类型错误：img 应该是 PIL 图像。得到 <class 'torch.Tensor'>答案

【问题标题】：TypeError: img should be PIL Image. Got <class 'torch.Tensor'>类型错误：img 应该是 PIL 图像。得到 <class 'torch.Tensor'>
【发布时间】：2021-05-24 07:13:50
【问题描述】：

import torch
import torch.nn as nn

import torchvision.transforms.functional as TF


class DoubleConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(DoubleConv, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, 3, 1, 1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, 3, 1, 1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
        )

    def forward(self, x):
        return self.conv(x)

class UNET(nn.Module):
    def __init__(
            self, in_channels=3, out_channels=1, features=[64, 128, 256, 512],
    ):
        super(UNET, self).__init__()
        self.ups = nn.ModuleList()
        self.downs = nn.ModuleList()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)

        # Down part of UNET
        for feature in features:
            self.downs.append(DoubleConv(in_channels, feature))
            in_channels = feature

        # Up part of UNET
        for feature in reversed(features):
            self.ups.append(
                nn.ConvTranspose2d(
                    feature*2, feature, kernel_size=2, stride=2,
                )
            )
            self.ups.append(DoubleConv(feature*2, feature))

        self.bottleneck = DoubleConv(features[-1], features[-1]*2)
        self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1)

    def forward(self, x):
        skip_connections = []

        for down in self.downs:
            x = down(x)
            skip_connections.append(x)
            x = self.pool(x)

        x = self.bottleneck(x)
        skip_connections = skip_connections[::-1]

        for idx in range(0, len(self.ups), 2):
            x = self.ups[idx](x)
            skip_connection = skip_connections[idx//2]

            if x.shape != skip_connection.shape:
                x = TF.resize(x, size=skip_connection.shape[2:])
            concat_skip = torch.cat((skip_connection, x), dim=1)
            x = self.ups[idx+1](concat_skip)

        return self.final_conv(x)
        

def test():
    x = torch.randn((3, 1, 161, 161))
    model = UNET(in_channels=1, out_channels=1)
    preds = model(x)
    #print(preds.shape)
    print(x.shape)
    assert preds.shape == x.shape

if __name__ == "__main__":
    test()

这是我的 UNet 代码。我得到的错误消息是：

raise TypeError('img should be PIL Image. Got {}'.format(type(img)))

TypeError: img should be PIL Image. Got <class 'torch.Tensor'>

提到这一行的错误x = TF.resize(x, size=skip_connection.shape[2:]) 我在这里做错了什么？

【问题讨论】：

我认为您可能使用的是旧版本的 torchvision 模块。根据文档，您所做的似乎应该可以工作，因此您使用的库可能不像当前文档所说的那样。
非常感谢，我更新了 pytorch 并解决了特定问题。

标签： python pytorch python-imaging-library

【解决方案1】：

如您收到的错误中所述，Tf.resize 需要 PIL.Image 类型的输入

Pil 是在这里找到的一个 python 库，用于处理和处理图像。

https://python-pillow.org/

pytorch 中函数的文档：

https://pytorch.org/vision/0.8/_modules/torchvision/transforms/functional.html#resize

【讨论】：