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# -*- coding: utf-8 -*-

"""

Created on Tue Mar 12 12:53:53 2019

@author: ita

"""

#----------------------Imports------------------------------

import numpy as np

import torch

import torch.nn as nn

import torch.nn.functional as F

from torch.autograd import Variable

import torch.utils.data as data

import torchvision

from torchvision import transforms

from torchvision import *

import torch

import math

import numpy as np

import matplotlib.pyplot as plt

import time as t

import torch.optim as optim

from scipy import ndimage as ndimage

from PIL import Image, ImageOps

#--------------------Data Loading and Splitting ---------------------------------

def get_data_loader(batch_size):

train_path = 'trainData'

val_path = 'valData'

#test_path = 'testData'

transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainSet = torchvision.datasets.ImageFolder(root=train_path, transform=transform)

train_data_loader = torch.utils.data.DataLoader(trainSet, batch_size=batch_size, shuffle=True)

valSet = torchvision.datasets.ImageFolder(root=val_path, transform=transform)

val_data_loader = torch.utils.data.DataLoader(valSet, batch_size=batch_size, shuffle=True)

# testSet = torchvision.datasets.ImageFolder(root=test_path, transform=transform)

# test_data_loader = torch.utils.data.DataLoader(testSet, batch_size=batch_size, shuffle=True)

return train_data_loader ,val_data_loader #,test_data_loader

#--------------------Base Model----------------------------------------------------

class BaseModel(nn.Module):

def __init__(self, input_size):

super(BaseModel, self).__init__()

self.name = "Base"

self.input_size = ((input_size - 2)/2)

self.conv1 = nn.Conv2d(1, 5, 3)

self.conv2 = nn.Conv2d(5, 7, 5)

self.pool = nn.MaxPool2d(2, 2)

self.fc1 = nn.Linear(int(7 * 147 * 147), 1000)

self.fc2 = nn.Linear(1000,2)

def forward(self, x):

x = self.pool(F.relu(self.conv1(x)))

x = self.pool(F.relu(self.conv2(x)))

x = x.view(-1,int(7*147 * 147) )

x = self.fc1(x)

x = self.fc2(x)

x = x.squeeze(1) # Flatten to [batch_size]

return x

#-------------------Filter (HP)----------------------------------------

def HPFilter(img):

weights = torch.tensor([[[-1.,2.,-2.,2.,-1.],

[2.,-6.,8.,-6.,2.],

[-2.,8.,-12.,8.,-2.],

[2.,-6.,8.,-6.,2.],

[-1.,2.,-2.,2.,1.]],

[[-1.,2.,-2.,2.,-1.],

[2.,-6.,8.,-6.,2.],

[-2.,8.,-12.,8.,-2.],

[2.,-6.,8.,-6.,2.],

[-1.,2.,-2.,2.,1.]],

[[-1.,2.,-2.,2.,-1.],

[2.,-6.,8.,-6.,2.],

[-2.,8.,-12.,8.,-2.],

[2.,-6.,8.,-6.,2.],

[-1.,2.,-2.,2.,1.]]])

weights=1/12*weights

weights=weights.unsqueeze(dim=0).cuda()

filteredimgs = F.conv2d(img, weights, padding=2).cuda()

return filteredimgs

def HPFilter2(img):

weights = torch.tensor([[-1.,2.,-2.,2.,-1.],

[2.,-6.,8.,-6.,2.],

[-2.,8.,-12.,8.,-2.],

[2.,-6.,8.,-6.,2.],

[-1.,2.,-2.,2.,1.]])

filteredimgs=[]

for im in img:

im=np.transpose(im,[1,2,0])

im=im/2+0.5

im = im.squeeze()

result=ndimage.convolve(im, np.atleast_3d(weights))

result = torch.from_numpy(result)

result=np.transpose(result,[2,0,1])

filteredimgs.append(result)

filteredimgs = torch.stack(filteredimgs)

return filteredimgs.cuda()

def LPFilter(img):

weights2_m1 = np.array([[0.,0.,0.,0.,0.],

[0.,-1.,2.,-1.,0.],

[0.,2.,-4.,2.,0],

[0.,-1.,2.,-1.,0.],

[0.,0.,0.,0.,0.]])

weights2_m1=1/4*weights2_m1

weights2_m2 = np.array([[-1.,2.,-2.,2.,-1],

[2.,-6.,8.,-6.,2.],

[-2.,8.,-12.,8.,-2.],

[2.,-6.,8.,-6.,2.],

[-1.,2.,-2.,2.,-1],])

weights2_m2=1/12*weights2_m2

weights2_m3 = np.array([[0.,0.,0.,0.,0.],

[0.,0.,0.,0.,0.],

[0.,1.,-2.,1.,0],

[0.,0.,0.,0.,0.],

[0.,0.,0.,0.,0.]])

weights2_m3=1/2*weights2_m3

weights=np.dot(weights2_m1, weights2_m2, weights2_m3)

weights=torch.from_numpy(weights)

weights3=[weights,weights,weights]

weights3=torch.stack(weights3).float()

weights3=weights3.unsqueeze(dim=0).cuda()

filteredimgs = F.conv2d(img, weights3, padding=2).cuda()

return filteredimgs

#-------------------Train Loop (Ft. Get Accuracy & Plotting)----------------------------------------

def get_accuracy(mdl,set_, batch_size):

mdl.cuda()

batch_size=16

label_ = [0]*(batch_size*2)

for i in range(batch_size,batch_size*2):

label_[i] = 1

label = torch.tensor(label_).cuda()

trainSet_,valSet_ = get_data_loader(batch_size)

if set_ == "train":

data_ = trainSet_

elif set_ == "val":

data_ = valSet_

correct = 0

total = 0

for img,batch in data_:

img,batch=img.cuda(),batch.cuda()

if(len(batch)==batch_size):

b = torch.split(img,600,dim=3)

img = torch.cat(b, 0)

filteredimgs=LPFilter(img)

output= mdl(filteredimgs).cuda()

# output = mdl(img).cuda()

pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability

correct += pred.eq(label.view_as(pred)).sum().item() #compute how many predictions were correct

total += img.shape[0] #get the total ammount of predictions

return correct / total

def train(mdl,epochs= 20,batch_size = 32,learning_rate =0.0001):

mdl.cuda()

criterion = nn.CrossEntropyLoss()

optimizer = optim.SGD(mdl.parameters(), lr=learning_rate, momentum=0.9)

trainSet,valSet = get_data_loader(batch_size)

train_acc, val_acc = [], []

n = 0 # the number of iterations

label_ = [0]*(batch_size*2)

for i in range(batch_size,batch_size*2):

label_[i] = 1

label = torch.tensor(label_).cuda()

print("--------------Starting--------------")

for epoch in range(epochs): # loop over the dataset multiple times

t1 = t.time()

itera = 0

filteredimg=[]

for img,batch in iter(trainSet):

if(len(batch)!=batch_size):

break

img,batch=img.cuda(),batch.cuda()

b = torch.split(img,600,dim=3)

img = torch.cat(b, 0)

# print(label)

itera += batch_size*2

filteredimgs=LPFilter(img).cuda()

out = mdl(filteredimgs).cuda()

loss = criterion(out, label)

loss.backward()

optimizer.step()

optimizer.zero_grad()

# print(itera)

# Calculate the statistics

train_acc.append(get_accuracy(mdl,"train", batch_size))

val_acc.append(get_accuracy(mdl,"val", batch_size)) # compute validation accuracy

n += 1

print("Epoch",n,"Done in:",t.time() - t1, "With Training Accuracy:",train_acc[-1], "And Validation Accuracy:",val_acc[-1])

# Save the current model (checkpoint) to a file

model_path = "model_{0}_bs{1}_lr{2}_epoch{3}".format(mdl.name,batch_size,learning_rate,epoch)

torch.save(mdl.state_dict(), model_path)

iterations = list(range(1,epochs + 1))

print("--------------Finished--------------")

return iterations,train_acc , val_acc

def plot(iterations,train_acc, val_acc):

plt.title("Training Curve")

plt.plot(iterations, train_acc, label="Train")

plt.plot(iterations, val_acc, label="Validation")

plt.xlabel("Epochs")

plt.ylabel("Training Accuracy")

plt.legend(loc='best')

plt.show()

print("Final Training Accuracy: {}".format(train_acc[-1]))

print("Final Validation Accuracy: {}".format(val_acc[-1]))