J3-DenseNet实战

🍨 本文为🔗365天深度学习训练营中的学习记录博客
🍖 原作者：K同学啊 | 接辅导、项目定制

环境

系统: Linux
语言: Python3.8.10
深度学习框架: Pytorch2.0.0+cu118
显卡：GTX2080TI

步骤

环境设置

包引用

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import DataLoader, random_splitfrom torchvision import datasets, transforms
import numpy as np
import matplotlib.pyplot as plt
from torchinfo import summaryimport random, pathlib, collections, copy
from PIL import Image

全局设备对象

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

数据准备

从K同学提供的网盘中下载乳腺癌数据集，解压到data目录下，数据集的结构如下：

其中1是乳腺癌，0不是乳腺癌，这个目录结构可以使用torchvision.datasets.ImageFolder直接加载

图像信息查看

获取到所有的图像

root_dir = 'J3-data'
root_directory = pathlib.Path(root_dir)
image_list = root_directory.glob("*/*")

随机打印5个图像的尺寸

for _ in range(5):print(np.array(Image.open(str(random.choice(image_list)))).shape)

图像尺寸

发现输入并不是224大小的三通道图像，所以我们可以在数据集处理时需要Resize这一步
3. 随机打印20个图像

plt.figure(figsize=(20, 4))
for i in range(20):plt.subplot(2, 10, i+1)plt.axis('off')image = random.choice(image_list)class_name = image.parts[-2]plt.title('normal' if class_name == '0' else 'abnormal')plt.imshow(Image.open(str(image)))

图像展示

创建数据集
首先定义一个图像的预处理

transform = transforms.Compose([transforms.Resize([224, 224]),transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225],),
])

然后通过datasets.ImageFolder加载文件夹

dataset = datasets.ImageFolder(root_dir, transform=transform)

从数据中提取图像不同的分类名称，并转换成文字

class_names = ['正常细胞' if x =='0' else '乳腺癌细胞' for x in dataset.class_to_idx]

划分训练集和验证集

train_size = int(len(dataset) * 0.8)
test_size = len(dataset) - train_sizetrain_dataset, test_dataset = random_split(dataset, [train_size, test_size])

最后，将数据集划分批次

batch_size = 8
train_loader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size)
test_loader = DataLoader(test_dataset, batch_size=batch_size)

模型构建

模型的参数列表
模型参数

首先编写DenseLayer，它生成自己本层的特征值并与上层的拼接到一起输出

class DenseLayer(nn.Sequential):def __init__(self, input_size, growth_rate, bn_size, drop_rate):super().__init__()self.add_module('norm1', nn.BatchNorm2d(input_size)),self.add_module('relu1', nn.ReLU(inplace=True)),self.add_module('conv1', nn.Conv2d(input_size, bn_size*growth_rate, kernel_size=1, stride=1, bias=False))self.add_module('norm2', nn.BatchNorm2d(bn_size*growth_rate))self.add_module('relu2', nn.ReLU(inplace=True)),self.add_module('conv2', nn.Conv2d(bn_size*growth_rate, growth_rate, kernel_size=3, stride=1, padding=1, bias=False))self.drop_rate = drop_ratedef forward(self, x):features = super().forward(x)if self.drop_rate > 0:features = F.dropout(features, p = self.drop_rate, training=self.training)return torch.concat([x, features], 1)

然后编写DenseBlock，它根据参数对DenseLayer进行堆叠

class DenseBlock(nn.Sequential):def __init__(self, num_layers, input_size, growth_rate, bn_size, drop_rate):super().__init__()for i in range(num_layers):layer = DenseLayer(input_size + i * growth_rate, growth_rate, bn_size, drop_rate)self.add_module('denselayer%d' % (i + 1,), layer)

然后编写Transition，用来连接不同的DenseBlock，缩小特征图的维度

class Transition(nn.Sequential):def __init__(self, input_size, output_size):super().__init__()self.add_module('norm', nn.BatchNorm2d(input_size))self.add_module('relu', nn.ReLU(inplace=True))self.add_module('conv', nn.Conv2d(input_size, output_size, kernel_size=1, stride=1, bias=False))self.add_module('pool', nn.AvgPool2d(2, stride=2))

最后编写DenseNet模块

class DenseNet(nn.Module):def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), init_features=64, bn_size=4, compress_rate=0.5, drop_rate = 0, num_class=1000):super().__init__()self.features = nn.Sequential(collections.OrderedDict([('conv0', nn.Conv2d(3, init_features, kernel_size=7, stride=2, padding=3, bias=False)),('norm0', nn.BatchNorm2d(init_features)),('relu0', nn.ReLU(inplace=True)),('pool0', nn.MaxPool2d(3, stride=2, padding=1))]))num_features = init_featuresfor i, layer_conf in enumerate(block_config):block = DenseBlock(layer_conf, num_features, growth_rate, bn_size, drop_rate)self.features.add_module('denseblock%d' % (i + 1,), block)num_features += layer_conf*growth_rateif i != len(block_config) - 1:transition = Transition(num_features, int(num_features*compress_rate))self.features.add_module('transition%d' % (i + 1,), transition)num_features = int(num_features*compress_rate)self.features.add_module('norm5', nn.BatchNorm2d(num_features))self.features.add_module('relu5', nn.ReLU(inplace=True))self.classifier = nn.Linear(num_features, num_class)# 参数初始化for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight)elif isinstance(m, nn.BatchNorm2d):nn.init.constant_(m.bias, 0)nn.init.constant_(m.weight, 1)elif isinstance(m, nn.Linear):nn.init.constant_(m.bias, 0)def forward(self, x):features = self.features(x)out = F.avg_pool2d(features, 7, stride=1).view(features.size(0), -1)out = self.classifier(out)return out

由于默认的参数直接对应的就是DenseNet121的，我们直接创建模型对象

model = DenseNet(num_class=len(class_names))
model

打印一下模型的结构如下

DenseNet((features): Sequential((conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)(norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu0): ReLU(inplace=True)(pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)(denseblock1): DenseBlock((denselayer1): DenseLayer((norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer2): DenseLayer((norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer3): DenseLayer((norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer4): DenseLayer((norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer5): DenseLayer((norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer6): DenseLayer((norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)))(transition1): Transition((norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(pool): AvgPool2d(kernel_size=2, stride=2, padding=0))(denseblock2): DenseBlock((denselayer1): DenseLayer((norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer2): DenseLayer((norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer3): DenseLayer((norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer4): DenseLayer((norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer5): DenseLayer((norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer6): DenseLayer((norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer7): DenseLayer((norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer8): DenseLayer((norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer9): DenseLayer((norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer10): DenseLayer((norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer11): DenseLayer((norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer12): DenseLayer((norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)))(transition2): Transition((norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(pool): AvgPool2d(kernel_size=2, stride=2, padding=0))(denseblock3): DenseBlock((denselayer1): DenseLayer((norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer2): DenseLayer((norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer3): DenseLayer((norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer4): DenseLayer((norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer5): DenseLayer((norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer6): DenseLayer((norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer7): DenseLayer((norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer8): DenseLayer((norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer9): DenseLayer((norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer10): DenseLayer((norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer11): DenseLayer((norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer12): DenseLayer((norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer13): DenseLayer((norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer14): DenseLayer((norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer15): DenseLayer((norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer16): DenseLayer((norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer17): DenseLayer((norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer18): DenseLayer((norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer19): DenseLayer((norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer20): DenseLayer((norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer21): DenseLayer((norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer22): DenseLayer((norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer23): DenseLayer((norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer24): DenseLayer((norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)))(transition3): Transition((norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)(pool): AvgPool2d(kernel_size=2, stride=2, padding=0))(denseblock4): DenseBlock((denselayer1): DenseLayer((norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer2): DenseLayer((norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer3): DenseLayer((norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer4): DenseLayer((norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer5): DenseLayer((norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer6): DenseLayer((norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer7): DenseLayer((norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer8): DenseLayer((norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer9): DenseLayer((norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer10): DenseLayer((norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer11): DenseLayer((norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer12): DenseLayer((norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer13): DenseLayer((norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer14): DenseLayer((norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer15): DenseLayer((norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer16): DenseLayer((norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)))(norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu5): ReLU(inplace=True))(classifier): Linear(in_features=1024, out_features=2, bias=True)
)

通过torchinfo的summary预估参数量

summary(model, input_size=(32, 3, 224, 224))

打印结果如下：

==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
DenseNet                                 [32, 2]                   --
├─Sequential: 1-1                        [32, 1024, 7, 7]          --
│    └─Conv2d: 2-1                       [32, 64, 112, 112]        9,408
│    └─BatchNorm2d: 2-2                  [32, 64, 112, 112]        128
│    └─ReLU: 2-3                         [32, 64, 112, 112]        --
│    └─MaxPool2d: 2-4                    [32, 64, 56, 56]          --
│    └─DenseBlock: 2-5                   [32, 256, 56, 56]         --
│    │    └─DenseLayer: 3-1              [32, 96, 56, 56]          45,440
│    │    └─DenseLayer: 3-2              [32, 128, 56, 56]         49,600
│    │    └─DenseLayer: 3-3              [32, 160, 56, 56]         53,760
│    │    └─DenseLayer: 3-4              [32, 192, 56, 56]         57,920
│    │    └─DenseLayer: 3-5              [32, 224, 56, 56]         62,080
│    │    └─DenseLayer: 3-6              [32, 256, 56, 56]         66,240
│    └─Transition: 2-6                   [32, 128, 28, 28]         --
│    │    └─BatchNorm2d: 3-7             [32, 256, 56, 56]         512
│    │    └─ReLU: 3-8                    [32, 256, 56, 56]         --
│    │    └─Conv2d: 3-9                  [32, 128, 56, 56]         32,768
│    │    └─AvgPool2d: 3-10              [32, 128, 28, 28]         --
│    └─DenseBlock: 2-7                   [32, 512, 28, 28]         --
│    │    └─DenseLayer: 3-11             [32, 160, 28, 28]         53,760
│    │    └─DenseLayer: 3-12             [32, 192, 28, 28]         57,920
│    │    └─DenseLayer: 3-13             [32, 224, 28, 28]         62,080
│    │    └─DenseLayer: 3-14             [32, 256, 28, 28]         66,240
│    │    └─DenseLayer: 3-15             [32, 288, 28, 28]         70,400
│    │    └─DenseLayer: 3-16             [32, 320, 28, 28]         74,560
│    │    └─DenseLayer: 3-17             [32, 352, 28, 28]         78,720
│    │    └─DenseLayer: 3-18             [32, 384, 28, 28]         82,880
│    │    └─DenseLayer: 3-19             [32, 416, 28, 28]         87,040
│    │    └─DenseLayer: 3-20             [32, 448, 28, 28]         91,200
│    │    └─DenseLayer: 3-21             [32, 480, 28, 28]         95,360
│    │    └─DenseLayer: 3-22             [32, 512, 28, 28]         99,520
│    └─Transition: 2-8                   [32, 256, 14, 14]         --
│    │    └─BatchNorm2d: 3-23            [32, 512, 28, 28]         1,024
│    │    └─ReLU: 3-24                   [32, 512, 28, 28]         --
│    │    └─Conv2d: 3-25                 [32, 256, 28, 28]         131,072
│    │    └─AvgPool2d: 3-26              [32, 256, 14, 14]         --
│    └─DenseBlock: 2-9                   [32, 1024, 14, 14]        --
│    │    └─DenseLayer: 3-27             [32, 288, 14, 14]         70,400
│    │    └─DenseLayer: 3-28             [32, 320, 14, 14]         74,560
│    │    └─DenseLayer: 3-29             [32, 352, 14, 14]         78,720
│    │    └─DenseLayer: 3-30             [32, 384, 14, 14]         82,880
│    │    └─DenseLayer: 3-31             [32, 416, 14, 14]         87,040
│    │    └─DenseLayer: 3-32             [32, 448, 14, 14]         91,200
│    │    └─DenseLayer: 3-33             [32, 480, 14, 14]         95,360
│    │    └─DenseLayer: 3-34             [32, 512, 14, 14]         99,520
│    │    └─DenseLayer: 3-35             [32, 544, 14, 14]         103,680
│    │    └─DenseLayer: 3-36             [32, 576, 14, 14]         107,840
│    │    └─DenseLayer: 3-37             [32, 608, 14, 14]         112,000
│    │    └─DenseLayer: 3-38             [32, 640, 14, 14]         116,160
│    │    └─DenseLayer: 3-39             [32, 672, 14, 14]         120,320
│    │    └─DenseLayer: 3-40             [32, 704, 14, 14]         124,480
│    │    └─DenseLayer: 3-41             [32, 736, 14, 14]         128,640
│    │    └─DenseLayer: 3-42             [32, 768, 14, 14]         132,800
│    │    └─DenseLayer: 3-43             [32, 800, 14, 14]         136,960
│    │    └─DenseLayer: 3-44             [32, 832, 14, 14]         141,120
│    │    └─DenseLayer: 3-45             [32, 864, 14, 14]         145,280
│    │    └─DenseLayer: 3-46             [32, 896, 14, 14]         149,440
│    │    └─DenseLayer: 3-47             [32, 928, 14, 14]         153,600
│    │    └─DenseLayer: 3-48             [32, 960, 14, 14]         157,760
│    │    └─DenseLayer: 3-49             [32, 992, 14, 14]         161,920
│    │    └─DenseLayer: 3-50             [32, 1024, 14, 14]        166,080
│    └─Transition: 2-10                  [32, 512, 7, 7]           --
│    │    └─BatchNorm2d: 3-51            [32, 1024, 14, 14]        2,048
│    │    └─ReLU: 3-52                   [32, 1024, 14, 14]        --
│    │    └─Conv2d: 3-53                 [32, 512, 14, 14]         524,288
│    │    └─AvgPool2d: 3-54              [32, 512, 7, 7]           --
│    └─DenseBlock: 2-11                  [32, 1024, 7, 7]          --
│    │    └─DenseLayer: 3-55             [32, 544, 7, 7]           103,680
│    │    └─DenseLayer: 3-56             [32, 576, 7, 7]           107,840
│    │    └─DenseLayer: 3-57             [32, 608, 7, 7]           112,000
│    │    └─DenseLayer: 3-58             [32, 640, 7, 7]           116,160
│    │    └─DenseLayer: 3-59             [32, 672, 7, 7]           120,320
│    │    └─DenseLayer: 3-60             [32, 704, 7, 7]           124,480
│    │    └─DenseLayer: 3-61             [32, 736, 7, 7]           128,640
│    │    └─DenseLayer: 3-62             [32, 768, 7, 7]           132,800
│    │    └─DenseLayer: 3-63             [32, 800, 7, 7]           136,960
│    │    └─DenseLayer: 3-64             [32, 832, 7, 7]           141,120
│    │    └─DenseLayer: 3-65             [32, 864, 7, 7]           145,280
│    │    └─DenseLayer: 3-66             [32, 896, 7, 7]           149,440
│    │    └─DenseLayer: 3-67             [32, 928, 7, 7]           153,600
│    │    └─DenseLayer: 3-68             [32, 960, 7, 7]           157,760
│    │    └─DenseLayer: 3-69             [32, 992, 7, 7]           161,920
│    │    └─DenseLayer: 3-70             [32, 1024, 7, 7]          166,080
│    └─BatchNorm2d: 2-12                 [32, 1024, 7, 7]          2,048
│    └─ReLU: 2-13                        [32, 1024, 7, 7]          --
├─Linear: 1-2                            [32, 2]                   2,050
==========================================================================================
Total params: 6,955,906
Trainable params: 6,955,906
Non-trainable params: 0
Total mult-adds (G): 90.66
==========================================================================================
Input size (MB): 19.27
Forward/backward pass size (MB): 5777.06
Params size (MB): 27.82
Estimated Total Size (MB): 5824.16
==========================================================================================

模型训练

编写训练函数

def train(train_loader, model, loss_fn, optimizer):size = len(train_loader.dataset)num_batches = len(train_loader)train_loss, train_acc = 0, 0for x, y in train_loader:x, y = x.to(device), y.to(device)pred = model(x)loss = loss_fn(pred, y)optimizer.zero_grad()loss.backward()optimizer.step()train_loss += loss.item()train_acc += (pred.argmax(1) == y).type(torch.float).sum().item()train_loss /= num_batchestrain_acc /= sizereturn train_loss, train_acc

编写测试函数

def test(test_loader, model, loss_fn):size = len(test_loader.dataset)num_batches = len(test_loader)test_loss, test_acc = 0, 0for x, y in test_loader:x, y = x.to(device), y.to(device)pred = model(x)loss = loss_fn(pred, y)test_loss += loss.item()test_acc += (pred.argmax(1) == y).type(torch.float).sum().item()test_loss /= num_batchestest_acc /= sizereturn test_loss, test_acc

正式训练

optimizer = optim.Adam(model.parameters(), lr=1e-4)
loss_fn = nn.CrossEntropyLoss()epochs = 20train_loss, train_acc = [], []
test_loss, test_acc = [], []best_acc = 0
for epoch in range(epochs):model.train()epoch_train_loss, epoch_train_acc = train(train_loader, model, loss_fn, optimizer)model.eval()with torch.no_grad():epoch_test_loss, epoch_test_acc = test(test_loader, model, loss_fn)if epoch_test_acc > best_acc:best_acc = epoch_test_accbest_model = copy.deepcopy(model)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)lr = optimizer.state_dict['param_groups'][0]['lr']print(f"Epoch:{epoch+1:2d}, Train_acc:{epoch_train_acc*100:.1f}, Train_loss: {epoch_train_loss:.3f}, Test_acc: {epoch_test_acc*100:.1f}, Test_loss: {epoch_test_loss:.3f}, Lr: {lr:.2E}")PATH = './best_model.pth'
torch.save(best_model.state_dict(), PATH)print('Done')

过程日志如下：

Epoch: 1, Train_acc:87.4, Train_loss: 0.304, Test_acc: 87.7, Test_loss: 0.302, Lr: 1.00E-04
Epoch: 2, Train_acc:88.9, Train_loss: 0.273, Test_acc: 86.0, Test_loss: 0.314, Lr: 1.00E-04
Epoch: 3, Train_acc:89.9, Train_loss: 0.244, Test_acc: 89.0, Test_loss: 0.268, Lr: 1.00E-04
Epoch: 4, Train_acc:90.5, Train_loss: 0.230, Test_acc: 88.7, Test_loss: 0.255, Lr: 1.00E-04
Epoch: 5, Train_acc:91.3, Train_loss: 0.213, Test_acc: 88.5, Test_loss: 0.293, Lr: 1.00E-04
Epoch: 6, Train_acc:91.6, Train_loss: 0.205, Test_acc: 89.9, Test_loss: 0.265, Lr: 1.00E-04
Epoch: 7, Train_acc:92.5, Train_loss: 0.191, Test_acc: 89.3, Test_loss: 0.267, Lr: 1.00E-04
Epoch: 8, Train_acc:92.9, Train_loss: 0.176, Test_acc: 88.7, Test_loss: 0.277, Lr: 1.00E-04
Epoch: 9, Train_acc:93.3, Train_loss: 0.166, Test_acc: 89.9, Test_loss: 0.236, Lr: 1.00E-04
Epoch:10, Train_acc:93.8, Train_loss: 0.157, Test_acc: 90.5, Test_loss: 0.248, Lr: 1.00E-04
Epoch:11, Train_acc:94.5, Train_loss: 0.141, Test_acc: 91.6, Test_loss: 0.219, Lr: 1.00E-04
Epoch:12, Train_acc:95.2, Train_loss: 0.122, Test_acc: 90.2, Test_loss: 0.274, Lr: 1.00E-04
Epoch:13, Train_acc:95.3, Train_loss: 0.128, Test_acc: 90.0, Test_loss: 0.302, Lr: 1.00E-04
Epoch:14, Train_acc:95.5, Train_loss: 0.115, Test_acc: 92.3, Test_loss: 0.229, Lr: 1.00E-04
Epoch:15, Train_acc:96.5, Train_loss: 0.090, Test_acc: 90.2, Test_loss: 0.311, Lr: 1.00E-04
Epoch:16, Train_acc:97.0, Train_loss: 0.087, Test_acc: 89.6, Test_loss: 0.297, Lr: 1.00E-04
Epoch:17, Train_acc:96.4, Train_loss: 0.096, Test_acc: 93.0, Test_loss: 0.216, Lr: 1.00E-04
Epoch:18, Train_acc:97.7, Train_loss: 0.067, Test_acc: 90.5, Test_loss: 0.324, Lr: 1.00E-04
Epoch:19, Train_acc:97.0, Train_loss: 0.081, Test_acc: 91.6, Test_loss: 0.272, Lr: 1.00E-04
Epoch:20, Train_acc:98.2, Train_loss: 0.060, Test_acc: 89.9, Test_loss: 0.352, Lr: 1.00E-04
Done

模型效果展示

展示Loss和Accuracy图

epochs_range = range(epochs)
plt.figure(figsize=(12,3))plt.subplot(1,2,1)
plt.plot(epochs_range, train_acc, label='Training Accuracy')
plt.plot(epochs_range, test_acc, label='Test Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Test Accuracy')plt.subplot(1,2,2)
plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Traning and Test Loss')plt.show()

训练过程

模型评估

best_model.load_state_dict(torch.load(PATH, map_location=device))
epoch_test_acc, epoch_test_loss =test(test_loader, best_model, loss_fn)
print(epoch_test_acc, epoch_test_loss)

评估结果

总结与心得体会

DenseNet在跨层连接也是使用了RestNet2的BN-RELU-CONV的顺序，但是在ResNet的基础上把前面模型的输出堆叠起来，使得层间的连接更加密集。这种连接让我感觉和U-Net等连接有些同样的妙处，可以减少模型特征图中的特征丢失。但是如此密集的连接会增大参数量，训练的速度显著的变慢了（或许下次应该弄个新的机器），因此还有很大的改进空间。