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Razvan27
/
ML4SE-Python

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hkgdifyu_pFedPT/system/flcore/servers/__pycache__/serverproto.cpython-39.pyc
a f¾`cÜã@shddlmZddlmZddlmZddlmZddlZddl Z ddl m Z Gdd„deƒZ d d „ZdS) é)Ú clientProto)ÚServer)Úread_client_data)ÚThreadN)Ú defaultdictcs>eZdZ‡fdd„Zdd„Zdd„Zdd„Zd d d „Z‡ZS...
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serverdynpt-checkpoint.py
hkgdifyu_pFedPT/system/flcore/servers/.ipynb_checkpoints/serverdynpt-checkpoint.py
import copy import torch from system.flcore.clients.clientdynpt import clientDynPT from system.flcore.servers.serverbase import Server from threading import Thread import time import h5py import copy import os class FedDynPT(Server): def __init__(self, args, times): super().__init__(args, times) #...
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serverbabu-checkpoint.py
hkgdifyu_pFedPT/system/flcore/servers/.ipynb_checkpoints/serverbabu-checkpoint.py
from flcore.clients.clientbabu import clientBABU from flcore.servers.serverbase import Server from threading import Thread class FedBABU(Server): def __init__(self, args, times): super().__init__(args, times) # select slow clients self.set_slow_clients() self.set_clients(args, cli...
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serverfedpt-checkpoint.py
hkgdifyu_pFedPT/system/flcore/servers/.ipynb_checkpoints/serverfedpt-checkpoint.py
from system.flcore.clients.clientt import clientT from system.flcore.servers.serverbase import Server from threading import Thread import time import torch import os import h5py import copy class FedPT(Server): def __init__(self, args, times): super().__init__(args, times) # select slow clients ...
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serverbase-checkpoint.py
hkgdifyu_pFedPT/system/flcore/servers/.ipynb_checkpoints/serverbase-checkpoint.py
import torch import os import numpy as np import h5py import copy import time import random from utils.data_utils import read_client_data class Server(object): def __init__(self, args, times): # Set up the main attributes self.device = args.device self.dataset = args.dataset self...
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serverpfedpt-checkpoint.py
hkgdifyu_pFedPT/system/flcore/servers/.ipynb_checkpoints/serverpfedpt-checkpoint.py
from system.flcore.clients.clientpt import clientPT from system.flcore.servers.serverbase import Server from threading import Thread import time import torch import os import h5py import copy class PFedPT(Server): def __init__(self, args, times): super().__init__(args, times) # select slow clients...
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serverreppt-checkpoint.py
hkgdifyu_pFedPT/system/flcore/servers/.ipynb_checkpoints/serverreppt-checkpoint.py
from system.flcore.clients.clientreppt import clientREPPT from system.flcore.servers.serverbase import Server from threading import Thread import time import copy import torch import os import h5py import copy class PFedRepPT(Server): def __init__(self, args, times): super().__init__(args, times) ...
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serverrep-checkpoint.py
hkgdifyu_pFedPT/system/flcore/servers/.ipynb_checkpoints/serverrep-checkpoint.py
from system.flcore.clients.clientrep import clientRep from system.flcore.servers.serverbase import Server from threading import Thread import time import copy class FedRep(Server): def __init__(self, args, times): super().__init__(args, times) # select slow clients self.set_slow_clients()...
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data_utils.py
hkgdifyu_pFedPT/system/utils/data_utils.py
import ujson import numpy as np import os import torch # IMAGE_SIZE = 28 # IMAGE_PIXELS = IMAGE_SIZE * IMAGE_SIZE # NUM_CHANNELS = 1 # IMAGE_SIZE_CIFAR = 32 # NUM_CHANNELS_CIFAR = 3 def batch_data(data, batch_size): ''' data is a dict := {'x': [numpy array], 'y': [numpy array]} (on one client) returns x...
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mem_utils.py
hkgdifyu_pFedPT/system/utils/mem_utils.py
import math import gc from collections import defaultdict from typing import Optional, Tuple, List import torch from math import isnan from calmsize import size as calmsize def readable_size(num_bytes: int) -> str: return '' if isnan(num_bytes) else '{:.2f}'.format(calmsize(num_bytes)) LEN = 79 # some pytorc...
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privacy.py
hkgdifyu_pFedPT/system/utils/privacy.py
from opacus import PrivacyEngine from opacus.dp_model_inspector import DPModelInspector MAX_GRAD_NORM = 1.0 EPSILON = 50.0 DELTA = 1e-7 EPOCHS = 1 * 100 N_ACCUMULATION_STEPS = 4 def initialize_dp(model, optimizer, sample_rate, dp_sigma): privacy_engine = PrivacyEngine( model, sample_rate = sample_...
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result_utils.py
hkgdifyu_pFedPT/system/utils/result_utils.py
import h5py import numpy as np import os def average_data(algorithm="", dataset="", goal="", times=10, length=800): test_acc = get_all_results_for_one_algo( algorithm, dataset, goal, times, int(length)) test_acc_data = np.average(test_acc, axis=0) max_accurancy = [] for i in range(times): ...
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data_utils.cpython-38.pyc
hkgdifyu_pFedPT/system/utils/__pycache__/data_utils.cpython-38.pyc
U ?şcbã@sZddlZddlZddlZddlZdd„Zdd„Zdd„Zdd d „Zdd d „Z dd d„Z dS)éNccs‚|d}|d}tj ¡}tj |¡tj |¡tj |¡tdt|ƒ|ƒD].}||||…}||||…}||fVqNdS)z— data is...
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data_utils.cpython-37.pyc
hkgdifyu_pFedPT/system/utils/__pycache__/data_utils.cpython-37.pyc
B Ê:cc®ã@sZddlZddlZddlZddlZdd„Zdd„Zdd„Zdd d „Zdd d „Z dd d„Z dS)éNccs†|d}|d}tj ¡}tj |¡tj |¡tj |¡xBtdt|ƒ|ƒD].}||||…}||||…}||fVqPWdS)z— data is a d...
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mem_utils.cpython-37.pyc
hkgdifyu_pFedPT/system/utils/__pycache__/mem_utils.cpython-37.pyc
B Ë:ccEã@s~ddlZddlZddlmZddlmZmZmZddlZddlm Z ddl m Z e e dœdd„Zd Zd Zd ZGd d „d ƒZdS)éN)Ú defaultdict)ÚOptionalÚTupleÚList)Úisnan)Úsize)Ú num_bytesÚreturncCst|ƒr dSd t|ƒ¡S)NÚz{:.2f})...
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data_utils.cpython-39.pyc
hkgdifyu_pFedPT/system/utils/__pycache__/data_utils.cpython-39.pyc
a f¾`c®ã@sZddlZddlZddlZddlZdd„Zdd„Zdd„Zdd d „Zdd d „Z dd d„Z dS)éNccs‚|d}|d}tj ¡}tj |¡tj |¡tj |¡tdt|ƒ|ƒD].}||||…}||||…}||fVqNdS)z— data is...
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result_utils.cpython-37.pyc
hkgdifyu_pFedPT/system/utils/__pycache__/result_utils.cpython-37.pyc
B Ë:ccÇã@s:ddlZddlZddlZd dd„Zd dd„Zdd d „ZdS)éNÚé é c Csnt||||t|ƒƒ}tj|dd�}g}x"t|ƒD]}| || ¡¡q0Wtdt |¡ƒtdt  |¡ƒdS)Nr)Úaxiszstd for best accurancy:zmean for best accuran...
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result_utils.cpython-38.pyc
hkgdifyu_pFedPT/system/utils/__pycache__/result_utils.cpython-38.pyc
U çYgcÈã@s:ddlZddlZddlZd dd„Zd dd„Zdd d „ZdS)éNÚé é c Csjt||||t|ƒƒ}tj|dd�}g}t|ƒD]}| || ¡¡q.tdt |¡ƒtdt  |¡ƒdS)Nr)Úaxiszstd for best accurancy:zmean for best acc...
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privacy.cpython-38.pyc
hkgdifyu_pFedPT/system/utils/__pycache__/privacy.cpython-38.pyc
U e¾`cã@sPddlmZddlmZdZdZdZdZdZdd „Z d d „Z d d „Z dd„Z dS)é)Ú PrivacyEngine)ÚDPModelInspectorgğ?gI@gH¯¼šò×z>édécCs$t||tt|td�}| |¡dS)N)Ú sample_rateZ target_deltaZnoise_multipl...
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mem_utils.cpython-38.pyc
hkgdifyu_pFedPT/system/utils/__pycache__/mem_utils.cpython-38.pyc
U –jfcEã@s~ddlZddlZddlmZddlmZmZmZddlZddlm Z ddl m Z e e dœdd„Zd Zd Zd ZGd d „d ƒZdS)éN)Ú defaultdict)ÚOptionalÚTupleÚList)Úisnan)Úsize)Ú num_bytesÚreturncCst|ƒr dSd t|ƒ¡S)NÚz...
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generate_tiny_imagenet.py
hkgdifyu_pFedPT/dataset/generate_tiny_imagenet.py
import numpy as np import os import sys import random import torch import torchvision import torchvision.transforms as transforms from utils.dataset_utils import check, separate_data, split_data, save_file from torchvision.datasets import ImageFolder, DatasetFolder random.seed(1) np.random.seed(1) num_clients = int(sy...
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2,286,854
generate_cifar10.py
hkgdifyu_pFedPT/dataset/generate_cifar10.py
import numpy as np import os import sys import random import torch import torchvision import torchvision.transforms as transforms from utils.dataset_utils import check, separate_data, split_data, save_file random.seed(1) np.random.seed(1) num_clients = int(sys.argv[4]) num_classes = 10 dir_path = "Cifar10/"+sys.argv[...
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generate_cifar100.py
hkgdifyu_pFedPT/dataset/generate_cifar100.py
import numpy as np import os import sys import random import torch import torchvision import torchvision.transforms as transforms from utils.dataset_utils import check, separate_data, split_data, save_file random.seed(1) np.random.seed(1) num_clients = int(sys.argv[4]) num_classes = 100 dir_path = "Cifar100/"+sys.ar...
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2,286,856
dataset_utils.py
hkgdifyu_pFedPT/dataset/utils/dataset_utils.py
import os import ujson import numpy as np import gc from sklearn.model_selection import train_test_split batch_size = 2 train_size = 0.75 # merge original training set and test set, then split it manually. least_samples = batch_size / (1-train_size) # least samples for each client # for Dirichlet distribution def ch...
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hkgdifyu/pFedPT
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dataset_utils.cpython-38.pyc
hkgdifyu_pFedPT/dataset/utils/__pycache__/dataset_utils.cpython-38.pyc
U )Qgcâã@sjddlZddlZddlZddlZddlmZdZdZedeZ dd d „Z dd d „Z d d„Z ddd„Z dS)éN)Útrain_test_splitégè?éFTçš™™™™™¹?c CsÔtj |¡rŒt|dƒ�} t | ¡} W5QRX| d|krŒ| d|krŒ| d|krŒ...
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dataset_utils.cpython-37.pyc
hkgdifyu_pFedPT/dataset/utils/__pycache__/dataset_utils.cpython-37.pyc
B 4ØccÊã@snddlZddlZddlZddlZddlmZdZdZedeZ dZ dd d „Z dd d „Z d d„Z ddd„ZdS)éN)Útrain_test_splitégè?égš™™™™™¹?FTc CsÔtj |¡rŒt|dƒ�}t |¡} WdQRX| d|krŒ| d|krŒ| d|krŒ| d...
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2,286,859
dataset_utils-checkpoint.py
hkgdifyu_pFedPT/dataset/utils/.ipynb_checkpoints/dataset_utils-checkpoint.py
import os import ujson import numpy as np import gc from sklearn.model_selection import train_test_split batch_size = 2 train_size = 0.75 # merge original training set and test set, then split it manually. least_samples = batch_size / (1-train_size) # least samples for each client alpha = 0.1 # for Dirichlet distribu...
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generate_cifar10-checkpoint.py
hkgdifyu_pFedPT/dataset/.ipynb_checkpoints/generate_cifar10-checkpoint.py
import numpy as np import os import sys import random import torch import torchvision import torchvision.transforms as transforms from utils.dataset_utils import check, separate_data, split_data, save_file random.seed(1) np.random.seed(1) num_clients = 200 num_classes = 10 dir_path = "Cifar10/" # Allocate data to u...
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2,286,861
generate_agnews-checkpoint.py
hkgdifyu_pFedPT/dataset/.ipynb_checkpoints/generate_agnews-checkpoint.py
import numpy as np import os import sys import random import torchtext from utils.dataset_utils import check, separate_data, split_data, save_file from torchtext.data.utils import get_tokenizer from torchtext.vocab import build_vocab_from_iterator random.seed(1) np.random.seed(1) num_clients = 20 num_classes = 4 max_...
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generate_cifar100-checkpoint.py
hkgdifyu_pFedPT/dataset/.ipynb_checkpoints/generate_cifar100-checkpoint.py
import numpy as np import os import sys import random import torch import torchvision import torchvision.transforms as transforms from utils.dataset_utils import check, separate_data, split_data, save_file random.seed(1) np.random.seed(1) num_clients = 20 num_classes = 100 dir_path = "Cifar100/" # Allocate data to ...
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fedoptimizer.cpython-39.pyc
hkgdifyu_pFedPT/system/flcore/optimizers/__pycache__/fedoptimizer.cpython-39.pyc
a fæ`c¸„@shddlZddlmZGddÑdeÉZGddÑdeÉZGddÑdeÉZGd d Ñd eÉZGd d Ñd eÉZdS) ÈN)⁄ Optimizercs&eZdZáfddÑZdddÑZáZS)⁄PerAvgOptimizercs t|dç}tt|Ɇ||°dS...
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fedoptimizer.cpython-38.pyc
hkgdifyu_pFedPT/system/flcore/optimizers/__pycache__/fedoptimizer.cpython-38.pyc
U îjfc¸„@shddlZddlmZGddÑdeÉZGddÑdeÉZGddÑdeÉZGd d Ñd eÉZGd d Ñd eÉZdS) ÈN)⁄ Optimizercs&eZdZáfddÑZdddÑZáZS)⁄PerAvgOptimizercs t|dç}tt|Ɇ||°dS...
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fedoptimizer.cpython-37.pyc
hkgdifyu_pFedPT/system/flcore/optimizers/__pycache__/fedoptimizer.cpython-37.pyc
B À:ccüã@shddlZddlmZGdd„deƒZGdd„deƒZGdd„deƒZGd d „d eƒZGd d „d eƒZdS) éN)Ú Optimizercs&eZdZ‡fdd„Zddd„Z‡ZS)ÚPerAvgOptimizercs t|d�}tt|ƒ ||¡dS)N)Úlr)Ú...
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clientamp.cpython-38.pyc
hkgdifyu_pFedPT/system/flcore/clients/__pycache__/clientamp.cpython-38.pyc
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clientdyn.cpython-38.pyc
hkgdifyu_pFedPT/system/flcore/clients/__pycache__/clientdyn.cpython-38.pyc
U ”jfc ã@sTddlZddlZddlmZddlZddlZddlmZGdd„deƒZ dd„Z dS)éN)ÚClientcs,eZdZ‡fdd„Zdd„Zdd„Z‡ZS)Ú clientDync sltƒj||||f|�t ¡|_tjj|j  ¡|j d�|...
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clientrep.cpython-38.pyc
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clientmoon.cpython-37.pyc
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