def inference(images, pool_type, use_lrn, keep_probability, phase_train=True, weight_decay=0.0):
""" Define an inference network for face recognition based
on inception modules using batch normalization
Args:
images: The images to run inference on, dimensions batch_size x height x width x channels
phase_train: True if batch normalization should operate in training mode
"""
conv1 = facenet.conv(images, 3, 64, 7, 7, 2, 2, 'SAME', 'conv1_7x7', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
pool1 = facenet.mpool(conv1, 3, 3, 2, 2, 'SAME', 'pool1')
if use_lrn:
lrn1 = tf.nn.local_response_normalization(pool1, depth_radius=5, bias=1.0, alpha=1e-4, beta=0.75)
else:
lrn1 = pool1
conv2 = facenet.conv(lrn1, 64, 64, 1, 1, 1, 1, 'SAME', 'conv2_1x1', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
conv3 = facenet.conv(conv2, 64, 192, 3, 3, 1, 1, 'SAME', 'conv3_3x3', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
if use_lrn:
lrn2 = tf.nn.local_response_normalization(conv3, depth_radius=5, bias=1.0, alpha=1e-4, beta=0.75)
else:
lrn2 = conv3
pool3 = facenet.mpool(lrn2, 3, 3, 2, 2, 'SAME', 'pool3')
incept3a = facenet.inception(pool3, 192, 1, 64, 96, 128, 16, 32, 3, 32, 1, 'MAX', 'incept3a', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
incept3b = facenet.inception(incept3a, 256, 1, 64, 96, 128, 32, 64, 3, 64, 1, pool_type, 'incept3b', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
incept3c = facenet.inception(incept3b, 320, 2, 0, 128, 256, 32, 64, 3, 0, 2, 'MAX', 'incept3c', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
incept4a = facenet.inception(incept3c, 640, 1, 256, 96, 192, 32, 64, 3, 128, 1, pool_type, 'incept4a', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
incept4b = facenet.inception(incept4a, 640, 1, 224, 112, 224, 32, 64, 3, 128, 1, pool_type, 'incept4b', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
incept4c = facenet.inception(incept4b, 640, 1, 192, 128, 256, 32, 64, 3, 128, 1, pool_type, 'incept4c', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
incept4d = facenet.inception(incept4c, 640, 1, 160, 144, 288, 32, 64, 3, 128, 1, pool_type, 'incept4d', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
incept4e = facenet.inception(incept4d, 640, 2, 0, 160, 256, 64, 128, 3, 0, 2, 'MAX', 'incept4e', phase_train=phase_train, use_batch_norm=True)
incept5a = facenet.inception(incept4e, 1024, 1, 384, 192, 384, 0, 0, 3, 128, 1, pool_type, 'incept5a', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
incept5b = facenet.inception(incept5a, 896, 1, 384, 192, 384, 0, 0, 3, 128, 1, 'MAX', 'incept5b', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
pool6 = facenet.apool(incept5b, 3, 3, 1, 1, 'VALID', 'pool6')
resh1 = tf.reshape(pool6, [-1, 896])
affn1 = facenet.affine(resh1, 896, 128, 'fc7', weight_decay=weight_decay)
dropout = tf.nn.dropout(affn1, keep_probability)
norm = tf.nn.l2_normalize(dropout, 1, 1e-10, name='embeddings')
return norm
def inference(images, pool_type, use_lrn, keep_probability, phase_train=True):
""" Define an inference network for face recognition based
on inception modules using batch normalization
Args:
images: The images to run inference on, dimensions batch_size x height x width x channels
phase_train: True if batch normalization should operate in training mode
"""
conv1 = facenet.conv(images,
3,
64,
7,
7,
2,
2,
'SAME',
'conv1_7x7',
phase_train=phase_train,
use_batch_norm=True)
pool1 = facenet.mpool(conv1, 3, 3, 2, 2, 'SAME')
if use_lrn:
lrn1 = tf.nn.local_response_normalization(pool1,
depth_radius=5,
bias=1.0,
alpha=1e-4,
beta=0.75)
else:
lrn1 = pool1
conv2 = facenet.conv(lrn1,
64,
64,
1,
1,
1,
1,
'SAME',
'conv2_1x1',
phase_train=phase_train,
use_batch_norm=True)
conv3 = facenet.conv(conv2,
64,
192,
3,
3,
1,
1,
'SAME',
'conv3_3x3',
phase_train=phase_train,
use_batch_norm=True)
if use_lrn:
lrn2 = tf.nn.local_response_normalization(conv3,
depth_radius=5,
bias=1.0,
alpha=1e-4,
beta=0.75)
else:
lrn2 = conv3
pool3 = facenet.mpool(lrn2, 3, 3, 2, 2, 'SAME')
incept3a = facenet.inception(pool3,
192,
1,
64,
96,
128,
16,
32,
3,
32,
1,
'MAX',
'incept3a',
phase_train=phase_train,
use_batch_norm=True)
incept3b = facenet.inception(incept3a,
256,
1,
64,
96,
128,
32,
64,
3,
64,
1,
pool_type,
'incept3b',
phase_train=phase_train,
use_batch_norm=True)
incept3c = facenet.inception(incept3b,
320,
2,
0,
128,
256,
32,
64,
3,
0,
2,
'MAX',
'incept3c',
phase_train=phase_train,
use_batch_norm=True)
incept4a = facenet.inception(incept3c,
640,
1,
256,
96,
192,
32,
64,
3,
128,
1,
pool_type,
'incept4a',
phase_train=phase_train,
use_batch_norm=True)
incept4b = facenet.inception(incept4a,
640,
1,
224,
112,
224,
32,
64,
3,
128,
1,
pool_type,
'incept4b',
phase_train=phase_train,
use_batch_norm=True)
incept4c = facenet.inception(incept4b,
640,
1,
192,
128,
256,
32,
64,
3,
128,
1,
pool_type,
'incept4c',
phase_train=phase_train,
use_batch_norm=True)
incept4d = facenet.inception(incept4c,
640,
1,
160,
144,
288,
32,
64,
3,
128,
1,
pool_type,
'incept4d',
phase_train=phase_train,
use_batch_norm=True)
incept4e = facenet.inception(incept4d,
640,
2,
0,
160,
256,
64,
128,
3,
0,
2,
'MAX',
'incept4e',
phase_train=phase_train,
use_batch_norm=True)
incept5a = facenet.inception(incept4e,
1024,
1,
384,
192,
384,
0,
0,
3,
128,
1,
pool_type,
'incept5a',
phase_train=phase_train,
use_batch_norm=True)
incept5b = facenet.inception(incept5a,
896,
1,
384,
192,
384,
0,
0,
3,
128,
1,
'MAX',
'incept5b',
phase_train=phase_train,
use_batch_norm=True)
pool6 = facenet.apool(incept5b, 3, 3, 1, 1, 'VALID')
resh1 = tf.reshape(pool6, [-1, 896])
affn1 = facenet.affine(resh1, 896, 128)
dropout = tf.nn.dropout(affn1, keep_probability)
norm = tf.nn.l2_normalize(dropout, 1, 1e-10, name='embeddings')
return norm