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