resnet网络中定义了downsample,详细解释其功能与使用情况?
时间: 2024-05-22 08:15:53 浏览: 16
在ResNet中,downsample是一种用于减小特征图尺寸的操作。当网络深度增加时,特征图的尺寸会不断减小,downsample操作可以在某些层中使用,以减小特征图的尺寸,从而使得特征图的尺寸与网络的深度相适应。
downsample通常在残差块中使用,用于减小特征图的尺寸。在残差块中,downsample可以通过卷积操作或者池化操作来实现。当使用卷积操作时,downsample会通过卷积核的步幅来减小特征图的尺寸;当使用池化操作时,downsample会通过池化层的参数来减小特征图的尺寸。
在使用downsample时,需要考虑到特征图的尺寸与网络深度的适应关系,以保证网络的训练效果。如果downsample的操作不当,会导致特征图尺寸过小,从而影响网络的训练效果。因此,在使用downsample时,需要对网络的结构和参数进行合理的调整,以达到最佳的训练效果。
相关问题
使用paddle框架写一段resnet网络代码
好的,以下是使用 PaddlePaddle 框架实现 ResNet 网络的代码:
```python
import paddle
import paddle.nn as nn
class BasicBlock(nn.Layer):
expansion = 1
def __init__(self, in_channels, out_channels, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2D(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias_attr=False)
self.bn1 = nn.BatchNorm2D(out_channels)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2D(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias_attr=False)
self.bn2 = nn.BatchNorm2D(out_channels)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class Bottleneck(nn.Layer):
expansion = 4
def __init__(self, in_channels, out_channels, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2D(in_channels, out_channels, kernel_size=1, bias_attr=False)
self.bn1 = nn.BatchNorm2D(out_channels)
self.conv2 = nn.Conv2D(out_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias_attr=False)
self.bn2 = nn.BatchNorm2D(out_channels)
self.conv3 = nn.Conv2D(out_channels, out_channels * self.expansion, kernel_size=1, bias_attr=False)
self.bn3 = nn.BatchNorm2D(out_channels * self.expansion)
self.relu = nn.ReLU()
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = 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:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResNet(nn.Layer):
def __init__(self, block, layers, num_classes=1000):
super(ResNet, self).__init__()
self.in_channels = 64
self.conv1 = nn.Conv2D(3, 64, kernel_size=7, stride=2, padding=3, bias_attr=False)
self.bn1 = nn.BatchNorm2D(64)
self.relu = nn.ReLU()
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.AdaptiveAvgPool2D((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
def _make_layer(self, block, out_channels, blocks, stride=1):
downsample = None
if stride != 1 or self.in_channels != out_channels * block.expansion:
downsample = nn.Sequential(
nn.Conv2D(self.in_channels, out_channels * block.expansion, kernel_size=1, stride=stride, bias_attr=False),
nn.BatchNorm2D(out_channels * block.expansion)
)
layers = []
layers.append(block(self.in_channels, out_channels, stride, downsample))
self.in_channels = out_channels * block.expansion
for _ in range(1, blocks):
layers.append(block(self.in_channels, out_channels))
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 = paddle.flatten(x, 1)
x = self.fc(x)
return x
def resnet18(num_classes=1000):
return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes)
def resnet34(num_classes=1000):
return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes)
def resnet50(num_classes=1000):
return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes)
def resnet101(num_classes=1000):
return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes)
def resnet152(num_classes=1000):
return ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes)
```
以上代码定义了 BasicBlock 和 Bottleneck 两个块,并使用它们构建了 ResNet 模型。其中,ResNet 可以选择使用不同的块和层数来构建不同版本的网络。通过调用 `resnet18()`,`resnet34()`,`resnet50()`,`resnet101()` 和 `resnet152()` 函数可以得到不同版本的 ResNet 网络。
使用python实现使用ResNet网络训练MNIST数据集,并进行测试
好的,以下是使用Python实现使用ResNet网络训练MNIST数据集的步骤:
1. 导入必要的库和模块:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR
from torchvision import datasets, transforms
```
2. 定义ResNet网络结构:
```python
class ResNet(nn.Module):
def __init__(self):
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(64, 2)
self.layer2 = self._make_layer(128, 2, stride=2)
self.layer3 = self._make_layer(256, 2, stride=2)
self.layer4 = self._make_layer(512, 2, stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512, 10)
def _make_layer(self, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes),
)
layers = []
layers.append(BasicBlock(self.inplanes, planes, stride, downsample))
self.inplanes = planes
for i in range(1, blocks):
layers.append(BasicBlock(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.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
class BasicBlock(nn.Module):
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
```
3. 加载MNIST数据集:
```python
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
train_dataset = datasets.MNIST('./data', train=True, download=True, transform=transform)
test_dataset = datasets.MNIST('./data', train=False, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=128, shuffle=False)
```
4. 定义损失函数和优化器:
```python
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = ResNet().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=0.0001)
scheduler = StepLR(optimizer, step_size=10, gamma=0.1)
```
5. 训练模型:
```python
def train(model, device, train_loader, optimizer, criterion, epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
if batch_idx % 100 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
def test(model, device, test_loader, criterion):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += criterion(output, target).item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
for epoch in range(1, 21):
scheduler.step()
train(model, device, train_loader, optimizer, criterion, epoch)
test(model, device, test_loader, criterion)
```
6. 测试模型:
```python
test(model, device, test_loader, criterion)
```
这样就完成了使用Python实现使用ResNet网络训练MNIST数据集,并进行测试的过程。
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