pytorch中ToTensor的作用

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之前过段时间就会忘记ToTensor是否对输入进行了transpose,因此在这里记录一下ToTensor的作用

ToTensor的作用

该数据增强能把输入的数据格式(numpy.ndarray或者PIL Image)转成tensor格式,输入的shape会从[H, W, C]变成]C, H, W],输入的数值范围会从[0, 255]变成[0, 1],

"""Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor. This transform does not support torchscript.

Converts a PIL Image or numpy.ndarray (H x W x C) in the range
[0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]
if the PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1)
or if the numpy.ndarray has dtype = np.uint8

In the other cases, tensors are returned without scaling.

.. note::
Because the input image is scaled to [0.0, 1.0], this transformation should not be used when transforming target image masks. See the `references`_ for implementing the transforms for image masks.

.. _references: https://github.com/pytorch/vision/tree/main/references/segmentation
"""

代码示例文章来源地址https://www.toymoban.com/news/detail-597363.html

def to_tensor(pic) -> Tensor:
    """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
    This function does not support torchscript.

    See :class:`~torchvision.transforms.ToTensor` for more details.

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

    Returns:
        Tensor: Converted image.
    """
    if not torch.jit.is_scripting() and not torch.jit.is_tracing():
        _log_api_usage_once(to_tensor)
    if not (F_pil._is_pil_image(pic) or _is_numpy(pic)):
        raise TypeError(f"pic should be PIL Image or ndarray. Got {type(pic)}")
    
    # 判断pic类是否符合要求
    if _is_numpy(pic) and not _is_numpy_image(pic):
        raise ValueError(f"pic should be 2/3 dimensional. Got {pic.ndim} dimensions.")

    default_float_dtype = torch.get_default_dtype()
    
    # 如果pic为np.ndarray
    if isinstance(pic, np.ndarray):
        # 如果pic的ndim为2, 则扩充一个维度
        if pic.ndim == 2:
            pic = pic[:, :, None]
        
        # [H, W, C] --> [C, H, W]
        img = torch.from_numpy(pic.transpose((2, 0, 1))).contiguous()

        # backward compatibility, 将pic的数值除以255
        if isinstance(img, torch.ByteTensor):
            return img.to(dtype=default_float_dtype).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).to(dtype=default_float_dtype)

    # handle PIL Image
    mode_to_nptype = {"I": np.int32, "I;16": np.int16, "F": np.float32}
    img = torch.from_numpy(np.array(pic, mode_to_nptype.get(pic.mode, np.uint8), copy=True))

    if pic.mode == "1":
        img = 255 * img
    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.to(dtype=default_float_dtype).div(255)
    else:
        return img
from torchvision.transforms import ToTensor
import numpy as np


x = np.arange(0, 81, dtype="uint8").reshape(9, 9)
print(x.shape)
print(x)

x_tensor = ToTensor()(x)
print(x_tensor.shape)
print(x_tensor)


"""
(9, 9)
[[ 0  1  2  3  4  5  6  7  8]
 [ 9 10 11 12 13 14 15 16 17]
 [18 19 20 21 22 23 24 25 26]
 [27 28 29 30 31 32 33 34 35]
 [36 37 38 39 40 41 42 43 44]
 [45 46 47 48 49 50 51 52 53]
 [54 55 56 57 58 59 60 61 62]
 [63 64 65 66 67 68 69 70 71]
 [72 73 74 75 76 77 78 79 80]]

torch.Size([1, 9, 9])
tensor([[[0.0000, 0.0039, 0.0078, 0.0118, 0.0157, 0.0196, 0.0235, 0.0275,
          0.0314],
         [0.0353, 0.0392, 0.0431, 0.0471, 0.0510, 0.0549, 0.0588, 0.0627,
          0.0667],
         [0.0706, 0.0745, 0.0784, 0.0824, 0.0863, 0.0902, 0.0941, 0.0980,
          0.1020],
         [0.1059, 0.1098, 0.1137, 0.1176, 0.1216, 0.1255, 0.1294, 0.1333,
          0.1373],
         [0.1412, 0.1451, 0.1490, 0.1529, 0.1569, 0.1608, 0.1647, 0.1686,
          0.1725],
         [0.1765, 0.1804, 0.1843, 0.1882, 0.1922, 0.1961, 0.2000, 0.2039,
          0.2078],
         [0.2118, 0.2157, 0.2196, 0.2235, 0.2275, 0.2314, 0.2353, 0.2392,
          0.2431],
         [0.2471, 0.2510, 0.2549, 0.2588, 0.2627, 0.2667, 0.2706, 0.2745,
          0.2784],
         [0.2824, 0.2863, 0.2902, 0.2941, 0.2980, 0.3020, 0.3059, 0.3098,
          0.3137]]])
"""

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