File size: 10,639 Bytes
7576d48 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 |
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.applications import MobileNetV2, ResNet50
from tensorflow.keras.layers import Input, Conv2D, ReLU, LeakyReLU
from retinaface.anchor import decode_tf, prior_box_tf
def _regularizer(weights_decay):
"""l2 regularizer"""
return tf.keras.regularizers.l2(weights_decay)
def _kernel_init(scale=1.0, seed=None):
"""He normal initializer"""
return tf.keras.initializers.he_normal()
class BatchNormalization(tf.keras.layers.BatchNormalization):
"""Make trainable=False freeze BN for real (the og version is sad).
ref: https://github.com/zzh8829/yolov3-tf2
"""
def __init__(self, axis=-1, momentum=0.9, epsilon=1e-5, center=True,
scale=True, name=None, **kwargs):
super(BatchNormalization, self).__init__(
axis=axis, momentum=momentum, epsilon=epsilon, center=center,
scale=scale, name=name, **kwargs)
def call(self, x, training=False):
if training is None:
training = tf.constant(False)
training = tf.logical_and(training, self.trainable)
return super().call(x, training)
def Backbone(backbone_type='ResNet50', use_pretrain=True):
"""Backbone Model"""
weights = None
if use_pretrain:
weights = 'imagenet'
def backbone(x):
if backbone_type == 'ResNet50':
extractor = ResNet50(
input_shape=x.shape[1:], include_top=False, weights=weights)
pick_layer1 = 80 # [80, 80, 512]
pick_layer2 = 142 # [40, 40, 1024]
pick_layer3 = 174 # [20, 20, 2048]
preprocess = tf.keras.applications.resnet.preprocess_input
elif backbone_type == 'MobileNetV2':
extractor = MobileNetV2(
input_shape=x.shape[1:], include_top=False, weights=weights)
pick_layer1 = 54 # [80, 80, 32]
pick_layer2 = 116 # [40, 40, 96]
pick_layer3 = 143 # [20, 20, 160]
preprocess = tf.keras.applications.mobilenet_v2.preprocess_input
else:
raise NotImplementedError(
'Backbone type {} is not recognized.'.format(backbone_type))
return Model(extractor.input,
(extractor.layers[pick_layer1].output,
extractor.layers[pick_layer2].output,
extractor.layers[pick_layer3].output),
name=backbone_type + '_extrator')(preprocess(x))
return backbone
class ConvUnit(tf.keras.layers.Layer):
"""Conv + BN + Act"""
def __init__(self, f, k, s, wd, act=None, **kwargs):
super(ConvUnit, self).__init__(**kwargs)
self.conv = Conv2D(filters=f, kernel_size=k, strides=s, padding='same',
kernel_initializer=_kernel_init(),
kernel_regularizer=_regularizer(wd),
use_bias=False)
self.bn = BatchNormalization()
if act is None:
self.act_fn = tf.identity
elif act == 'relu':
self.act_fn = ReLU()
elif act == 'lrelu':
self.act_fn = LeakyReLU(0.1)
else:
raise NotImplementedError(
'Activation function type {} is not recognized.'.format(act))
def call(self, x):
return self.act_fn(self.bn(self.conv(x)))
class FPN(tf.keras.layers.Layer):
"""Feature Pyramid Network"""
def __init__(self, out_ch, wd, **kwargs):
super(FPN, self).__init__(**kwargs)
act = 'relu'
self.out_ch = out_ch
self.wd = wd
if (out_ch <= 64):
act = 'lrelu'
self.output1 = ConvUnit(f=out_ch, k=1, s=1, wd=wd, act=act)
self.output2 = ConvUnit(f=out_ch, k=1, s=1, wd=wd, act=act)
self.output3 = ConvUnit(f=out_ch, k=1, s=1, wd=wd, act=act)
self.merge1 = ConvUnit(f=out_ch, k=3, s=1, wd=wd, act=act)
self.merge2 = ConvUnit(f=out_ch, k=3, s=1, wd=wd, act=act)
def call(self, x):
output1 = self.output1(x[0]) # [80, 80, out_ch]
output2 = self.output2(x[1]) # [40, 40, out_ch]
output3 = self.output3(x[2]) # [20, 20, out_ch]
up_h, up_w = tf.shape(output2)[1], tf.shape(output2)[2]
up3 = tf.image.resize(output3, [up_h, up_w], method='nearest')
output2 = output2 + up3
output2 = self.merge2(output2)
up_h, up_w = tf.shape(output1)[1], tf.shape(output1)[2]
up2 = tf.image.resize(output2, [up_h, up_w], method='nearest')
output1 = output1 + up2
output1 = self.merge1(output1)
return output1, output2, output3
def get_config(self):
config = {
'out_ch': self.out_ch,
'wd': self.wd,
}
base_config = super(FPN, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class SSH(tf.keras.layers.Layer):
"""Single Stage Headless Layer"""
def __init__(self, out_ch, wd, **kwargs):
super(SSH, self).__init__(**kwargs)
assert out_ch % 4 == 0
self.out_ch = out_ch
self.wd = wd
act = 'relu'
if (out_ch <= 64):
act = 'lrelu'
self.conv_3x3 = ConvUnit(f=out_ch // 2, k=3, s=1, wd=wd, act=None)
self.conv_5x5_1 = ConvUnit(f=out_ch // 4, k=3, s=1, wd=wd, act=act)
self.conv_5x5_2 = ConvUnit(f=out_ch // 4, k=3, s=1, wd=wd, act=None)
self.conv_7x7_2 = ConvUnit(f=out_ch // 4, k=3, s=1, wd=wd, act=act)
self.conv_7x7_3 = ConvUnit(f=out_ch // 4, k=3, s=1, wd=wd, act=None)
self.relu = ReLU()
def call(self, x):
conv_3x3 = self.conv_3x3(x)
conv_5x5_1 = self.conv_5x5_1(x)
conv_5x5 = self.conv_5x5_2(conv_5x5_1)
conv_7x7_2 = self.conv_7x7_2(conv_5x5_1)
conv_7x7 = self.conv_7x7_3(conv_7x7_2)
output = tf.concat([conv_3x3, conv_5x5, conv_7x7], axis=3)
output = self.relu(output)
return output
def get_config(self):
config = {
'out_ch': self.out_ch,
'wd': self.wd,
}
base_config = super(SSH, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class BboxHead(tf.keras.layers.Layer):
"""Bbox Head Layer"""
def __init__(self, num_anchor, wd, **kwargs):
super(BboxHead, self).__init__(**kwargs)
self.num_anchor = num_anchor
self.wd = wd
self.conv = Conv2D(filters=num_anchor * 4, kernel_size=1, strides=1)
def call(self, x):
h, w = tf.shape(x)[1], tf.shape(x)[2]
x = self.conv(x)
return tf.reshape(x, [-1, h * w * self.num_anchor, 4])
def get_config(self):
config = {
'num_anchor': self.num_anchor,
'wd': self.wd,
}
base_config = super(BboxHead, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class LandmarkHead(tf.keras.layers.Layer):
"""Landmark Head Layer"""
def __init__(self, num_anchor, wd, name='LandmarkHead', **kwargs):
super(LandmarkHead, self).__init__(name=name, **kwargs)
self.num_anchor = num_anchor
self.wd = wd
self.conv = Conv2D(filters=num_anchor * 10, kernel_size=1, strides=1)
def call(self, x):
h, w = tf.shape(x)[1], tf.shape(x)[2]
x = self.conv(x)
return tf.reshape(x, [-1, h * w * self.num_anchor, 10])
def get_config(self):
config = {
'num_anchor': self.num_anchor,
'wd': self.wd,
}
base_config = super(LandmarkHead, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class ClassHead(tf.keras.layers.Layer):
"""Class Head Layer"""
def __init__(self, num_anchor, wd, name='ClassHead', **kwargs):
super(ClassHead, self).__init__(name=name, **kwargs)
self.num_anchor = num_anchor
self.wd = wd
self.conv = Conv2D(filters=num_anchor * 2, kernel_size=1, strides=1)
def call(self, x):
h, w = tf.shape(x)[1], tf.shape(x)[2]
x = self.conv(x)
return tf.reshape(x, [-1, h * w * self.num_anchor, 2])
def get_config(self):
config = {
'num_anchor': self.num_anchor,
'wd': self.wd,
}
base_config = super(ClassHead, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def RetinaFaceModel(cfg, training=False, iou_th=0.4, score_th=0.02,
name='RetinaFaceModel'):
"""Retina Face Model"""
input_size = cfg['input_size'] if training else None
wd = cfg['weights_decay']
out_ch = cfg['out_channel']
num_anchor = len(cfg['min_sizes'][0])
backbone_type = cfg['backbone_type']
# define model
x = inputs = Input([input_size, input_size, 3], name='input_image')
x = Backbone(backbone_type=backbone_type)(x)
fpn = FPN(out_ch=out_ch, wd=wd)(x)
features = [SSH(out_ch=out_ch, wd=wd)(f)
for i, f in enumerate(fpn)]
bbox_regressions = tf.concat(
[BboxHead(num_anchor, wd=wd)(f)
for i, f in enumerate(features)], axis=1)
landm_regressions = tf.concat(
[LandmarkHead(num_anchor, wd=wd, name=f'LandmarkHead_{i}')(f)
for i, f in enumerate(features)], axis=1)
classifications = tf.concat(
[ClassHead(num_anchor, wd=wd, name=f'ClassHead_{i}')(f)
for i, f in enumerate(features)], axis=1)
classifications = tf.keras.layers.Softmax(axis=-1)(classifications)
if training:
out = (bbox_regressions, landm_regressions, classifications)
else:
# only for batch size 1
preds = tf.concat( # [bboxes, landms, landms_valid, conf]
[bbox_regressions[0],
landm_regressions[0],
tf.ones_like(classifications[0, :, 0][..., tf.newaxis]),
classifications[0, :, 1][..., tf.newaxis]], 1)
priors = prior_box_tf((tf.shape(inputs)[1], tf.shape(inputs)[2]), cfg['min_sizes'], cfg['steps'], cfg['clip'])
decode_preds = decode_tf(preds, priors, cfg['variances'])
selected_indices = tf.image.non_max_suppression(
boxes=decode_preds[:, :4],
scores=decode_preds[:, -1],
max_output_size=tf.shape(decode_preds)[0],
iou_threshold=iou_th,
score_threshold=score_th)
out = tf.gather(decode_preds, selected_indices)
return Model(inputs, out, name=name), Model(inputs, [bbox_regressions, landm_regressions, classifications], name=name + '_bb_only')
|