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definitions_v3_alex
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definitions_v3_alex
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# -*- coding: utf-8 -*-
"""
Created on Tue Mar 12 12:53:53 2019
@author: marc
"""
#----------------------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'F:\trainData'
val_path = r'F:\valData'
test_path = r'F:\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(3, 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
#-------------------Train Loop (Ft. Get Accuracy & Plotting)----------------------------------------
def get_accuracy(model,set_):
label_ = [0]*(300)
for i in range(0,300,2):
label_[i] = 1
label = torch.tensor(label_)
trainSet_,valSet_,__ = get_data_loader(150)
if set_ == "train":
data_ = trainSet_
elif set_ == "val":
data_ = valSet_
correct = 0
total = 0
for img, _ in data_:
b = torch.split(img,600,dim=3)
img = torch.cat(b, 0)
output = model(img)
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
break
return correct / total
def train(mdl,epochs= 20,batch_size = 32,learning_rate =0.01):
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
label_ = [0]*(batch_size*2)
for i in range(0,batch_size*2,2):
label_[i] = 1
label = torch.tensor(label_)
print("--------------Starting--------------")
for epoch in range(epochs): # loop over the dataset multiple times
t1 = t.time()
itera = 0
for img,_ in iter(trainSet):
b = torch.split(img,600,dim=3)
img = torch.cat(b, 0)
print(img.size())
itera += batch_size*2
out = mdl(img)
loss = criterion(out, label)
loss.backward()
optimizer.step()
optimizer.zero_grad()
print(itera)
break
# Calculate the statistics
train_acc.append(get_accuracy(mdl,"train"))
val_acc.append(get_accuracy(mdl,"val")) # 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]))
from a3code import AlexNetFeatures
myfeature_model = AlexNetFeatures() #loads pre-trained weights
atrain_loader, aval_loader, atest_loader = get_data_loader(1)
a=0
for img, l in atest_loader:
features=myfeature_model(img)
i=str(a)
label=l[0].item()
print(features.shape)
break
class AlexNet(nn.Module):
def __init__(self):
super(AlexNet, self).__init__()
self.layer1 = nn.Linear(256*6*6, 50)
self.layer2 = nn.Linear(50, 20)
self.layer3 = nn.Linear(20, 2)
def forward(self, img):
flattened = img.view(-1,256*6*6)
activation1 = F.relu(self.layer1(flattened))
activation2 = F.relu(self.layer2(activation1))
output = self.layer3(activation2)
return output
def get_alex_data_loader(batch_size, shuffle=True):
np.random.seed(1000) # Fixed numpy random seed for reproducible shuffling
train_sampler = torchvision.datasets.DatasetFolder(root='trainData', loader=torch.load, extensions=list(['']))
alex_train_loader = torch.utils.data.DataLoader(train_sampler, batch_size=batch_size, shuffle=shuffle)
val_sampler = torchvision.datasets.DatasetFolder(root='valData', loader=torch.load, extensions=list(['']))
alex_val_loader = torch.utils.data.DataLoader(val_sampler, batch_size=batch_size, shuffle=shuffle)
test_sampler = torchvision.datasets.DatasetFolder(root='testData', loader=torch.load, extensions=list(['']))
alex_test_loader = torch.utils.data.DataLoader(test_sampler, batch_size=batch_size, shuffle=shuffle)
return alex_train_loader, alex_train_loader, alex_test_loader
def get_alex_accuracy(model, train=True):
batch_size=16
label_ = [0]*(batch_size*2)
for i in range(0,batch_size*2,2):
label_[i] = 1
if train:
data = alex_train_loader
else:
train, data, test = alex_train_loader, alex_val_loader, alex_test_loader = get_alex_data_loader(1)
correct = 0
total = 0
for inputs, labels in data:
output = model(inputs) # We don't need to run F.softmax
pred = output.argmax()
correct += pred.eq(labels.view_as(pred)).sum().item()
total += inputs.shape[0]
return correct / total
def alextrain(model, batch_size=32, num_epochs=15, lr=0.0001):
atrain_loader, aval_loader, atest_loader = get_alex_data_loader(1)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9)
iters, losses, train_acc, val_acc = [], [], [], []
# training
n = 0 # the number of iterations
label_ = [0]*(batch_size*2)
for i in range(0,batch_size*2,2):
label_[i] = 1
label = torch.tensor(label_).cuda()
for epoch in range(num_epochs):
correct=0
total=0
#to store the iteration that reached 100% accuracy first.
for inputs, labels in iter(atrain_loader):
out = model(inputs) # forward pass
loss = criterion(out, labels) # compute the total loss
loss.backward() # backward pass (compute parameter updates)
optimizer.step() # make the updates for each parameter
optimizer.zero_grad() # a clean up step for PyTorch
# save the current training information
pred = out.argmax()
correct += pred.eq(labels.view_as(pred)).sum().item()
total += inputs.shape[0]
train_acc.append(correct/total) # compute training accuracy
val_acc.append(get_alex_accuracy(model, train=False)) # compute validation accuracy
n += 1
iters.append(n)
plt.title("Training Curve")
plt.plot(iters, train_acc, label="Train")
plt.plot(iters, val_acc, label="Validation")
plt.xlabel("Iterations")
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]))