Main_Triplet.py
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Created: Tue, May 7, 18:59

Main_Triplet.py
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	#https://github.com/adambielski/siamese-triplet


	# from torchvision.datasets import MNIST
	from torchvision import transforms

	import torch
	from torch.optim import lr_scheduler
	import torch.optim as optim
	from torch.autograd import Variable

	from trainer import fit
	import numpy as np
	cuda = torch.cuda.is_available()

	import matplotlib
	import matplotlib.pyplot as plt
	import torchvision.transforms as transforms
	import torchvision
	from torchvision import datasets
	from torch.utils.data import Dataset, DataLoader, random_split
	from os import walk
	import os
	from torch.optim.lr_scheduler import StepLR

	from datasets import TripletMNIST,TripletPlot
	import pandas as pd
	from matplotlib import animation
	from Plots import *


	#%%

	#%%
	# setting the root directories and categories of the images
	#%% Folder pointing to the data

	# Data--> https://polybox.ethz.ch/index.php/s/HUcJ7cJ18K0MrEn

	datadir = '../Data/' #place in a folder
	traindir = datadir + 'train/'
	testdir = datadir + 'test/'

	categories = [[folder, os.listdir(traindir + folder)] for folder in os.listdir(traindir) if not folder.startswith('.') ]

	mnist_classes = ['1', '2', '3', '4','5','6']



	#%%
	# Datasize preparation
	# Set up data loaders



	transformations = transforms.Compose([
	torchvision.transforms.Resize((480,320)),
	transforms.ToTensor()])


	train_loader_folder = datasets.ImageFolder(root=traindir, transform=transformations)
	test_loader_folder = datasets.ImageFolder(root=testdir, transform=transformations)

	#%%

	batch_size = 256
	kwargs = {'num_workers': 0, 'pin_memory': True} if cuda else {}
	trainloader = torch.utils.data.DataLoader(train_loader_folder, batch_size=batch_size, shuffle=True, **kwargs)
	testloader = torch.utils.data.DataLoader(test_loader_folder, batch_size=batch_size, shuffle=True, **kwargs)

	dataSize = len(trainloader.dataset) # self-defined dataset size
	TRAIN_PCT = 0.9 # percentage of entire dataset for training
	train_size = int(dataSize * TRAIN_PCT)
	val_size = dataSize - train_size

	Triplet_train_dataset = TripletMNIST(categories, traindir, dataSize, transformations)
	train_set, val_set = random_split(Triplet_train_dataset, [train_size, val_size])

	batch_size = 250

	kwargs = {'num_workers': 0, 'pin_memory': True} if cuda else {}
	train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True, **kwargs)
	test_loader = torch.utils.data.DataLoader(val_set, batch_size=batch_size, shuffle=False, **kwargs)

	#%%

	# Setting up the network and training parameters
	from networks import EmbeddingNet, TripletNet
	from losses import TripletLoss

	#Network
	embedding_net = EmbeddingNet(dropout=0.1)
	model = TripletNet(embedding_net)
	if cuda:
	model.cuda()
	#Loss function
	margin = 1.
	loss_fn = TripletLoss(margin)

	#Training parameters
	lr = 1e-3
	# optimizer = optim.Adam(model.parameters(), lr=lr)
	# scheduler = lr_scheduler.StepLR(optimizer, 8, gamma=0.1, last_epoch=-1)

	optimizer = torch.optim.SGD(model.parameters(),lr=0.0005,momentum=0.9)
	scheduler = StepLR(optimizer, step_size = 200, gamma= 0.25 )
	n_epochs = 1
	log_interval = 25


	#%%

	#Training of the Network
	train_losses,val_losses=fit(train_loader, test_loader, model, loss_fn, optimizer, scheduler, n_epochs, cuda, log_interval)


	train_plot = 'train_losses'+'_'+ '.npy'
	val_plot = 'val_losses'+'_'+'.npy'


	np.save(train_plot,train_losses, allow_pickle=True)
	np.save(val_plot,val_losses, allow_pickle=True)

	#%%
	#figures


	Triplet_train = TripletPlot(categories, traindir, dataSize, transformations) # Returns pairs of images and target same/different
	train_set_1, val_set_1 = random_split(Triplet_train, [train_size, val_size])

	batch_size = 128
	kwargs = {'num_workers': 0, 'pin_memory': True} if cuda else {}
	train_loader_1 = torch.utils.data.DataLoader(train_set_1, batch_size=1, shuffle=True, **kwargs)



	for img1, img2, img3 in train_loader_1:
	i= 1
	if i == 1:
	plt.subplot(1,3,1)
	plt.imshow(np.transpose(img1[0][0]))
	plt.subplot(1,3,2)
	plt.imshow(np.transpose(img2[0][0]))

	plt.subplot(1,3,3)
	plt.imshow(np.transpose(img3[0][0]))
	plt.savefig('Trainingimage.png', dpi=600,bbox_inches='tight')
	plt.show()

	break


	#%%
	train_losses=np.load('train_losses_.npy')
	plt.xlabel('Epoch')
	plt.ylabel('Loss values')
	plt.plot(train_losses, c='b', label='Triplet Loss',linewidth =2.0)
	# plt.plot(val_losses, label="Validation Loss",linewidth =2.0)
	plt.legend( loc='upper right')
	plt.savefig('Paper_Loss_Triplet.png', dpi=600,bbox_inches='tight')
	plt.show()


	train_plot = 'train_losses'+'_'+ '.npy'
	val_plot = 'val_losses'+'_'+'.npy'

	np.save(train_plot,train_losses, allow_pickle=True)
	np.save(val_plot,val_losses, allow_pickle=True)
	#%%


	color = [ 'purple','orange','green','red', 'blue', 'cyan']
	marker= ["d","s","*",">","X","o"]

	mnist_classes = ['P1', 'P2', 'P3', 'P4','P5','P6']
	graph_name_2D='Training_Feature_2D' +'_'+'.png'
	graph_title = "Feature space distribution"

	def plot_embeddings(embeddings, targets, xlim=None, ylim=None):
	plt.figure(figsize=(7,5))
	j=0
	for i in range(len(train_loader_folder.classes)):
	inds = np.where(targets==i)[0]
	plt.scatter(embeddings[inds,0], embeddings[inds,1], alpha=0.7, color=color[j],marker=marker[j],s=100)
	j=j+1

	if xlim:
	plt.xlim(xlim[0], xlim[1])
	if ylim:
	plt.ylim(ylim[0], ylim[1])
	plt.legend(mnist_classes,bbox_to_anchor=(1.32, 1.05))
	plt.xlabel ('Weights_1', labelpad=10)
	plt.ylabel ('Weights_2', labelpad=10)
	plt.title(str(graph_title),fontsize = 15)
	plt.savefig(graph_name_2D, bbox_inches='tight',dpi=600)
	plt.show()

	def extract_embeddings(dataloader, model):
	with torch.no_grad():
	model.eval()
	embeddings = np.zeros((len(dataloader.dataset), 4))
	labels = np.zeros(len(dataloader.dataset))
	k = 0
	for images, target in dataloader:
	images1 = images
	target=target
	if cuda:
	images = images.cuda()
	embeddings[k:k+len(images)] = model.get_embedding(images).data.cpu().numpy()
	labels[k:k+len(images)] = target.numpy()
	k += len(images)
	return embeddings, labels

	PATH = './Triplet.pth'
	torch.save(model.state_dict(), PATH)
	torch.save(model, PATH)



	#%%

	train_embeddings_baseline, train_labels_baseline = extract_embeddings(trainloader, model)

	train_embeddings_baseline=train_embeddings_baseline.astype(np.float64)
	train_labels_baseline=train_labels_baseline.astype(np.float64)
	train_embeddings = 'train_embeddings'+'_'+ '.npy'
	train_labelsname = 'train_labels'+'_'+'.npy'
	np.save(train_embeddings,train_embeddings_baseline, allow_pickle=True)
	np.save(train_labelsname,train_labels_baseline, allow_pickle=True)
	plot_embeddings(train_embeddings_baseline, train_labels_baseline)

	test_embeddings_baseline, test_labels_baseline = extract_embeddings(testloader, model)

	test_embeddings_baseline=test_embeddings_baseline.astype(np.float64)
	test_labels_baseline=test_labels_baseline.astype(np.float64)
	test_embeddings = 'test_embeddings'+'_'+ '.npy'
	test_labelsname = 'test_labels'+'_'+'.npy'
	np.save(test_embeddings,test_embeddings_baseline, allow_pickle=True)
	np.save(test_labelsname,test_labels_baseline, allow_pickle=True)



	#%%


	graph_name='Training_Feature' +'_'+'.png'
	ax,fig=Three_embeddings(train_embeddings_baseline, train_labels_baseline,graph_name,ang=45)
	gif1_name= str('Training_Feature')+'.gif'

	#%%
	def rotate(angle):
	ax.view_init(azim=angle)

	angle = 3
	ani = animation.FuncAnimation(fig, rotate, frames=np.arange(0, 360, angle), interval=50)
	ani.save(gif1_name, writer=animation.PillowWriter(fps=20))

	#%%

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