请给我一个fcn-32s的完整代码
时间: 2024-05-08 22:21:32 浏览: 14
好的,以下是一个fcn-32s的完整代码示例,使用的是PyTorch框架:
```
import torch
import torch.nn as nn
import torch.nn.functional as F
class FCN32s(nn.Module):
def __init__(self, num_classes):
super(FCN32s, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=100)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
self.relu2 = nn.ReLU(inplace=True)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.relu3 = nn.ReLU(inplace=True)
self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
self.relu4 = nn.ReLU(inplace=True)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
self.conv5 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
self.relu5 = nn.ReLU(inplace=True)
self.conv6 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.relu6 = nn.ReLU(inplace=True)
self.conv7 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.relu7 = nn.ReLU(inplace=True)
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
self.conv8 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
self.relu8 = nn.ReLU(inplace=True)
self.conv9 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.relu9 = nn.ReLU(inplace=True)
self.conv10 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.relu10 = nn.ReLU(inplace=True)
self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
self.conv11 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.relu11 = nn.ReLU(inplace=True)
self.conv12 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.relu12 = nn.ReLU(inplace=True)
self.conv13 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.relu13 = nn.ReLU(inplace=True)
self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
self.fc14 = nn.Conv2d(512, 4096, kernel_size=7)
self.relu14 = nn.ReLU(inplace=True)
self.drop14 = nn.Dropout2d()
self.fc15 = nn.Conv2d(4096, 4096, kernel_size=1)
self.relu15 = nn.ReLU(inplace=True)
self.drop15 = nn.Dropout2d()
self.score_fr = nn.Conv2d(4096, num_classes, kernel_size=1)
self.upscore = nn.ConvTranspose2d(num_classes, num_classes, kernel_size=64, stride=32, bias=False)
self._initialize_weights()
def forward(self, x):
x = self.conv1(x)
x = self.relu1(x)
x = self.conv2(x)
x = self.relu2(x)
x = self.pool1(x)
x = self.conv3(x)
x = self.relu3(x)
x = self.conv4(x)
x = self.relu4(x)
x = self.pool2(x)
x = self.conv5(x)
x = self.relu5(x)
x = self.conv6(x)
x = self.relu6(x)
x = self.conv7(x)
x = self.relu7(x)
x = self.pool3(x)
x = self.conv8(x)
x = self.relu8(x)
x = self.conv9(x)
x = self.relu9(x)
x = self.conv10(x)
x = self.relu10(x)
x = self.pool4(x)
x = self.conv11(x)
x = self.relu11(x)
x = self.conv12(x)
x = self.relu12(x)
x = self.conv13(x)
x = self.relu13(x)
x = self.pool5(x)
x = self.fc14(x)
x = self.relu14(x)
x = self.drop14(x)
x = self.fc15(x)
x = self.relu15(x)
x = self.drop15(x)
x = self.score_fr(x)
x = self.upscore(x)
x = x[:, :, 19:19+x.size()[2], 19:19+x.size()[3]].contiguous()
return x
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
m.weight.data.zero_()
if m.bias is not None:
m.bias.data.zero_()
if isinstance(m, nn.ConvTranspose2d):
assert m.kernel_size[0] == m.kernel_size[1]
initial_weight = get_upsampling_weight(m.in_channels, m.out_channels, m.kernel_size[0])
m.weight.data.copy_(initial_weight)
def get_upsampling_weight(in_channels, out_channels, kernel_size):
factor = (kernel_size + 1) // 2
if kernel_size % 2 == 1:
center = factor - 1
else:
center = factor - 0.5
og = torch.ogrid[:kernel_size, :kernel_size]
filter = (1 - abs(og[0] - center) / factor) * (1 - abs(og[1] - center) / factor)
weight = torch.zeros((in_channels, out_channels, kernel_size, kernel_size))
weight[range(in_channels), range(out_channels), :, :] = filter
return weight
```
这个代码实现了FCN32s模型,可以输入一张图片,输出对应的语义分割结果。