二者的代码实现
时间: 2024-02-27 12:54:56 浏览: 16
以下是在 PyTorch 中实现 FPN 和 PANet 的代码示例:
## FPN
```python
import torch.nn as nn
import torch.nn.functional as F
class FPN(nn.Module):
def __init__(self, in_channels_list, out_channels):
super(FPN, self).__init__()
self.lateral_convs = nn.ModuleList()
self.fpn_convs = nn.ModuleList()
for in_channels in in_channels_list:
lateral_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
fpn_conv = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
self.lateral_convs.append(lateral_conv)
self.fpn_convs.append(fpn_conv)
def forward(self, inputs):
laterals = [lateral_conv(inputs[i]) for i, lateral_conv in enumerate(self.lateral_convs)]
num_levels = len(laterals)
for i in range(num_levels - 1, 0, -1):
laterals[i - 1] += F.interpolate(laterals[i], scale_factor=2, mode='nearest')
fpn_outs = [self.fpn_convs[i](laterals[i]) for i in range(num_levels)]
for i in range(num_levels - 1, 0, -1):
fpn_outs[i - 1] += F.interpolate(fpn_outs[i], scale_factor=2, mode='nearest')
return fpn_outs
```
## PANet
```python
import torch.nn as nn
import torch.nn.functional as F
class PANet(nn.Module):
def __init__(self, in_channels_list, out_channels):
super(PANet, self).__init__()
self.lateral_convs = nn.ModuleList()
self.fpn_convs = nn.ModuleList()
self.pan_convs = nn.ModuleList()
for in_channels in in_channels_list:
lateral_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
fpn_conv = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
pan_conv = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
self.lateral_convs.append(lateral_conv)
self.fpn_convs.append(fpn_conv)
self.pan_convs.append(pan_conv)
def forward(self, inputs):
laterals = [lateral_conv(inputs[i]) for i, lateral_conv in enumerate(self.lateral_convs)]
num_levels = len(laterals)
for i in range(num_levels - 1, 0, -1):
laterals[i - 1] += F.interpolate(laterals[i], scale_factor=2, mode='nearest')
fpn_outs = [self.fpn_convs[i](laterals[i]) for i in range(num_levels)]
for i in range(num_levels - 1, 0, -1):
fpn_outs[i - 1] += F.interpolate(fpn_outs[i], scale_factor=2, mode='nearest')
pan_outs = []
for i in range(num_levels):
pan_out = fpn_outs[i]
for j in range(i + 1, num_levels):
pan_out += F.interpolate(self.pan_convs[j - i - 1](fpn_outs[j]), scale_factor=2**(j - i), mode='nearest')
pan_outs.append(pan_out)
return pan_outs
```
需要注意的是,这里的代码仅为示例,具体实现方式可能会有所不同。
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