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arcface.py
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import torch
import torch.nn as nn
from collections import namedtuple
class Flatten(nn.Module):
def forward(self, input):
return input.view(input.size(0), -1)
def l2_norm(input,axis=1):
norm = torch.norm(input,2,axis,True)
output = torch.div(input, norm)
return output
class SEModule(nn.Module):
def __init__(self, channels, reduction):
super(SEModule, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1, padding=0 ,bias=False)
self.relu = nn.ReLU(inplace=True)
self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1, padding=0 ,bias=False)
self.sigmoid = nn.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
class bottleneck_IR(nn.Module):
def __init__(self, in_channel, depth, stride):
super(bottleneck_IR, self).__init__()
if in_channel == depth:
self.shortcut_layer = nn.MaxPool2d(1, stride)
else:
self.shortcut_layer = nn.Sequential(nn.Conv2d(in_channel, depth, (1, 1), stride ,bias=False),
nn.BatchNorm2d(depth))
self.res_layer = nn.Sequential(nn.BatchNorm2d(in_channel),
nn.Conv2d(in_channel, depth, (3, 3), (1, 1), 1 ,bias=False),
nn.PReLU(depth),
nn.Conv2d(depth, depth, (3, 3), stride, 1 ,bias=False),
nn.BatchNorm2d(depth))
def forward(self, x):
shortcut = self.shortcut_layer(x)
res = self.res_layer(x)
return res + shortcut
class bottleneck_IR_SE(nn.Module):
def __init__(self, in_channel, depth, stride):
super(bottleneck_IR_SE, self).__init__()
if in_channel == depth:
self.shortcut_layer = nn.MaxPool2d(1, stride)
else:
self.shortcut_layer = nn.Sequential(nn.Conv2d(in_channel, depth, (1, 1), stride ,bias=False),
nn.BatchNorm2d(depth))
self.res_layer = nn.Sequential(nn.BatchNorm2d(in_channel),
nn.Conv2d(in_channel, depth, (3,3), (1,1),1 ,bias=False),
nn.PReLU(depth),nn.Conv2d(depth, depth, (3,3), stride, 1 ,bias=False),
nn.BatchNorm2d(depth),
SEModule(depth,16))
def forward(self,x):
shortcut = self.shortcut_layer(x)
res = self.res_layer(x)
return res + shortcut
class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
'''A named tuple describing a ResNet block.'''
def get_block(in_channel, depth, num_units, stride = 2):
return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units-1)]
def get_blocks(num_layers):
if num_layers == 50:
blocks = [
get_block(in_channel=64, depth=64, num_units = 3),
get_block(in_channel=64, depth=128, num_units=4),
get_block(in_channel=128, depth=256, num_units=14),
get_block(in_channel=256, depth=512, num_units=3)
]
elif num_layers == 100:
blocks = [
get_block(in_channel=64, depth=64, num_units=3),
get_block(in_channel=64, depth=128, num_units=13),
get_block(in_channel=128, depth=256, num_units=30),
get_block(in_channel=256, depth=512, num_units=3)
]
elif num_layers == 152:
blocks = [
get_block(in_channel=64, depth=64, num_units=3),
get_block(in_channel=64, depth=128, num_units=8),
get_block(in_channel=128, depth=256, num_units=36),
get_block(in_channel=256, depth=512, num_units=3)
]
return blocks
class Backbone(nn.Module):
def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True):
super(Backbone, self).__init__()
assert input_size in [112, 224], "input_size should be 112 or 224"
assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152"
assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se"
blocks = get_blocks(num_layers)
if mode == 'ir':
unit_module = bottleneck_IR
elif mode == 'ir_se':
unit_module = bottleneck_IR_SE
self.input_layer = nn.Sequential(nn.Conv2d(3, 64, (3, 3), 1, 1, bias=False),
nn.BatchNorm2d(64),
nn.PReLU(64))
if input_size == 112:
self.output_layer = nn.Sequential(nn.BatchNorm2d(512),
nn.Dropout(drop_ratio),
nn.Flatten(),
nn.Linear(512 * 7 * 7, 512),
nn.BatchNorm1d(512, affine=affine))
else:
self.output_layer = nn.Sequential(nn.BatchNorm2d(512),
nn.Dropout(drop_ratio),
Flatten(),
nn.Linear(512 * 14 * 14, 512),
nn.BatchNorm1d(512, affine=affine))
modules = []
for block in blocks:
for bottleneck in block:
modules.append(unit_module(bottleneck.in_channel,
bottleneck.depth,
bottleneck.stride))
self.body = nn.Sequential(*modules)
def forward(self, x):
x = self.input_layer(x)
x = self.body(x)
x = self.output_layer(x)
return l2_norm(x)
def IR_101(input_size):
model = Backbone(input_size, 100, mode="ir", drop_ratio=0.4, affine=False)
return model
class IDLoss(nn.Module):
def __init__(self):
super(IDLoss, self).__init__()
self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se') # modified resnet50
self.facenet.load_state_dict(torch.load('model_ir_se50.pth'))
self.face_pool = nn.AdaptiveAvgPool2d((112, 112))
self.facenet.eval()
def extract_feats(self, x):
x = torch.nn.functional.interpolate(x, (256, 256), mode='bilinear')
x = x[:, :, 35:223, 32:220] # Crop interesting region
x = self.face_pool(x)
x_feats = self.facenet(x)
return x_feats
def forward(self, y_hat, y):
n_samples = y_hat.size(0)
y_hat_feats = self.extract_feats(y_hat)
y_feats = self.extract_feats(y) # Otherwise use the feature from there
y_feats = y_feats.detach()
loss = 0
count = 0
for i in range(n_samples):
diff_target = y_hat_feats[i].dot(y_feats[i])
loss += 1 - diff_target
count += 1
return loss / count