[人工智能]pytorch得到中间层输出

讲解视频

https://www.bilibili.com/video/BV1z7411f7za?spm_id_from=333.999.0.0

关键代码

import torch.nn as nn
import torch


class AlexNet(nn.Module):
    def __init__(self, num_classes=1000, init_weights=False):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 48, kernel_size=11, stride=4, padding=2),  # input[3, 224, 224]  output[48, 55, 55]
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),                  # output[48, 27, 27]
            nn.Conv2d(48, 128, kernel_size=5, padding=2),           # output[128, 27, 27]
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),                  # output[128, 13, 13]
            nn.Conv2d(128, 192, kernel_size=3, padding=1),          # output[192, 13, 13]
            nn.ReLU(inplace=True),
            nn.Conv2d(192, 192, kernel_size=3, padding=1),          # output[192, 13, 13]
            nn.ReLU(inplace=True),
            nn.Conv2d(192, 128, kernel_size=3, padding=1),          # output[128, 13, 13]
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),                  # output[128, 6, 6]
        )
        self.classifier = nn.Sequential(
            nn.Dropout(p=0.5),
            nn.Linear(128 * 6 * 6, 2048),
            nn.ReLU(inplace=True),
            nn.Dropout(p=0.5),
            nn.Linear(2048, 2048),
            nn.ReLU(inplace=True),
            nn.Linear(2048, num_classes),
        )
        if init_weights:
            self._initialize_weights()

    def forward(self, x):
        outputs = []
        for name, module in self.features.named_children():
            x = module(x)
            if name in ["0", "3", "6"]:
                outputs.append(x)

        return outputs

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.constant_(m.bias, 0)

# -*- encoding: utf-8 -*-
"""
@File    : 123.py
@Time    : 2021-12-08 21:18
@Author  : XD
@Email   : gudianpai@qq.com
@Software: PyCharm
"""
import torch
import torch.nn as nn


class TestModule(nn.Module):
    def __init__(self):
        super(TestModule, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(16, 32, 3, 1),
            nn.ReLU(inplace=True)
        )
        self.layer2 = nn.Sequential(
            nn.Linear(32, 10)
        )

    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)


model = TestModule()

# for name, module in model.named_children():
#     print('children module:', name)
#
# for name, module in model.named_modules():
#     print('modules:', name)
for name, module in model.layer1.named_children():
    print('children module:', name)
    print('module:', module)

children module: 0
module: Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1))
children module: 1
module: ReLU(inplace=True)