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`def fusion_two_layer(C_i, P_j, scope):
'''
i = j+1
:param C_i: shape is [1, h, w, c]
:param P_j: shape is [1, h/2, w/2, 256]
:return:
P_i
'''
with tf.variable_scope(scope):
level_name = scope.split('')[1]
h, w = tf.shape(C_i)[1], tf.shape(C_i)[2]
upsample_p = tf.image.resize_bilinear(P_j,
size=[h, w],
name='up_sample'+level_name)
reduce_dim_c = slim.conv2d(C_i,
num_outputs=256,
kernel_size=[1, 1], stride=1,
scope='reduce_dim_'+level_name)
add_f = 0.5*upsample_p + 0.5*reduce_dim_c
# P_i = slim.conv2d(add_f,
# num_outputs=256, kernel_size=[3, 3], stride=1,
# padding='SAME',
# scope='fusion_'+level_name)
return add_f`
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