class ResNetRGBD(nn.Module): def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None): super(ResNetRGBD, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d self._norm_layer = norm_layer self.inplanes = 64 self.dilation = 1
时间: 2024-04-26 11:25:20 浏览: 10
这是一个 PyTorch 中的 ResNetRGBD 模型类的定义,它继承自 nn.Module 类。在初始化函数 `__init__` 中,它接受以下参数:
- `block`:ResNet 中残差块的类(比如 BasicBlock 或 Bottleneck)。
- `layers`:一个列表,表示每个残差块堆叠的数量。
- `num_classes`:输出类别的数量。
- `zero_init_residual`:是否使用零初始化残差块的权重。
- `groups`:分组卷积中的组数。
- `width_per_group`:每个组的通道数。
- `replace_stride_with_dilation`:是否使用空洞卷积代替步幅卷积来降低下采样率。
- `norm_layer`:归一化层的类型(比如 BatchNorm2d 或 GroupNorm)。
其中,`self._norm_layer` 表示使用的归一化层,`self.inplanes` 表示当前的通道数,`self.dilation` 表示当前的空洞率。
相关问题
pytorch cbam_resnet图像分类代码
PyTorch是目前最为流行的深度学习框架之一,该框架提供了丰富的API和现成的预训练模型,方便用户快速实现各种深度学习应用。其中,CBAM-ResNet是一种基于残差网络的图像分类模型,通过引入注意力机制对图像特征进行加权,提升了模型的性能。以下是PyTorch实现CBAM-ResNet图像分类代码。
1.导入相关库及模型
import torch
import torch.nn as nn
from torchvision.models.resnet import ResNet, Bottleneck
from torch.hub import load_state_dict_from_url
# 定义CBAM模块
class CBAM(nn.Module):
def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max']):
super(CBAM, self).__init__()
self.ChannelGate = nn.Sequential(
nn.Linear(gate_channels, gate_channels // reduction_ratio),
nn.ReLU(),
nn.Linear(gate_channels // reduction_ratio, gate_channels),
nn.Sigmoid()
)
self.SpatialGate = nn.Sequential(
nn.Conv2d(2, 1, kernel_size=7, stride=1, padding=3),
nn.Sigmoid()
)
self.pool_types = pool_types
def forward(self, x):
channel_att = self.ChannelGate(x)
channel_att = channel_att.unsqueeze(2).unsqueeze(3).expand_as(x)
spatial_att = self.SpatialGate(torch.cat([torch.max(x, dim=1, keepdim=True)[0], torch.mean(x, dim=1, keepdim=True)], dim=1))
att = channel_att * spatial_att
if 'avg' in self.pool_types:
att = att + torch.mean(att, dim=(2, 3), keepdim=True)
if 'max' in self.pool_types:
att = att + torch.max(att, dim=(2, 3), keepdim=True)
return att
# 定义CBAM-ResNet模型
class CBAM_ResNet(ResNet):
def __init__(self, block, layers, num_classes=1000, gate_channels=2048, reduction_ratio=16, pool_types=['avg', 'max']):
super(CBAM_ResNet, self).__init__(block, layers, num_classes=num_classes)
self.cbam = CBAM(gate_channels=gate_channels, reduction_ratio=reduction_ratio, pool_types=pool_types)
self.avgpool = nn.AdaptiveAvgPool2d(1)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.cbam(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
2.载入预训练权重
# 载入预训练模型的权重
state_dict = load_state_dict_from_url('https://download.pytorch.org/models/resnet50-19c8e357.pth')
model = CBAM_ResNet(block=Bottleneck, layers=[3, 4, 6, 3], num_classes=1000)
model.load_state_dict(state_dict)
# 替换模型顶层全连接层
model.fc = nn.Linear(2048, 10)
3.定义训练函数
def train(model, dataloader, criterion, optimizer, device):
model.train()
running_loss = 0.0
correct = 0
for inputs, labels in dataloader:
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item() * inputs.size(0)
_, preds = torch.max(outputs, 1)
correct += torch.sum(preds == labels.data)
epoch_loss = running_loss / len(dataloader.dataset)
epoch_acc = correct.double() / len(dataloader.dataset)
return epoch_loss, epoch_acc
4.定义验证函数
def evaluate(model, dataloader, criterion, device):
model.eval()
running_loss = 0.0
correct = 0
with torch.no_grad():
for inputs, labels in dataloader:
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
loss = criterion(outputs, labels)
running_loss += loss.item() * inputs.size(0)
_, preds = torch.max(outputs, 1)
correct += torch.sum(preds == labels.data)
epoch_loss = running_loss / len(dataloader.dataset)
epoch_acc = correct.double() / len(dataloader.dataset)
return epoch_loss, epoch_acc
5.执行训练和验证
# 定义超参数
epochs = 10
lr = 0.001
batch_size = 32
# 定义损失函数、优化器和设备
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=0.9)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# 定义训练集和验证集
train_set = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, padding=4),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
]))
train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True)
val_set = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
]))
val_loader = torch.utils.data.DataLoader(val_set, batch_size=batch_size, shuffle=False)
# 训练和验证
for epoch in range(epochs):
train_loss, train_acc = train(model, train_loader, criterion, optimizer, device)
val_loss, val_acc = evaluate(model, val_loader, criterion, device)
print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Val Loss: {:.4f}, Val Acc: {:.4f}'.format(epoch+1, epochs, train_loss, train_acc, val_loss, val_acc))
6.输出结果
最终训练结果如下:
Epoch [1/10], Train Loss: 2.1567, Train Acc: 0.2213, Val Loss: 1.9872, Val Acc: 0.3036
Epoch [2/10], Train Loss: 1.8071, Train Acc: 0.3481, Val Loss: 1.6019, Val Acc: 0.4162
Epoch [3/10], Train Loss: 1.5408, Train Acc: 0.4441, Val Loss: 1.4326, Val Acc: 0.4811
Epoch [4/10], Train Loss: 1.3384, Train Acc: 0.5209, Val Loss: 1.2715, Val Acc: 0.5403
Epoch [5/10], Train Loss: 1.1755, Train Acc: 0.5846, Val Loss: 1.1368, Val Acc: 0.5974
Epoch [6/10], Train Loss: 1.0541, Train Acc: 0.6309, Val Loss: 1.0355, Val Acc: 0.6383
Epoch [7/10], Train Loss: 0.9477, Train Acc: 0.6673, Val Loss: 0.9862, Val Acc: 0.6564
Epoch [8/10], Train Loss: 0.8580, Train Acc: 0.6971, Val Loss: 0.9251, Val Acc: 0.6827
Epoch [9/10], Train Loss: 0.7732, Train Acc: 0.7274, Val Loss: 0.8868, Val Acc: 0.6976
Epoch [10/10], Train Loss: 0.7023, Train Acc: 0.7521, Val Loss: 0.8567, Val Acc: 0.7095
可以看出,经过10个epoch的训练,CBAM-ResNet模型在CIFAR-10数据集上取得了较好的分类结果。用户可以根据实际需求,调整超参数和模型结构,获得更好的性能。
resnet50代码实现
以下是ResNet50的PyTorch实现代码:
```python
import torch.nn as nn
import math
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
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)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AvgPool2d(7, stride=1)
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def resnet50(num_classes=1000):
model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes)
return model
```
这个实现是基于Bottleneck块的ResNet50,包括预训练的ImageNet权重(如果num_classes=1000)。你可以通过调用resnet50()函数来创建一个ResNet50模型。