Open HUI11126 opened 3 years ago
`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): levelname = 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='upsample'+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`
`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): levelname = 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='upsample'+level_name)