310 lines
12 KiB
Python
310 lines
12 KiB
Python
from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, ReLU, Sigmoid, Dropout, \
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MaxPool2d, AdaptiveAvgPool2d, Sequential, Module, Parameter
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import torch
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from collections import namedtuple
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import math
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################################## Original Arcface Model ############################################################
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class Flatten(Module):
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def forward(self, input):
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return input.view(input.size(0), -1)
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def l2_norm(input, axis=1):
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norm = torch.norm(input, 2, axis, True)
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output = torch.div(input, norm)
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return output
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class SEModule(Module):
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def __init__(self, channels, reduction):
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super(SEModule, self).__init__()
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self.avg_pool = AdaptiveAvgPool2d(1)
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self.fc1 = Conv2d(channels, channels // reduction, kernel_size=1, padding=0, bias=False)
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self.relu = ReLU(inplace=True)
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self.fc2 = Conv2d(channels // reduction, channels, kernel_size=1, padding=0, bias=False)
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self.sigmoid = Sigmoid()
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def forward(self, x):
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module_input = x
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x = self.avg_pool(x)
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x = self.fc1(x)
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x = self.relu(x)
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x = self.fc2(x)
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x = self.sigmoid(x)
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return module_input * x
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class bottleneck_IR(Module):
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def __init__(self, in_channel, depth, stride):
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super(bottleneck_IR, self).__init__()
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if in_channel == depth:
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self.shortcut_layer = MaxPool2d(1, stride)
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else:
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self.shortcut_layer = Sequential(
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Conv2d(in_channel, depth, (1, 1), stride, bias=False), BatchNorm2d(depth))
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self.res_layer = Sequential(
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BatchNorm2d(in_channel),
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Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth),
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Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth))
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def forward(self, x):
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shortcut = self.shortcut_layer(x)
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res = self.res_layer(x)
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return res + shortcut
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class bottleneck_IR_SE(Module):
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def __init__(self, in_channel, depth, stride):
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super(bottleneck_IR_SE, self).__init__()
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if in_channel == depth:
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self.shortcut_layer = MaxPool2d(1, stride)
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else:
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self.shortcut_layer = Sequential(Conv2d(in_channel, depth, (1, 1), stride, bias=False), BatchNorm2d(depth))
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self.res_layer = Sequential(
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BatchNorm2d(in_channel),
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Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
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PReLU(depth),
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Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
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BatchNorm2d(depth),
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SEModule(depth, 16)
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)
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def forward(self, x):
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shortcut = self.shortcut_layer(x)
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res = self.res_layer(x)
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return res + shortcut
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class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
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'''A named tuple describing a ResNet block.'''
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def get_block(in_channel, depth, num_units, stride=2):
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return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)]
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def get_blocks(num_layers):
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if num_layers == 50:
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blocks = [
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get_block(in_channel=64, depth=64, num_units=3),
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get_block(in_channel=64, depth=128, num_units=4),
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get_block(in_channel=128, depth=256, num_units=14),
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get_block(in_channel=256, depth=512, num_units=3)
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]
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elif num_layers == 100:
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blocks = [
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get_block(in_channel=64, depth=64, num_units=3),
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get_block(in_channel=64, depth=128, num_units=13),
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get_block(in_channel=128, depth=256, num_units=30),
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get_block(in_channel=256, depth=512, num_units=3)
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]
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elif num_layers == 152:
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blocks = [
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get_block(in_channel=64, depth=64, num_units=3),
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get_block(in_channel=64, depth=128, num_units=8),
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get_block(in_channel=128, depth=256, num_units=36),
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get_block(in_channel=256, depth=512, num_units=3)
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]
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return blocks
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class Backbone(Module):
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def __init__(self, num_layers, drop_ratio, mode='ir'):
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super(Backbone, self).__init__()
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assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152'
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assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se'
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blocks = get_blocks(num_layers)
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if mode == 'ir':
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unit_module = bottleneck_IR
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elif mode == 'ir_se':
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unit_module = bottleneck_IR_SE
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self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False), BatchNorm2d(64), PReLU(64))
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self.output_layer = Sequential(BatchNorm2d(512),
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Dropout(drop_ratio),
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Flatten(),
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Linear(512 * 7 * 7, 512),
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BatchNorm1d(512))
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modules = []
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for block in blocks:
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for bottleneck in block:
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modules.append(unit_module(bottleneck.in_channel, bottleneck.depth, bottleneck.stride))
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self.body = Sequential(*modules)
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def forward(self, x):
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x = self.input_layer(x)
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x = self.body(x)
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x = self.output_layer(x)
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return l2_norm(x)
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################################## MobileFaceNet #############################################################
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class Conv_block(Module):
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def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
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super(Conv_block, self).__init__()
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self.conv = Conv2d(in_c, out_channels=out_c, kernel_size=kernel, groups=groups, stride=stride, padding=padding,
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bias=False)
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self.bn = BatchNorm2d(out_c)
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self.prelu = PReLU(out_c)
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def forward(self, x):
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x = self.conv(x)
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x = self.bn(x)
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x = self.prelu(x)
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return x
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class Linear_block(Module):
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def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
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super(Linear_block, self).__init__()
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self.conv = Conv2d(in_c, out_channels=out_c, kernel_size=kernel, groups=groups, stride=stride, padding=padding,
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bias=False)
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self.bn = BatchNorm2d(out_c)
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def forward(self, x):
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x = self.conv(x)
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x = self.bn(x)
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return x
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class Depth_Wise(Module):
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def __init__(self, in_c, out_c, residual=False, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=1):
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super(Depth_Wise, self).__init__()
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self.conv = Conv_block(in_c, out_c=groups, kernel=(1, 1), padding=(0, 0), stride=(1, 1))
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self.conv_dw = Conv_block(groups, groups, groups=groups, kernel=kernel, padding=padding, stride=stride)
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self.project = Linear_block(groups, out_c, kernel=(1, 1), padding=(0, 0), stride=(1, 1))
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self.residual = residual
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def forward(self, x):
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if self.residual:
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short_cut = x
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x = self.conv(x)
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x = self.conv_dw(x)
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x = self.project(x)
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if self.residual:
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output = short_cut + x
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else:
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output = x
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return output
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class Residual(Module):
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def __init__(self, c, num_block, groups, kernel=(3, 3), stride=(1, 1), padding=(1, 1)):
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super(Residual, self).__init__()
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modules = []
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for _ in range(num_block):
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modules.append(
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Depth_Wise(c, c, residual=True, kernel=kernel, padding=padding, stride=stride, groups=groups))
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self.model = Sequential(*modules)
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def forward(self, x):
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return self.model(x)
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class MobileFaceNet(Module):
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def __init__(self, embedding_size):
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super(MobileFaceNet, self).__init__()
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self.conv1 = Conv_block(3, 64, kernel=(3, 3), stride=(2, 2), padding=(1, 1))
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self.conv2_dw = Conv_block(64, 64, kernel=(3, 3), stride=(1, 1), padding=(1, 1), groups=64)
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self.conv_23 = Depth_Wise(64, 64, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=128)
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self.conv_3 = Residual(64, num_block=4, groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1))
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self.conv_34 = Depth_Wise(64, 128, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=256)
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self.conv_4 = Residual(128, num_block=6, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1))
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self.conv_45 = Depth_Wise(128, 128, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=512)
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self.conv_5 = Residual(128, num_block=2, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1))
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self.conv_6_sep = Conv_block(128, 512, kernel=(1, 1), stride=(1, 1), padding=(0, 0))
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self.conv_6_dw = Linear_block(512, 512, groups=512, kernel=(7, 7), stride=(1, 1), padding=(0, 0))
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self.conv_6_flatten = Flatten()
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self.linear = Linear(512, embedding_size, bias=False)
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self.bn = BatchNorm1d(embedding_size)
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def forward(self, x):
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out = self.conv1(x)
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out = self.conv2_dw(out)
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out = self.conv_23(out)
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out = self.conv_3(out)
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out = self.conv_34(out)
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out = self.conv_4(out)
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out = self.conv_45(out)
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out = self.conv_5(out)
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out = self.conv_6_sep(out)
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out = self.conv_6_dw(out)
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out = self.conv_6_flatten(out)
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out = self.linear(out)
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out = self.bn(out)
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return l2_norm(out)
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################################## Arcface head #############################################################
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class Arcface(Module):
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# implementation of additive margin softmax loss in https://arxiv.org/abs/1801.05599
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def __init__(self, embedding_size=512, classnum=51332, s=64., m=0.5):
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super(Arcface, self).__init__()
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self.classnum = classnum
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self.kernel = Parameter(torch.Tensor(embedding_size, classnum))
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# initial kernel
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self.kernel.data.uniform_(-1, 1).renorm_(2, 1, 1e-5).mul_(1e5)
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self.m = m # the margin value, default is 0.5
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self.s = s # scalar value default is 64, see normface https://arxiv.org/abs/1704.06369
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self.cos_m = math.cos(m)
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self.sin_m = math.sin(m)
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self.mm = self.sin_m * m # issue 1
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self.threshold = math.cos(math.pi - m)
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def forward(self, embbedings, label):
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# weights norm
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nB = len(embbedings)
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kernel_norm = l2_norm(self.kernel, axis=0)
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# cos(theta+m)
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cos_theta = torch.mm(embbedings, kernel_norm)
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# output = torch.mm(embbedings,kernel_norm)
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cos_theta = cos_theta.clamp(-1, 1) # for numerical stability
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cos_theta_2 = torch.pow(cos_theta, 2)
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sin_theta_2 = 1 - cos_theta_2
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sin_theta = torch.sqrt(sin_theta_2)
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cos_theta_m = (cos_theta * self.cos_m - sin_theta * self.sin_m)
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# this condition controls the theta+m should in range [0, pi]
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# 0<=theta+m<=pi
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# -m<=theta<=pi-m
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cond_v = cos_theta - self.threshold
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cond_mask = cond_v <= 0
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keep_val = (cos_theta - self.mm) # when theta not in [0,pi], use cosface instead
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cos_theta_m[cond_mask] = keep_val[cond_mask]
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output = cos_theta * 1.0 # a little bit hacky way to prevent in_place operation on cos_theta
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idx_ = torch.arange(0, nB, dtype=torch.long)
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output[idx_, label] = cos_theta_m[idx_, label]
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output *= self.s # scale up in order to make softmax work, first introduced in normface
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return output
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################################## Cosface head #############################################################
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class Am_softmax(Module):
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# implementation of additive margin softmax loss in https://arxiv.org/abs/1801.05599
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def __init__(self, embedding_size=512, classnum=51332):
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super(Am_softmax, self).__init__()
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self.classnum = classnum
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self.kernel = Parameter(torch.Tensor(embedding_size, classnum))
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# initial kernel
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self.kernel.data.uniform_(-1, 1).renorm_(2, 1, 1e-5).mul_(1e5)
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self.m = 0.35 # additive margin recommended by the paper
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self.s = 30. # see normface https://arxiv.org/abs/1704.06369
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def forward(self, embbedings, label):
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kernel_norm = l2_norm(self.kernel, axis=0)
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cos_theta = torch.mm(embbedings, kernel_norm)
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cos_theta = cos_theta.clamp(-1, 1) # for numerical stability
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phi = cos_theta - self.m
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label = label.view(-1, 1) # size=(B,1)
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index = cos_theta.data * 0.0 # size=(B,Classnum)
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index.scatter_(1, label.data.view(-1, 1), 1)
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index = index.byte()
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output = cos_theta * 1.0
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output[index] = phi[index] # only change the correct predicted output
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output *= self.s # scale up in order to make softmax work, first introduced in normface
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return output
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