Page MenuHomec4science
Files F79266092

Network.py
No OneTemporary
Actions

File Metadata

Created
Sun, Aug 25, 13:50

Network.py
View Options

# -*- 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
import matplotlib.pyplot as plt
import numpy as np
from prettytable import PrettyTable
import torch.nn as nn
import torch
class PrintLayer(torch.nn.Module):
def __init__(self):
super(PrintLayer, self).__init__()
def forward(self, x):
# Do your print / debug stuff here
print(x.shape)
return x
class Network(nn.Module):
"""
This class represents a network model for multi-material composition monitoring using sensor fusion.
Args:
droupout (float): The dropout rate for the network.
emb_dim (int): The dimension of the output embedding.
Inputs:
x (torch.Tensor): The input tensor of shape (batch_size, 1, 5000).
Returns:
torch.Tensor: The normalized output tensor of shape (batch_size, emb_dim).
"""
def __init__(self, droupout, emb_dim):
super(Network, self).__init__()
#torch.Size([100, 1, 5000])
self.dropout = droupout
self.conv = nn.Sequential(
nn.Conv1d(in_channels=2, out_channels=4, kernel_size=16),
nn.BatchNorm1d(4),
nn.ReLU(),
nn.Dropout(self.dropout),
nn.MaxPool1d(3),
# PrintLayer(),
nn.Conv1d(in_channels=4, out_channels=8, kernel_size=16),
nn.BatchNorm1d(8),
nn.ReLU(),
nn.Dropout(self.dropout),
nn.MaxPool1d(3),
# PrintLayer(),
nn.Conv1d(in_channels=8, out_channels=16, kernel_size=16),
nn.BatchNorm1d(16),
nn.ReLU(),
nn.Dropout(self.dropout),
nn.MaxPool1d(3),
# PrintLayer(),
nn.Conv1d(in_channels=16, out_channels=32, kernel_size=16),
nn.BatchNorm1d(32),
nn.ReLU(),
nn.Dropout(self.dropout),
nn.MaxPool1d(3),
# PrintLayer(),
nn.Conv1d(in_channels=32, out_channels=64, kernel_size=16),
nn.BatchNorm1d(64),
nn.ReLU(),
nn.Dropout(self.dropout),
nn.MaxPool1d(3),
# PrintLayer(),
)
self.fc = nn.Sequential(
nn.Linear(64*13, emb_dim),
nn.ReLU(),
nn.Linear(emb_dim, emb_dim),
# nn.ReLU(),
# nn.Linear(32, emb_dim),
# PrintLayer(),
)
def forward(self, x):
"""
Applies forward pass on the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, channels, sequence_length).
Returns:
torch.Tensor: Normalized output tensor of shape (batch_size, embedding_size).
"""
PrintLayer(),
x = x.permute(0, 2, 1)
PrintLayer(),
x = self.conv(x)
x = x.view(-1, 832)
x = self.fc(x)
return nn.functional.normalize(x)

Event Timeline

c4science · Help