64
>>> c = torch.tensor([1., 2.])
>>> c.dtype
torch.float32

Tensor：
这个主要是类，默认是单精度

transpose每次只能指定两个维度交换

torch.transpose(input, dim0, dim1) → Tensor

用transpose可能会带来的问题

>>> a = torch.arange(24).reshape(2,3,4)
>>> a.shape
torch.Size([2, 3, 4])
>>> b = torch.transpose(a,0,1)
>>> b.shape
torch.Size([3, 2, 4])
>>> c = b.view(2,3,4)
Traceback (most recent call last):
  File "", line 1, in 
RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.
>>> c 
tensor([1., 2.])
# 用reshape改变视图或者用.contiguous()，让存储数据连续
>>> c = b.reshape(4,6)
>>> b = b.contiguous()
>>> d = b.view(4,6)
>>> c
tensor([[ 0,  1,  2,  3, 12, 13],
        [14, 15,  4,  5,  6,  7],
        [16, 17, 18, 19,  8,  9],
        [10, 11, 20, 21, 22, 23]])
>>> b
tensor([[[ 0,  1,  2,  3],
         [12, 13, 14, 15]],

        [[ 4,  5,  6,  7],
         [16, 17, 18, 19]],

        [[ 8,  9, 10, 11],
         [20, 21, 22, 23]]])
>>> d
tensor([[ 0,  1,  2,  3, 12, 13],
        [14, 15,  4,  5,  6,  7],
        [16, 17, 18, 19,  8,  9],
        [10, 11, 20, 21, 22, 23]])
>>> 

permute可以一次奖Tensor变换到任意的dimension sequence的排列

tensor.permute(*dims) → Tensor

参考：https://blog.csdn.net/qq_50001789/article/details/120451717

ToTensor实际开发中经常用到toTensor操作，把读进来的图片直接编程tensor，非常好用

from torchvision.transforms import Compose, ToTensor
def transform():
    return Compose([
        ToTensor(),
    ])
  
  
class DatasetFromFoldereval(data.Dataset):
    def __init__(self, hr_dir, lr_dir, upscale_factor, transform=None):
        super(DatasetFromFoldereval, self).__init__()
        self.hr_dir = hr_dir
        self.lr_dir = lr_dir
        self.HR_img = sorted(os.listdir(self.hr_dir))
        self.LR_img = sorted(os.listdir(self.lr_dir))

        self.hr_filenames = [join(self.hr_dir, x) for x in self.HR_img if
                             is_image_file(x)]
        self.lr_filenames = [join(self.lr_dir, x) for x in self.LR_img if
                             is_image_file(x)]
        self.upscale_factor = upscale_factor
        self.transform = transform

    def __getitem__(self, index):
        input = load_img(self.lr_filenames[index])
        _, file = os.path.split(self.lr_filenames[index])
        lr_prefix, lr_postfix = file.split('.')
        hr_prefix = lr_prefix[0:-2] + 'HR'
        hrfile = hr_prefix + "." + lr_postfix
        bicubic = rescale_img(input, self.upscale_factor)
        hr_path = os.path.join(self.hr_dir, hrfile)
        # print('hr_path = ', hr_path)
        target = load_img(hr_path)
        # print('target.shape ', target.size)
        if self.transform:
            input = self.transform(input)
            bicubic = self.transform(bicubic)
            target = self.transform(target)
        # print('input shape ----- ', input.shape)
        return input, target, bicubic, file

    def __len__(self):
        return len(self.lr_filenames)

ToTensor实际调用的是functional中的to_tensor

def to_tensor(pic):
    """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.

    See ``ToTensor`` for more details.

    Args:
        pic (PIL Image or numpy.ndarray): Image to be converted to tensor.

    Returns:
        Tensor: Converted image.
    """
    if not(F_pil._is_pil_image(pic) or _is_numpy(pic)):
        raise TypeError('pic should be PIL Image or ndarray. Got {}'.format(type(pic)))

    if _is_numpy(pic) and not _is_numpy_image(pic):
        raise ValueError('pic should be 2/3 dimensional. Got {} dimensions.'.format(pic.ndim))

    if isinstance(pic, np.ndarray):
        # handle numpy array
        if pic.ndim == 2:
            pic = pic[:, :, None]

        img = torch.from_numpy(pic.transpose((2, 0, 1))).contiguous()
        # backward compatibility
        if isinstance(img, torch.ByteTensor):
            return img.float().div(255)
        else:
            return img

    if accimage is not None and isinstance(pic, accimage.Image):
        nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=np.float32)
        pic.copyto(nppic)
        return torch.from_numpy(nppic)

    # handle PIL Image
    if pic.mode == 'I':
        img = torch.from_numpy(np.array(pic, np.int32, copy=False))
    elif pic.mode == 'I;16':
        img = torch.from_numpy(np.array(pic, np.int16, copy=False))
    elif pic.mode == 'F':
        img = torch.from_numpy(np.array(pic, np.float32, copy=False))
    elif pic.mode == '1':
        img = 255 * torch.from_numpy(np.array(pic, np.uint8, copy=False))
    else:
        img = torch.ByteTensor(torch.ByteStorage.from_buffer(pic.tobytes()))

    img = img.view(pic.size[1], pic.size[0], len(pic.getbands()))
    # put it from HWC to CHW format
    img = img.permute((2, 0, 1)).contiguous()
    if isinstance(img, torch.ByteTensor):
        return img.float().div(255)
    else:
        return img

参考：https://www.cnblogs.com/ocean1100/p/9494640.html