def model_InceptionResNet_BLSTM(images, weight_decay=1e-5, is_training=True):
'''
相对不对称的U-Net结构, 并在Inception提出特征后,使用BLSTM提取全局的一个特征
:param images:
:param weight_decay:
:param is_training:
:return:
'''
images = mean_image_subtraction(images)
logits, end_points = InceptionResNet.model(images,
is_training=is_training,
weight_decay=weight_decay)
print 'end_points is ', end_points
print 'end_points key are', end_points.keys()
print 'logits is ', logits
with tf.variable_scope('feature_fusion', values=[end_points.values]):
batch_norm_params = {
'decay': 0.997,
'epsilon': 1e-5,
'scale': True,
'is_training': is_training
}
# 提取Global的特征
# end_points['Scale-5'] = Bilstm(end_points['Scale-5'], d_i=1536, d_h=256, d_o=256, name='Bilstm',
# weight_decay=weight_decay)
with slim.arg_scope(
[slim.conv2d],
activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params,
weights_regularizer=slim.l2_regularizer(weight_decay)):
f = [
end_points['Scale-5'], end_points['Scale-4'],
end_points['Scale-3'], end_points['Scale-2'],
end_points['Scale-1'], end_points['Scale-0']
]
for i in range(4):
print('Shape of f_{} {}'.format(i, f[i].shape))
g = [None, None, None, None, None, None]
h = [None, None, None, None, None, None]
num_inputs = [1536, 1088, 320, 192, 64, 64]
num_outputs = [None, 128, 64, 32, 32, 32]
g_last = 6
for i in range(g_last):
if i == 0:
h[i] = f[i]
else:
f[i] = slim.conv2d(f[i],
num_outputs=num_outputs[i],
kernel_size=1)
f[i] = Bilstm(f[i],
num_outputs[i],
num_outputs[i] * 2,
num_outputs[i],
name='Bilstm-' + str(i))
c1_1 = slim.conv2d(tf.concat([g[i - 1], f[i]], axis=-1),
num_outputs[i], 1)
h[i] = slim.conv2d(c1_1, num_outputs[i], 3)
if i <= g_last - 2:
g[i] = unpool(h[i])
if i == 0:
continue
# g[i] = Bilstm(g[i], num_outputs[i], num_outputs[i] * 2, num_outputs[i], name='Bilstm-' + str(i))
else:
g[i] = slim.conv2d(h[i], num_outputs[i], 3)
print('Shape of h_{} {}, g_{} {}'.format(
i, h[i].shape, i, g[i].shape))
print 'g[', str(g_last - 1), '] shape is ', g[g_last - 1]
# here we use a slightly different way for regression part,
# we first use a sigmoid to limit the regression range, and also
# this is do with the angle map
if is_training:
if FLAGS.cross_loss:
print 'loss: cross_loss'
F_score = slim.conv2d(g[g_last - 1],
1,
1,
activation_fn=None,
normalizer_fn=None)
else:
print 'loss: IoU loss'
F_score = slim.conv2d(g[g_last - 1],
1,
1,
activation_fn=tf.nn.sigmoid,
normalizer_fn=None)
else:
F_score = slim.conv2d(g[g_last - 1],
1,
1,
activation_fn=tf.nn.sigmoid,
normalizer_fn=None)
# 4 channel of axis aligned bbox and 1 channel rotation angle
input_tensor_for_geo = tf.concat([g[g_last - 1], F_score], axis=-1)
input_tensor_for_geo = slim.conv2d(input_tensor_for_geo,
33,
kernel_size=3,
stride=1,
activation_fn=tf.nn.relu,
scope='merge_conv1')
input_tensor_for_geo = slim.conv2d(input_tensor_for_geo,
33,
kernel_size=3,
stride=1,
activation_fn=tf.nn.relu,
scope='merge_conv2')
geo_map = slim.conv2d(input_tensor_for_geo,
4,
1,
activation_fn=tf.nn.sigmoid,
normalizer_fn=None) * FLAGS.text_scale
angle_map = (slim.conv2d(input_tensor_for_geo,
1,
1,
activation_fn=tf.nn.sigmoid,
normalizer_fn=None) -
0.5) * np.pi / 2 # angle is between [-45, 45]
F_geometry = tf.concat([geo_map, angle_map], axis=-1)
return F_score, F_geometry
def model_InceptionResNet_symmetry(images,
weight_decay=1e-5,
is_training=True):
'''
相对不对称的U-Net结构
:param images:
:param weight_decay:
:param is_training:
:return:
'''
images = mean_image_subtraction(images)
logits, end_points = InceptionResNet.model(images,
is_training=is_training,
weight_decay=weight_decay)
print 'end_points is ', end_points
print 'end_points key are', end_points.keys()
print 'logits is ', logits
with tf.variable_scope('feature_fusion', values=[end_points.values]):
batch_norm_params = {
'decay': 0.997,
'epsilon': 1e-5,
'scale': True,
'is_training': is_training
}
with slim.arg_scope(
[slim.conv2d],
activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params,
weights_regularizer=slim.l2_regularizer(weight_decay)):
f = [
end_points['Scale-5'], end_points['Scale-4'],
end_points['Scale-3'], end_points['Scale-2'],
end_points['Scale-1'], end_points['Scale-0']
]
for i in range(4):
print('Shape of f_{} {}'.format(i, f[i].shape))
# Scale-5:
output_channel = 256
h = end_points['Scale-5']
h = slim.conv2d(h, output_channel, kernel_size=3)
h = slim.conv2d(tf.concat([h, end_points['Scale-5-2']], axis=-1),
output_channel, 1)
h = slim.conv2d(h, output_channel, kernel_size=3)
h = slim.conv2d(tf.concat([h, end_points['Scale-5-1']], axis=-1),
output_channel, 1)
h = slim.conv2d(h, output_channel, kernel_size=3)
h = slim.conv2d(tf.concat([h, end_points['Scale-5-0']], axis=-1),
output_channel, 1)
h = slim.conv2d(h, output_channel, kernel_size=3)
g = unpool(h)
# Scale-4
output_channel = 128
h = slim.conv2d(tf.concat([g, end_points['Scale-4']], axis=-1),
output_channel, 1)
h = slim.conv2d(h, output_channel, kernel_size=3)
h = slim.conv2d(tf.concat([h, end_points['Scale-4-0']], axis=-1),
output_channel, 1)
h = slim.conv2d(h, output_channel, kernel_size=3)
g = unpool(h)
# Scale-3
output_channel = 64
h = slim.conv2d(tf.concat([g, end_points['Scale-3']], axis=-1),
output_channel, 1)
h = slim.conv2d(h, output_channel, kernel_size=3)
h = slim.conv2d(tf.concat([h, end_points['Scale-3-1']], axis=-1),
output_channel, 1)
h = slim.conv2d(h, output_channel, kernel_size=3)
h = slim.conv2d(tf.concat([h, end_points['Scale-3-0']], axis=-1),
output_channel, 1)
h = slim.conv2d(h, output_channel, kernel_size=3)
g = unpool(h)
# Scale-2
output_channel = 32
h = slim.conv2d(tf.concat([g, end_points['Scale-2']], axis=-1),
output_channel, 1)
h = slim.conv2d(h, output_channel, kernel_size=3)
h = slim.conv2d(tf.concat([h, end_points['Scale-2-1']], axis=-1),
output_channel, 1)
h = slim.conv2d(h, output_channel, kernel_size=3)
h = slim.conv2d(tf.concat([h, end_points['Scale-2-0']], axis=-1),
output_channel, 1)
h = slim.conv2d(h, output_channel, kernel_size=3)
g = unpool(h)
# Scale-1
output_channel = 32
h = slim.conv2d(tf.concat([g, end_points['Scale-1']], axis=-1),
output_channel, 1)
h = slim.conv2d(h, output_channel, kernel_size=3)
h = slim.conv2d(tf.concat([h, end_points['Scale-1-1']], axis=-1),
output_channel, 1)
h = slim.conv2d(h, output_channel, kernel_size=3)
h = slim.conv2d(tf.concat([h, end_points['Scale-1-0']], axis=-1),
output_channel, 1)
h = slim.conv2d(h, output_channel, kernel_size=3)
g = unpool(h)
# Scale-1
output_channel = 32
h = slim.conv2d(tf.concat([g, end_points['Scale-0']], axis=-1),
output_channel, 1)
h = slim.conv2d(h, output_channel, kernel_size=3)
h = slim.conv2d(tf.concat([h, end_points['Scale-0-1']], axis=-1),
output_channel, 1)
h = slim.conv2d(h, output_channel, kernel_size=3)
h = slim.conv2d(tf.concat([h, end_points['Scale-0-0']], axis=-1),
output_channel, 1)
h = slim.conv2d(h, output_channel, kernel_size=3)
g = slim.conv2d(h, output_channel, 3)
if is_training:
if FLAGS.cross_loss:
print 'loss: cross_loss'
F_score = slim.conv2d(g,
1,
1,
activation_fn=None,
normalizer_fn=None)
else:
print 'loss: IoU loss'
F_score = slim.conv2d(g,
1,
1,
activation_fn=tf.nn.sigmoid,
normalizer_fn=None)
else:
F_score = slim.conv2d(g,
1,
1,
activation_fn=tf.nn.sigmoid,
normalizer_fn=None)
# 4 channel of axis aligned bbox and 1 channel rotation angle
input_tensor_for_geo = tf.concat([g, F_score], axis=-1)
input_tensor_for_geo = slim.conv2d(input_tensor_for_geo,
33,
kernel_size=3,
stride=1,
activation_fn=tf.nn.relu,
scope='merge_conv1')
input_tensor_for_geo = slim.conv2d(input_tensor_for_geo,
33,
kernel_size=3,
stride=1,
activation_fn=tf.nn.relu,
scope='merge_conv2')
geo_map = slim.conv2d(input_tensor_for_geo,
4,
1,
activation_fn=tf.nn.sigmoid,
normalizer_fn=None) * FLAGS.text_scale
angle_map = (slim.conv2d(input_tensor_for_geo,
1,
1,
activation_fn=tf.nn.sigmoid,
normalizer_fn=None) -
0.5) * np.pi / 2 # angle is between [-45, 45]
F_geometry = tf.concat([geo_map, angle_map], axis=-1)
return F_score, F_geometry