Nach einer Weile werde ich vergessen, ob ToTensor die Eingabe transponiert hat, also notieren Sie hier die Rolle von ToTensor
Die Rolle von ToTensor
Diese Datenerweiterung kann das Eingabedatenformat (numpy.ndarray oder PIL Image) in das Tensorformat konvertieren, die Eingabeform ändert sich von [H, W, C] zu] C, H, W] und der Eingabewertbereich ändert sich von [0, 255] zu [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
"""Codebeispiel
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 imgfrom 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]]])
"""