Datasets:

FelixOtt
/

gnss_federated_learning

ArXiv:

Dataset card Files Files and versions

xet

Community

gnss_federated_learning / Test /embeddings.py

FelixOtt

Upload folder using huggingface_hub

29d330e verified 18 days ago

Raw History Blame Contribute Delete

5.18 kB

import sys
import os
import numpy as np
import torch
from torch import nn
from tqdm import tqdm
from torch.utils.data import DataLoader
import torch.optim as optim
from itertools import chain, combinations
import utilities_raw as utilities  # Assuming utilities_raw is in the same directory as this script
from torchvision.models import resnet18
from easyfsl.methods import PrototypicalNetworks

#
def main(strategy, run_no,station_num):
    # Modify these paths based on the strategy and run_no
    if station_num ==0:
        folder = f"./workspace/Jammer_detection/Src/federated_darcy/{strategy}/model/"
        #folder = f"./workspace/Jammer_detection/Src/federated_darcy/Pytorch_federated_manual/{strategy}/model/"
    elif station_num==11:
        folder = f"./workspace/Jammer_detection/Src/federated_darcy/Pytorch_federated_manual/{strategy}/model/"
    elif station_num == 12:
        folder = "./workspace/Jammer_detection/Src/outputs/2023-12-11/16-17-23/model_global_500/"
        startweight = "./workspace/Jammer_detection/Src/outputs/2023-12-11/16-17-23/model_global_500/model_weights314epoch75.h5"
    else:
        folder = f"./workspace/Jammer_detection/Src/federated_darcy/{strategy}/local_models/station_{station_num}_models/"
    result_folder = f"{folder}/embtsne_{run_no}/"
    if station_num != 12:
        startweight = f"{folder}round--{run_no}_weights.h5"

    torch.autograd.set_detect_anomaly(True)
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print(device)

    norm_data = "./workspace/Jammer_detection/Datasets/norm_vals.npy"
    batchSize = 40
    time_size = 243

    dp_base = "./workspace/Jammer_detection/Datasets/data/"
    split_file = "./workspace/Jammer_detection/Datasets/train_test_splits/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"


    

    if not os.path.exists(result_folder):
        os.makedirs(result_folder)

    # startweight = f"{folder}round--{run_no}_weights.h5"
    print(startweight)

    # Create Test data lists
    print("Test File: ", split_file)
    with open(split_file, "r") as f:
        test_info = f.readlines()
    test_info = np.array([x.split("\n")[0].split("\t") for x in test_info])
    test_list_raw = test_info[:, 0]
    test_classes = test_info[:, 1].astype(int)
    test_list = []
    for tlr in test_list_raw:
        ms = int(tlr.split("_")[-1][-5:])
        test_list.append([dp_base + tlr.split("_")[0], ms])
    model =resnet18(pretrained=False)
    model.fc = nn.Identity()
    few_shot_classifier = PrototypicalNetworks(model).to(device)
    few_shot_classifier.load_state_dict(torch.load(startweight, map_location=device))
    few_shot_classifier.eval()

    save_file_all_vals = f"{result_folder}/emb_"
    save_file_all_vals_ind = f"{result_folder}/ylabels_"
    results = []

    # for ai, add_class in enumerate(additional_classes):
    #     print("Dealing with classes: ", ai, add_class)
    #     cur_classes = np.concatenate([old_classes, add_class])
    #     cur_classes.sort()
    #     cur_support_list_files = []
    #     cur_support_list_classes = []

    #     for ci, cc in enumerate(cur_classes):
    #         cur_support_list_files.extend(support_list[cc])
    #         cur_support_list_classes.extend([ci] * len(support_list[cc]))

    #     cur_support_list = [cur_support_list_files, cur_support_list_classes]

        # # Prepare support images
        # support_images, support_labels = utilities.get_support_data(cur_support_list, time_size, norm=norm_data)
        # support_images = torch.tensor(support_images).to(device)
        # support_labels = torch.tensor(support_labels).to(device)
        # few_shot_classifier.process_support_set(support_images, support_labels)

    datagen = utilities.gen_data_batch_test(test_list, test_classes, time_size, batchSize, norm=norm_data)
    pred = []
    lab = []
    inds = []
    bcount = 0

    for X_batch, Y_batch, ind_batch in datagen:
        if bcount % 100 == 0:
            print("Dealing with batch: %d" % bcount)
        bcount += 1
        inputs = torch.tensor(X_batch).to(device)
        out = few_shot_classifier.backbone(inputs).float()
        pred_x = out.detach().cpu().numpy()
        print(pred_x.shape)
        pred.extend(pred_x)
        lab.extend(Y_batch)
        inds.extend(ind_batch)
        del inputs, out

    pred = np.array(pred)
    lab = np.array(lab)
    inds = np.array(inds)

    np.save(save_file_all_vals + ".npy", pred)
    np.save(save_file_all_vals_ind + ".npy", lab)
       

if __name__ == "__main__":
    if len(sys.argv) != 4:
        print("Usage: python script.py <strategy> <run_no>")
        sys.exit(1)

    strategy = sys.argv[1]
    run_no = sys.argv[2]
    station_no = sys.argv[3]

    try:
        run_no = int(run_no)
        station_num = int(station_no)
    except ValueError:
        print("Error: run_no must be an integer.")
        sys.exit(1)

    # # main(strategy, run_no,station_num)
    # rounds = [11, 12,13, 14, 15]#[1, 2, 3, 4, 5]#[6, 7, 8, 9, 10]  # List of rounds
    # for round_num in rounds:
    #     main(strategy, round_num, station_num)
    main(strategy, run_no, station_num)

	import sys
	import os
	import numpy as np
	import torch
	from torch import nn
	from tqdm import tqdm
	from torch.utils.data import DataLoader
	import torch.optim as optim
	from itertools import chain, combinations
	import utilities_raw as utilities # Assuming utilities_raw is in the same directory as this script
	from torchvision.models import resnet18
	from easyfsl.methods import PrototypicalNetworks

	#
	def main(strategy, run_no,station_num):
	# Modify these paths based on the strategy and run_no
	if station_num ==0:
	folder = f"./workspace/Jammer_detection/Src/federated_darcy/{strategy}/model/"
	#folder = f"./workspace/Jammer_detection/Src/federated_darcy/Pytorch_federated_manual/{strategy}/model/"
	elif station_num==11:
	folder = f"./workspace/Jammer_detection/Src/federated_darcy/Pytorch_federated_manual/{strategy}/model/"
	elif station_num == 12:
	folder = "./workspace/Jammer_detection/Src/outputs/2023-12-11/16-17-23/model_global_500/"
	startweight = "./workspace/Jammer_detection/Src/outputs/2023-12-11/16-17-23/model_global_500/model_weights314epoch75.h5"
	else:
	folder = f"./workspace/Jammer_detection/Src/federated_darcy/{strategy}/local_models/station_{station_num}_models/"
	result_folder = f"{folder}/embtsne_{run_no}/"
	if station_num != 12:
	startweight = f"{folder}round--{run_no}_weights.h5"

	torch.autograd.set_detect_anomaly(True)
	device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
	print(device)

	norm_data = "./workspace/Jammer_detection/Datasets/norm_vals.npy"
	batchSize = 40
	time_size = 243

	dp_base = "./workspace/Jammer_detection/Datasets/data/"
	split_file = "./workspace/Jammer_detection/Datasets/train_test_splits/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"




	if not os.path.exists(result_folder):
	os.makedirs(result_folder)

	# startweight = f"{folder}round--{run_no}_weights.h5"
	print(startweight)

	# Create Test data lists
	print("Test File: ", split_file)
	with open(split_file, "r") as f:
	test_info = f.readlines()
	test_info = np.array([x.split("\n")[0].split("\t") for x in test_info])
	test_list_raw = test_info[:, 0]
	test_classes = test_info[:, 1].astype(int)
	test_list = []
	for tlr in test_list_raw:
	ms = int(tlr.split("_")[-1][-5:])
	test_list.append([dp_base + tlr.split("_")[0], ms])
	model =resnet18(pretrained=False)
	model.fc = nn.Identity()
	few_shot_classifier = PrototypicalNetworks(model).to(device)
	few_shot_classifier.load_state_dict(torch.load(startweight, map_location=device))
	few_shot_classifier.eval()

	save_file_all_vals = f"{result_folder}/emb_"
	save_file_all_vals_ind = f"{result_folder}/ylabels_"
	results = []

	# for ai, add_class in enumerate(additional_classes):
	# print("Dealing with classes: ", ai, add_class)
	# cur_classes = np.concatenate([old_classes, add_class])
	# cur_classes.sort()
	# cur_support_list_files = []
	# cur_support_list_classes = []

	# for ci, cc in enumerate(cur_classes):
	# cur_support_list_files.extend(support_list[cc])
	# cur_support_list_classes.extend([ci] * len(support_list[cc]))

	# cur_support_list = [cur_support_list_files, cur_support_list_classes]

	# # Prepare support images
	# support_images, support_labels = utilities.get_support_data(cur_support_list, time_size, norm=norm_data)
	# support_images = torch.tensor(support_images).to(device)
	# support_labels = torch.tensor(support_labels).to(device)
	# few_shot_classifier.process_support_set(support_images, support_labels)

	datagen = utilities.gen_data_batch_test(test_list, test_classes, time_size, batchSize, norm=norm_data)
	pred = []
	lab = []
	inds = []
	bcount = 0

	for X_batch, Y_batch, ind_batch in datagen:
	if bcount % 100 == 0:
	print("Dealing with batch: %d" % bcount)
	bcount += 1
	inputs = torch.tensor(X_batch).to(device)
	out = few_shot_classifier.backbone(inputs).float()
	pred_x = out.detach().cpu().numpy()
	print(pred_x.shape)
	pred.extend(pred_x)
	lab.extend(Y_batch)
	inds.extend(ind_batch)
	del inputs, out

	pred = np.array(pred)
	lab = np.array(lab)
	inds = np.array(inds)

	np.save(save_file_all_vals + ".npy", pred)
	np.save(save_file_all_vals_ind + ".npy", lab)


	if __name__ == "__main__":
	if len(sys.argv) != 4:
	print("Usage: python script.py <strategy> <run_no>")
	sys.exit(1)

	strategy = sys.argv[1]
	run_no = sys.argv[2]
	station_no = sys.argv[3]

	try:
	run_no = int(run_no)
	station_num = int(station_no)
	except ValueError:
	print("Error: run_no must be an integer.")
	sys.exit(1)

	# # main(strategy, run_no,station_num)
	# rounds = [11, 12,13, 14, 15]#[1, 2, 3, 4, 5]#[6, 7, 8, 9, 10] # List of rounds
	# for round_num in rounds:
	# main(strategy, round_num, station_num)
	main(strategy, run_no, station_num)