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definitions.py
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definitions.py
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#----------------------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 PIL import Image, ImageOps
#--------------------Data Loading and Splitting ---------------------------------
def get_data_loader(batch_size):
train_path = r'C:\Users\chris\OneDrive\Documents\3rd Year Labs\AI Project\trainData'
val_path = r'C:\Users\chris\OneDrive\Documents\3rd Year Labs\AI Project\valData'
test_path = r'C:\Users\chris\OneDrive\Documents\3rd Year Labs\AI Project\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=False)
valSet = torchvision.datasets.ImageFolder(root=val_path, transform=transform)
val_data_loader = torch.utils.data.DataLoader(valSet, batch_size=batch_size, shuffle=False)
testSet = torchvision.datasets.ImageFolder(root=test_path, transform=transform)
test_data_loader = torch.utils.data.DataLoader(testSet, batch_size=batch_size, shuffle=False)
return train_data_loader ,val_data_loader,test_data_loader
#--------------------Base Model----------------------------------------------------
class BaseModel(nn.Module):
def __init__(self, input_size = 400):
super(BaseModel, self).__init__()
self.name = "Base"
self.input_size = input_size
self.conv = nn.Conv2d(3, 5, 3)
self.pool = nn.MaxPool2d(2, 2)
self.fc = nn.Linear(5 * 99 * 99, 2)
def forward(self, x):
x = self.pool(F.relu(self.conv(x)))
x = self.pool(x)
x = x.view(-1, 5 * 99 * 99)
x = self.fc(x)
x = x.squeeze(1) # Flatten to [batch_size]
return x
#-------------------Train Loop (Ft. Get Accuracy & Plotting)----------------------------------------
def get_accuracy(model):
_,valSet_,__ = get_data_loader(189)
data_ = valSet_
correct = 0
total = 0
for imgs, labels in data_:
output = model(imgs)
pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability
correct += pred.eq(labels.view_as(pred)).sum().item() #compute how many predictions were correct
total += imgs.shape[0] #get the total ammount of predictions
break
return correct / total
def train(mdl,epochs= 20,batch_size = 64,learning_rate =0.1):
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(mdl.parameters(), lr=learning_rate, momentum=0.9)
trainSet,valSet,testSet = get_data_loader(batch_size)
train_acc, val_acc = [], []
n = 0 # the number of iterations
print("--------------Starting--------------")
for epoch in range(epochs): # loop over the dataset multiple times
t1 = t.time()
correct = 0
total = 0
for img, label in iter(trainSet):
out = mdl(img)
#---------Get statistics for train---------
pred = out.max(1, keepdim=True)[1] # get the index of the max log-probability
correct += pred.eq(label.view_as(pred)).sum().item()
total += img.shape[0]
#-----------------Done------------------
loss = criterion(out, label)
loss.backward()
optimizer.step()
optimizer.zero_grad()
# Calculate the statistics
train_acc.append(correct/total)
val_acc.append(get_accuracy(mdl)) # 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]))