import os
import shutil
import torch
import collections
from torchvision import transforms,datasets
from __future__ import print_function, division
import os
import torch
import pylab
import pandas as pd
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from skimage import io, transform
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
 
# Ignore warnings
import warnings
warnings.filterwarnings("ignore")
 
plt.ion() # interactive mode

import math
from PIL import Image
 
class Resize(object):
 """Resize the input PIL Image to the given size.
 Args:
 size (sequence or int): Desired output size. If size is a sequence like
  (h, w), output size will be matched to this. If size is an int,
  smaller edge of the image will be matched to this number.
  i.e, if height > width, then image will be rescaled to
  (size * height / width, size)
 interpolation (int, optional): Desired interpolation. Default is
  ``PIL.Image.BILINEAR``
 """
 
 def __init__(self, size, interpolation=Image.BILINEAR):
 # assert isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)
 self.size = size
 self.interpolation = interpolation
 
 def __call__(self, img):
 w,h = img.size
 
 min_edge = min(img.size)
 rate = min_edge / self.size
 
 new_w = math.ceil(w / rate)
 new_h = math.ceil(h / rate)
 
 return img.resize((new_w,new_h))

data_transform = transforms.Compose([
 Resize(84),
 transforms.CenterCrop(84),
 transforms.ToTensor(),
 transforms.Normalize(mean = [0.5,0.5,0.5],std = [0.5,0.5,0.5])
])
 
train_dataset = datasets.ImageFolder(root = 'train/',transform = data_transform)
train_loader = torch.utils.data.DataLoader(train_dataset,batch_size = 4,shuffle = True,num_workers = 4)
 
test_dataset = datasets.ImageFolder(root = 'test/',transform = data_transform)
test_loader = torch.utils.data.DataLoader(test_dataset,batch_size = 4,shuffle = True,num_workers = 4)

class Net(nn.Module):
 def __init__(self):
 super(Net,self).__init__()
 
 self.conv1 = nn.Conv2d(3,6,5)
 self.pool = nn.MaxPool2d(2,2)
 self.conv2 = nn.Conv2d(6,16,5)
 self.fc1 = nn.Linear(16 * 18 * 18,800)
 self.fc2 = nn.Linear(800,120)
 self.fc3 = nn.Linear(120,2)
 
 def forward(self,x):
 x = self.pool(F.relu(self.conv1(x)))
 x = self.pool(F.relu(self.conv2(x)))
 x = x.view(-1,16 * 18 * 18)
 x = F.relu(self.fc1(x))
 x = F.relu(self.fc2(x))
 x = self.fc3(x)
 
 return x
 
net = Net()

transforms.CenterCrop(84)

import torch.optim as optim
 
cirterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(),lr = 0.0001,momentum = 0.9)
 
for epoch in range(3):
 running_loss = 0.0
 
 for i,data in enumerate(train_loader,0):
 inputs,labels = data
 inputs,labels = Variable(inputs),Variable(labels)
 optimizer.zero_grad()
 outputs = net(inputs)
 loss = cirterion(outputs,labels)
 loss.backward()
 optimizer.step()
 
 running_loss += loss.data[0]
 
 if i % 2000 == 1999:
  print('[%d %5d] loss: %.3f' % (epoch + 1,i + 1,running_loss / 2000))
  running_loss = 0.0
 
print('finished training!')

[1 2000] loss: 0.691
[1 4000] loss: 0.687
[2 2000] loss: 0.671
[2 4000] loss: 0.657
[3 2000] loss: 0.628
[3 4000] loss: 0.626
finished training!

correct = 0
total = 0
 
for data in test_loader:
 images,labels = data
 outputs = net(Variable(images))
 _,predicted = torch.max(outputs.data,1)
 total += labels.size(0)
 correct += (predicted == labels).sum()
 
print('Accuracy of the network on the 5000 test images: %d %%' % (100 * correct / total))