python中一个文件（A.py）怎么调用另一个文件（B.py）中定义的类AA详解和示例

本文主要讲解python文件中怎么调用另外一个py文件中定义的类，将通过代码和示例解读，帮助大家理解和使用。

代码

B.py

如在文件B.py,定义了类别Bottleneck，其包含卷积层、正则化和激活函数层，主要对输入图像进行处理。但没有读取图像等代码。


from torch import nn

def autopad(k, p=None, d=1):  # kernel, padding, dilation
    # Pad to 'same' shape outputs
    if d > 1:
        k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k]  # actual kernel-size
    if p is None:
        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
    return p

class Conv(nn.Module):
    # Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)
    default_act = nn.SiLU()  # default activation

    def __init__(self, c1, c2, k=3, s=2, p=None, g=1, d=1, act=True):
        super().__init__()
        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)
        self.bn = nn.BatchNorm2d(c2)
        self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()

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

    def forward_fuse(self, x):
        return self.act(self.conv(x))

class Bottleneck(nn.Module):
    # Standard bottleneck
    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c_, c2, 3, 2, g=g)
        self.add = shortcut and c1 == c2

    def forward(self, x):
        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))

A.py

文件A.py，在此部分，加载一幅图像并对其进行卷积处理。在代码中，没有卷积等层的定义，通过调用B.py中定义的类进行。

import B
from PIL import Image
from torchvision import transforms
import math
if __name__ == '__main__':
    image = Image.open("../11111.jpg")
    transform = transforms.Compose([
        transforms.ToTensor()
    ])

    # 对图像应用转换操作
    input_image = transform(image)
    input_image = input_image.unsqueeze(0)
    CBR=B.Bottleneck(3,64)
    x=CBR(input_image )
    print(x.shape)