298 lines
10 KiB
Python
Executable File
298 lines
10 KiB
Python
Executable File
import torch
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import torchvision
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import numpy as np
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from tqdm import tqdm
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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import torchvision.transforms as transforms
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import matplotlib.pyplot as plt
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device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
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################### 数据集初始化与读入 ###################
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train_transform = transforms.Compose([
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transforms.RandomHorizontalFlip(),
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transforms.RandomCrop(32, padding=4),
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transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2),
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transforms.ToTensor()
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])
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train_dset = torchvision.datasets.CIFAR10(root='./CIFAR10',train=True,download=False,transform=train_transform)
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test_dset = torchvision.datasets.CIFAR10(root='./CIFAR10',train=False,download=False,transform=transforms.ToTensor())
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train_loader = torch.utils.data.DataLoader(train_dset, batch_size=128, shuffle=True, num_workers=0)
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test_loader = torch.utils.data.DataLoader(test_dset, batch_size=128, shuffle=False, num_workers=0)
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#######################################################
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################### 构建模型 ###################
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class Net(nn.Module):
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def __init__(self,act):
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super(Net, self).__init__()
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# 卷积层 (32x32x3的图像)
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self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
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# 卷积层(16x16x16)
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self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
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# 卷积层(8x8x32)
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self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
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# 最大池化层
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self.pool = nn.MaxPool2d(2, 2)
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# linear layer (64 * 4 * 4 -> 500)
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self.fc1 = nn.Linear(64 * 4 * 4, 500)
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# linear layer (500 -> 10)
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self.fc2 = nn.Linear(500, 10)
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if act == 'relu':
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self.act = F.relu
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elif act == 'tanh':
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self.act = torch.tanh
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elif act == 'sigmoid':
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self.act = F.sigmoid
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def forward(self, x):
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# add sequence of convolutional and max pooling layers
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x = self.pool(self.act(self.conv1(x)))
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x = self.pool(self.act(self.conv2(x)))
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x = self.pool(self.act(self.conv3(x)))
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# flatten image input
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x = x.view(-1, 64 * 4 * 4)
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x = self.act(self.fc1(x))
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x = self.fc2(x)
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return x
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#######################################################
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################### 模型加入batchnorm ###################
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class BnNet(nn.Module):
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def __init__(self, act):
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super(BnNet, self).__init__()
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# 卷积层 (32x32x3的图像)
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self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
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self.bn1 = nn.BatchNorm2d(16)
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# 卷积层(16x16x16)
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self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
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self.bn2 = nn.BatchNorm2d(32)
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# 卷积层(8x8x32)
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self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
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self.bn3 = nn.BatchNorm2d(64)
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# 最大池化层
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self.pool = nn.MaxPool2d(2, 2)
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# linear layer (64 * 4 * 4 -> 500)
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self.fc1 = nn.Linear(64 * 4 * 4, 500)
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self.bn4 = nn.BatchNorm1d(500)
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# linear layer (500 -> 10)
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self.fc2 = nn.Linear(500, 10)
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if act == 'relu':
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self.act = F.relu
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elif act == 'tanh':
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self.act = torch.tanh
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elif act == 'sigmoid':
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self.act = F.sigmoid
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def forward(self, x):
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# add sequence of convolutional and max pooling layers
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x = self.pool(self.act(self.bn1(self.conv1(x))))
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x = self.pool(self.act(self.bn2(self.conv2(x))))
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x = self.pool(self.act(self.bn3(self.conv3(x))))
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# flatten image input
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x = x.view(-1, 64 * 4 * 4)
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x = self.act(self.bn4(self.fc1(x)))
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x = self.fc2(x)
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return x
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################### 构建模型 ###################
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class DeepNet(nn.Module):
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def __init__(self,act):
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super(DeepNet, self).__init__()
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################### 代码填空：请在此填补模型定义代码 ###################
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# 卷积层 (32x32x3的图像)
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self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
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# 卷积层(32x32x16)
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self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
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# 卷积层(16x16x32)
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self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
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# 卷积层(16x16x64)
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self.conv4 = nn.Conv2d(64, 128, 3, padding=1)
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# 卷积层(8x8x128)
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self.conv5 = nn.Conv2d(128, 256, 3, padding=1)
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# 卷积层(8x8x256)
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self.conv6 = nn.Conv2d(256, 512, 3, padding=1)
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# 最大池化层
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self.pool = nn.MaxPool2d(2, 2)
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# 自适应平均池化层
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self.apool = nn.AdaptiveAvgPool2d((1, 1))
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# linear layer (512 -> 256)
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self.fc1 = nn.Linear(512, 256)
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# linear layer (256 -> 128)
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self.fc2 = nn.Linear(256, 128)
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# linear layer (128 -> 10)
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self.fc3 = nn.Linear(128, 10)
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if act == 'relu':
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self.act = F.relu
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elif act == 'tanh':
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self.act = torch.tanh
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elif act == 'sigmoid':
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self.act = F.sigmoid
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##################################################################
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def forward(self, x):
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# convolutional layers
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################### 代码填空：请在此填补前向计算代码 ###################
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x = self.pool(self.act(self.conv2(self.act(self.conv1(x)))))
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x = self.pool(self.act(self.conv4(self.act(self.conv3(x)))))
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x = self.apool(self.act(self.conv6(self.act(self.conv5(x)))))
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# flatten image input
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x = x.view(-1, 512)
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x = self.act(self.fc1(x))
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x = self.act(self.fc2(x))
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x = self.fc3(x)
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return x
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##################################################################
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class BnDeepNet(nn.Module):
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def __init__(self,act):
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super(BnDeepNet, self).__init__()
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################### 代码填空：请在此填补模型定义代码 ###################
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# 卷积层 (32x32x3的图像)
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self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
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self.bn1 = nn.BatchNorm2d(16)
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# 卷积层(32x32x16)
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self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
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self.bn2 = nn.BatchNorm2d(32)
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# 卷积层(16x16x32)
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self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
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self.bn3 = nn.BatchNorm2d(64)
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# 卷积层(16x16x64)
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self.conv4 = nn.Conv2d(64, 128, 3, padding=1)
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self.bn4 = nn.BatchNorm2d(128)
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# 卷积层(8x8x128)
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self.conv5 = nn.Conv2d(128, 256, 3, padding=1)
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self.bn5 = nn.BatchNorm2d(256)
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# 卷积层(8x8x256)
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self.conv6 = nn.Conv2d(256, 512, 3, padding=1)
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self.bn6 = nn.BatchNorm2d(512)
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# 最大池化层
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self.pool = nn.MaxPool2d(2, 2)
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# 自适应平均池化层
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self.apool = nn.AdaptiveAvgPool2d((1, 1))
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# linear layer (512 -> 256)
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self.fc1 = nn.Linear(512, 256)
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self.bn7 = nn.BatchNorm1d(256)
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# linear layer (256 -> 128)
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self.fc2 = nn.Linear(256, 128)
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self.bn8 = nn.BatchNorm1d(128)
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# linear layer (128 -> 10)
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self.fc3 = nn.Linear(128, 10)
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if act == 'relu':
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self.act = F.relu
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elif act == 'tanh':
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self.act = torch.tanh
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elif act == 'sigmoid':
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self.act = F.sigmoid
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###################################################################
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def forward(self, x):
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# convolutional layers
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################### 代码填空：请在此填补前向计算代码 ###################
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x = self.pool(self.act(self.bn2(self.conv2(self.act(self.bn1(self.conv1(x)))))))
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x = self.pool(self.act(self.bn4(self.conv4(self.act(self.bn3(self.conv3(x)))))))
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x = self.apool(self.act(self.bn6(self.conv6(self.act(self.bn5(self.conv5(x)))))))
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# flatten image input
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x = x.view(-1, 512)
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x = self.act(self.bn7(self.fc1(x)))
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x = self.act(self.bn8(self.fc2(x)))
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x = self.fc3(x)
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return x
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##################################################################
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################### 训练前准备 ###################
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# model = Net('tanh')
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# model = BnNet('relu')
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# model = DeepNet('tanh')
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model = BnDeepNet('relu')
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model.to(device)
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criterion = nn.CrossEntropyLoss()
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optimizer_type = "SGD"
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# optimizer_type = "Adam"
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if optimizer_type == "SGD":
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optimizer = optim.SGD(model.parameters(), lr=0.001)
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elif optimizer_type == "Adam":
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########## 代码填空：请在此填补Adam优化器计算代码, lr=0.0001 ###########
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optimizer = optim.Adam(model.parameters(), lr = 0.0001)
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##################################################################
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n_epochs = 100
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train_losses = []
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valid_losses = []
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accuracies = []
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################################################
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################### 训练+验证 ###################
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for epoch in range(n_epochs):
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train_loss = 0.0
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valid_loss = 0.0
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model.train()
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for idx,(img,label) in tqdm(enumerate(train_loader)):
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img, label=img.to(device), label.to(device)
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optimizer.zero_grad()
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output = model(img)
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loss = criterion(output,label)
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loss.backward()
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optimizer.step()
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train_loss += loss.item() * img.shape[0]
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model.eval()
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correct = 0
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total = 0
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for idx,(img,label) in tqdm(enumerate(test_loader)):
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img, label=img.to(device), label.to(device)
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output = model(img)
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loss = criterion(output, label)
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valid_loss += loss.item() * img.shape[0]
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_, predicted = torch.max(output.data, 1)
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total += label.size(0)
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correct += (predicted == label).sum().item()
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train_loss = train_loss / len(train_dset)
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valid_loss = valid_loss / len(test_dset)
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train_losses.append(train_loss)
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valid_losses.append(valid_loss)
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accuracy = correct / total
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accuracies.append(accuracy)
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print(f"Epoch:{epoch}, Acc:{correct/total}, Train Loss:{train_loss}, Test Loss:{valid_loss}")
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################################################
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################### 曲线绘制 ###################
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print("MAX ACC: ",np.max(accuracies))
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plt.plot(range(n_epochs), train_losses, label='Train Loss')
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plt.plot(range(n_epochs), valid_losses, label='Valid Loss')
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plt.xlabel('Epoch')
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plt.ylabel('Loss')
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plt.legend()
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plt.savefig("Loss.png")
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plt.clf()
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# 绘制验证集准确率随epoch的变化曲线
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plt.plot(range(n_epochs), accuracies, label='Accuracy')
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plt.xlabel('Epoch')
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plt.ylabel('Accuracy')
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plt.legend()
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plt.savefig("Acc.png")
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################################################
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