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R12999/Classification/Multivariate/Main_CNN_Multivariate.py
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Main_CNN_Multivariate.py
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# -*- coding: utf-8 -*-
"""
@author: srpv
contact: vigneashwara.solairajapandiyan@empa.ch, vigneashpandiyan@gmail.com
The codes in this following script will be used for the publication of the following work
"Qualify-As-You-Go: Sensor Fusion of Optical and Acoustic Signatures with Contrastive Deep Learning for Multi-Material Composition Monitoring in Laser Powder Bed Fusion Process"
@any reuse of this code should be authorized by the first owner, code author
"""
# libraries to import
from Trainer.Trainer import *
from Dataloader.Dataloader import *
from Network.Network import *
from Utils.Utils import *
from tqdm.auto import tqdm
from prettytable import PrettyTable
from torchsummary import summary
import os
import torch
import numpy as np
import pandas as pd
import time
import matplotlib.pyplot as plt
import seaborn as sns
# %%
# Clearing the cache
torch.cuda.empty_cache()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
# %%
# Defining the Hyperparameters for training the network
batch_size = 256
epoch = 300
windowsize = 5000
Material_1 = "D1"
Material_2 = "D2"
embedding_dims = 32
# %%
# Checking the availability of the GPU device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if device.type == "cuda":
torch.cuda.get_device_name()
# %%
# Defining the path for the data ---> Folder path
#http://dx.doi.org/10.5281/zenodo.11094814
total_path = r"C:\Users\srpv\Desktop\Git\Additive-Manufacturing-Multi-Material-Composition-Monitoring-Using-Sensor-Fusion\Data"
# Data_torch function helps to convert the data into torch dataset
Featurespace_1, classspace = data_pipeline(Material_1, total_path, windowsize)
Featurespace_2, classspace = data_pipeline(Material_2, total_path, windowsize)
trainset, testset = Data_torch(classspace, Featurespace_1, Featurespace_2)
# %%
# Defining the network and if the GPU device is avaialble then the network will be loaded and trained on the GPU
if torch.cuda.device_count() > 0:
print("Let's use", torch.cuda.device_count(), "GPUs!")
net = CNN(len(np.unique(classspace)), embedding_dims, dropout_rate=0.1)
# net = nn.DataParallel(net)
net.to(device)
summary(net, (2, 5000))
# %%
# Training the network
model, iteration, Loss_value, Total_Epoch, Accuracy, Learning_rate, Training_loss_mean, Training_loss_std = Network_trainer(
net, trainset, testset, device, epoch=epoch)
PATH = './CNN_Multi_variate'+'.pth'
torch.save(model.state_dict(), PATH)
torch.save(model, PATH)
model = torch.load(PATH)
correctHits = 0
total = 0
for batches in testset:
data, output = batches
data, output = data.to(device, dtype=torch.float), output.to(device, dtype=torch.long)
output = output.squeeze()
prediction = model(data)
prediction = torch.argmax(prediction, dim=1)
total += output.size(0)
correctHits += (prediction == output).sum().item()
print('Accuracy = '+str((correctHits/total)*100))
print('Finished Training')
# %%
# Plotting the confusion matrix and the training loss, learning rate and accuracy
folder_created = os.path.join('Figures/', str(Material_1)+str(Material_2))
print(folder_created)
try:
os.makedirs(folder_created, exist_ok=True)
print("Directory created....")
except OSError as error:
print("Directory already exists....")
plt.rcParams.update(plt.rcParamsDefault)
plots(iteration, Loss_value, Total_Epoch, Accuracy, Learning_rate,
Training_loss_mean, Training_loss_std, folder_created)
count_parameters(net)
classes = ('1', '2', '3', '4', '5') # Change the classes based on the dataset
plotname = 'Multi_variate'+'_confusion_matrix'+'.png'
plot_confusion_matrix(model, testset, classes, device, plotname)
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