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from torch import nn as nn
from torch.nn import functional as F
from models.layers import FocalConv2d
class FrameLevelPartFeatureExtractor(nn.Module):
def __init__(self, in_channels: int):
super(FrameLevelPartFeatureExtractor, self).__init__()
nf = 32
self.focal_conv1 = FocalConv2d(in_channels, nf, kernel_size=5,
padding=2, halving=1)
self.focal_conv2 = FocalConv2d(nf, nf, kernel_size=3,
padding=1, halving=1)
self.focal_conv3 = FocalConv2d(nf, nf * 2, kernel_size=3,
padding=1, halving=4)
self.focal_conv4 = FocalConv2d(nf * 2, nf * 2, kernel_size=3,
padding=1, halving=4)
self.focal_conv5 = FocalConv2d(nf * 2, nf * 4, kernel_size=3,
padding=1, halving=8)
self.focal_conv6 = FocalConv2d(nf * 4, nf * 4, kernel_size=3,
padding=1, halving=8)
self.max_pool = nn.MaxPool2d(kernel_size=2, stride=2)
def forward(self, x):
x = F.leaky_relu(self.focal_conv1(x))
x = F.leaky_relu(self.focal_conv2(x))
x = self.max_pool(x)
x = F.leaky_relu(self.focal_conv3(x))
x = F.leaky_relu(self.focal_conv4(x))
x = self.max_pool(x)
x = F.leaky_relu(self.focal_conv5(x))
x = F.leaky_relu(self.focal_conv6(x))
return x
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