如何使用空间变换来裁剪pytorch中的图像？

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from torch.autograd import Variable

class SpatialTransformer(nn.Module):
    def __init__(self):
        super(SpatialTransformer, self).__init__()
        self.localization = nn.Sequential(
            nn.Conv2d(3, 8, kernel_size=7),
            nn.MaxPool2d(2, stride=2),
            nn.ReLU(True),
            nn.Conv2d(8, 10, kernel_size=5),
            nn.MaxPool2d(2, stride=2),
            nn.ReLU(True)
        )
        self.fc_loc = nn.Sequential(
            nn.Linear(10 * 3 * 3, 32),
            nn.ReLU(True),
            nn.Linear(32, 3 * 2)
        )
        self.fc_loc[2].weight.data.zero_()
        self.fc_loc[2].bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))

    def stn(self, x):
        xs = self.localization(x)
        xs = xs.view(-1, 10 * 3 * 3)
        theta = self.fc_loc(xs)
        theta = theta.view(-1, 2, 3)

        grid = F.affine_grid(theta, x.size())
        x = F.grid_sample(x, grid)

        return x

    def forward(self, x):
        x = self.stn(x)
        return x

# 假设有一张图像img，可以通过transforms对其进行预处理
preprocess = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
img = Image.open('image.jpg')
img = preprocess(img)
img = img.unsqueeze(0)  # 添加一个维度，变成4D张量

stn = SpatialTransformer()
output = stn(img)