03、图像风格转换的模型模块分析_笔记

东方耀 · 发表于 2019-3-13 10:30:25

03、图像风格转换的模型模块分析_笔记

模块（一个一个实现来写代码）：

1、定义输入文件与输出目录

2、管理模型的超参

3、数据的提供（内容图像风格图像随机初始化的图像）

4、构建计算图（数据流图、定义loss、train_op）

5、训练执行过程（会话中执行设备：cpu或gpu或tpu）

图像大小要求：224*224的（vgg net的要求）原本应该是224*224*3的由于我是用qq的截图的图片默认是224*224*4（4通道的 RGBA）所以需要进行处理请看我的视频操作

# -*- coding: utf-8 -*-
__author__ = 'dongfangyao'
__date__ = '2019/3/13 上午10:33'
__product__ = 'PyCharm'
__filename__ = '2_image_style_conver'
import tensorflow as tf
from tensorflow import logging
import os
from tensorflow import gfile
from PIL import Image
import time
import numpy as np
logging.set_verbosity(logging.INFO)
# logging.info('dfy_88888')
# vgg net 中写死的归一化的数据预处理
VGG_MEAN = [103.939, 116.779, 123.68]
class VGGNet:
"""
构建VGG16的网络结构并从预训练好的模型提取参数加载
"""
def __init__(self, data_dict):
self.data_dict = data_dict
def get_conv_kernel(self, name):
# 卷积核的参数：w 0 b 1
return tf.constant(self.data_dict[name][0], name='conv')
def get_fc_weight(self, name):
return tf.constant(self.data_dict[name][0], name='fc')
def get_bias(self, name):
return tf.constant(self.data_dict[name][1], name='bias')
def conv_layer(self, inputs, name):
"""
构建一个卷积计算层
:param inputs: 输入的feature_map
:param name: 卷积层的名字也是获得参数的key 不能出错
:return:
"""
with tf.name_scope(name):
"""
多使用name_scope的好处：1、防止参数命名冲突 2、tensorboard可视化时很规整
如果scope里面有变量需要训练时则用tf.variable_scope
"""
conv_w = self.get_conv_kernel(name)
conv_b = self.get_bias(name)
# tf.layers.conv2d() 这是一个封装更高级的api
# 里面并没有提供接口来输入卷积核参数这里不能用平时训练cnn网络时非常好用
result = tf.nn.conv2d(input=inputs, filter=conv_w, strides=[1, 1, 1, 1], padding='SAME', name=name)
result = tf.nn.bias_add(result, conv_b)
result = tf.nn.relu(result)
return result
def pooling_layer(self, inputs, name):
# tf.layers.max_pooling2d()
# tf.nn.max_pool 这里的池化层没有参数两套api都可以用
return tf.nn.max_pool(inputs, [1, 2, 2, 1], [1, 2, 2, 1], padding='SAME', name=name)
def fc_layer(self, inputs, name, activation=tf.nn.relu):
"""
构建全连接层
:param inputs: 输入
:param name:
:param activation: 是否有激活函数的封装
:return:
"""
with tf.name_scope(name):
fc_w = self.get_fc_weight(name)
fc_b = self.get_bias(name)
# fc: wx+b 线性变换
result = tf.nn.bias_add(tf.matmul(inputs, fc_w), fc_b)
if activation is None:
# vgg16的最后是不需relu激活的
return result
else:
return activation(result)
def flatten_op(self, inputs, name):
# 展平操作为了后续的fc层必须将维度展平
with tf.name_scope(name):
# [NHWC]---> [N, H*W*C]
x_shape = inputs.get_shape().as_list()
dim = 1
for d in x_shape[1:]:
dim *= d
inputs = tf.reshape(inputs, shape=[-1, dim])
# 直接用现成api也是可以的
# return tf.layers.flatten(inputs)
return inputs
def build(self, input_rgb):
"""
构建vgg16网络结构抽取特征 FP过程
:param input_rgb: [1, 224, 224, 3] [NHWC]
:return:
"""
start_time = time.time()
logging.info('building start...')
# 在通道维度上分离深度可分离卷积中也需要用到这个api
r, g, b = tf.split(input_rgb, num_or_size_splits=3, axis=3)
# 在通道维度上拼接
# 输入vgg网络的图像是bgr的（与OpenCV一样倒序的）而不是rgb
x_bgr = tf.concat(values=[
b - VGG_MEAN[0],
g - VGG_MEAN[1],
r - VGG_MEAN[2],
], axis=3)
assert x_bgr.get_shape().as_list()[1:] == [224, 224, 3]
# 构建网络
# stage 1
self.conv1_1 = self.conv_layer(x_bgr, 'conv1_1')
self.conv1_2 = self.conv_layer(self.conv1_1, 'conv1_2')
self.pool1 = self.pooling_layer(self.conv1_2, 'pool1')
# stage 2
self.conv2_1 = self.conv_layer(self.pool1, 'conv2_1')
self.conv2_2 = self.conv_layer(self.conv2_1, 'conv2_2')
self.pool2 = self.pooling_layer(self.conv2_2, 'pool2')
# stage 3
self.conv3_1 = self.conv_layer(self.pool2, 'conv3_1')
self.conv3_2 = self.conv_layer(self.conv3_1, 'conv3_2')
self.conv3_3 = self.conv_layer(self.conv3_2, 'conv3_3')
self.pool3 = self.pooling_layer(self.conv3_3, 'pool3')
# stage 4
self.conv4_1 = self.conv_layer(self.pool3, 'conv4_1')
self.conv4_2 = self.conv_layer(self.conv4_1, 'conv4_2')
self.conv4_3 = self.conv_layer(self.conv4_2, 'conv4_3')
self.pool4 = self.pooling_layer(self.conv4_3, 'pool4')
# stage 5
self.conv5_1 = self.conv_layer(self.pool4, 'conv5_1')
self.conv5_2 = self.conv_layer(self.conv5_1, 'conv5_2')
self.conv5_3 = self.conv_layer(self.conv5_2, 'conv5_3')
self.pool5 = self.pooling_layer(self.conv5_3, 'pool5')
# flatten_op
# self.flatten = self.flatten_op(self.pool5, 'flatten_op')
#
# # fc
# self.fc6 = self.fc_layer(self.flatten, 'fc6')
# self.fc7 = self.fc_layer(self.fc6, 'fc7')
# self.fc8 = self.fc_layer(self.fc7, 'fc8', activation=None)
# self.logits = tf.nn.softmax(self.fc8, name='logits')
logging.info('building end... 耗时%3d秒' % (time.time() - start_time))
#
# vgg16_for_result = VGGNet(data_dict)
# image_rgb = tf.placeholder(dtype=tf.float32, shape=[1, 224, 224, 3], name='image_rgb')
# vgg16_for_result.build(image_rgb)
# print(vgg16_for_result.conv1_1)
# print(vgg16_for_result.flatten)
# print(vgg16_for_result.fc6)
"""
模块（一个一个实现来写代码）：
1、定义输入文件与输出目录
2、管理模型的超参
3、数据的提供（内容图像风格图像随机初始化的图像）
4、构建计算图（数据流图、定义loss、train_op）
5、训练执行过程（会话中执行设备：cpu或gpu或tpu）
"""
vgg16_npy_path = './vgg16.npy'
content_img_path = './dfy_88888.png'
style_img_path = './style.png'
output_dir = './output_imgs'
if not gfile.Exists(output_dir):
gfile.MakeDirs(output_dir)
def get_default_params():
return tf.contrib.training.HParams(
learning_rate=10,
lambda_content_loss=0.05,
lambda_style_loss=2000,
)
hps = get_default_params()
print(hps.learning_rate)
print(hps.lambda_content_loss)
def read_img(image_name):
img = Image.open(image_name)
np_img = np.array(img)
# [224, 224, 4]
print('np_img shape:', np_img.shape, image_name)
# RGBA--->RGB [224, 224, 3]
np_img = np_img[:, :, 0:3]
np_img = np.asarray([np_img], dtype=np.float32)
print('np_img shape: ', np_img.shape)
# (1, 224, 224, 3)
return np_img
# read_img(content_img_path)
content_img_arr_val = read_img(content_img_path)
style_img_arr_val = read_img(style_img_path)
def initial_image(shape, mean, stddev):
# 截断的随机的正态分布数据产生
initial_img = tf.truncated_normal(shape=shape, mean=mean, stddev=stddev, dtype=tf.float32)
return tf.Variable(initial_value=initial_img, trainable=True)
result_img_val = initial_image([1, 224, 224, 3], mean=255//2, stddev=20)
# 用占位符具体的值在sess中通过feed_dict喂养后面执行阶段会有
content_img = tf.placeholder(dtype=tf.float32, shape=[1, 224, 224, 3], name='content_img')
style_img = tf.placeholder(dtype=tf.float32, shape=[1, 224, 224, 3], name='style_img')
# 提取图像的卷积层的特征
vgg16_data = np.load(vgg16_npy_path, encoding='latin1')
data_dict = vgg16_data.item()
vgg16_for_result_img = VGGNet(data_dict)
vgg16_for_content_img = VGGNet(data_dict)
vgg16_for_style_img = VGGNet(data_dict)
# 结果图像vgg16的构建
vgg16_for_result_img.build(result_img_val)
# 内容图像vgg16的构建
vgg16_for_content_img.build(content_img)
# 风格图像vgg16的构建
vgg16_for_style_img.build(style_img)
# 定义需要提取哪些层的特征了 cnn
# 内容图像的内容特征抽取越低层效果越好
content_features = [
vgg16_for_content_img.conv1_1,
vgg16_for_content_img.conv2_1,
# vgg16_for_content_img.conv3_1,
# vgg16_for_content_img.conv3_2,
# vgg16_for_content_img.conv5_1,
# vgg16_for_content_img.conv5_3,
]
# 结果图像的内容特征抽取必须一致
result_content_features = [
vgg16_for_result_img.conv1_1,
vgg16_for_result_img.conv2_1,
# vgg16_for_result_img.conv3_1,
# vgg16_for_result_img.conv3_2,
# vgg16_for_result_img.conv5_1,
# vgg16_for_result_img.conv5_3,
]
# 风格图像的风格特征抽取越高层越好
style_features = [
# vgg16_for_style_img.conv1_1,
# vgg16_for_style_img.conv2_1,
# vgg16_for_style_img.conv3_1,
# vgg16_for_style_img.conv4_2,
vgg16_for_style_img.conv4_3,
vgg16_for_style_img.conv5_3,
]
# 结果图像的风格特征抽取必须一致
result_style_features = [
# vgg16_for_result_img.conv1_1,
# vgg16_for_result_img.conv2_1,
# vgg16_for_result_img.conv3_1,
# vgg16_for_result_img.conv4_2,
vgg16_for_result_img.conv4_3,
vgg16_for_result_img.conv5_3,
]
# loss = loss_content + loss_style

复制代码

		自动登录	找回密码
密码			立即注册

[课堂笔记] 03、图像风格转换的模型模块分析_笔记