Spaces:
Build error
Build error
File size: 5,874 Bytes
83d8d3c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 |
"""
Adapted from https://github.com/cavalleria/cavaface.pytorch/blob/master/backbone/mobilefacenet.py
Original author cavalleria
"""
import torch
import torch.nn as nn
from torch.nn import BatchNorm1d
from torch.nn import BatchNorm2d
from torch.nn import Conv2d
from torch.nn import Linear
from torch.nn import Module
from torch.nn import PReLU
from torch.nn import Sequential
class Flatten(Module):
def forward(self, x):
return x.view(x.size(0), -1)
class ConvBlock(Module):
def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
super(ConvBlock, self).__init__()
self.layers = nn.Sequential(
Conv2d(in_c, out_c, kernel, groups=groups, stride=stride, padding=padding, bias=False),
BatchNorm2d(num_features=out_c),
PReLU(num_parameters=out_c),
)
def forward(self, x):
return self.layers(x)
class LinearBlock(Module):
def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
super(LinearBlock, self).__init__()
self.layers = nn.Sequential(
Conv2d(in_c, out_c, kernel, stride, padding, groups=groups, bias=False), BatchNorm2d(num_features=out_c)
)
def forward(self, x):
return self.layers(x)
class DepthWise(Module):
def __init__(self, in_c, out_c, residual=False, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=1):
super(DepthWise, self).__init__()
self.residual = residual
self.layers = nn.Sequential(
ConvBlock(in_c, out_c=groups, kernel=(1, 1), padding=(0, 0), stride=(1, 1)),
ConvBlock(groups, groups, groups=groups, kernel=kernel, padding=padding, stride=stride),
LinearBlock(groups, out_c, kernel=(1, 1), padding=(0, 0), stride=(1, 1)),
)
def forward(self, x):
short_cut = None
if self.residual:
short_cut = x
x = self.layers(x)
if self.residual:
output = short_cut + x
else:
output = x
return output
class Residual(Module):
def __init__(self, c, num_block, groups, kernel=(3, 3), stride=(1, 1), padding=(1, 1)):
super(Residual, self).__init__()
modules = []
for _ in range(num_block):
modules.append(DepthWise(c, c, True, kernel, stride, padding, groups))
self.layers = Sequential(*modules)
def forward(self, x):
return self.layers(x)
class GDC(Module):
def __init__(self, embedding_size):
super(GDC, self).__init__()
self.layers = nn.Sequential(
LinearBlock(512, 512, groups=512, kernel=(7, 7), stride=(1, 1), padding=(0, 0)),
Flatten(),
Linear(512, embedding_size, bias=False),
BatchNorm1d(embedding_size),
)
def forward(self, x):
return self.layers(x)
class MobileFaceNet(Module):
def __init__(self, fp16=False, num_features=512, blocks=(1, 4, 6, 2), scale=2):
super(MobileFaceNet, self).__init__()
self.scale = scale
self.fp16 = fp16
self.layers = nn.ModuleList()
self.layers.append(ConvBlock(3, 64 * self.scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1)))
if blocks[0] == 1:
self.layers.append(
ConvBlock(64 * self.scale, 64 * self.scale, kernel=(3, 3), stride=(1, 1), padding=(1, 1), groups=64)
)
else:
self.layers.append(
Residual(
64 * self.scale, num_block=blocks[0], groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1)
),
)
self.layers.extend(
[
DepthWise(64 * self.scale, 64 * self.scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=128),
Residual(
64 * self.scale, num_block=blocks[1], groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1)
),
DepthWise(64 * self.scale, 128 * self.scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=256),
Residual(
128 * self.scale, num_block=blocks[2], groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)
),
DepthWise(128 * self.scale, 128 * self.scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=512),
Residual(
128 * self.scale, num_block=blocks[3], groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)
),
]
)
self.conv_sep = ConvBlock(128 * self.scale, 512, kernel=(1, 1), stride=(1, 1), padding=(0, 0))
self.features = GDC(num_features)
self._initialize_weights()
def _initialize_weights(self):
for m in self.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_()
def forward(self, x):
with torch.cuda.amp.autocast(self.fp16):
for func in self.layers:
x = func(x)
x = self.conv_sep(x.float() if self.fp16 else x)
x = self.features(x)
return x
def get_mbf(fp16, num_features, blocks=(1, 4, 6, 2), scale=2):
return MobileFaceNet(fp16, num_features, blocks, scale=scale)
def get_mbf_large(fp16, num_features, blocks=(2, 8, 12, 4), scale=4):
return MobileFaceNet(fp16, num_features, blocks, scale=scale)
|