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# The implementation is adopted from TFace,made pubicly available under the Apache-2.0 license at
# https://github.com/Tencent/TFace/blob/master/recognition/torchkit/backbone/common.py
import torch.nn as nn
from torch.nn import Conv2d, Module, ReLU, Sigmoid
def initialize_weights(modules):
""" Weight initilize, conv2d and linear is initialized with kaiming_normal
"""
for m in modules:
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(
m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
nn.init.kaiming_normal_(
m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
m.bias.data.zero_()
class Flatten(Module):
""" Flat tensor
"""
def forward(self, input):
return input.view(input.size(0), -1)
class SEModule(Module):
""" SE block
"""
def __init__(self, channels, reduction):
super(SEModule, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc1 = Conv2d(
channels,
channels // reduction,
kernel_size=1,
padding=0,
bias=False)
nn.init.xavier_uniform_(self.fc1.weight.data)
self.relu = ReLU(inplace=True)
self.fc2 = Conv2d(
channels // reduction,
channels,
kernel_size=1,
padding=0,
bias=False)
self.sigmoid = Sigmoid()
def forward(self, x):
module_input = x
x = self.avg_pool(x)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.sigmoid(x)
return module_input * x
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