# library
# standard library
import os 
# third-party library
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
import torchvision
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
# torch.manual_seed(1)  # reproducible 
# Hyper Parameters
EPOCH = 1        # train the training data n times, to save time, we just train 1 epoch
BATCH_SIZE = 50
LR = 0.001       # learning rate 
 
root = "./mnist/raw/"
 
def default_loader(path):
  # return Image.open(path).convert('RGB')
  return Image.open(path)
 
class MyDataset(Dataset):
  def __init__(self, txt, transform=None, target_transform=None, loader=default_loader):
    fh = open(txt, 'r')
    imgs = []
    for line in fh:
      line = line.strip('\n')
      line = line.rstrip()
      words = line.split()
      imgs.append((words[0], int(words[1])))
    self.imgs = imgs
    self.transform = transform
    self.target_transform = target_transform
    self.loader = loader
    fh.close()
  def __getitem__(self, index):
    fn, label = self.imgs[index]
    img = self.loader(fn)
    img = Image.fromarray(np.array(img), mode='L')
    if self.transform is not None:
      img = self.transform(img)
    return img,label
  def __len__(self):
    return len(self.imgs)
 
train_data = MyDataset(txt= root + 'train.txt', transform = torchvision.transforms.ToTensor())
train_loader = DataLoader(dataset = train_data, batch_size=BATCH_SIZE, shuffle=True)
 
test_data = MyDataset(txt= root + 'test.txt', transform = torchvision.transforms.ToTensor())
test_loader = DataLoader(dataset = test_data, batch_size=BATCH_SIZE)
 
class CNN(nn.Module):
  def __init__(self):
    super(CNN, self).__init__()
    self.conv1 = nn.Sequential(     # input shape (1, 28, 28)
      nn.Conv2d(
        in_channels=1,       # input height
        out_channels=16,      # n_filters
        kernel_size=5,       # filter size
        stride=1,          # filter movement/step
        padding=2,         # if want same width and length of this image after con2d, padding=(kernel_size-1)/2 if stride=1
      ),               # output shape (16, 28, 28)
      nn.ReLU(),           # activation
      nn.MaxPool2d(kernel_size=2),  # choose max value in 2x2 area, output shape (16, 14, 14)
    )
    self.conv2 = nn.Sequential(     # input shape (16, 14, 14)
      nn.Conv2d(16, 32, 5, 1, 2),   # output shape (32, 14, 14)
      nn.ReLU(),           # activation
      nn.MaxPool2d(2),        # output shape (32, 7, 7)
    )
    self.out = nn.Linear(32 * 7 * 7, 10)  # fully connected layer, output 10 classes
 
  def forward(self, x):
    x = self.conv1(x)
    x = self.conv2(x)
    x = x.view(x.size(0), -1)      # flatten the output of conv2 to (batch_size, 32 * 7 * 7)
    output = self.out(x)
    return output, x  # return x for visualization 
cnn = CNN()
print(cnn) # net architecture
 
optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)  # optimize all cnn parameters
loss_func = nn.CrossEntropyLoss()            # the target label is not one-hotted 
 
# training and testing
for epoch in range(EPOCH):
  for step, (x, y) in enumerate(train_loader):  # gives batch data, normalize x when iterate train_loader
    b_x = Variable(x)  # batch x
    b_y = Variable(y)  # batch y
 
    output = cnn(b_x)[0]        # cnn output
    loss = loss_func(output, b_y)  # cross entropy loss
    optimizer.zero_grad()      # clear gradients for this training step
    loss.backward()         # backpropagation, compute gradients
    optimizer.step()        # apply gradients
 
    if step % 50 == 0:
      cnn.eval()
      eval_loss = 0.
      eval_acc = 0.
      for i, (tx, ty) in enumerate(test_loader):
        t_x = Variable(tx)
        t_y = Variable(ty)
        output = cnn(t_x)[0]
        loss = loss_func(output, t_y)
        eval_loss += loss.data[0]
        pred = torch.max(output, 1)[1]
        num_correct = (pred == t_y).sum()
        eval_acc += float(num_correct.data[0])
      acc_rate = eval_acc / float(len(test_data))
      print('Test Loss: {:.6f}, Acc: {:.6f}'.format(eval_loss / (len(test_data)), acc_rate))