# -*- coding: utf-8 -*-
"""
Created on Sat Oct 13 10:22:45 2018
@author: www
"""
 
import torch
from torch import nn
from torch.autograd import Variable
 
import torchvision.transforms as tfs
from torch.utils.data import DataLoader, sampler
from torchvision.datasets import MNIST
 
import numpy as np
 
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
 
plt.rcParams['figure.figsize'] = (10.0, 8.0) # 設(shè)置畫圖的尺寸
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
 
def show_images(images): # 定義畫圖工具
  images = np.reshape(images, [images.shape[0], -1])
  sqrtn = int(np.ceil(np.sqrt(images.shape[0])))
  sqrtimg = int(np.ceil(np.sqrt(images.shape[1])))
 
  fig = plt.figure(figsize=(sqrtn, sqrtn))
  gs = gridspec.GridSpec(sqrtn, sqrtn)
  gs.update(wspace=0.05, hspace=0.05)
 
  for i, img in enumerate(images):
    ax = plt.subplot(gs[i])
    plt.axis('off')
    ax.set_xticklabels([])
    ax.set_yticklabels([])
    ax.set_aspect('equal')
    plt.imshow(img.reshape([sqrtimg,sqrtimg]))
  return 
  
def preprocess_img(x):
  x = tfs.ToTensor()(x)
  return (x - 0.5) / 0.5
 
def deprocess_img(x):
  return (x + 1.0) / 2.0
 
class ChunkSampler(sampler.Sampler): # 定義一個(gè)取樣的函數(shù)
  """Samples elements sequentially from some offset. 
  Arguments:
    num_samples: # of desired datapoints
    start: offset where we should start selecting from
  """
  def __init__(self, num_samples, start=0):
    self.num_samples = num_samples
    self.start = start
 
  def __iter__(self):
    return iter(range(self.start, self.start + self.num_samples))
 
  def __len__(self):
    return self.num_samples
    
NUM_TRAIN = 50000
NUM_VAL = 5000
 
NOISE_DIM = 96
batch_size = 128
 
train_set = MNIST('E:/data', train=True, transform=preprocess_img)
 
train_data = DataLoader(train_set, batch_size=batch_size, sampler=ChunkSampler(NUM_TRAIN, 0))
 
val_set = MNIST('E:/data', train=True, transform=preprocess_img)
 
val_data = DataLoader(val_set, batch_size=batch_size, sampler=ChunkSampler(NUM_VAL, NUM_TRAIN))
 
imgs = deprocess_img(train_data.__iter__().next()[0].view(batch_size, 784)).numpy().squeeze() # 可視化圖片效果
show_images(imgs)
 
#判別網(wǎng)絡(luò)
def discriminator():
  net = nn.Sequential(    
      nn.Linear(784, 256),
      nn.LeakyReLU(0.2),
      nn.Linear(256, 256),
      nn.LeakyReLU(0.2),
      nn.Linear(256, 1)
    )
  return net
  
#生成網(wǎng)絡(luò)
def generator(noise_dim=NOISE_DIM):  
  net = nn.Sequential(
    nn.Linear(noise_dim, 1024),
    nn.ReLU(True),
    nn.Linear(1024, 1024),
    nn.ReLU(True),
    nn.Linear(1024, 784),
    nn.Tanh()
  )
  return net
  
#判別器的 loss 就是將真實(shí)數(shù)據(jù)的得分判斷為 1，假的數(shù)據(jù)的得分判斷為 0，而生成器的 loss 就是將假的數(shù)據(jù)判斷為 1
 
bce_loss = nn.BCEWithLogitsLoss()#交叉熵?fù)p失函數(shù)
 
def discriminator_loss(logits_real, logits_fake): # 判別器的 loss
  size = logits_real.shape[0]
  true_labels = Variable(torch.ones(size, 1)).float()
  false_labels = Variable(torch.zeros(size, 1)).float()
  loss = bce_loss(logits_real, true_labels) + bce_loss(logits_fake, false_labels)
  return loss
  
def generator_loss(logits_fake): # 生成器的 loss 
  size = logits_fake.shape[0]
  true_labels = Variable(torch.ones(size, 1)).float()
  loss = bce_loss(logits_fake, true_labels)
  return loss
  
# 使用 adam 來進(jìn)行訓(xùn)練，學(xué)習(xí)率是 3e-4, beta1 是 0.5, beta2 是 0.999
def get_optimizer(net):
  optimizer = torch.optim.Adam(net.parameters(), lr=3e-4, betas=(0.5, 0.999))
  return optimizer
  
def train_a_gan(D_net, G_net, D_optimizer, G_optimizer, discriminator_loss, generator_loss, show_every=250, 
        noise_size=96, num_epochs=10):
  iter_count = 0
  for epoch in range(num_epochs):
    for x, _ in train_data:
      bs = x.shape[0]
      # 判別網(wǎng)絡(luò)
      real_data = Variable(x).view(bs, -1) # 真實(shí)數(shù)據(jù)
      logits_real = D_net(real_data) # 判別網(wǎng)絡(luò)得分
      
      sample_noise = (torch.rand(bs, noise_size) - 0.5) / 0.5 # -1 ~ 1 的均勻分布
      g_fake_seed = Variable(sample_noise)
      fake_images = G_net(g_fake_seed) # 生成的假的數(shù)據(jù)
      logits_fake = D_net(fake_images) # 判別網(wǎng)絡(luò)得分
 
      d_total_error = discriminator_loss(logits_real, logits_fake) # 判別器的 loss
      D_optimizer.zero_grad()
      d_total_error.backward()
      D_optimizer.step() # 優(yōu)化判別網(wǎng)絡(luò)
      
      # 生成網(wǎng)絡(luò)
      g_fake_seed = Variable(sample_noise)
      fake_images = G_net(g_fake_seed) # 生成的假的數(shù)據(jù)
 
      gen_logits_fake = D_net(fake_images)
      g_error = generator_loss(gen_logits_fake) # 生成網(wǎng)絡(luò)的 loss
      G_optimizer.zero_grad()
      g_error.backward()
      G_optimizer.step() # 優(yōu)化生成網(wǎng)絡(luò)
 
      if (iter_count % show_every == 0):
        print('Iter: {}, D: {:.4}, G:{:.4}'.format(iter_count, d_total_error.item(), g_error.item()))
        imgs_numpy = deprocess_img(fake_images.data.cpu().numpy())
        show_images(imgs_numpy[0:16])
        plt.show()
        print()
      iter_count += 1
 
D = discriminator()
G = generator()
 
D_optim = get_optimizer(D)
G_optim = get_optimizer(G)
 
train_a_gan(D, G, D_optim, G_optim, discriminator_loss, generator_loss)