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Main.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
"Acoustic emission signature of martensitic transformation in Laser Powder Bed Fusion of Ti6Al4V-Fe, supported by operando X-ray diffraction"
@any reuse of this code should be authorized by the first owner, code author
"""
# libraries to import
import time
import torch
import random
import numpy as np
import pandas as pd
import torch.nn as nn
import torch.optim as optim
from tqdm.notebook import tqdm
import matplotlib.pyplot as plt
from torchvision import transforms
from torch.utils.data import DataLoader, Dataset
from torch.nn import functional as F
from Utils import *
from Network import *
from Dataloader import *
from Loss import *
from Classifier import *
from Trainer import *
from matplotlib import animation
import os
# %%
# Clearing the cache
torch.cuda.empty_cache()
torch.manual_seed(2020)
np.random.seed(2020)
random.seed(2020)
# %%
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if device.type == "cuda":
torch.cuda.get_device_name()
print('Using device:', device)
# %%
datapath = r'C:\Users\srpv\Desktop\Git\Additive-Manufacturing-Acoustic-Dynamics-of-in-situ-alloying-of-Titanium-Fe\Data_preprocessing'
embedding_dims = 8
batch_size = 64
epochs = 5
Datasets = ['CM', 'KM']
for Exptype in Datasets:
category = ['Ti64', 'Ti64-3Fe', 'Ti64-6Fe']
class_names = []
for x in category:
x = str(Exptype)+'-'+x
class_names.append(x)
# %%
# folder creation to store latent spaces, graphs and figure.
file = os.path.join(os.getcwd(), os.listdir(os.getcwd())[0])
total_path = os.path.dirname(file)
print(total_path)
folder_created = os.path.join(total_path, Exptype)
print(folder_created)
try:
os.makedirs(folder_created, exist_ok=True)
print("Directory created....")
except OSError as error:
print("Directory already exists....")
# %%
# Data loading and preprocessing
data_plot(datapath, Exptype, folder_created)
train_df, test_df = data_extract(datapath, Exptype)
train_loader, test_loader = torch_loader(batch_size, train_df, test_df)
# %%
# Model training
model = Network(embedding_dims)
model.apply(init_weights)
model = torch.jit.script(model).to(device)
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = torch.jit.script(ContrastiveLoss())
model.train()
Training_loss, Training_loss_mean, Training_loss_std, running_loss, model = model_trainer(
epochs, train_loader, device, optimizer, model, criterion, Exptype, folder_created)
# %%
# Plotting the training loss
plot_function(Exptype, folder_created, Training_loss,
Training_loss_mean, Training_loss_std, running_loss)
# %%
# Saving the model embeddings
train_results, train_labels = save_train_embeddings(
device, Exptype, folder_created, train_loader, model)
test_results, test_labels = save_test_embeddings(
device, Exptype, folder_created, test_loader, model)
# %%
# Plotting the latent space
graph_name_2D = os.path.join(folder_created, 'Training_Feature_2D.png')
plot_embeddings(train_results, train_labels, graph_name_2D, class_names)
graph_name_2D = os.path.join(folder_created, 'Testing_Feature_2D.png')
plot_embeddings(test_results, test_labels, graph_name_2D, class_names)
# %%
# Plotting the latent space in 3D
latent_animation(class_names, folder_created, train_results,
train_labels, test_results, test_labels)
# %%
# Model parameters
count_parameters(model)
# %%
# Classifier training
classifier_linear(Exptype, folder_created)

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