data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)

"""
Explanation: 人脸生成（Face Generation）
在该项目中，你将使用生成式对抗网络（Generative Adversarial Nets）来生成新的人脸图像。
获取数据
该项目将使用以下数据集：
- MNIST
- CelebA
由于 CelebA 数据集比较复杂，而且这是你第一次使用 GANs。我们想让你先在 MNIST 数据集上测试你的 GANs 模型，以让你更快的评估所建立模型的性能。
如果你在使用 FloydHub, 请将 data_dir 设置为 "/input" 并使用 FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
End of explanation
"""


show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')

"""
Explanation: 探索数据（Explore the Data）
MNIST
MNIST 是一个手写数字的图像数据集。你可以更改 show_n_images 探索此数据集。
End of explanation
"""


show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))

"""
Explanation: CelebA
CelebFaces Attributes Dataset (CelebA) 是一个包含 20 多万张名人图片及相关图片说明的数据集。你将用此数据集生成人脸，不会用不到相关说明。你可以更改 show_n_images 探索此数据集。
End of explanation
"""


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))

"""
Explanation: 预处理数据（Preprocess the Data）
由于该项目的重点是建立 GANs 模型，我们将为你预处理数据。
经过数据预处理，MNIST 和 CelebA 数据集的值在 28×28 维度图像的 [-0.5, 0.5] 范围内。CelebA 数据集中的图像裁剪了非脸部的图像部分，然后调整到 28x28 维度。
MNIST 数据集中的图像是单[通道](https://en.wikipedia.org/wiki/Channel_(digital_image%29)的黑白图像，CelebA 数据集中的图像是 [三通道的 RGB 彩色图像](https://en.wikipedia.org/wiki/Channel_(digital_image%29#RGB_Images)。
建立神经网络（Build the Neural Network）
你将通过部署以下函数来建立 GANs 的主要组成部分:
- model_inputs
- discriminator
- generator
- model_loss
- model_opt
- train
检查 TensorFlow 版本并获取 GPU 型号
检查你是否使用正确的 TensorFlow 版本，并获取 GPU 型号
End of explanation
"""


import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    x = tf.placeholder(dtype=tf.float32,shape=(None,image_width,image_height,image_channels),name="inputs_x")
    z = tf.placeholder(dtype=tf.float32,shape=(None,z_dim),name="z_dim")
    learning_rate = tf.placeholder(dtype=tf.float32,shape=(),name="learning_rate")
    return x, z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)

"""
Explanation: 输入（Input）
部署 model_inputs 函数以创建用于神经网络的 占位符 (TF Placeholders)。请创建以下占位符：
- 输入图像占位符: 使用 image_width，image_height 和 image_channels 设置为 rank 4。
- 输入 Z 占位符: 设置为 rank 2，并命名为 z_dim。
- 学习速率占位符: 设置为 rank 0。
返回占位符元组的形状为 (tensor of real input images, tensor of z data, learning rate)。
End of explanation
"""


def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha=0.2
    with tf.variable_scope("discriminator",reuse=reuse):
        conv_1 = tf.layers.conv2d(images, 64, 4, strides=2, padding='same')
        conv_1_bn = tf.layers.batch_normalization(conv_1, training=True)
        relu1 = tf.maximum(alpha * conv_1_bn, conv_1_bn)
        # 14x14x64
        
        conv_2 = tf.layers.conv2d(relu1, 128, 4, strides=2, padding='same')
        conv_2_bn = tf.layers.batch_normalization(conv_2, training=True)
        relu2 = tf.maximum(alpha * conv_2_bn, conv_2_bn)
        # 7x7x128
        
        conv_3 = tf.layers.conv2d(relu2, 256, 4, strides=2, padding='same')
        conv_3_bn = tf.layers.batch_normalization(conv_3, training=True)
        relu3 = tf.maximum(alpha * conv_3_bn, conv_3_bn)
        # 4x4x256

        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)

"""
Explanation: 辨别器（Discriminator）
部署 discriminator 函数创建辨别器神经网络以辨别 images。该函数应能够重复使用神经网络中的各种变量。 在 tf.variable_scope 中使用 "discriminator" 的变量空间名来重复使用该函数中的变量。 
该函数应返回形如 (tensor output of the discriminator, tensor logits of the discriminator) 的元组。
End of explanation
"""


def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha=0.2
    with tf.variable_scope('generator',reuse=not(is_train)):
        # Reshape it to start the convolutional stack
        fc = tf.layers.dense(z, 4*4*512)
        x = tf.reshape(fc, (-1, 4, 4, 512))
        x = tf.layers.batch_normalization(x, training=is_train)
        relu_x = tf.maximum(alpha * x, x)
        # 4x4x512 now

        #conv2d_1_t = tf.nn.conv2d_transpose(relu_x, kernel, output_shape=[64,7,7,256], strides=[1,2,2,1], padding='SAME')
        conv2d_1_t = tf.layers.conv2d_transpose(relu_x, 256,4, strides=1, padding='valid')
        conv2d_1_t_bn = tf.layers.batch_normalization(conv2d_1_t, training=is_train)
        relu1 = tf.maximum(alpha * conv2d_1_t_bn, conv2d_1_t_bn)
        # 7x7x256 now
        
        conv2d_2_t = tf.layers.conv2d_transpose(relu1, 128, 4, strides=2, padding='same')
        conv2d_2_t_bn = tf.layers.batch_normalization(conv2d_2_t, training=is_train)
        relu2 = tf.maximum(alpha * conv2d_2_t_bn, conv2d_2_t_bn)
        # 14x14x128
        
        # Output layer
        logits = tf.layers.conv2d_transpose(relu2, out_channel_dim, 4, strides=2, padding='same')
        # 28x28x3 now
        out = tf.tanh(logits)

    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)

"""
Explanation: 生成器（Generator）
部署 generator 函数以使用 z 生成图像。该函数应能够重复使用神经网络中的各种变量。
在 tf.variable_scope 中使用 "generator" 的变量空间名来重复使用该函数中的变量。 
该函数应返回所生成的 28 x 28 x out_channel_dim 维度图像。
End of explanation
"""


def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_f = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_logits_real)*(1 - 0.1)))
    
    d_loss_f = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_f, labels=tf.zeros_like(d_logits_f)))
    
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_f, labels=tf.ones_like(d_logits_f)))

    d_loss = d_loss_real + d_loss_f
    return d_loss, g_loss



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)

"""
Explanation: 损失函数（Loss）
部署 model_loss 函数训练并计算 GANs 的损失。该函数应返回形如 (discriminator loss, generator loss) 的元组。
使用你已实现的函数：
- discriminator(images, reuse=False)
- generator(z, out_channel_dim, is_train=True)
End of explanation
"""


def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
        
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)

"""
Explanation: 优化（Optimization）
部署 model_opt 函数实现对 GANs 的优化。使用 tf.trainable_variables 获取可训练的所有变量。通过变量空间名 discriminator 和 generator 来过滤变量。该函数应返回形如 (discriminator training operation, generator training operation) 的元组。
End of explanation
"""


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

"""
Explanation: 训练神经网络（Neural Network Training）
输出显示
使用该函数可以显示生成器 (Generator) 在训练过程中的当前输出，这会帮你评估 GANs 模型的训练程度。
End of explanation
"""


def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    
    input_real,input_z, lr = model_inputs(*data_shape[1:], z_dim)
    
    out_channel_dim = 1
    if data_image_mode == "RGB" : 
        out_channel_dim = 3
    d_loss, g_loss = model_loss(input_real,input_z,out_channel_dim)
    d_train_opt, g_train_opt = model_opt(d_loss,g_loss,lr,beta1)

    steps = 0
    start = time.clock()
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1

                #print(batch_images)
                batch_images = batch_images * 2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr :learning_rate})
                _ = sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr :learning_rate})
                if steps % 100 == 0:
                    end = time.clock()
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})
                    print("Time: {:.4f}s ...Epoch {}/{}...setp:{}...".format((end - start),epoch_i+1, epoch_count,steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    start = time.clock()
                if steps % 500 == 0:
                    show_generator_output(sess,4,input_z,out_channel_dim,data_image_mode)
        show_generator_output(sess,4,input_z,out_channel_dim,data_image_mode)
                
                

"""
Explanation: 训练
部署 train 函数以建立并训练 GANs 模型。记得使用以下你已完成的函数：
- model_inputs(image_width, image_height, image_channels, z_dim)
- model_loss(input_real, input_z, out_channel_dim)
- model_opt(d_loss, g_loss, learning_rate, beta1)
使用 show_generator_output 函数显示 generator 在训练过程中的输出。
注意：在每个批次 (batch) 中运行 show_generator_output 函数会显著增加训练时间与该 notebook 的体积。推荐每 100 批次输出一次 generator 的输出。
End of explanation
"""


import time
batch_size = 32
z_dim = 100
learning_rate = 0.001
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)

"""
Explanation: MNIST
在 MNIST 上测试你的 GANs 模型。经过 2 次迭代，GANs 应该能够生成类似手写数字的图像。确保生成器 (generator) 低于辨别器 (discriminator) 的损失，或接近 0。
End of explanation
"""


batch_size = None
z_dim = None
learning_rate = None
beta1 = None


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)

"""
Explanation: CelebA
在 CelebA 上运行你的 GANs 模型。在一般的GPU上运行每次迭代大约需要 20 分钟。你可以运行整个迭代，或者当 GANs 开始产生真实人脸图像时停止它。
End of explanation
"""


