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| import gradio as gr | |
| import torch | |
| import cv2 | |
| import numpy as np | |
| import pandas as pd | |
| from scipy.signal import find_peaks, savgol_filter | |
| from collections import Counter | |
| from tqdm import tqdm | |
| import time | |
| import os | |
| import torch | |
| import torch.nn as nn | |
| import torch.fft as fft | |
| import xgboost as xgb | |
| from torch.utils.data import DataLoader, TensorDataset | |
| import time | |
| # Define the TCN model | |
| class TCN(nn.Module): | |
| def __init__(self, input_size, hidden_size, output_size, num_layers=3, dropout=0.1): | |
| super(TCN, self).__init__() | |
| # List to hold convolutional layers | |
| self.convs = nn.ModuleList() | |
| dropout = dropout if num_layers > 1 else 0 # No dropout if only one layer | |
| self.dropout = nn.Dropout(dropout) | |
| # Create the convolutional layers | |
| for i in range(num_layers): | |
| in_channels = input_size if i == 0 else hidden_size # First layer uses input_size, others use hidden_size | |
| out_channels = hidden_size # All layers have the same hidden size | |
| self.convs.append(nn.Conv1d(in_channels, out_channels, kernel_size=2, padding=1)) | |
| # Fully connected output layer | |
| self.fc = nn.Linear(hidden_size, output_size) | |
| def forward(self, x): | |
| x = x.permute(0, 2, 1) # Change to (batch_size, features, timesteps) | |
| # Apply each convolutional layer followed by dropout | |
| for conv in self.convs: | |
| x = torch.relu(conv(x)) | |
| x = self.dropout(x) # Apply dropout after each convolution | |
| x = torch.mean(x, dim=2) # Global average pooling | |
| x = self.fc(x) # Output layer | |
| return x |