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resnet_batchRenorm.py
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resnet_batchRenorm.py
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import tensorflow as tf
import os
from arcface import Arcfacelayer
bn_axis = -1
initializer = 'glorot_normal'
# initializer = tf.keras.initializers.TruncatedNormal(
# mean=0.0, stddev=0.05, seed=None)
# initializer = tf.keras.initializers.VarianceScaling(
# scale=0.05, mode='fan_avg', distribution='normal', seed=None)
# initializer = tf.keras.initializers.RandomNormal(
# mean=0.0, stddev=0.01, seed=None)
# initializer = tf.keras.initializers.RandomUniform(
# minval=-0.0001, maxval=0.0001, seed=None)
gammaInit = 'ones'
# gammaInit = tf.keras.initializers.Constant(value=1.0)
# maxNorm = 0.1
def residual_unit_v3(input, num_filter, stride, dim_match, name):
x = tf.keras.layers.BatchNormalization(axis=bn_axis,
scale=True,
momentum=0.9,
epsilon=2e-5,
renorm=True,
renorm_clipping={'rmax': 3,
'rmin': 0.3333,
'dmax': 5},
renorm_momentum=0.9,
beta_regularizer=tf.keras.regularizers.l2(
l=5e-4),
gamma_regularizer=tf.keras.regularizers.l2(
l=5e-4),
gamma_initializer=gammaInit,
name=name + '_bn1')(input)
x = tf.keras.layers.ZeroPadding2D(
padding=(1, 1), name=name + '_conv1_pad')(x)
x = tf.keras.layers.Conv2D(num_filter, (3, 3),
strides=(1, 1),
padding='valid',
kernel_initializer=initializer,
use_bias=False,
kernel_regularizer=tf.keras.regularizers.l2(
l=5e-4),
name=name + '_conv1')(x)
x = tf.keras.layers.BatchNormalization(axis=bn_axis,
scale=True,
momentum=0.9,
epsilon=2e-5,
renorm=True,
renorm_clipping={'rmax': 3,
'rmin': 0.3333,
'dmax': 5},
renorm_momentum=0.9,
beta_regularizer=tf.keras.regularizers.l2(
l=5e-4),
gamma_regularizer=tf.keras.regularizers.l2(
l=5e-4),
gamma_initializer=gammaInit,
name=name + '_bn2')(x)
x = tf.keras.layers.PReLU(name=name + '_relu1',
alpha_regularizer=tf.keras.regularizers.l2(
l=5e-4))(x)
x = tf.keras.layers.ZeroPadding2D(
padding=(1, 1), name=name + '_conv2_pad')(x)
x = tf.keras.layers.Conv2D(num_filter, (3, 3),
strides=stride,
padding='valid',
kernel_initializer=initializer,
use_bias=False,
kernel_regularizer=tf.keras.regularizers.l2(
l=5e-4),
name=name + '_conv2')(x)
x = tf.keras.layers.BatchNormalization(axis=bn_axis,
scale=True,
momentum=0.9,
epsilon=2e-5,
renorm=True,
renorm_clipping={'rmax': 3,
'rmin': 0.3333,
'dmax': 5},
renorm_momentum=0.9,
beta_regularizer=tf.keras.regularizers.l2(
l=5e-4),
gamma_regularizer=tf.keras.regularizers.l2(
l=5e-4),
gamma_initializer=gammaInit,
name=name + '_bn3')(x)
if (dim_match):
shortcut = input
else:
shortcut = tf.keras.layers.Conv2D(num_filter, (1, 1),
strides=stride,
padding='valid',
kernel_initializer=initializer,
use_bias=False,
kernel_regularizer=tf.keras.regularizers.l2(
l=5e-4),
name=name + '_conv1sc')(input)
shortcut = tf.keras.layers.BatchNormalization(axis=bn_axis,
scale=True,
momentum=0.9,
epsilon=2e-5,
renorm=True,
renorm_clipping={
'rmax': 3,
'rmin': 0.3333,
'dmax': 5},
renorm_momentum=0.9,
beta_regularizer=tf.keras.regularizers.l2(
l=5e-4),
gamma_regularizer=tf.keras.regularizers.l2(
l=5e-4),
gamma_initializer=gammaInit,
name=name + '_sc')(shortcut)
return x + shortcut
def get_fc1(input):
x = tf.keras.layers.BatchNormalization(axis=bn_axis,
scale=True,
momentum=0.9,
epsilon=2e-5,
renorm=True,
renorm_clipping={'rmax': 3,
'rmin': 0.3333,
'dmax': 5},
renorm_momentum=0.9,
beta_regularizer=tf.keras.regularizers.l2(
l=5e-4),
gamma_regularizer=tf.keras.regularizers.l2(
l=5e-4),
gamma_initializer=gammaInit,
name='bn1')(input)
x = tf.keras.layers.Dropout(0.4)(x)
resnet_shape = input.shape
x = tf.keras.layers.Reshape(
[resnet_shape[1] * resnet_shape[2] * resnet_shape[3]], name='reshapelayer')(x)
x = tf.keras.layers.Dense(512,
name='E_DenseLayer', kernel_initializer=initializer,
kernel_regularizer=tf.keras.regularizers.l2(
l=5e-4),
bias_regularizer=tf.keras.regularizers.l2(
l=5e-4))(x)
x = tf.keras.layers.BatchNormalization(axis=-1,
scale=False,
momentum=0.9,
epsilon=2e-5,
renorm=True,
renorm_clipping={'rmax': 3,
'rmin': 0.3333,
'dmax': 5},
renorm_momentum=0.9,
beta_regularizer=tf.keras.regularizers.l2(
l=5e-4),
name='fc1')(x)
return x
def ResNet50():
input_shape = [112, 112, 3]
filter_list = [64, 64, 128, 256, 512]
units = [3, 4, 14, 3]
num_stages = 4
img_input = tf.keras.layers.Input(shape=input_shape)
x = tf.keras.layers.ZeroPadding2D(
padding=(1, 1), name='conv0_pad')(img_input)
x = tf.keras.layers.Conv2D(64, (3, 3),
strides=(1, 1),
padding='valid',
kernel_initializer=initializer,
use_bias=False,
kernel_regularizer=tf.keras.regularizers.l2(
l=5e-4),
name='conv0')(x)
x = tf.keras.layers.BatchNormalization(axis=bn_axis,
scale=True,
momentum=0.9,
epsilon=2e-5,
renorm=True,
renorm_clipping={'rmax': 3,
'rmin': 0.3333,
'dmax': 5},
renorm_momentum=0.9,
beta_regularizer=tf.keras.regularizers.l2(
l=5e-4),
gamma_regularizer=tf.keras.regularizers.l2(
l=5e-4),
gamma_initializer=gammaInit,
name='bn0')(x)
x = tf.keras.layers.PReLU(
name='prelu0',
alpha_regularizer=tf.keras.regularizers.l2(
l=5e-4))(x)
for i in range(num_stages):
x = residual_unit_v3(x, filter_list[i + 1], (2, 2), False,
name='stage%d_unit%d' % (i + 1, 1))
for j in range(units[i] - 1):
x = residual_unit_v3(x, filter_list[i + 1], (1, 1),
True, name='stage%d_unit%d' % (i + 1, j + 2))
x = get_fc1(x)
# Create model.
model = tf.keras.models.Model(img_input, x, name='resnet50')
model.trainable = True
for i in range(len(model.layers)):
model.layers[i].trainable = True
# if ('conv0' in model.layers[i].name):
# model.layers[i].trainable = False
# if ('bn0' in model.layers[i].name):
# model.layers[i].trainable = False
# if ('prelu0' in model.layers[i].name):
# model.layers[i].trainable = False
# if ('stage1' in model.layers[i].name):
# model.layers[i].trainable = False
# if ('stage2' in model.layers[i].name):
# model.layers[i].trainable = False
# if ('stage3' in model.layers[i].name):
# model.layers[i].trainable = False
# if ('stage4' in model.layers[i].name):
# model.layers[i].trainable = False
return model
class train_model(tf.keras.Model):
def __init__(self):
super(train_model, self).__init__()
self.resnet = ResNet50()
self.arcface = Arcfacelayer()
def call(self, x, y):
x = self.resnet(x)
return self.arcface(x, y)