def blockC(name, x):
with tf.variable_scope(name) as scope:
layer = avg_pool('avg_pool', x, k=2, s=1)
layer = convolution2d('cnn', layer, 256, k=1, s=1)
layer_ = convolution2d('cnn_0', x, 256, k=1, s=1)
layer__ = convolution2d('cnn__0', x, 384, k=1, s=1)
layer__0 = convolution2d_manual('cnn__1_0',
layer__,
256,
k_h=1,
k_w=3,
s=1)
layer__1 = convolution2d_manual('cnn__1_1',
layer__,
256,
k_h=3,
k_w=1,
s=1)
layer___ = convolution2d('cnn___0', x, 384, k=1, s=1)
layer___ = convolution2d_manual('cnn___1',
layer___,
448,
k_h=1,
k_w=3,
s=1)
layer___ = convolution2d_manual('cnn___2',
layer___,
512,
k_h=3,
k_w=1,
s=1)
layer___0 = convolution2d_manual('cnn___3_0',
layer___,
256,
k_h=3,
k_w=1,
s=1)
layer___1 = convolution2d_manual('cnn___3_1',
layer___,
256,
k_h=1,
k_w=3,
s=1)
layer_join = tf.concat(
[layer, layer_, layer__0, layer__1, layer___0, layer___1],
axis=3,
name='join')
print 'layer_name :', 'join'
print 'layer_shape :', layer_join.get_shape()
return layer_join
def blockB(name, x):
with tf.variable_scope(name) as scope:
layer = avg_pool('avg_pool', x, k=2, s=1)
layer = convolution2d('cnn', layer, 128, k=1, s=1)
layer_ = convolution2d('cnn_0', x, 384, k=1, s=1)
layer__ = convolution2d('cnn__0', x, 192, k=1, s=1)
layer__ = convolution2d_manual('cnn__1',
layer__,
224,
k_h=1,
k_w=7,
s=1)
layer__ = convolution2d_manual('cnn__2',
layer__,
256,
k_h=1,
k_w=7,
s=1)
layer___ = convolution2d('cnn___0', x, 192, k=1, s=1)
layer___ = convolution2d_manual('cnn___1',
layer___,
192,
k_h=1,
k_w=7,
s=1)
layer___ = convolution2d_manual('cnn___2',
layer___,
224,
k_h=7,
k_w=1,
s=1)
layer___ = convolution2d_manual('cnn___3',
layer___,
224,
k_h=1,
k_w=7,
s=1)
layer___ = convolution2d_manual('cnn___4',
layer___,
256,
k_h=7,
k_w=1,
s=1)
layer_join = tf.concat([layer, layer_, layer__, layer___],
axis=3,
name='join')
print 'layer_name :', 'join'
print 'layer_shape :', layer_join.get_shape()
return layer_join
def blockA(name , x):
with tf.variable_scope(name) as scope:
layer = avg_pool('avg_pool', x, k=2, s=1)
layer = convolution2d('cnn', layer, 96, k=1, s=1)
layer_ = convolution2d('cnn_0', x, 96, k=1, s=1)
layer__ = convolution2d('cnn__0', x, 64, k=1, s=1)
layer__ = convolution2d('cnn__1', layer__,96, k=3, s=1)
layer___ = convolution2d('cnn___0', x,64, k=1, s=1)
layer___ = convolution2d('cnn___1',layer___,96, k=3, s=1)
layer___ = convolution2d('cnn___2',layer___,96, k=3, s=1)
layer_join = tf.concat([layer, layer_, layer__, layer___], axis=3, name='join')
print 'layer_name :', 'join'
print 'layer_shape :', layer_join.get_shape()
return layer_join