WithTensorflow/InceptionV3demo.py

# _*_ coding:utf-8 _*_

import math
import time
import tensorflow as tf
from datetime import datetime

batch_size = 32
num_batches = 100
height, width = 299, 299

slim = tf.contrib.slim
#  产生截断的正态分布
trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)

def print_activations(t):
    print(t.op.name, '  ', t.get_shape().as_list())

# 生成网络中经常用到的函数的默认参数
'''
    卷积的激活函数
    权重初始化方式
    标准化器
'''
def inception_v3_arg_scope(weight_decay=0.00004,
                           stddev=0.1,
                           batch_norm_var_collection='moving_vars'):
    batch_norm_params = {
        'decay': 0.997,  # 衰减系数
        'epsilon': 0.001,
        'updates_collections': tf.GraphKeys.UPDATE_OPS,
        'variables_collections': {
            'beta': None,
            'gamma': None,
            'moving_mean': [batch_norm_var_collection],
            'moving_variance': [batch_norm_var_collection]
        }
    }

    # 使用slim.arg_scope给函数赋予默认值
    with slim.arg_scope([slim.conv2d, slim.fully_connected],
                        weights_regularizer=slim.l2_regularizer(weight_decay)):
        with slim.arg_scope([slim.conv2d],
                            weights_initializer=tf.truncated_normal_initializer(stddev=stddev),
                            activation_fn=tf.nn.relu,
                            normalizer_fn=slim.batch_norm,
                            normalizer_params=batch_norm_params) as sc:
            return sc

# 定义Inception V3
def inception_v3_base(inputs, scope=None):
    end_points = {}
    with tf.variable_scope(scope, 'InceptionV3', [inputs]):
        # 最开始的卷积部分
        with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                            stride=1, padding='VALID'):
            net = slim.conv2d(inputs, 32, [3, 3], stride=2, scope='Conv2d_1a-3x3')
            net = slim.conv2d(net, 32, [3, 3], scope='Conv2d_2a_3x3')
            net = slim.conv2d(net, 64, [3, 3], padding='SAME', scope='Conv2d_2b_3x3')
            net = slim.max_pool2d(net, [3, 3], stride=2, scope='MaxPool_3a_3x3')
            net = slim.conv2d(net, 80, [1, 1], scope='Conv2d_3b_1x1')
            net = slim.conv2d(net, 192, [3, 3], scope='Conv2d_4a_3x3')
            net = slim.max_pool2d(net, [3, 3], stride=2, scope="MaxPool_5a_3x3")

        # 三个连续的inception模块组
        with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                            stride=1, padding='SAME'):
            # 第一个Inception模块的第一个Inception Model
            with tf.variable_scope('Mixed_5b'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv2d_0b_5x5')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv2d_0b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
                print_activations(net)

            # 第一个Inception模块的第二个Inception Model
            with tf.variable_scope('Mixed_5c'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0b_1x1')
                    branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv2d_0b_5x5')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
                print_activations(net)

            # 第一个Inception模块的第三个Inception Model
            with tf.variable_scope('Mixed_5d'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0b_1x1')
                    branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv2d_0b_5x5')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
                print_activations(net)

            # 第二个Inception模块的第一个Inception Model
            with tf.variable_scope('Mixed_6a'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 384, [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_3x3')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_1a_1x1')
                    branch_1 = slim.conv2d(branch_1, 96, [3, 3], scope='Conv2d_1b_3x3')
                    branch_1 = slim.conv2d(branch_1, 96, [3, 3], stride=2, padding='VALID', scope='Conv2d_1c_3x3')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='MacPool_1a_3x3')
                net = tf.concat([branch_0, branch_1, branch_2], 3)
                print_activations(net)

            # 第二个Inception模块的第二个Inception Model
            with tf.variable_scope('Mixed_6b'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_1a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_1a_1x1')
                    branch_1 = slim.conv2d(branch_1, 128, [1, 7], scope='Conv2d_1b_1x7')
                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_1c_7x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_1a_1x1')
                    branch_2 = slim.conv2d(branch_2, 128, [7, 1], scope='Conv2d_1b_7x1')
                    branch_2 = slim.conv2d(branch_2, 128, [1, 7], scope='Conv2d_1c_1x7')
                    branch_2 = slim.conv2d(branch_2, 128, [7, 1], scope='Conv2d_1d_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_1e_1x7')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_1a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_1b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
                print_activations(net)

            # 第二个Inception模块的第三个Inception Model
            with tf.variable_scope('Mixed_6c'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_1a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_1a_1x1')
                    branch_1 = slim.conv2d(branch_1, 160, [1, 7], scope='Conv2d_1b_1x7')
                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_1c_7x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_1a_1x1')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_1b_7x1')
                    branch_2 = slim.conv2d(branch_2, 160, [1, 7], scope='Conv2d_1c_1x7')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_1d_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_1e_1x7')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_1a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_1b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
                print_activations(net)

            # 第二个Inception模块的第四个Inception Model
            with tf.variable_scope('Mixed_6d'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_1a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_1a_1x1')
                    branch_1 = slim.conv2d(branch_1, 160, [1, 7], scope='Conv2d_1b_1x7')
                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_1c_7x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_1a_1x1')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_1b_7x1')
                    branch_2 = slim.conv2d(branch_2, 160, [1, 7], scope='Conv2d_1c_1x7')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_1d_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_1e_1x7')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_1a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_1b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
                print_activations(net)

            # 第二个Inception模块的第五个Inception Model
            with tf.variable_scope('Mixed_6e'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_1a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_1a_1x1')
                    branch_1 = slim.conv2d(branch_1, 160, [1, 7], scope='Conv2d_1b_1x7')
                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_1c_7x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_1a_1x1')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_1b_7x1')
                    branch_2 = slim.conv2d(branch_2, 160, [1, 7], scope='Conv2d_1c_1x7')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_1d_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_1e_1x7')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_1a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_1b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
                print_activations(net)

            # 将Mixed_6e作为Auxiliary Classifier辅助模型的分类
            end_points['Mixed_6e'] = net

            # 第三个Inception模块的第一个Inception Model
            with tf.variable_scope('Mixed_7a'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_2a_1x1')
                    branch_0 = slim.conv2d(branch_0, 320, [3, 3], stride=2, padding='VALID', scope='Conv2d_2b_3x3')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_2a_1x1')
                    branch_1 = slim.conv2d(branch_1, 192, [1, 7], scope='Conv2d_2b_1x7')
                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_2c_7x1')
                    branch_1 = slim.conv2d(branch_1, 192, [3, 3], stride=2, padding='VALID', scope='Conv2d_2d_3x3')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='Conv2d_2a_3x3')
                net = tf.concat([branch_0, branch_1, branch_2], 3)
                print_activations(net)

            # 第三个Inception模块的第二个Inception Model
            with tf.variable_scope('Mixed_7b'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 320, [1, 1], scope='Conv2d_2a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_2a_1x1')
                    branch_1 = tf.concat([
                        slim.conv2d(branch_1, 384, [1, 3], scope='Conv2d_2b_1x3'),
                        slim.conv2d(branch_1, 384, [3, 1], scope='Conv2d_2b_3x1')], 3
                    )
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 448, [1, 1], scope='Conv2d_2a_1x1')
                    branch_2 = slim.conv2d(branch_2, 348, [3, 3], scope='Conv2d_2b_3x3')
                    branch_2 = tf.concat([
                        slim.conv2d(branch_2, 384, [1, 3], scope='Conv2d_2c_1x3'),
                        slim.conv2d(branch_2, 384, [3, 1], scope='Conv2d_2c_3x1')], 3
                    )
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_2a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_2a_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
                print_activations(net)

            # 第三个Inception模块的第三个Inception Model
            with tf.variable_scope('Mixed_7c'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 320, [1, 1], scope='Conv2d_2a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_2a_1x1')
                    branch_1 = tf.concat([
                        slim.conv2d(branch_1, 384, [1, 3], scope='Conv2d_2b_1x3'),
                        slim.conv2d(branch_1, 384, [3, 1], scope='Conv2d_2b_3x1')], 3
                    )
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 448, [1, 1], scope='Conv2d_2a_1x1')
                    branch_2 = slim.conv2d(branch_2, 348, [3, 3], scope='Conv2d_2b_3x3')
                    branch_2 = tf.concat([
                        slim.conv2d(branch_2, 384, [1, 3], scope='Conv2d_2c_1x3'),
                        slim.conv2d(branch_2, 384, [3, 1], scope='Conv2d_2c_3x1')], 3
                    )
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_2a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_2a_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
                print_activations(net)

            # 返回Inception Model模型的输出结果
            return net, end_points

# 全局平均池化、Softamx和Auxiliary Logits
def inception_v3(inputs,
                 num_classes=1000,
                 is_training=True,
                 dropout_keep_prob=0.8,
                 prediction_fn=slim.softmax,
                 spatial_squeeze=True,
                 reuse=None,
                 scope='InceptionV3'):
    with tf.variable_scope(scope, 'InceptionV3', [inputs, num_classes], reuse=reuse) as scope:
        with slim.arg_scope([slim.batch_norm, slim.dropout], is_training=is_training):
            net, end_points = inception_v3_base(inputs, scope=scope)

            with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], stride=1, padding='SAME'):

                # 辅助分类节点
                aux_logits = end_points['Mixed_6e']
                with tf.variable_scope('AuxLogits'):
                    aux_logits = slim.avg_pool2d(aux_logits, [5, 5], stride=3, padding='VALID', scope='AvgPool_1a_5x5')
                    aux_logits = slim.conv2d(aux_logits, 128, [1, 1], scope='Conv2d_1b_1x1')
                    aux_logits = slim.conv2d(aux_logits, 768, [5, 5], weights_initializer=trunc_normal(0.01),
                                             padding='VALID', scope='Conv2d_2a_5x5')
                    aux_logits = slim.conv2d(aux_logits, num_classes, [1, 1], activation_fn=None, normalizer_fn=None,
                                             weights_initializer=trunc_normal(0.001), scope='Conv2d_2b_1x1')
                    if spatial_squeeze:
                        aux_logits = tf.squeeze(aux_logits, [1, 2], name='SpatialSqueeze')
                        print_activations(aux_logits)
                    end_points['AuxLogits'] = aux_logits

                # 正常预测分类节点
                with tf.variable_scope('Logits'):
                    net = slim.avg_pool2d(net, [8, 8], padding='VALID', scope='AvgPool_1a_8x8')
                    net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
                    end_points['PredLogits'] = net
                    logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
                                         normalizer_fn=None, scope='Conv2d_1c_1x1')
                    if spatial_squeeze:
                        logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
                        print_activations(logits)
                    end_points['Logits'] = logits
                    end_points['Predictions'] = prediction_fn(logits, scope='Predictions')

        return logits, end_points

def time_tensorflow_run(session, target, info_string):
    num_steps_burn_in = 10
    total_duration = 0.0
    total_durtaion_squared = 0.0
    for i in range(num_batches + num_steps_burn_in):
        start_time = time.time()
        _ = session.run(target)
        duration = time.time() - start_time
        if i >= num_steps_burn_in:
            if not i % 10:
                print('%s: step %d,  duration = %.3f' % (datetime.now(), i - num_steps_burn_in, duration))
            total_duration += duration
            total_durtaion_squared += duration * duration

    mn = total_duration / num_batches
    vr = total_durtaion_squared / num_batches - mn * mn
    sd = math.sqrt(vr)
    print('%s: %s across %d steps, %.3f +/- %.3f sec / batch' % (datetime.now(), info_string, num_batches, mn, sd))

def run_benchmark():

        inputs = tf.Variable(tf.random_normal([batch_size, height, width, 3], dtype=tf.float32, stddev=1e-1))
        print(inputs)
        with slim.arg_scope(inception_v3_arg_scope()):
            logits, end_points = inception_v3(inputs, is_training=False)

        init = tf.global_variables_initializer()
        sess = tf.Session()
        sess.run(init)
        time_tensorflow_run(sess, logits, "Forward")


if __name__ == '__main__':
    run_benchmark()
    #inputs = tf.Variable(tf.random_normal([batch_size, height, width, 3], dtype=tf.float32, stddev=1e-1))
    # net, end_points = inception_v3_base(inputs)
    # print(net)
    # print(end_points['Mixed_6e'])