基于Pytorch框架实现ResNet18中嵌入视觉注意力机制python源码+项目说明.zip

import torch.nn as nn import math try: from torch.hub import load_state_dict_from_url except ImportError: from torch.utils.model_zoo import load_url as load_state_dict_from_url import torch import torchvision import torchvision.transforms as transforms import torch.nn.functional as F import matplotlib.pyplot as plt import numpy as np #通道注意力机制 class ChannelAttention(nn.Module): def __init__(self, in_planes, ratio=32 ): super(ChannelAttention, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.max_pool = nn.AdaptiveMaxPool2d(1) self.fc1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False) self.relu1 = nn.ReLU() self.fc2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False) #self.sigmoid = nn.ReLU() #self.sigmoid = nn.Tanh() self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x)))) max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x)))) out = avg_out + max_out return self.sigmoid(out) #空间注意力机制 class SpatialAttention(nn.Module): def __init__(self, kernel_size=7): super(SpatialAttention, self).__init__() assert kernel_size in (3, 7), 'kernel size must be 3 or 7' padding = 3 if kernel_size == 7 else 1 self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False) #self.sigmoid = nn.ReLU() #self.sigmoid = nn.Tanh() self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = torch.mean(x, dim=1, keepdim=True) max_out, _ = torch.max(x, dim=1, keepdim=True) x = torch.cat([avg_out, max_out], dim=1) x = self.conv1(x) return self.sigmoid(x) # 判断是否有GPU device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') num_epochs = 50 #50轮 batch_size = 50 #50步长 learning_rate = 0.01 #学习率0.01 # 3x3 卷积定义 def conv3x3(in_channels, out_channels, stride=1): return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) # Resnet 的残差块 class ResidualBlock(nn.Module): def __init__(self, in_channels, out_channels, stride=1, downsample=None): super(ResidualBlock, self).__init__() self.conv1 = conv3x3(in_channels, out_channels, stride) self.bn1 = nn.BatchNorm2d(out_channels) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(out_channels, out_channels) self.bn2 = nn.BatchNorm2d(out_channels) self.downsample = downsample def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample: residual = self.downsample(x) out += residual out = self.relu(out) return out # ResNet定义 class ResNet(nn.Module): def __init__(self, block, layers, num_classes=100, inter_layer=False): super(ResNet, self).__init__() self.in_channels = 64 self.conv = conv3x3(3,64) self.bn = nn.BatchNorm2d(64) self.relu = nn.ReLU(inplace=True) # 网络的第一层加入注意力机制 self.ca = ChannelAttention(self.in_channels) self.sa = SpatialAttention() self.layer1 = self.make_layer(block, 64, layers[0]) self.layer2 = self.make_layer(block, 128, layers[1], 2) self.layer3 = self.make_layer(block, 256, layers[2], 2) self.layer4 = self.make_layer(block, 512, layers[2], 2)#4 #网络的卷积层的最后一层加入注意力机制 self.ca1 = ChannelAttention(self.in_channels) self.sa1 = SpatialAttention() self.avg_pool = nn.AvgPool2d(4) self.fc = nn.Linear(512, num_classes) def make_layer(self, block, out_channels, blocks, stride=1): downsample = None if (stride != 1) or (self.in_channels != out_channels): downsample = nn.Sequential( conv3x3(self.in_channels, out_channels, stride=stride), nn.BatchNorm2d(out_channels)) layers = [] layers.append(block(self.in_channels, out_channels, stride, downsample)) self.in_channels = out_channels for i in range(1, blocks): layers.append(block(out_channels, out_channels)) return nn.Sequential(*layers) def forward(self, x): out = self.conv(x) out = self.bn(out) out = self.relu(out) out = self.ca(out) * out out = self.sa(out) * out out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = self.ca1(out) * out out = self.sa1(out) * out out = self.avg_pool(out) out = out.view(out.size(0), -1) out = self.fc(out) return out def get_acc(outputs, label): total = outputs.shape[0] probs, pred_y = outputs.data.max(dim=1) # 得到概率 correct = (pred_y == label).sum().data return torch.div(correct, total) # 更新学习率 def update_lr(optimizer, lr): for param_group in optimizer.param_groups: param_group['lr'] = lr def train(model,device,train_loader,optimizer,epoch,criterion,curr_lr): total_step = len(train_loader) model.train() train_loss=0 train_acc=0 acc1=0 for i, (images, labels) in enumerate(train_loader): images = images.to(device) labels = labels.to(device) # Forward pass outputs = model(images) loss = criterion(outputs, labels) # Backward and optimize optimizer.zero_grad() loss.backward() optimizer.step() train_loss += loss.item() train_acc += get_acc(outputs,labels).item() if (i+1) % 200 == 0: print ("Epoch [{}/{}], Step [{}/{}] Loss: {:.4f}" .format(epoch+1, num_epochs, i+1, total_step, loss.item())) train_loss = train_loss / len(train_loader) train_acc = train_acc * 100 / len(train_loader) print("Train Acc {:.4f}%, Train Loss {:.4f}".format(train_acc/(epoch+1),train_loss/(epoch+1))) # 延迟学习率 if (epoch+1) % 20 == 0: curr_lr /= 3 update_lr(optimizer, curr_lr) return train_loss,100-train_acc def test(model,devive,test_loader,criterion): # 测试网络模型 erro=[] model.eval() with torch.no_grad(): correct = 0 total = 0 test_loss=0 test_acc=0 for images, labels in test_loader: images = images.to(device) labels = labels.to(device) outputs = model(images) _, predicted = torch.max(outputs.data, 1) loss = criterion(outputs,labels) total += labels.size(0) correct += (predicted == labels).sum().item() test_loss += loss.item() test_acc += get_acc(outputs,labels).item() erro.append(100 *(1- correct / total)) test_loss = test_loss / len(test_loader) test_acc = test_acc * 100 / len(test_loader) print('Accuracy of the model on the test images: {} %, test_loss: {}'.format(100 * correct / total,test_loss)) print('test_acc of the model on the test images: {} %'.format(test_acc)) return test_loss,100-test_acc def draw(train_data,test_data,x_data,x_lable,y_lable,title): #设置图像属性 y1 = train_data y2 = test_data #绘制图像