import matplotlib.pyplot as plt import csv from collections import OrderedDict from typing import List, Tuple import torch import numpy as np import subprocess import os import random from Dataset import Jammer_Dataset from torch.utils.data import DataLoader from easyfsl.samplers import TaskSampler from Dataset import balance_data import torch.nn.functional as F def visualization(image, label): # Create a plot plt.figure(figsize=(10, 6)) # Adjust the figure size as needed plt.imshow(image.permute(1, 2, 0), origin='lower') plt.colorbar() plt.title(f'Spectrogram of class {label} ') plt.xlabel('Time') plt.ylabel('Channel') plt.show() # def save_balanced_data_to_csv(data, csv_filename): # os.makedirs("dataset_training",exist_ok=True) # csv_filepath = os.path.join("dataset_training", csv_filename) # file_exists = os.path.isfile(csv_filepath) # with open(csv_filepath, 'w', newline='') as csvfile: # fieldnames = ['Filename', 'Label'] # writer = csv.DictWriter(csvfile, fieldnames=fieldnames) # if data: # writer.writeheader() # for filename, label in data: # writer.writerow({'Filename': filename, 'Label': label}) def save_balanced_data_to_csv(data, csv_filename): # Create the folder if it doesn't exist dataset_training = "../../../../Linkhalle_fewshot_dataset" os.makedirs(dataset_training,exist_ok=True) folder_path = dataset_training os.makedirs(folder_path, exist_ok=True) csv_filepath = os.path.join(folder_path, csv_filename) # Check if the file already exists file_exists = os.path.isfile(csv_filepath) # Check if data is not empty and the file doesn't exist if data and not file_exists: with open(csv_filepath, 'w', newline='') as csvfile: fieldnames = ['Filename', 'Label'] writer = csv.DictWriter(csvfile, fieldnames=fieldnames) # Write the header writer.writeheader() # Write the data for filename, label in data: writer.writerow({'Filename': filename, 'Label': label}) elif not data: print("No data to save.") elif file_exists: print("File already exists. Data not written.") def load_data_from_csv(csv_filename): dataset_training = "../../../../Linkhalle_fewshot_dataset" csv_filepath = os.path.join(dataset_training, csv_filename) data = [] with open(csv_filepath, 'r') as csvfile: reader = csv.DictReader(csvfile) for row in reader: if row['Filename'] and row['Label']: filename = row['Filename'] label = int(row['Label']) data.append((filename, label)) return data def update(data, target, model, optimizer): model.send(data.location) optimizer.zero_grad() prediction = model(data) loss = F.mse_loss(prediction.view(-1), target) loss.backward() optimizer.step() return model def get_parameters(net) -> List[np.ndarray]: return [val.cpu().numpy() for _, val in net.state_dict().items()] def set_parameters(net, parameters: List[np.ndarray]): params_dict = zip(net.state_dict().keys(), parameters) state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict}) net.load_state_dict(state_dict, strict=True) def check_client_availability(client_name): try: # Run 'ping' command with a timeout of 2 seconds (adjust as needed) subprocess.check_call(["ping", "-c", "1", "-W", "2", client_name]) return True # Client is reachable except subprocess.CalledProcessError: return False # Client is not reachable # # Check availability of each client in the list # for client in clients: # if check_client_availability(client): # print(f"{client} is available and reachable.") # else: # print(f"{client} is not available or not reachable.") # aggregated_model = history[-1].model # # Save the aggregated model to a file # torch.save(aggregated_model.state_dict(), "aggregated_model.pth") def calculate_binary_metrics(true_labels, predicted_labels): """ Calculate binary classification metrics using NumPy. """ # print(true_labels) # print(predicted_labels) # True positive, false positive, false negative counts tp = np.sum(np.logical_and(predicted_labels == 1, true_labels == 1)) fp = np.sum(np.logical_and(predicted_labels == 1, true_labels == 0)) fn = np.sum(np.logical_and(predicted_labels == 0, true_labels == 1)) tn = np.sum(np.logical_and(predicted_labels == 0, true_labels == 0)) #print(tp,fp,fn,tn) # Precision, recall, F1, and F2 scores precision = tp / (tp + fp) if (tp + fp) > 0 else 0 recall = tp / (tp + fn) if (tp + fn) > 0 else 0 f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0 # F2 score with more emphasis on recall beta = 2 f2 = (1 + beta**2) * (precision * recall) / ((beta**2 * precision) + recall) if (precision + recall) > 0 else 0 # Accuracy accuracy = (tp + tn) / (tp + tn + fp + fn) if (tp + tn + fp + fn) > 0 else 0 return precision, recall, f1, f2, accuracy def create_test_set( root_dir, train_test_split_dir ): required_train_txt = ["test_files_0.8_0.8_0.8_0.8_0.8_0.8_0.0_0.8_0.8_0.8_0.8_0.8_split4.txt"] # Initialize lists to store filenames and labels print(f"required file ------------------------{required_train_txt}") filenames = [] labels = [] for filename in required_train_txt: with open(os.path.join(train_test_split_dir, filename), 'r') as file: lines = file.readlines() for line in lines: parts = line.strip().split() if len(parts) == 2: filenames.append(os.path.join(root_dir, parts[0])) labels.append(int(parts[1])) # Randomly shuffle the data and labels combined_data = list(zip(filenames, labels)) #random.shuffle(combined_data) # combined_data = balance_data(n_shot,n_query,combined_data) testing_path = "tesing_file" os.makedirs(testing_path, exist_ok=True) test_file = os.path.join(testing_path, f'tesing_data_{4}.csv') save_balanced_data_to_csv(combined_data, test_file) def create_test_dataloder( root_dir, path, batch, n_workers, norm_vals, transforms=None, ): train_data = load_data_from_csv(path) train_dataset = Jammer_Dataset(root_dir, train_data, norm_vals,transform=transforms) # Create DataLoader for training train_loader = DataLoader( train_dataset, batch_size=batch, num_workers=n_workers, pin_memory=True, shuffle=False, ) return train_loader