# def max_pool2d(inputs,#                kernel_size,#                stride=2,#                padding='VALID',#                data_format=DATA_FORMAT_NHWC,#                outputs_collections=None,#                scope=None):#"VALID"模式下#输出图像大小 out_height = round((in_height - floor(filter_height / 2) * 2) / strides_height) floor表示下取整 round表示四舍五入input = tf.Variable(tf.round(10 * tf.random_normal([1, 7, 7, 1])))#filter = tf.Variable(tf.round(5 * tf.random_normal([3, 3, 1, 1])))#op2 = tf.nn.conv2d(input, filter, strides=[1, 1, 1, 1], padding='VALID')slim_max_pool2d = slim.max_pool2d(input, [3, 3], [1, 1], scope='pool1')#slim_conv2d_SAME = slim.conv2d(input, 1, [3, 3], [1, 1], weights_initializer=tf.ones_initializer, padding='SAME')with tf.Session() as sess:    sess.run(tf.global_variables_initializer())    slim_max_pool2d_value = \        sess.run(slim_max_pool2d)    print(slim_max_pool2d_value.shape)