torchvision/transforms/functional.py
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(_is_pil_image(pic) or _is_numpy_image(pic)):
        raise TypeError('pic should be PIL Image or ndarray. Got {}'.format(type(pic)))

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

        img = torch.from_numpy(pic.transpose((2, 0, 1)))
        # 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()))
    # PIL image mode: L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK
    if pic.mode == 'YCbCr':
        nchannel = 3
    elif pic.mode == 'I;16':
        nchannel = 1
    else:
        nchannel = len(pic.mode)
    img = img.view(pic.size[1], pic.size[0], nchannel)
    # put it from HWC to CHW format
    # yikes, this transpose takes 80% of the loading time/CPU
    img = img.transpose(0, 1).transpose(0, 2).contiguous()
    if isinstance(img, torch.ByteTensor):
        return img.float().div(255)
    else:
        return img
def to_tensor(pic):
    if not(_is_pil_image(pic) or _is_numpy_image(pic)):
        raise TypeError('pic should be PIL Image or ndarray. Got {}'.format(type(pic)))

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

        img = torch.from_numpy(pic.transpose((2, 0, 1)))
        # 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()))
    # PIL image mode: L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK
    if pic.mode == 'YCbCr':
        nchannel = 3
    elif pic.mode == 'I;16':
        nchannel = 1
    else:
        nchannel = len(pic.mode)
    img = img.view(pic.size[1], pic.size[0], nchannel)
    # put it from HWC to CHW format
    # yikes, this transpose takes 80% of the loading time/CPU
    img = img.transpose(0, 1).transpose(0, 2).contiguous()
    if isinstance(img, torch.ByteTensor):
        return img.float().div(255)
    else:
        return img
def to_tensor ( pic ) : if not ( _is_pil_image ( pic ) or _is_numpy_image ( pic ) ) : raise TypeError ( 'pic should be PIL Image or ndarray. Got {}' . format ( type ( pic ) ) ) if isinstance ( pic , np . ndarray ) : # handle numpy array if pic . ndim == 2 : pic = pic [ : , : , None ] img = torch . from_numpy ( pic . transpose ( ( 2 , 0 , 1 ) ) ) # 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 ( ) ) ) # PIL image mode: L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK if pic . mode == 'YCbCr' : nchannel = 3 elif pic . mode == 'I;16' : nchannel = 1 else : nchannel = len ( pic . mode ) img = img . view ( pic . size [ 1 ] , pic . size [ 0 ] , nchannel ) # put it from HWC to CHW format # yikes, this transpose takes 80% of the loading time/CPU img = img . transpose ( 0 , 1 ) . transpose ( 0 , 2 ) . contiguous ( ) if isinstance ( img , torch . ByteTensor ) : return img . float ( ) . div ( 255 ) else : return img
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.
Convert a PIL Image or numpy . ndarray to tensor .