用 Python 能够完成侧脸转正脸？我也要试一下！

作者 | 李秋键

责编 | Carol

封图 | CSDN 下载自视觉中国

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近几年来GAN图像生成应用越来越广泛 ，其中主要得益于GAN 在博弈下不断提高建模能力
，最终实现以假乱真的图像生成。 GAN 由两个神经网络组成，一个生成器和一个判别器组成 ，其中生成器试图产生欺骗判别器的真实样本
，而判别器试图区分真实样本和生成样本 。这种对抗博弈下使得生成器和判别器不断提高性能，在达到纳什平衡后生成器可以实现以假乱真的输出
。

其中GAN 在图像生成应用最为突出 ，当然在计算机视觉中还有许多其他应用
，如图像绘画，图像标注 ，物体检测和语义分割。在自然语言处理中应用 GAN 的研究也是一种增长趋势
， 如文本建模，对话生成 ，问答和机器翻译 。 然而
，在 NLP 任务中训练 GAN 更加困难并且需要更多技术，这也使其成为具有挑战性但有趣的研究领域 。

而今天我们就将利用CC-GAN训练将侧脸生成正脸的模型 ，其中迭代20次结果如下：

实验前的准备

首先我们使用的python版本是3.6.5所用到的模块如下：tensorflow用来模型训练和网络层建立；numpy模块用来处理矩阵运算；OpenCV用来读取图片和图像处理；os模块用来读取数据集等本地文件操作
。

素材准备

其中准备训练的不同角度人脸图片放入以下文件夹作为训练集 ，如下图可见：

测试集图片如下可见：  

模型搭建

原始GAN（GAN 简介与代码实战）在理论上可以完全逼近真实数据
，但它的可控性不强（生成小图片还行，生成的大图片可能是不合逻辑的） ，因此需要对gan加一些约束
，能生成我们想要的图片，这个时候 ，CGAN就横空出世了。其中CCGAN整体模型结构如下：  

1 、网络结构参数的搭建：

首先是定义标准化
、激活函数和池化层等函数：Batch_Norm是对其进行规整
，是为了防止同一个batch间的梯度相互抵消 。其将不同batch规整到同一个均值0和方差1
。InstanceNorm是将输入在深度方向上减去均值除以标准差，可以加快网络的训练速度。

def instance_norm(x, scope='instance_norm'): return tf_contrib.layers.instance_norm(x, epsilon=1e-05, center=True, scale=True, scope=scope) def batch_norm(x, scope='batch_norm'): return tf_contrib.layers.batch_norm(x, decay=0.9, epsilon=1e-05, center=True, scale=True, scope=scope) def flatten(x) : return tf.layers.flatten(x) def lrelu(x, alpha=0.2): return tf.nn.leaky_relu(x, alpha) def relu(x): return tf.nn.relu(x) def global_avg_pooling(x): gap = tf.reduce_mean(x, axis=[1, 2], keepdims=True) return gap def resblock(x_init, c, scope='resblock'): with tf.variable_scope(scope): with tf.variable_scope('res1'): x = slim.conv2d(x_init, c, kernel_size=[3,3], stride=1, activation_fn = None) x = batch_norm(x) x = relu(x) with tf.variable_scope('res2'): x = slim.conv2d(x, c, kernel_size=[3,3], stride=1, activation_fn = None) x = batch_norm(x) return x + x_init

然后是卷积层的定义：

def conv(x, c): x1 = slim.conv2d(x, c, kernel_size=[5,5], stride=2, padding = 'SAME', activation_fn=relu) # print(x1.shape) x2 = slim.conv2d(x, c, kernel_size=[3,3], stride=2, padding = 'SAME', activation_fn=relu) # print(x2.shape) x3 = slim.conv2d(x, c, kernel_size=[1,1], stride=2, padding = 'SAME', activation_fn=relu) # print(x3.shape) out = tf.concat([x1, x2, x3],axis = 3) out = slim.conv2d(out, c, kernel_size=[1,1], stride=1, padding = 'SAME', activation_fn=None) # print(out.shape) return out

生成器函数定义：

def mixgenerator(x_init, c, org_pose, trg_pose): reuse = len([t for t in tf.global_variables() if t.name.startswith('generator')]) > 0 with tf.variable_scope('generator', reuse = reuse): org_pose = tf.cast(tf.reshape(org_pose, shape=[-1, 1, 1, org_pose.shape[-1]]), tf.float32) print(org_pose.shape) org_pose = tf.tile(org_pose, [1, x_init.shape[1], x_init.shape[2], 1]) print(org_pose.shape) x = tf.concat([x_init, org_pose], axis=-1) print(x.shape) x = conv(x, c) x = batch_norm(x, scope='bat_norm_1') x = relu(x)#64 print('----------------') print(x.shape) x = conv(x, c*2) x = batch_norm(x, scope='bat_norm_2') x = relu(x)#32 print(x.shape) x = conv(x, c*4) x = batch_norm(x, scope='bat_norm_3') x = relu(x)#16 print(x.shape) f_org = x x = conv(x, c*8) x = batch_norm(x, scope='bat_norm_4') x = relu(x)#8 print(x.shape) x = conv(x, c*8) x = batch_norm(x, scope='bat_norm_5') x = relu(x)#4 print(x.shape) for i in range(6): x = resblock(x, c*8, scope = str(i)+"_resblock") trg_pose = tf.cast(tf.reshape(trg_pose, shape=[-1, 1, 1, trg_pose.shape[-1]]), tf.float32) print(trg_pose.shape) trg_pose = tf.tile(trg_pose, [1, x.shape[1], x.shape[2], 1]) print(trg_pose.shape) x = tf.concat([x, trg_pose], axis=-1) print(x.shape) x = slim.conv2d_transpose(x, c*8, kernel_size=[3, 3], stride=2, activation_fn=None) x = batch_norm(x, scope='bat_norm_8') x = relu(x)#8 print(x.shape) x = slim.conv2d_transpose(x, c*4, kernel_size=[3, 3], stride=2, activation_fn=None) x = batch_norm(x, scope='bat_norm_9') x = relu(x)#16 print(x.shape) f_trg =x x = slim.conv2d_transpose(x, c*2, kernel_size=[3, 3], stride=2, activation_fn=None) x = batch_norm(x, scope='bat_norm_10') x = relu(x)#32 print(x.shape) x = slim.conv2d_transpose(x, c, kernel_size=[3, 3], stride=2, activation_fn=None) x = batch_norm(x, scope='bat_norm_11') x = relu(x)#64 print(x.shape) z = slim.conv2d_transpose(x, 3 , kernel_size=[3,3], stride=2, activation_fn = tf.nn.tanh) f = tf.concat([f_org, f_trg], axis=-1) print(f.shape) return z, f

下面还有判别器等函数定义 ，不加赘述 。

2、VGG程序设立：

VGG模型网络层的搭建：

def build(self, rgb, include_fc=False): """ load variable from npy to build the VGG input format: bgr image with shape [batch_size, h, w, 3] scale: (-1, 1) """ start_time = time.time() rgb_scaled = (rgb + 1) / 2 # [-1, 1] ~ [0, 1] # blue, green, red = tf.split(axis=3, num_or_size_splits=3, value=rgb_scaled) # bgr = tf.concat(axis=3, values=[blue - VGG_MEAN[0], # green - VGG_MEAN[1], # red - VGG_MEAN[2]]) self.conv1_1 = self.conv_layer(rgb_scaled, "conv1_1") self.conv1_2 = self.conv_layer(self.conv1_1, "conv1_2") self.pool1 = self.max_pool(self.conv1_2, 'pool1') self.conv2_1 = self.conv_layer(self.pool1, "conv2_1") self.conv2_2 = self.conv_layer(self.conv2_1, "conv2_2") self.pool2 = self.max_pool(self.conv2_2, 'pool2') self.conv3_1 = self.conv_layer(self.pool2, "conv3_1") self.conv3_2_no_activation = self.no_activation_conv_layer(self.conv3_1, "conv3_2") self.conv3_2 = self.conv_layer(self.conv3_1, "conv3_2") self.conv3_3 = self.conv_layer(self.conv3_2, "conv3_3") self.conv3_4 = self.conv_layer(self.conv3_3, "conv3_4") self.pool3 = self.max_pool(self.conv3_4, 'pool3') 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.conv4_4_no_activation = self.no_activation_conv_layer(self.conv4_3, "conv4_4") self.conv4_4 = self.conv_layer(self.conv4_3, "conv4_4") self.pool4 = self.max_pool(self.conv4_4, 'pool4') 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.conv5_4_no_activation = self.no_activation_conv_layer(self.conv5_3, "conv5_4") self.conv5_4 = self.conv_layer(self.conv5_3, "conv5_4") self.pool5 = self.max_pool(self.conv5_4, 'pool5') if include_fc: self.fc6 = self.fc_layer(self.pool5, "fc6") assert self.fc6.get_shape().as_list()[1:] == [4096] self.relu6 = tf.nn.relu(self.fc6) self.fc7 = self.fc_layer(self.relu6, "fc7") self.relu7 = tf.nn.relu(self.fc7) self.fc8 = self.fc_layer(self.relu7, "fc8") self.prob = tf.nn.softmax(self.fc8, name="prob") self.data_dict = None print(("Finished building vgg19: %ds" % (time.time() - start_time)))

池化层 、卷积层函数的定义：

def avg_pool(self, bottom, name): return tf.nn.avg_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name) def max_pool(self, bottom, name): return tf.nn.max_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name) def conv_layer(self, bottom, name): with tf.variable_scope(name): filt = self.get_conv_filter(name) conv = tf.nn.conv2d(bottom, filt, [1, 1, 1, 1], padding='SAME') conv_biases = self.get_bias(name) bias = tf.nn.bias_add(conv, conv_biases) relu = tf.nn.relu(bias) return relu def no_activation_conv_layer(self, bottom, name): with tf.variable_scope(name): filt = self.get_conv_filter(name) conv = tf.nn.conv2d(bottom, filt, [1, 1, 1, 1], padding='SAME') conv_biases = self.get_bias(name) x = tf.nn.bias_add(conv, conv_biases) return x def fc_layer(self, bottom, name): with tf.variable_scope(name): shape = bottom.get_shape().as_list() dim = 1 for d in shape[1:]: dim *= d x = tf.reshape(bottom, [-1, dim]) weights = self.get_fc_weight(name) biases = self.get_bias(name) # Fully connected layer. Note that the '+' operation automatically # broadcasts the biases. fc = tf.nn.bias_add(tf.matmul(x, weights), biases) return fc def get_conv_filter(self, name): return tf.constant(self.data_dict[name][0], name="filter") def get_bias(self, name): return tf.constant(self.data_dict[name][1], name="biases") def get_fc_weight(self, name): return tf.constant(self.data_dict[name][0], name="weights")

模型的训练

设置GPU加速训练
，需要配置好CUDA环境，并按照tensorflow-gpu版本
。

os.environ["CUDA_VISIBLE_DEVICES"] = "0" config = tf.ConfigProto() config.gpu_options.allow_growth = True tf.reset_default_graph() model = Sequential() #创建一个神经网络对象 #添加一个卷积层 ，传入固定宽高三通道的 数据集读取和训练批次的划分： imagedir = './data/' img_label_org, label_trg, img = reader.images_list(imagedir) epoch = 800 batch_size = 10 total_sample_num = len(img_label_org) if total_sample_num % batch_size == 0: n_batch = int(total_sample_num / batch_size) else: n_batch = int(total_sample_num / batch_size) + 1

输入输出神经元和判别器等初始化：

org_image = tf.placeholder(tf.float32,[None,128,128,3], name='org_image') trg_image = tf.placeholder(tf.float32,[None,128,128,3], name='trg_image') org_pose = tf.placeholder(tf.float32,[None,9], name='org_pose') trg_pose = tf.placeholder(tf.float32,[None,9], name='trg_pose') gen_trg, feat = model.mixgenerator(org_image, 32, org_pose, trg_pose) out_trg = model.generator(feat, 32, trg_pose) #D_ab D_r, real_logit, real_pose = model.snpixdiscriminator(trg_image) D_f, fake_logit, fake_pose = model.snpixdiscriminator(gen_trg) D_f_, fake_logit_, fake_pose_ = model.snpixdiscriminator(out_trg) # fake or real D_LOSS loss_pred_r = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=real_logit, labels=tf.ones_like(D_r))) loss_pred_f = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_logit_, labels=tf.zeros_like(D_f_))) loss_d_pred = loss_pred_r + loss_pred_f #pose loss loss_d_pose = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=real_pose, labels=trg_pose)) loss_g_pose_ = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=fake_pose_, labels=trg_pose)) loss_g_pose = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=fake_pose, labels=trg_pose)) #G_LOSS loss_g_pred = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_logit_, labels=tf.ones_like(D_f_))) out_pix_loss = ops.L2_loss(out_trg, trg_image) out_pre_loss, out_feat_texture = ops.vgg_loss(out_trg, trg_image) out_loss_texture = ops.texture_loss(out_feat_texture) out_loss_tv = 0.0002 * tf.reduce_mean(ops.tv_loss(out_trg)) gen_pix_loss = ops.L2_loss(gen_trg, trg_image) out_g_loss = 100*gen_pix_loss + 100*out_pix_loss + loss_g_pred + out_pre_loss + out_loss_texture + out_loss_tv + loss_g_pose_ gen_g_loss = 100 * gen_pix_loss + loss_g_pose # d_loss disc_loss = loss_d_pred + loss_d_pose out_global_step = tf.Variable(0, trainable=False) gen_global_step = tf.Variable(0, trainable=False) disc_global_step = tf.Variable(0, trainable=False) start_decay_step = 500000 start_learning_rate = 0.0001 decay_steps = 500000 end_learning_rate = 0.0 out_lr = (tf.where(tf.greater_equal(out_global_step, start_decay_step), tf.train.polynomial_decay(start_learning_rate, out_global_step-start_decay_step, decay_steps, end_learning_rate, power=1.0),start_learning_rate)) gen_lr = (tf.where(tf.greater_equal(gen_global_step, start_decay_step), tf.train.polynomial_decay(start_learning_rate, gen_global_step-start_decay_step, decay_steps, end_learning_rate, power=1.0),start_learning_rate)) disc_lr = (tf.where(tf.greater_equal(disc_global_step, start_decay_step), tf.train.polynomial_decay(start_learning_rate, disc_global_step-start_decay_step, decay_steps, end_learning_rate, power=1.0),start_learning_rate)) t_vars = tf.trainable_variables() g_gen_vars = [var for var in t_vars if 'generator' in var.name] g_out_vars = [var for var in t_vars if 'generator_1' in var.name] d_vars = [var for var in t_vars if 'discriminator' in var.name] train_gen = tf.train.AdamOptimizer(gen_lr, beta1=0.5, beta2=0.999).minimize(gen_g_loss, var_list = g_gen_vars, global_step = gen_global_step) train_out = tf.train.AdamOptimizer(out_lr, beta1=0.5, beta2=0.999).minimize(out_g_loss, var_list = g_out_vars, global_step = out_global_step) train_disc = tf.train.AdamOptimizer(disc_lr, beta1=0.5, beta2=0.999).minimize(disc_loss, var_list = d_vars, global_step = disc_global_step) saver = tf.train.Saver(tf.global_variables())

模型训练
、图片生成和模型的保存：

with tf.Session(config=config) as sess: for d in ['/gpu:0']: with tf.device(d): ckpt = tf.train.get_checkpoint_state('./models/') if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path): saver.restore(sess, ckpt.model_checkpoint_path) print('Import models successful!') else: sess.run(tf.global_variables_initializer()) print('Initialize successful!') for i in range(epoch): random.shuffle(img_label_org) random.shuffle(label_trg) for j in range(n_batch): if j == n_batch - 1: n = total_sample_num else: n = j * batch_size + batch_size img_org_output, img_trg_output, label_org_output, label_trg_output, image_name_output = reader.images_read(img_label_org[j*batch_size:n], label_trg[j*batch_size:n], img, imagedir) feeds = {org_image:img_org_output, trg_image:img_trg_output, org_pose:label_org_output,trg_pose:label_trg_output} if i < 400: sess.run(train_disc, feed_dict=feeds) sess.run(train_gen, feed_dict=feeds) sess.run(train_out, feed_dict=feeds) else: sess.run(train_gen, feed_dict=feeds) sess.run(train_out, feed_dict=feeds) if j%10==0: sess.run(train_disc, feed_dict=feeds) if j%2==0: gen_g_loss_,out_g_loss_, disc_loss_, org_image_, gen_trg_, out_trg_, trg_image_ = sess.run([gen_g_loss, out_g_loss, disc_loss, org_image, gen_trg, out_trg, trg_image],feeds) print("epoch:", i, "iter:", j, "gen_g_loss_:", gen_g_loss_, "out_g_loss_:", out_g_loss_, "loss_disc:", disc_loss_) for n in range(batch_size): org_image_output = (org_image_[n] + 1)*127.5 gen_trg_output = (gen_trg_[n] + 1)*127.5 out_trg_output = (out_trg_[n] + 1)*127.5 trg_image_output = (trg_image_[n] + 1)*127.5 temp = np.concatenate([org_image_output, gen_trg_output, out_trg_output, trg_image_output], 1) cv.imwrite("./record/%d_%d_%d_image.jpg" %(i, j, n), temp) if i%10==0 or i==epoch-1: saver.save(sess, './models/wssGAN.ckpt', global_step=gen_global_step) print("Finish!")

最终运行程序结果如下：

初始训练一次结果：

训练20次结果：

经过对比
，可以发现有明显的提升！

源码地址：

https://pan.baidu.com/s/1cpRJlk7yUwhYJSIkRpbNpg

提取码：kdxe

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