hourglass_tiny.py 文件源码

def _attention_iter(self, inputs, lrnSize, itersize, name = 'attention_iter'):
        with tf.name_scope(name):
            numIn = inputs.get_shape().as_list()[3]
            padding = np.floor(lrnSize/2)
            pad = tf.pad(inputs, np.array([[0,0],[1,1],[1,1],[0,0]]))
            U = self._conv(pad, filters=1, kernel_size=3, strides=1)
            pad_2 = tf.pad(U, np.array([[0,0],[padding,padding],[padding,padding],[0,0]]))
            sharedK = tf.Variable(tf.contrib.layers.xavier_initializer(uniform=False)([lrnSize,lrnSize, 1, 1]), name= 'shared_weights')
            Q = []
            C = []
            for i in range(itersize):
                if i ==0:
                    conv = tf.nn.conv2d(pad_2, sharedK, [1,1,1,1], padding='VALID', data_format='NHWC')
                else:
                    conv = tf.nn.conv2d(Q[i-1], sharedK, [1,1,1,1], padding='SAME', data_format='NHWC')
                C.append(conv)
                Q_tmp = tf.nn.sigmoid(tf.add_n([C[i], U]))
                Q.append(Q_tmp)
            stacks = []
            for i in range(numIn):
                stacks.append(Q[-1]) 
            pfeat = tf.multiply(inputs,tf.concat(stacks, axis = 3) )
        return pfeat