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2,286,714 | clientavg-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientavg-checkpoint.py | import torch
import torch.nn as nn
import numpy as np
import time
from flcore.clients.clientbase import Client
class clientAVG(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
self.loss = nn.CrossEntropyLoss()
self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.learning_rate)
# # differential privacy
# if self.privacy:
# check_dp(self.model)
# initialize_dp(self.model, self.optimizer, self.sample_rate, self.dp_sigma)
def train(self):
trainloader = self.load_train_data()
start_time = time.time()
self.model.to(self.device)
self.model.train()
max_local_steps = self.local_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for step in range(max_local_steps):
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
if self.privacy:
dp_step(self.optimizer, i, len(trainloader))
else:
self.optimizer.step()
self.model.cpu()
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
# if self.privacy:
# res, DELTA = get_dp_params(self.optimizer)
# print(f"Client {self.id}", f"(ε = {res[0]:.2f}, δ = {DELTA}) for α = {res[1]}") | 1,962 | Python | .py | 45 | 31.866667 | 93 | 0.54878 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,715 | clientditto-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientditto-checkpoint.py | import torch
import numpy as np
import time
import copy
import torch.nn as nn
from flcore.optimizers.fedoptimizer import PerturbedGradientDescent
from flcore.clients.clientbase import Client
import torch.nn.functional as F
from sklearn.preprocessing import label_binarize
from sklearn import metrics
# from utils.privacy import *
class clientDitto(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
self.mu = args.mu
self.plocal_steps = args.plocal_steps
self.pmodel = copy.deepcopy(self.model)
self.loss = nn.CrossEntropyLoss()
self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.learning_rate)
self.poptimizer = PerturbedGradientDescent(
self.pmodel.parameters(), lr=self.learning_rate, mu=self.mu)
# # differential privacy
# if self.privacy:
# check_dp(self.model)
# initialize_dp(self.model, self.optimizer, self.sample_rate, self.dp_sigma)
def train(self):
trainloader = self.load_train_data()
start_time = time.time()
self.model.to(self.device)
self.model.train()
max_local_steps = self.local_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for step in range(max_local_steps):
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
if self.privacy:
dp_step(self.optimizer, i, len(trainloader))
else:
self.optimizer.step()
self.model.cpu()
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
#
# if self.privacy:
# res, DELTA = get_dp_params(self.optimizer)
# print(f"Client {self.id}", f"(ε = {res[0]:.2f}, δ = {DELTA}) for α = {res[1]}")
def ptrain(self):
trainloader = self.load_train_data()
start_time = time.time()
self.pmodel.to(self.device)
self.pmodel.train()
max_local_steps = self.plocal_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for step in range(max_local_steps):
for x, y in trainloader:
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.poptimizer.zero_grad()
output = self.pmodel(x)
loss = self.loss(output, y)
loss.backward()
self.poptimizer.step(self.model.parameters(), self.device)
self.pmodel.cpu()
self.train_time_cost['total_cost'] += time.time() - start_time
def test_metrics(self):
testloaderfull = self.load_test_data()
# self.model = self.load_model('model')
self.pmodel.to(self.device)
self.pmodel.eval()
test_acc = 0
test_num = 0
y_prob = []
y_true = []
with torch.no_grad():
for x, y in testloaderfull:
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
output = self.pmodel(x)
test_acc += (torch.sum(torch.argmax(output, dim=1) == y)).item()
test_num += y.shape[0]
y_prob.append(F.softmax(output).detach().cpu().numpy())
y_true.append(label_binarize(y.detach().cpu().numpy(), classes=np.arange(self.num_classes)))
self.pmodel.cpu()
y_prob = np.concatenate(y_prob, axis=0)
y_true = np.concatenate(y_true, axis=0)
auc = metrics.roc_auc_score(y_true, y_prob, average='micro')
return test_acc, test_num, auc | 4,593 | Python | .py | 107 | 30.897196 | 108 | 0.550567 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,716 | clientpFedMe-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientpFedMe-checkpoint.py | import numpy as np
import time
import copy
import torch
import torch.nn as nn
from flcore.optimizers.fedoptimizer import pFedMeOptimizer
from flcore.clients.clientbase import Client
class clientpFedMe(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
self.lamda = args.lamda
self.K = args.K
self.personalized_learning_rate = args.p_learning_rate
# these parameters are for personalized federated learing.
self.local_params = copy.deepcopy(list(self.model.parameters()))
self.personalized_params = copy.deepcopy(list(self.model.parameters()))
self.loss = nn.CrossEntropyLoss()
self.optimizer = pFedMeOptimizer(
self.model.parameters(), lr=self.personalized_learning_rate, lamda=self.lamda)
def train(self):
trainloader = self.load_train_data()
start_time = time.time()
# self.model.to(self.device)
self.model.train()
max_local_steps = self.local_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for step in range(max_local_steps): # local update
for x, y in trainloader:
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
# K is number of personalized steps
for i in range(self.K):
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
# finding aproximate theta
self.personalized_params = self.optimizer.step(self.local_params, self.device)
# update local weight after finding aproximate theta
for new_param, localweight in zip(self.personalized_params, self.local_params):
localweight = localweight.to(self.device)
localweight.data = localweight.data - self.lamda * self.learning_rate * (localweight.data - new_param.data)
# self.model.cpu()
self.update_parameters(self.model, self.local_params)
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
def set_parameters(self, model):
for new_param, old_param, local_param in zip(model.parameters(), self.model.parameters(), self.local_params):
old_param.data = new_param.data.clone()
local_param.data = new_param.data.clone()
def test_metrics_personalized(self):
testloaderfull = self.load_test_data()
self.update_parameters(self.model, self.personalized_params)
# self.model.to(self.device)
self.model.eval()
test_acc = 0
test_num = 0
with torch.no_grad():
for x, y in testloaderfull:
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
output = self.model(x)
test_acc += (torch.sum(torch.argmax(output, dim=1) == y)).item()
test_num += y.shape[0]
# self.model.cpu()
return test_acc, test_num
# def train_metrics_personalized(self):
# self.update_parameters(self.model, self.personalized_params)
# # self.model.to(self.device)
# self.model.eval()
# train_acc = 0
# train_num = 0
# loss = 0
# for x, y in trainloaderfull:
# if type(x) == type([]):
# x[0] = x[0].to(self.device)
# else:
# x = x.to(self.device)
# y = y.to(self.device)
# output = self.model(x)
# train_acc += (torch.sum(torch.argmax(output, dim=1) == y)).item()
# train_num += y.shape[0]
# loss += self.loss(output, y).item() * y.shape[0]
# # self.model.cpu()
# return train_acc, loss, train_num
| 4,350 | Python | .py | 94 | 35.308511 | 127 | 0.566912 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,717 | clientrep-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientrep-checkpoint.py | import torch
import torch.nn as nn
import numpy as np
import time
from system.flcore.clients.clientbase import Client
# from utils.privacy import *
class clientRep(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
self.loss = nn.CrossEntropyLoss()
self.optimizer = torch.optim.SGD(self.model.base.parameters(), lr=self.learning_rate)
self.poptimizer = torch.optim.SGD(self.model.predictor.parameters(), lr=self.learning_rate)
self.plocal_steps = args.plocal_steps
def train(self):
trainloader = self.load_train_data()
start_time = time.time()
self.model.to(self.device)
self.model.train()
for param in self.model.base.parameters():
param.requires_grad = False
for param in self.model.predictor.parameters():
param.requires_grad = True
for step in range(self.plocal_steps):
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.poptimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
self.poptimizer.step()
max_local_steps = self.local_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for param in self.model.base.parameters():
param.requires_grad = True
for param in self.model.predictor.parameters():
param.requires_grad = False
for step in range(max_local_steps):
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
self.optimizer.step()
self.model.cpu()
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
def set_parameters(self, base):
for new_param, old_param in zip(base.parameters(), self.model.base.parameters()):
old_param.data = new_param.data.clone() | 2,827 | Python | .py | 63 | 32.095238 | 99 | 0.559867 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,718 | clientpt-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientpt-checkpoint.py | import torch
import torch.nn as nn
import numpy as np
import time
from system.flcore.clients.clientbase import Client
import copy
# from utils.privacy import *
class clientPT(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
self.loss = nn.CrossEntropyLoss()
self.poptimizer = torch.optim.SGD(self.model.generator.parameters(), lr=self.learning_rate)
self.optimizer = torch.optim.SGD(self.model.base.parameters(), lr=self.learning_rate)
self.plocal_steps = args.plocal_steps
def train(self):
trainloader = self.load_train_data()
start_time = time.time()
old_prompt = copy.deepcopy(self.model.generator)
self.model.to(self.device)
self.model.train()
for param in self.model.base.parameters():
param.requires_grad = False
for param in self.model.generator.parameters():
param.requires_grad = True
for step in range(self.plocal_steps):
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.poptimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
self.poptimizer.step()
max_local_steps = self.local_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for param in self.model.base.parameters():
param.requires_grad = True
for param in self.model.generator.parameters():
param.requires_grad = False
for step in range(max_local_steps):
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
self.optimizer.step()
self.model.cpu()
new_prompt = copy.deepcopy(self.model.generator)
diff_provalue = 0
for new_param, old_param in zip(old_prompt.parameters(), new_prompt.parameters()):
diff_pro = new_param - old_param
diff_pro = torch.where(diff_pro > 0, diff_pro, torch.zeros_like(diff_pro)-diff_pro)
diff_provalue = torch.sum(diff_pro)
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
return diff_provalue
def set_parameters(self, base):
for new_param, old_param in zip(base.parameters(), self.model.base.parameters()):
old_param.data = new_param.data.clone() | 3,248 | Python | .py | 72 | 33.291667 | 99 | 0.571745 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,719 | clientfomo-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientfomo-checkpoint.py | import torch
import torch.nn as nn
import numpy as np
import time
import copy
from flcore.clients.clientbase import Client
from torch.utils.data import DataLoader
from utils.data_utils import read_client_data
class clientFomo(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
self.num_clients = args.num_clients
self.old_model = copy.deepcopy(self.model)
self.received_ids = []
self.received_models = []
self.weight_vector = torch.zeros(self.num_clients, device=self.device)
self.loss = nn.CrossEntropyLoss()
self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.learning_rate)
self.val_ratio = 0.2
self.train_samples = self.train_samples * (1-self.val_ratio)
def train(self):
trainloader, val_loader = self.load_train_data()
start_time = time.time()
self.aggregate_parameters(val_loader)
self.clone_model(self.model, self.old_model)
self.model.to(self.device)
self.model.train()
max_local_steps = self.local_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for step in range(max_local_steps):
for x, y in trainloader:
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
self.optimizer.step()
self.model.cpu()
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
def load_train_data(self, batch_size=None):
if batch_size == None:
batch_size = self.batch_size
train_data = read_client_data(self.dataset, self.id, is_train=True)
val_idx = -int(self.val_ratio*len(train_data))
val_data = train_data[val_idx:]
train_data = train_data[:val_idx]
trainloader = DataLoader(train_data, self.batch_size, drop_last=True, shuffle=True)
val_loader = DataLoader(val_data, self.batch_size, drop_last=self.has_BatchNorm, shuffle=True)
return trainloader, val_loader
def train_metrics(self):
trainloader, val_loader = self.load_train_data()
# self.model = self.load_model('model')
self.model.to(self.device)
self.model.eval()
train_num = 0
loss = 0
for x, y in trainloader:
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
output = self.model(x)
train_num += y.shape[0]
loss += self.loss(output, y).item() * y.shape[0]
self.model.cpu()
# self.save_model(self.model, 'model')
return loss, train_num
def receive_models(self, ids, models):
self.received_ids = ids
self.received_models = models
def weight_cal(self, val_loader):
weight_list = []
L = self.recalculate_loss(self.old_model, val_loader)
for received_model in self.received_models:
params_dif = []
for param_n, param_i in zip(received_model.parameters(), self.old_model.parameters()):
params_dif.append((param_n - param_i).view(-1))
params_dif = torch.cat(params_dif)
weight_list.append((L - self.recalculate_loss(received_model, val_loader)) / (torch.norm(params_dif) + 1e-5))
# import torch.autograd.profiler as profiler
# with profiler.profile(profile_memory=True, record_shapes=True) as prof:
# self.weight_vector_update(weight_list)
# print(prof.key_averages().table(sort_by="cuda_memory_usage", row_limit=10))
# from pytorch_memlab import LineProfiler
# with LineProfiler(self.weight_vector_update(weight_list)) as prof:
# self.weight_vector_update(weight_list)
# prof.display()
self.weight_vector_update(weight_list)
return torch.tensor(weight_list)
# from pytorch_memlab import profile
# @profile
def weight_vector_update(self, weight_list):
# self.weight_vector = torch.zeros(self.num_clients, device=self.device)
# for w, id in zip(weight_list, self.received_ids):
# self.weight_vector[id] += w.clone()
self.weight_vector = np.zeros(self.num_clients)
for w, id in zip(weight_list, self.received_ids):
self.weight_vector[id] += w.item()
self.weight_vector = torch.tensor(self.weight_vector).to(self.device)
def recalculate_loss(self, new_model, val_loader):
L = 0
new_model.to(self.device)
for x, y in val_loader:
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
output = new_model(x)
loss = self.loss(output, y)
L += loss.item()
new_model.cpu()
return L / len(val_loader)
def add_parameters(self, w, received_model):
for param, received_param in zip(self.model.parameters(), received_model.parameters()):
param.data += received_param.data.clone() * w
def aggregate_parameters(self, val_loader):
weights = self.weight_scale(self.weight_cal(val_loader))
if len(weights) > 0:
for param in self.model.parameters():
param.data.zero_()
for w, received_model in zip(weights, self.received_models):
self.add_parameters(w, received_model)
def weight_scale(self, weights):
weights = torch.maximum(weights, torch.tensor(0))
w_sum = torch.sum(weights)
if w_sum > 0:
weights = [w/w_sum for w in weights]
return torch.tensor(weights)
else:
return torch.tensor([])
| 6,368 | Python | .py | 140 | 34.857143 | 121 | 0.596191 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,720 | clientper-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientper-checkpoint.py | import copy
import torch
import torch.nn as nn
import numpy as np
import time
from flcore.clients.clientbase import Client
class clientPer(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
self.loss = nn.CrossEntropyLoss()
self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.learning_rate)
def train(self):
trainloader = self.load_train_data()
start_time = time.time()
# self.model.to(self.device)
self.model.train()
max_local_steps = self.local_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for step in range(max_local_steps):
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
self.optimizer.step()
# self.model.cpu()
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
def set_parameters(self, model):
for new_param, old_param in zip(model.parameters(), self.model.base.parameters()):
old_param.data = new_param.data.clone()
| 1,668 | Python | .py | 39 | 31.74359 | 90 | 0.573034 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,721 | clientbase-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientbase-checkpoint.py | import copy
import torch
import torch.nn as nn
import numpy as np
import os
import torch.nn.functional as F
from torch.utils.data import DataLoader
from sklearn.preprocessing import label_binarize
from sklearn import metrics
from utils.data_utils import read_client_data
class Client(object):
"""
Base class for clients in federated learning.
"""
def __init__(self, args, id, train_samples, test_samples, **kwargs):
self.model = copy.deepcopy(args.model)
self.dataset = args.dataset
self.device = args.device
self.id = id # integer
self.save_folder_name = args.save_folder_name
self.num_classes = args.num_classes
self.train_samples = train_samples
self.test_samples = test_samples
self.batch_size = args.batch_size
self.learning_rate = args.local_learning_rate
self.local_steps = args.local_steps
# check BatchNorm
self.has_BatchNorm = False
for layer in self.model.children():
if isinstance(layer, nn.BatchNorm2d):
self.has_BatchNorm = True
break
self.train_slow = kwargs['train_slow']
self.send_slow = kwargs['send_slow']
self.train_time_cost = {'num_rounds': 0, 'total_cost': 0.0}
self.send_time_cost = {'num_rounds': 0, 'total_cost': 0.0}
self.privacy = args.privacy
self.dp_sigma = args.dp_sigma
self.sample_rate = self.batch_size / self.train_samples
def load_train_data(self, batch_size=None):
if batch_size == None:
batch_size = self.batch_size
train_data = read_client_data(self.dataset, self.id, is_train=True)
return DataLoader(train_data, batch_size, drop_last=True, shuffle=True)
def load_test_data(self, batch_size=None):
if batch_size == None:
batch_size = self.batch_size
test_data = read_client_data(self.dataset, self.id, is_train=False)
return DataLoader(test_data, batch_size, drop_last=False, shuffle=True)
def set_parameters(self, model):
for new_param, old_param in zip(model.parameters(), self.model.parameters()):
old_param.data = new_param.data.clone()
def clone_model(self, model, target):
for param, target_param in zip(model.parameters(), target.parameters()):
target_param.data = param.data.clone()
# target_param.grad = param.grad.clone()
def update_parameters(self, model, new_params):
for param, new_param in zip(model.parameters(), new_params):
param.data = new_param.data.clone()
def test_metrics(self):
testloaderfull = self.load_test_data()
# self.model = self.load_model('model')
self.model.to(self.device)
self.model.eval()
test_acc = 0
test_num = 0
y_prob = []
y_true = []
with torch.no_grad():
for x, y in testloaderfull:
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
output = self.model(x)
test_acc += (torch.sum(torch.argmax(output, dim=1) == y)).item()
test_num += y.shape[0]
y_prob.append(output.detach().cpu().numpy())
y_true.append(label_binarize(y.detach().cpu().numpy(), classes=np.arange(self.num_classes)))
self.model.cpu()
# self.save_model(self.model, 'model')
y_prob = np.concatenate(y_prob, axis=0)
y_true = np.concatenate(y_true, axis=0)
auc = metrics.roc_auc_score(y_true, y_prob, average='micro')
return test_acc, test_num, auc
def train_metrics(self):
trainloader = self.load_train_data()
# self.model = self.load_model('model')
self.model.to(self.device)
self.model.eval()
train_num = 0
loss = 0
for x, y in trainloader:
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
output = self.model(x)
train_num += y.shape[0]
loss += self.loss(output, y).item() * y.shape[0]
self.model.cpu()
# self.save_model(self.model, 'model')
return loss, train_num
# def get_next_train_batch(self):
# try:
# # Samples a new batch for persionalizing
# (x, y) = next(self.iter_trainloader)
# except StopIteration:
# # restart the generator if the previous generator is exhausted.
# self.iter_trainloader = iter(self.trainloader)
# (x, y) = next(self.iter_trainloader)
# if type(x) == type([]):
# x = x[0]
# x = x.to(self.device)
# y = y.to(self.device)
# return x, y
def save_item(self, item, item_name, item_path=None):
if item_path == None:
item_path = self.save_folder_name
if not os.path.exists(item_path):
os.makedirs(item_path)
torch.save(item, os.path.join(item_path, "client_" + str(self.id) + "_" + item_name + ".pt"))
def load_item(self, item_name, item_path=None):
if item_path == None:
item_path = self.save_folder_name
return torch.load(os.path.join(item_path, "client_" + str(self.id) + "_" + item_name + ".pt"))
# @staticmethod
# def model_exists():
# return os.path.exists(os.path.join("models", "server" + ".pt"))
| 5,643 | Python | .py | 131 | 33.778626 | 108 | 0.586036 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,722 | clientdynpt-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientdynpt-checkpoint.py | import copy
import torch
import torch.nn as nn
import numpy as np
import time
from flcore.clients.clientbase import Client
# from utils.privacy import *
class clientDynPT(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
self.loss = nn.CrossEntropyLoss()
self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.learning_rate)
# differential privacy
# if self.privacy:
# check_dp(self.model)
# initialize_dp(self.model, self.optimizer, self.sample_rate, self.dp_sigma)
self.alpha = args.alpha
self.global_model_vector = None
old_grad = copy.deepcopy(self.model)
old_grad = model_parameter_vector(old_grad)
self.old_grad = torch.zeros_like(old_grad)
self.poptimizer = torch.optim.SGD(self.model.generator.parameters(), lr=self.learning_rate)
self.optimizer = torch.optim.SGD(self.model.base.parameters(), lr=self.learning_rate)
self.plocal_steps = args.plocal_steps
def train(self):
trainloader = self.load_train_data()
start_time = time.time()
old_prompt = copy.deepcopy(self.model.generator)
self.model.to(self.device)
self.old_grad = self.old_grad.to(self.device)
self.global_model_vector = self.global_model_vector.to(self.device)
self.model.train()
for param in self.model.base.parameters():
param.requires_grad = False
for param in self.model.generator.parameters():
param.requires_grad = True
for step in range(self.plocal_steps):
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.poptimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
self.poptimizer.step()
max_local_steps = self.local_steps
for param in self.model.base.parameters():
param.requires_grad = True
for param in self.model.generator.parameters():
param.requires_grad = False
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for step in range(max_local_steps):
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
if self.global_model_vector != None:
v1 = model_parameter_vector(self.model)
loss += self.alpha / 2 * torch.norm(v1 - self.global_model_vector, 2)
loss -= torch.dot(v1, self.old_grad)
loss.backward()
self.optimizer.step()
if self.global_model_vector != None:
v1 = model_parameter_vector(self.model).detach()
self.old_grad = self.old_grad - self.alpha * (v1 - self.global_model_vector)
self.model.cpu()
self.old_grad = self.old_grad.cpu()
self.global_model_vector = self.global_model_vector.cpu()
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
new_prompt = copy.deepcopy(self.model.generator)
diff_provalue = 0
for new_param, old_param in zip(old_prompt.parameters(), new_prompt.parameters()):
diff_pro = new_param - old_param
diff_pro = torch.where(diff_pro > 0, diff_pro, torch.zeros_like(diff_pro) - diff_pro)
diff_provalue = torch.sum(diff_pro)
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
return diff_provalue
#
# if self.privacy:
# res, DELTA = get_dp_params(self.optimizer)
# print(f"Client {self.id}", f"(ε = {res[0]:.2f}, δ = {DELTA}) for α = {res[1]}")
def set_parameters(self, model):
for new_param, old_param in zip(model.base.parameters(), self.model.base.parameters()):
old_param.data = new_param.data.clone()
self.global_model_vector = model_parameter_vector(model).detach().clone()
def model_parameter_vector(model):
param = [p.view(-1) for p in model.parameters()]
return torch.cat(param, dim=0) | 4,956 | Python | .py | 103 | 36.747573 | 99 | 0.583144 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,723 | clientbn-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientbn-checkpoint.py | import torch
import torch.nn as nn
import numpy as np
import time
from flcore.clients.clientbase import Client
# from utils.privacy import *
class clientBN(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
self.loss = nn.CrossEntropyLoss()
self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.learning_rate)
# # differential privacy
# if self.privacy:
# check_dp(self.model)
# initialize_dp(self.model, self.optimizer, self.sample_rate, self.dp_sigma)
def train(self):
trainloader = self.load_train_data()
start_time = time.time()
# self.model.to(self.device)
self.model.train()
max_local_steps = self.local_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for step in range(max_local_steps):
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
if self.privacy:
dp_step(self.optimizer, i, len(trainloader))
else:
self.optimizer.step()
# self.model.cpu()
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
if self.privacy:
res, DELTA = get_dp_params(self.optimizer)
print(f"Client {self.id}", f"(ε = {res[0]:.2f}, δ = {DELTA}) for α = {res[1]}")
def set_parameters(self, model):
for (nn, np), (on, op) in zip(model.named_parameters(), self.model.named_parameters()):
if 'bn' not in nn:
op.data = np.data.clone() | 2,207 | Python | .py | 50 | 32.28 | 95 | 0.55298 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,724 | clientphp-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientphp-checkpoint.py | import copy
import torch
import torch.nn as nn
import numpy as np
import time
from flcore.clients.clientbase import Client
# from utils.privacy import *
class clientPHP(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
self.loss = nn.CrossEntropyLoss()
self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.learning_rate)
self.mu = args.mu / args.global_rounds
self.lamda = args.lamda
self.model_s = copy.deepcopy(self.model)
for param in self.model_s.parameters():
param.requires_grad = False
# self.model_s.eval()
def train(self):
trainloader = self.load_train_data()
start_time = time.time()
self.model.to(self.device)
self.model_s.to(self.device)
self.model.train()
max_local_steps = self.local_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for step in range(max_local_steps):
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y) * (1 - self.lamda)
loss += MMD(self.model.base(x), self.model_s.base(x), 'rbf', self.device) * self.lamda
loss.backward()
self.optimizer.step()
self.model.cpu()
self.model_s.cpu()
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
def set_parameters(self, model, R):
mu = self.mu * R
for new_param, old_param in zip(model.parameters(), self.model_s.parameters()):
old_param.data = new_param.data.clone()
for new_param, old_param in zip(model.parameters(), self.model.parameters()):
old_param.data = new_param * (1 - mu) + old_param * mu
def MMD(x, y, kernel, device='cpu'):
"""Emprical maximum mean discrepancy. The lower the result
the more evidence that distributions are the same.
Args:
x: first sample, distribution P
y: second sample, distribution Q
kernel: kernel type such as "multiscale" or "rbf"
"""
xx, yy, zz = torch.mm(x, x.t()), torch.mm(y, y.t()), torch.mm(x, y.t())
rx = (xx.diag().unsqueeze(0).expand_as(xx))
ry = (yy.diag().unsqueeze(0).expand_as(yy))
dxx = rx.t() + rx - 2. * xx # Used for A in (1)
dyy = ry.t() + ry - 2. * yy # Used for B in (1)
dxy = rx.t() + ry - 2. * zz # Used for C in (1)
XX, YY, XY = (torch.zeros(xx.shape).to(device),
torch.zeros(xx.shape).to(device),
torch.zeros(xx.shape).to(device))
if kernel == "multiscale":
bandwidth_range = [0.2, 0.5, 0.9, 1.3]
for a in bandwidth_range:
XX += a**2 * (a**2 + dxx)**-1
YY += a**2 * (a**2 + dyy)**-1
XY += a**2 * (a**2 + dxy)**-1
if kernel == "rbf":
bandwidth_range = [10, 15, 20, 50]
for a in bandwidth_range:
XX += torch.exp(-0.5*dxx/a)
YY += torch.exp(-0.5*dyy/a)
XY += torch.exp(-0.5*dxy/a)
return torch.mean(XX + YY - 2. * XY) | 3,672 | Python | .py | 82 | 34.085366 | 102 | 0.555238 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,725 | clientreppt-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientreppt-checkpoint.py | import torch
import torch.nn as nn
import numpy as np
import time
from system.flcore.clients.clientbase import Client
import copy
# from utils.privacy import *
class clientREPPT(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
self.loss = nn.CrossEntropyLoss()
self.poptimizer = torch.optim.SGD(self.model.generator.parameters(), lr=self.learning_rate)
self.optimizer = torch.optim.SGD(self.model.base.parameters(), lr=self.learning_rate)
self.plocal_steps = args.plocal_steps
def train(self):
trainloader = self.load_train_data()
start_time = time.time()
old_prompt = copy.deepcopy(self.model.generator)
self.model.to(self.device)
self.model.train()
for param in self.model.base.parameters():
param.requires_grad = False
for param in self.model.generator.parameters():
param.requires_grad = True
for param in self.model.predictor.parameters():
param.requires_grad = True
for step in range(self.plocal_steps):
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.poptimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
self.poptimizer.step()
max_local_steps = self.local_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for param in self.model.base.parameters():
param.requires_grad = True
for param in self.model.generator.parameters():
param.requires_grad = False
for param in self.model.predictor.parameters():
param.requires_grad = False
for step in range(max_local_steps):
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
self.optimizer.step()
self.model.cpu()
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
new_prompt = copy.deepcopy(self.model.generator)
diff_provalue = 0
for new_param, old_param in zip(old_prompt.parameters(), new_prompt.parameters()):
diff_pro = new_param - old_param
diff_pro = torch.where(diff_pro > 0, diff_pro, torch.zeros_like(diff_pro) - diff_pro)
diff_provalue = torch.sum(diff_pro)
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
return diff_provalue
def set_parameters(self, base):
for new_param, old_param in zip(base.parameters(), self.model.base.parameters()):
old_param.data = new_param.data.clone() | 3,562 | Python | .py | 78 | 33.974359 | 99 | 0.576168 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,726 | clientdyn-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientdyn-checkpoint.py | import copy
import torch
import torch.nn as nn
import numpy as np
import time
from flcore.clients.clientbase import Client
# from utils.privacy import *
class clientDyn(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
self.loss = nn.CrossEntropyLoss()
self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.learning_rate)
# differential privacy
# if self.privacy:
# check_dp(self.model)
# initialize_dp(self.model, self.optimizer, self.sample_rate, self.dp_sigma)
self.alpha = args.alpha
self.global_model_vector = None
old_grad = copy.deepcopy(self.model)
old_grad = model_parameter_vector(old_grad)
self.old_grad = torch.zeros_like(old_grad)
def train(self):
trainloader = self.load_train_data()
start_time = time.time()
self.model.to(self.device)
self.old_grad = self.old_grad.to(self.device)
self.global_model_vector = self.global_model_vector.to(self.device)
self.model.train()
max_local_steps = self.local_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for step in range(max_local_steps):
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
if self.global_model_vector != None:
v1 = model_parameter_vector(self.model)
loss += self.alpha/2 * torch.norm(v1 - self.global_model_vector, 2)
loss -= torch.dot(v1, self.old_grad)
loss.backward()
self.optimizer.step()
if self.global_model_vector != None:
v1 = model_parameter_vector(self.model).detach()
self.old_grad = self.old_grad - self.alpha * (v1 - self.global_model_vector)
self.model.cpu()
self.old_grad = self.old_grad.cpu()
self.global_model_vector= self.global_model_vector.cpu()
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
#
# if self.privacy:
# res, DELTA = get_dp_params(self.optimizer)
# print(f"Client {self.id}", f"(ε = {res[0]:.2f}, δ = {DELTA}) for α = {res[1]}")
def set_parameters(self, model):
for new_param, old_param in zip(model.parameters(), self.model.parameters()):
old_param.data = new_param.data.clone()
self.global_model_vector = model_parameter_vector(model).detach().clone()
def model_parameter_vector(model):
param = [p.view(-1) for p in model.parameters()]
return torch.cat(param, dim=0) | 3,199 | Python | .py | 68 | 36.132353 | 93 | 0.587796 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,727 | clientproto-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientproto-checkpoint.py | from collections import defaultdict
import copy
import torch
import torch.nn as nn
import numpy as np
import time
from flcore.clients.clientbase import Client
# from utils.privacy import *
class clientProto(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
self.loss = nn.CrossEntropyLoss()
self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.learning_rate)
self.feature_dim = list(self.model.predictor.parameters())[0].shape[1]
self.protos = None
self.global_protos = None
self.loss_mse = nn.MSELoss()
self.lamda = args.lamda
def train(self):
trainloader = self.load_train_data()
start_time = time.time()
# self.model.to(self.device)
self.model.train()
max_local_steps = self.local_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
protos = defaultdict(list)
for step in range(max_local_steps):
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
rep = self.model.base(x)
output = self.model.predictor(rep)
loss = self.loss(output, y)
if self.global_protos != None:
proto_new = torch.zeros_like(rep)
for i, yy in enumerate(y):
y_c = yy.item()
proto_new[i, :] = self.global_protos[y_c].data
loss += self.loss_mse(proto_new, rep) * self.lamda
for i, yy in enumerate(y):
y_c = yy.item()
protos[y_c].append(rep[i, :].detach().data)
loss.backward()
self.optimizer.step()
# self.model.cpu()
# rep = self.model.base(x)
# print(torch.sum(rep!=0).item() / rep.numel())
# self.collect_protos()
self.protos = agg_func(protos)
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
def set_protos(self, global_protos):
self.global_protos = copy.deepcopy(global_protos)
def collect_protos(self):
trainloader = self.load_train_data()
self.model.eval()
protos = defaultdict(list)
with torch.no_grad():
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
rep = self.model.base(x)
for i, yy in enumerate(y):
y_c = yy.item()
protos[y_c].append(rep[i, :].detach().data)
self.protos = agg_func(protos)
def test_metrics(self, model=None):
testloader = self.load_test_data()
if model == None:
model = self.model
model.eval()
test_acc = 0
test_num = 0
with torch.no_grad():
for x, y in testloader:
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
rep = self.model.base(x)
output = float('inf') * torch.ones(y.shape[0], self.num_classes).to(self.device)
for i, r in enumerate(rep):
for j, pro in self.global_protos.items():
output[i, j] = self.loss_mse(r, pro)
test_acc += (torch.sum(torch.argmin(output, dim=1) == y)).item()
test_num += y.shape[0]
return test_acc, test_num, 0
# https://github.com/yuetan031/fedproto/blob/main/lib/utils.py#L205
def agg_func(protos):
"""
Returns the average of the weights.
"""
for [label, proto_list] in protos.items():
if len(proto_list) > 1:
proto = 0 * proto_list[0].data
for i in proto_list:
proto += i.data
protos[label] = proto / len(proto_list)
else:
protos[label] = proto_list[0]
return protos | 4,762 | Python | .py | 115 | 28.756522 | 96 | 0.519461 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,728 | clientmoon-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientmoon-checkpoint.py | import copy
import torch
import torch.nn as nn
import numpy as np
import time
import torch.nn.functional as F
from flcore.clients.clientbase import Client
# from utils.privacy import *
class clientMOON(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
self.loss = nn.CrossEntropyLoss()
self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.learning_rate)
# # differential privacy
# if self.privacy:
# check_dp(self.model)
# initialize_dp(self.model, self.optimizer, self.sample_rate, self.dp_sigma)
self.tau = args.tau
self.mu = args.mu
self.global_model = None
self.old_model = copy.deepcopy(self.model)
def train(self):
trainloader = self.load_train_data()
start_time = time.time()
self.model.to(self.device)
self.global_model.to(self.device)
self.old_model.to(self.device)
self.model.train()
max_local_steps = self.local_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for step in range(max_local_steps):
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
rep = self.model.base(x)
output = self.model.predictor(rep)
loss = self.loss(output, y)
rep_old = self.old_model.base(x).detach()
rep_global = self.global_model.base(x).detach()
loss_con = - torch.log(torch.exp(F.cosine_similarity(rep, rep_global) / self.tau) / (torch.exp(F.cosine_similarity(rep, rep_global) / self.tau) + torch.exp(F.cosine_similarity(rep, rep_old) / self.tau)))
loss += self.mu * torch.mean(loss_con)
loss.backward()
if self.privacy:
dp_step(self.optimizer, i, len(trainloader))
else:
self.optimizer.step()
self.model.cpu()
self.global_model.cpu()
self.old_model.cpu()
self.old_model = copy.deepcopy(self.model)
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
# if self.privacy:
# res, DELTA = get_dp_params(self.optimizer)
# print(f"Client {self.id}", f"(ε = {res[0]:.2f}, δ = {DELTA}) for α = {res[1]}")
def set_parameters(self, model):
for new_param, old_param in zip(model.parameters(), self.model.parameters()):
old_param.data = new_param.data.clone()
self.global_model = model | 3,023 | Python | .py | 66 | 34.757576 | 219 | 0.574403 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,729 | clientperavg-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientperavg-checkpoint.py | import numpy as np
import torch
import time
import copy
import torch.nn as nn
from flcore.optimizers.fedoptimizer import PerAvgOptimizer
from flcore.clients.clientbase import Client
class clientPerAvg(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
# self.beta = args.beta
self.beta = self.learning_rate
self.loss = nn.CrossEntropyLoss()
self.optimizer = PerAvgOptimizer(self.model.parameters(), lr=self.learning_rate)
def train(self):
trainloader = self.load_train_data(self.batch_size*2)
start_time = time.time()
# self.model.to(self.device)
self.model.train()
max_local_steps = self.local_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for step in range(max_local_steps): # local update
for X, Y in trainloader:
temp_model = copy.deepcopy(list(self.model.parameters()))
# step 1
if type(X) == type([]):
x = [None, None]
x[0] = X[0][:self.batch_size].to(self.device)
x[1] = X[1][:self.batch_size]
else:
x = X[:self.batch_size].to(self.device)
y = Y[:self.batch_size].to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
self.optimizer.step()
# step 2
if type(X) == type([]):
x = [None, None]
x[0] = X[0][self.batch_size:].to(self.device)
x[1] = X[1][self.batch_size:]
else:
x = X[self.batch_size:].to(self.device)
y = Y[self.batch_size:].to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
# restore the model parameters to the one before first update
for old_param, new_param in zip(self.model.parameters(), temp_model):
old_param.data = new_param.data.clone()
self.optimizer.step(beta=self.beta)
# self.model.cpu()
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
def train_one_step(self):
testloader = self.load_test_data(self.batch_size)
iter_testloader = iter(testloader)
# self.model.to(self.device)
self.model.train()
# step 1
(x, y) = next(iter_testloader)
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
self.optimizer.step()
# step 2
(x, y) = next(iter_testloader)
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
self.optimizer.step(beta=self.beta)
# self.model.cpu()
| 3,719 | Python | .py | 91 | 28.758242 | 88 | 0.529787 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,730 | clientmtl-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientmtl-checkpoint.py | import torch
import torch.nn as nn
from flcore.clients.clientbase import Client
import numpy as np
import time
import math
import copy
class clientMTL(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
self.omega = None
self.W_glob = None
self.idx = 0
self.itk = args.itk
self.lamba = 1e-4
self.loss = nn.CrossEntropyLoss()
self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.learning_rate, momentum=0.5)
def train(self):
trainloader = self.load_train_data()
start_time = time.time()
# self.model = self.load_model('model')
# self.model.to(self.device)
self.model.train()
max_local_steps = self.local_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for step in range(max_local_steps):
for x, y in trainloader:
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
self.W_glob[:, self.idx] = flatten(self.model)
loss_regularizer = 0
loss_regularizer += self.W_glob.norm() ** 2
# for i in range(self.W_glob.shape[0] // self.itk):
# x = self.W_glob[i * self.itk:(i+1) * self.itk, :]
# loss_regularizer += torch.sum(torch.sum((x*self.omega), 1)**2)
loss_regularizer += torch.sum(torch.sum((self.W_glob*self.omega), 1)**2)
f = (int)(math.log10(self.W_glob.shape[0])+1) + 1
loss_regularizer *= 10 ** (-f)
loss += loss_regularizer
loss.backward()
self.optimizer.step()
# self.model.cpu()
# self.save_model(self.model, 'model')
self.omega = None
self.W_glob = None
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
def receive_values(self, W_glob, omega, idx):
self.omega = torch.sqrt(omega[0][0])
self.W_glob = copy.deepcopy(W_glob)
self.idx = idx
def flatten(model):
state_dict = model.state_dict()
keys = state_dict.keys()
W = [state_dict[key].flatten() for key in keys]
return torch.cat(W) | 2,734 | Python | .py | 65 | 31.030769 | 102 | 0.549183 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,731 | clientapfl-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientapfl-checkpoint.py | import copy
import torch
import torch.nn as nn
import numpy as np
import time
from flcore.clients.clientbase import Client
class clientAPFL(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
self.loss = nn.CrossEntropyLoss()
self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.learning_rate)
self.alpha = args.alpha
self.model_per = copy.deepcopy(self.model)
self.optimizer_per = torch.optim.SGD(self.model_per.parameters(), lr=self.learning_rate)
def train(self):
trainloader = self.load_train_data()
start_time = time.time()
self.model_per.to(self.device)
self.model.to(self.device)
self.model.train()
max_local_steps = self.local_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for step in range(max_local_steps):
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
self.optimizer.step()
self.optimizer_per.zero_grad()
output_per = self.model_per(x)
loss_per = self.loss(output_per, y)
loss_per.backward()
self.optimizer_per.step()
self.alpha_update()
for lp, p in zip(self.model_per.parameters(), self.model.parameters()):
lp.data = (1 - self.alpha) * p + self.alpha * lp
self.model.cpu()
self.model_per.cpu()
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
# https://github.com/MLOPTPSU/FedTorch/blob/b58da7408d783fd426872b63fbe0c0352c7fa8e4/fedtorch/comms/utils/flow_utils.py#L240
def alpha_update(self):
grad_alpha = 0
for l_params, p_params in zip(self.model.parameters(), self.model_per.parameters()):
dif = p_params.data - l_params.data
grad = self.alpha * p_params.grad.data + (1-self.alpha) * l_params.grad.data
grad_alpha += dif.view(-1).T.dot(grad.view(-1))
grad_alpha += 0.02 * self.alpha
self.alpha = self.alpha - self.learning_rate * grad_alpha
self.alpha = np.clip(self.alpha.item(), 0.0, 1.0) | 2,786 | Python | .py | 59 | 35.627119 | 128 | 0.58352 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,732 | clientt-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientt-checkpoint.py | import torch
import torch.nn as nn
import numpy as np
import time
from system.flcore.clients.clientbase import Client
import copy
# from utils.privacy import *
class clientT(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
self.loss = nn.CrossEntropyLoss()
self.poptimizer = torch.optim.SGD(self.model.generator.parameters(), lr=self.learning_rate)
self.optimizer = torch.optim.SGD(self.model.base.parameters(), lr=self.learning_rate)
self.ser_para()
self.plocal_steps = args.plocal_steps
def ser_para(self):
torch.manual_seed(self.id)
self.model.generator.pad_down.data = torch.rand_like(self.model.generator.pad_down.data )
self.model.generator.pad_left.data = torch.rand_like(self.model.generator.pad_left.data)
self.model.generator.pad_right.data = torch.rand_like(self.model.generator.pad_right.data)
self.model.generator.pad_up.data = torch.rand_like(self.model.generator.pad_up.data)
def train(self):
trainloader = self.load_train_data()
start_time = time.time()
old_prompt = copy.deepcopy(self.model.generator)
self.model.to(self.device)
self.model.train()
max_local_steps = self.local_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for param in self.model.base.parameters():
param.requires_grad = True
for param in self.model.generator.parameters():
param.requires_grad = False
for step in range(max_local_steps):
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
self.optimizer.step()
self.model.cpu()
new_prompt = copy.deepcopy(self.model.generator)
diff_provalue = 0
for new_param, old_param in zip(old_prompt.parameters(), new_prompt.parameters()):
diff_pro = new_param - old_param
diff_pro = torch.where(diff_pro > 0, diff_pro, torch.zeros_like(diff_pro)-diff_pro)
diff_provalue = diff_provalue + torch.sum(diff_pro)
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
return diff_provalue
def set_parameters(self, base):
for new_param, old_param in zip(base.parameters(), self.model.base.parameters()):
old_param.data = new_param.data.clone() | 2,962 | Python | .py | 61 | 38.245902 | 99 | 0.619229 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,733 | clientbabu-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientbabu-checkpoint.py | import copy
import torch
import torch.nn as nn
import numpy as np
import time
from flcore.clients.clientbase import Client
class clientBABU(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
self.loss = nn.CrossEntropyLoss()
self.optimizer = torch.optim.SGD(self.model.base.parameters(), lr=self.learning_rate)
self.fine_tuning_steps = args.fine_tuning_steps
for param in self.model.predictor.parameters():
param.requires_grad = False
def train(self):
trainloader = self.load_train_data()
start_time = time.time()
# self.model.to(self.device)
self.model.train()
max_local_steps = self.local_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for step in range(max_local_steps):
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
self.optimizer.step()
# self.model.cpu()
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
def set_parameters(self, base):
for new_param, old_param in zip(base.parameters(), self.model.base.parameters()):
old_param.data = new_param.data.clone()
def fine_tune(self, which_module=['base', 'predictor']):
trainloader = self.load_train_data()
self.model.train()
if 'predictor' in which_module:
for param in self.model.predictor.parameters():
param.requires_grad = True
if 'base' not in which_module:
for param in self.model.predictor.parameters():
param.requires_grad = False
for step in range(self.fine_tuning_steps):
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
self.optimizer.step()
| 2,762 | Python | .py | 63 | 31.285714 | 93 | 0.556186 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,734 | clientrod-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientrod-checkpoint.py | import copy
import torch
import torch.nn as nn
import numpy as np
import time
from flcore.clients.clientbase import Client
import torch.nn.functional as F
from sklearn.preprocessing import label_binarize
from sklearn import metrics
class clientROD(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
self.loss = nn.CrossEntropyLoss()
self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.learning_rate)
self.pred = copy.deepcopy(self.model.predictor)
self.opt_pred = torch.optim.SGD(self.pred.parameters(), lr=self.learning_rate)
self.sample_per_class = torch.zeros(self.num_classes)
trainloader = self.load_train_data()
for x, y in trainloader:
for yy in y:
self.sample_per_class[yy.item()] += 1
def train(self):
trainloader = self.load_train_data()
start_time = time.time()
self.model.to(self.device)
self.pred.to(self.device)
self.model.train()
max_local_steps = self.local_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for step in range(max_local_steps):
for i, (x, y) in enumerate(trainloader):
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
rep = self.model.base(x)
out_g = self.model.predictor(rep)
loss_bsm = balanced_softmax_loss(y, out_g, self.sample_per_class)
self.optimizer.zero_grad()
loss_bsm.backward()
self.optimizer.step()
out_p = self.pred(rep.detach())
loss = self.loss(out_g.detach() + out_p, y)
self.opt_pred.zero_grad()
loss.backward()
self.opt_pred.step()
self.model.cpu()
self.pred.cpu()
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
def test_metrics(self, model=None):
testloader = self.load_test_data()
if model == None:
model = self.model
self.model.to(self.device)
self.pred.to(self.device)
model.eval()
test_acc = 0
test_num = 0
y_prob = []
y_true = []
with torch.no_grad():
for x, y in testloader:
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
rep = self.model.base(x)
out_g = self.model.predictor(rep)
out_p = self.pred(rep.detach())
output = out_g.detach() + out_p
test_acc += (torch.sum(torch.argmax(output, dim=1) == y)).item()
test_num += y.shape[0]
y_prob.append(F.softmax(output).detach().cpu().numpy())
nc = self.num_classes
if self.num_classes == 2:
nc += 1
lb = label_binarize(y.detach().cpu().numpy(), classes=np.arange(nc))
if self.num_classes == 2:
lb = lb[:, :2]
y_true.append(lb)
self.model.cpu()
self.pred.cpu()
y_prob = np.concatenate(y_prob, axis=0)
y_true = np.concatenate(y_true, axis=0)
auc = metrics.roc_auc_score(y_true, y_prob, average='micro')
return test_acc, test_num, auc
# https://github.com/jiawei-ren/BalancedMetaSoftmax-Classification
def balanced_softmax_loss(labels, logits, sample_per_class, reduction="mean"):
"""Compute the Balanced Softmax Loss between `logits` and the ground truth `labels`.
Args:
labels: A int tensor of size [batch].
logits: A float tensor of size [batch, no_of_classes].
sample_per_class: A int tensor of size [no of classes].
reduction: string. One of "none", "mean", "sum"
Returns:
loss: A float tensor. Balanced Softmax Loss.
"""
spc = sample_per_class.type_as(logits)
spc = spc.unsqueeze(0).expand(logits.shape[0], -1)
logits = logits + spc.log()
loss = F.cross_entropy(input=logits, target=labels, reduction=reduction)
return loss
| 4,529 | Python | .py | 106 | 31.443396 | 88 | 0.563661 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,735 | clientprox-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientprox-checkpoint.py | import torch
import numpy as np
import time
import copy
import torch.nn as nn
from flcore.optimizers.fedoptimizer import PerturbedGradientDescent
from flcore.clients.clientbase import Client
class clientProx(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
self.mu = args.mu
self.global_params = copy.deepcopy(list(self.model.parameters()))
self.loss = nn.CrossEntropyLoss()
self.optimizer = PerturbedGradientDescent(
self.model.parameters(), lr=self.learning_rate, mu=self.mu)
def train(self):
trainloader = self.load_train_data()
start_time = time.time()
self.model.to(self.device)
self.model.train()
max_local_steps = self.local_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for step in range(max_local_steps):
for x, y in trainloader:
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
# if self.train_slow:
# time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
loss.backward()
self.optimizer.step(self.global_params, self.device)
self.model.cpu()
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
def set_parameters(self, model):
for new_param, global_param, param in zip(model.parameters(), self.global_params, self.model.parameters()):
global_param.data = new_param.data.clone()
param.data = new_param.data.clone()
| 1,947 | Python | .py | 44 | 34.613636 | 115 | 0.59651 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,736 | clientamp-checkpoint.py | hkgdifyu_pFedPT/system/flcore/clients/.ipynb_checkpoints/clientamp-checkpoint.py | import torch
import torch.nn as nn
from flcore.clients.clientbase import Client
import numpy as np
import time
import copy
class clientAMP(Client):
def __init__(self, args, id, train_samples, test_samples, **kwargs):
super().__init__(args, id, train_samples, test_samples, **kwargs)
self.alphaK = args.alphaK
self.lamda = args.lamda
self.client_u = copy.deepcopy(self.model)
self.loss = nn.CrossEntropyLoss()
self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.learning_rate)
def train(self):
trainloader = self.load_train_data()
start_time = time.time()
self.model.to(self.device)
self.client_u.to(self.device)
self.model.train()
max_local_steps = self.local_steps
if self.train_slow:
max_local_steps = np.random.randint(1, max_local_steps // 2)
for step in range(max_local_steps):
for x, y in trainloader:
if type(x) == type([]):
x[0] = x[0].to(self.device)
else:
x = x.to(self.device)
y = y.to(self.device)
if self.train_slow:
time.sleep(0.1 * np.abs(np.random.rand()))
self.optimizer.zero_grad()
output = self.model(x)
loss = self.loss(output, y)
params = weight_flatten(self.model)
params_ = weight_flatten(self.client_u)
sub = params - params_
loss += self.lamda/self.alphaK/2 * torch.dot(sub, sub)
loss.backward()
self.optimizer.step()
self.model.cpu()
self.client_u.cpu()
del trainloader
# print(torch.dot(sub, sub))
self.train_time_cost['num_rounds'] += 1
self.train_time_cost['total_cost'] += time.time() - start_time
def set_parameters(self, model, coef_self):
for new_param, old_param in zip(model.parameters(), self.client_u.parameters()):
old_param.data = (new_param.data + coef_self * old_param.data).clone()
def weight_flatten(model):
params = []
for u in model.parameters():
params.append(u.view(-1))
params = torch.cat(params)
return params
| 2,315 | Python | .py | 56 | 30.660714 | 88 | 0.576629 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,737 | models.py | hkgdifyu_pFedPT/system/flcore/trainmodel/models.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import logging
from functools import partial
from collections import OrderedDict
from transformers import ViTImageProcessor, ViTForImageClassification
import numpy as np
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.layers import StdConv2dSame, DropPath, to_2tuple, trunc_normal_
batch_size = 16
class LocalModel(nn.Module):
def __init__(self, base, predictor):
super(LocalModel, self).__init__()
self.base = base
self.predictor = predictor
def forward(self, x):
out = self.base(x)
out = self.predictor(out)
return out
class LocalModel_pt(nn.Module):
def __init__(self, generator, base):
super(LocalModel_pt, self).__init__()
self.generator = generator
self.base = base
def forward(self, x):
out = self.generator(x)
out = self.base(out)
return out
class FedAvgCNN(nn.Module):
def __init__(self, in_features=1, num_classes=10, dim=1024):
super().__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(in_features,
32,
kernel_size=5,
padding=0,
stride=1,
bias=True),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=(2, 2))
)
self.conv2 = nn.Sequential(
nn.Conv2d(32,
64,
kernel_size=5,
padding=0,
stride=1,
bias=True),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=(2, 2))
)
self.fc1 = nn.Sequential(
nn.Linear(dim, 512),
nn.ReLU(inplace=True)
)
self.fc = nn.Linear(512, num_classes)
def forward(self, x):
out = self.conv1(x)
out = self.conv2(out)
out = torch.flatten(out, 1)
out = self.fc1(out)
out = self.fc(out)
return out
# ====================================================================================================================
def init_weights(m):
classname = m.__class__.__name__
if classname.find('Conv2d') != -1 or classname.find('ConvTranspose2d') != -1:
nn.init.kaiming_uniform_(m.weight)
nn.init.zeros_(m.bias)
elif classname.find('BatchNorm') != -1:
nn.init.normal_(m.weight, 1.0, 0.02)
nn.init.zeros_(m.bias)
elif classname.find('Linear') != -1:
nn.init.xavier_normal_(m.weight)
nn.init.zeros_(m.bias)
class PadPrompter(nn.Module):
def __init__(self,inchanel = 1,pad_size=1,image_size=28,args =None):
super(PadPrompter, self).__init__()
pad_size = pad_size
image_size = image_size
self.args = args
self.inchanel =inchanel
self.base_size = image_size - pad_size*2
self.pad_up = nn.Parameter(torch.randn([1, self.inchanel, pad_size, image_size]))
self.pad_down = nn.Parameter(torch.randn([1, self.inchanel, pad_size, image_size]))
self.pad_left = nn.Parameter(torch.randn([1, self.inchanel, image_size - pad_size*2, pad_size]))
self.pad_right = nn.Parameter(torch.randn([1,self.inchanel, image_size - pad_size*2, pad_size]))
def forward(self, x):
base = torch.zeros(1, self.inchanel, self.base_size, self.base_size).to(self.args.device)
prompt = torch.cat([self.pad_left, base, self.pad_right], dim=3)
prompt = torch.cat([self.pad_up, prompt, self.pad_down], dim=2)
prompt = torch.cat(x.size(0) * [prompt])
return x + prompt
# ====================================================================================================================
class PatchEmbed(nn.Module): # PatchEmbed from timm
"""
Image to Patch Embedding
CNN_proj + Rearrange: [B, C, W, H] -> [B, D, Pn_W, Pn_H] -> [B, D, Pn] -> [B, Pn, D]
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = num_patches
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, x):
B, C, H, W = x.shape
# FIXME look at relaxing size constraints
assert H == self.img_size[0] and W == self.img_size[1], \
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
# CNN_proj + Rearrange: [B, C, W, H] -> [B, D, Pn_W, Pn_H] -> [B, D, Pn] -> [B, Pn, D]
x = self.proj(x).flatten(2).transpose(1, 2)
# x: (B, 14*14, 768)
return x
class FFN(nn.Module): # Mlp from timm
"""
FFN (from timm)
:param in_features:
:param hidden_features:
:param out_features:
:param act_layer:
:param drop:
"""
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
# MHSA
class Attention(nn.Module): # qkv Transform + MSA(MHSA) (Attention from timm)
"""
qkv Transform + MSA(MHSA) (from timm)
# input x.shape = batch, patch_number, patch_dim
# output x.shape = batch, patch_number, patch_dim
:param dim: dim=CNN feature dim, because the patch size is 1x1
:param num_heads:
:param qkv_bias:
:param qk_scale: by default head_dim ** -0.5 (squre root)
:param attn_drop: dropout rate after MHSA
:param proj_drop:
"""
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
self.scale = qk_scale or head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
# input x.shape = batch, patch_number, patch_dim
batch, patch_number, patch_dim = x.shape
# mlp transform + head split [B, Pn, D] -> [B, Pn, 3D] -> [B, Pn, 3, H, D/H] -> [3, B, H, Pn, D/H]
qkv = self.qkv(x).reshape(batch, patch_number, 3, self.num_heads, patch_dim //
self.num_heads).permute(2, 0, 3, 1, 4)
# 3 [B, H, Pn, D/H]
q, k, v = qkv[0], qkv[1], qkv[2]
# [B, H, Pn, D/H] -> [B, H, Pn, D/H]
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn) # Dropout
# head fusion: [B, H, Pn, D/H] -> [B, Pn, H, D/H] -> [B, Pn, D]
x = (attn @ v).transpose(1, 2).reshape(batch, patch_number, patch_dim)
x = self.proj(x)
x = self.proj_drop(x) # mlp
# output x.shape = batch, patch_number, patch_dim
return x
# Encoder_Block
class Block(nn.Module): # teansformer Block from timm
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
"""
# input x.shape = batch, patch_number, patch_dim
# output x.shape = batch, patch_number, patch_dim
:param dim: dim
:param num_heads:
:param mlp_ratio: FFN
:param qkv_bias:
:param qk_scale: by default head_dim ** -0.5 (squre root)
:param drop:
:param attn_drop: dropout rate after Attention
:param drop_path: dropout rate after sd
:param act_layer: FFN act
:param norm_layer: Pre Norm
"""
super().__init__()
# Pre Norm
self.norm1 = norm_layer(dim) # Transformer used the nn.LayerNorm
self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop,
proj_drop=drop)
# NOTE from timm: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() # stochastic depth
# Add & Norm
self.norm2 = norm_layer(dim)
# FFN
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = FFN(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
def forward(self, x):
# MHSA + Res_connection
x = x + self.drop_path(self.attn(self.norm1(x)))
# FFN + Res_connection
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class VisionTransformer(nn.Module): # From timm
"""
Vision Transformer
A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
- https://arxiv.org/abs/2010.11929
"""
def __init__(self, img_size=32, patch_size=4, in_chans=3, num_classes=10, embed_dim=128, depth=8,
num_heads=8, mlp_ratio=2., qkv_bias=True, representation_size=None, drop_rate=0., attn_drop_rate=0.,
drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None, act_layer=None):
"""
Args:
img_size (int, tuple): input image size
patch_size (int, tuple): patch size
in_chans (int): number of input channels
num_classes (int): number of classes for classification head
embed_dim (int): embedding dimension
depth (int): depth of transformer
num_heads (int): number of attention heads
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
qkv_bias (bool): enable bias for qkv if True
representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
drop_rate (float): dropout rate
attn_drop_rate (float): attention dropout rate
drop_path_rate (float): stochastic depth rate
embed_layer (nn.Module): patch embedding layer
norm_layer: (nn.Module): normalization layer
"""
super().__init__()
self.num_classes = num_classes
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
self.num_tokens = 1
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
act_layer = act_layer or nn.GELU
self.patch_embed = embed_layer(
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
num_patches = self.patch_embed.num_patches
# Embedding tokens
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
self.pos_drop = nn.Dropout(p=drop_rate)
# Stochastic Depth Decay rule
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
# Encoders
self.blocks = nn.Sequential(*[
Block(dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate,
attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer)
for i in range(depth)])
# last norm
self.norm = norm_layer(embed_dim)
# Representation layer
if representation_size:
self.num_features = representation_size
self.pre_logits = nn.Sequential(OrderedDict([
('fc', nn.Linear(embed_dim, representation_size)),
('act', nn.Tanh())
]))
else:
self.pre_logits = nn.Identity()
# Classifier head(s)
self.fc = nn.Linear(self.num_features, self.num_classes) if self.num_classes > 0 else nn.Identity()
self.head_dist = None
def forward_features(self, x):
x = self.patch_embed(x)
# print(x.shape,self.pos_embed.shape)
cls_token = self.cls_token.expand(x.shape[0], -1, -1)
x = torch.cat((cls_token, x), dim=1)
x = self.pos_drop(x + self.pos_embed)
x = self.blocks(x)
x = self.norm(x)
return x
def forward(self, x):
x = self.forward_features(x)
x = self.pre_logits(x[:, 0]) # use cls token for cls head [B,1,768]
x = self.fc(x)
return x
| 13,249 | Python | .py | 295 | 35.677966 | 118 | 0.577847 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,738 | bilstm.cpython-37.pyc | hkgdifyu_pFedPT/system/flcore/trainmodel/__pycache__/bilstm.cpython-37.pyc | B
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__classcell__r$ r$ )r# r% r s r )r r ÚModuler r$ r$ r$ r% Ú<module> s | 2,663 | Python | .py | 30 | 87.7 | 247 | 0.465452 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,739 | vit.cpython-38.pyc | hkgdifyu_pFedPT/system/flcore/trainmodel/__pycache__/vit.cpython-38.pyc | U
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img_size (int, tuple): input image size
patch_size (int, tuple): patch size
in_chans (int): number of input channels
num_classes (int): number of classes for classification head
embed_dim (int): embedding dimension
depth (int): depth of transformer
num_heads (int): number of attention heads
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
qkv_bias (bool): enable bias for qkv if True
representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
drop_rate (float): dropout rate
attn_drop_rate (float): attention dropout rate
drop_path_rate (float): stochastic depth rate
embed_layer (nn.Module): patch embedding layer
norm_layer: (nn.Module): normalization layer
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MobileNet V2 main class
Args:
num_classes (int): Number of classes
width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
inverted_residual_setting: Network structure
round_nearest (int): Round the number of channels in each layer to be a multiple of this number
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block: Module specifying inverted residual building block for mobilenet
norm_layer: Module specifying the normalization layer to use
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img_size (int, tuple): input image size
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num_classes (int): number of classes for classification head
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depth (int): depth of transformer
num_heads (int): number of attention heads
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
qkv_bias (bool): enable bias for qkv if True
representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
drop_rate (float): dropout rate
attn_drop_rate (float): attention dropout rate
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block: Module specifying inverted residual building block for mobilenet
norm_layer: Module specifying the normalization layer to use
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progress (bool): If True, displays a progress bar of the download to stderr
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:param mlp_ratio: FFN
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:param qk_scale: by default head_dim ** -0.5 (squre root)
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:param attn_drop: dropout rate after Attention
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- https://arxiv.org/abs/2010.11929
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Args:
img_size (int, tuple): input image size
patch_size (int, tuple): patch size
in_chans (int): number of input channels
num_classes (int): number of classes for classification head
embed_dim (int): embedding dimension
depth (int): depth of transformer
num_heads (int): number of attention heads
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
qkv_bias (bool): enable bias for qkv if True
representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
drop_rate (float): dropout rate
attn_drop_rate (float): attention dropout rate
drop_path_rate (float): stochastic depth rate
embed_layer (nn.Module): patch embedding layer
norm_layer: (nn.Module): normalization layer
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2,286,749 | vit-checkpoint.py | hkgdifyu_pFedPT/system/flcore/trainmodel/.ipynb_checkpoints/vit-checkpoint.py | import torch
from torch import nn
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
# helpers
def pair(t):
return t if isinstance(t, tuple) else (t, t)
# classes
class PreNorm(nn.Module):
def __init__(self, dim, fn):
super().__init__()
self.norm = nn.LayerNorm(dim)
self.fn = fn
def forward(self, x, **kwargs):
return self.fn(self.norm(x), **kwargs)
class FeedForward(nn.Module):
def __init__(self, dim, hidden_dim, dropout = 0.):
super().__init__()
self.net = nn.Sequential(
nn.Linear(dim, hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, dim),
nn.Dropout(dropout)
)
def forward(self, x):
return self.net(x)
class Attention(nn.Module):
def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.1):
super().__init__()
inner_dim = dim_head * heads
project_out = not (heads == 1 and dim_head == dim)
self.heads = heads
self.scale = dim_head ** -0.5
self.attend = nn.Softmax(dim = -1)
self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
self.to_out = nn.Sequential(
nn.Linear(inner_dim, dim),
nn.Dropout(dropout)
) if project_out else nn.Identity()
def forward(self, x):
qkv = self.to_qkv(x).chunk(3, dim = -1)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv)
dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
attn = self.attend(dots)
out = torch.matmul(attn, v)
# print("out: ", out)
# print("QKV output dimension: ",out.shape)
# for i in range(8):
# print("Head "+str(i+1)+": ")
# print("Total mean: ",torch.mean(out[0][i]))
# print("QKV output: ",out[0][i])
# print("Isinstance mean: ",torch.mean(out[0][i],dim=0))
# print("Whole activation: ",out)
out = rearrange(out, 'b h n d -> b n (h d)')
return self.to_out(out)
class Transformer(nn.Module):
def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
super().__init__()
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(nn.ModuleList([
PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
]))
def forward(self, x):
ll = 0
for attn, ff in self.layers:
ll += 1
x = attn(x) + x
x = ff(x) + x
return x
class ViT(nn.Module):
def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.):
super().__init__()
image_height, image_width = pair(image_size)
patch_height, patch_width = pair(patch_size)
assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'
num_patches = (image_height // patch_height) * (image_width // patch_width)
patch_dim = channels * patch_height * patch_width
assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'
self.to_patch_embedding = nn.Sequential(
Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width),
nn.Linear(patch_dim, dim),
)
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
self.dropout = nn.Dropout(emb_dropout)
self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
self.pool = pool
self.to_latent = nn.Identity()
self.norm = nn.LayerNorm(dim)
self.fc = nn.Linear(dim, num_classes)
def forward(self, img):
x = self.to_patch_embedding(img)
b, n, _ = x.shape
cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
x = torch.cat((cls_tokens, x), dim=1)
x += self.pos_embedding[:, :(n + 1)]
x = self.dropout(x)
x = self.transformer(x)
x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]
x = self.to_latent(x)
x = self.norm(x)
x = self.fc(x)
return x
def produce_feature(self, img):
x = self.to_patch_embedding(img)
b, n, _ = x.shape
cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
x = torch.cat((cls_tokens, x), dim=1)
x += self.pos_embedding[:, :(n + 1)]
x = self.dropout(x)
x = self.transformer(x)
x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]
x = self.to_latent(x)
return x
def cal_feature(self, img):
x = self.to_patch_embedding(img)
b, n, _ = x.shape
cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
x = torch.cat((cls_tokens, x), dim=1)
x += self.pos_embedding[:, :(n + 1)]
x = self.dropout(x)
x = self.transformer(x)
return x
def Show_detail(self, img):
x = self.to_patch_embedding(img)
b, n, _ = x.shape
cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
x = torch.cat((cls_tokens, x), dim=1)
x += self.pos_embedding[:, :(n + 1)]
x = self.dropout(x)
x = self.transformer(x)
return x | 5,687 | Python | .py | 135 | 33.274074 | 166 | 0.548481 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,750 | models-checkpoint.py | hkgdifyu_pFedPT/system/flcore/trainmodel/.ipynb_checkpoints/models-checkpoint.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import logging
from functools import partial
from collections import OrderedDict
import numpy as np
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.layers import StdConv2dSame, DropPath, to_2tuple, trunc_normal_
batch_size = 16
class LocalModel(nn.Module):
def __init__(self, base, predictor):
super(LocalModel, self).__init__()
self.base = base
self.predictor = predictor
def forward(self, x):
out = self.base(x)
out = self.predictor(out)
return out
class LocalModel_pt(nn.Module):
def __init__(self, generator, base):
super(LocalModel_pt, self).__init__()
self.generator = generator
self.base = base
def forward(self, x):
out = self.generator(x)
out = self.base(out)
return out
class LocalModel_reppt(nn.Module):
def __init__(self, generator, base, predictor):
super(LocalModel_reppt, self).__init__()
self.generator = generator
self.base = base
self.predictor = predictor
def forward(self, x):
out = self.generator(x)
out = self.base(out)
out = self.predictor(out)
return out
# # https://github.com/katsura-jp/fedavg.pytorch/blob/master/src/models/cnn.py
# class FedAvgCNN(nn.Module):
# def __init__(self, in_features=1, num_classes=10, dim=1024):
# super().__init__()
# self.conv1 = nn.Conv2d(in_features,
# 32,
# kernel_size=5,
# padding=0,
# stride=1,
# bias=True)
# self.conv2 = nn.Conv2d(32,
# 64,
# kernel_size=5,
# padding=0,
# stride=1,
# bias=True)
# self.fc1 = nn.Linear(dim, 512)
# self.fc = nn.Linear(512, num_classes)
# self.act = nn.ReLU(inplace=True)
# self.maxpool = nn.MaxPool2d(kernel_size=(2, 2))
# def forward(self, x):
# x = self.act(self.conv1(x))
# x = self.maxpool(x)
# x = self.act(self.conv2(x))
# x = self.maxpool(x)
# x = torch.flatten(x, 1)
# x = self.act(self.fc1(x))
# x = self.fc(x)
# return x
class FedAvgCNN(nn.Module):
def __init__(self, in_features=1, num_classes=10, dim=1024):
super().__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(in_features,
32,
kernel_size=5,
padding=0,
stride=1,
bias=True),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=(2, 2))
)
self.conv2 = nn.Sequential(
nn.Conv2d(32,
64,
kernel_size=5,
padding=0,
stride=1,
bias=True),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=(2, 2))
)
self.fc1 = nn.Sequential(
nn.Linear(dim, 512),
nn.ReLU(inplace=True)
)
self.fc = nn.Linear(512, num_classes)
def forward(self, x):
out = self.conv1(x)
out = self.conv2(out)
out = torch.flatten(out, 1)
out = self.fc1(out)
out = self.fc(out)
return out
# ====================================================================================================================
# https://github.com/katsura-jp/fedavg.pytorch/blob/master/src/models/mlp.py
class FedAvgMLP(nn.Module):
def __init__(self, in_features=784, num_classes=10, hidden_dim=200):
super().__init__()
self.fc1 = nn.Linear(in_features, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, num_classes)
self.act = nn.ReLU(inplace=True)
def forward(self, x):
if x.ndim == 4:
x = x.view(x.size(0), -1)
x = self.act(self.fc1(x))
x = self.fc2(x)
return x
# ====================================================================================================================
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, batch_size, 2, 1)
self.conv2 = nn.Conv2d(batch_size, 32, 2, 1)
self.dropout1 = nn.Dropout(0.25)
self.dropout2 = nn.Dropout(0.5)
self.fc1 = nn.Linear(18432, 128)
self.fc = nn.Linear(128, 10)
def forward(self, x):
x = self.conv1(x)
x = nn.ReLU()(x)
x = nn.MaxPool2d(2, 1)(x)
x = self.dropout1(x)
x = self.conv2(x)
x = nn.ReLU()(x)
x = nn.MaxPool2d(2, 1)(x)
x = self.dropout2(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = nn.ReLU()(x)
x = self.fc(x)
output = F.log_softmax(x, dim=1)
return output
# ====================================================================================================================
class Mclr_Logistic(nn.Module):
def __init__(self, input_dim=1*28*28, num_classes=10):
super(Mclr_Logistic, self).__init__()
self.fc = nn.Linear(input_dim, num_classes)
def forward(self, x):
x = torch.flatten(x, 1)
x = self.fc(x)
output = F.log_softmax(x, dim=1)
return output
# ====================================================================================================================
class DNN(nn.Module):
def __init__(self, input_dim=1*28*28, mid_dim=100, num_classes=10):
super(DNN, self).__init__()
self.fc1 = nn.Linear(input_dim, mid_dim)
self.fc = nn.Linear(mid_dim, num_classes)
def forward(self, x):
x = torch.flatten(x, 1)
x = F.relu(self.fc1(x))
x = self.fc(x)
x = F.log_softmax(x, dim=1)
return x
# ====================================================================================================================
class CifarNet(nn.Module):
def __init__(self, num_classes=10):
super(CifarNet, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, batch_size, 5)
self.fc1 = nn.Linear(batch_size * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc = nn.Linear(84, num_classes)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, batch_size * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc(x)
x = F.log_softmax(x, dim=1)
return x
# ====================================================================================================================
# cfg = {
# 'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
# 'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
# 'VGGbatch_size': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
# 'VGG19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
# }
# class VGG(nn.Module):
# def __init__(self, vgg_name):
# super(VGG, self).__init__()
# self.features = self._make_layers(cfg[vgg_name])
# self.classifier = nn.Sequential(
# nn.Linear(512, 512),
# nn.ReLU(True),
# nn.Linear(512, 512),
# nn.ReLU(True),
# nn.Linear(512, 10)
# )
# def forward(self, x):
# out = self.features(x)
# out = out.view(out.size(0), -1)
# out = self.classifier(out)
# output = F.log_softmax(out, dim=1)
# return output
# def _make_layers(self, cfg):
# layers = []
# in_channels = 3
# for x in cfg:
# if x == 'M':
# layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
# else:
# layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1),
# nn.BatchNorm2d(x),
# nn.ReLU(inplace=True)]
# in_channels = x
# layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
# return nn.Sequential(*layers)
# ====================================================================================================================
def init_weights(m):
classname = m.__class__.__name__
if classname.find('Conv2d') != -1 or classname.find('ConvTranspose2d') != -1:
nn.init.kaiming_uniform_(m.weight)
nn.init.zeros_(m.bias)
elif classname.find('BatchNorm') != -1:
nn.init.normal_(m.weight, 1.0, 0.02)
nn.init.zeros_(m.bias)
elif classname.find('Linear') != -1:
nn.init.xavier_normal_(m.weight)
nn.init.zeros_(m.bias)
class PadPrompter(nn.Module):
def __init__(self,inchanel = 1,pad_size=1,image_size=28,args =None):
super(PadPrompter, self).__init__()
pad_size = pad_size
image_size = image_size
self.args = args
self.inchanel =inchanel
self.base_size = image_size - pad_size*2
self.pad_up = nn.Parameter(torch.randn([1, self.inchanel, pad_size, image_size]))
self.pad_down = nn.Parameter(torch.randn([1, self.inchanel, pad_size, image_size]))
self.pad_left = nn.Parameter(torch.randn([1, self.inchanel, image_size - pad_size*2, pad_size]))
self.pad_right = nn.Parameter(torch.randn([1,self.inchanel, image_size - pad_size*2, pad_size]))
def forward(self, x):
base = torch.zeros(1, self.inchanel, self.base_size, self.base_size).to(self.args.device)
prompt = torch.cat([self.pad_left, base, self.pad_right], dim=3)
prompt = torch.cat([self.pad_up, prompt, self.pad_down], dim=2)
prompt = torch.cat(x.size(0) * [prompt])
return x + prompt
class LeNet(nn.Module):
def __init__(self, feature_dim=50*4*4, bottleneck_dim=256, num_classes=10, iswn=None):
super(LeNet, self).__init__()
self.conv_params = nn.Sequential(
nn.Conv2d(1, 20, kernel_size=5),
nn.MaxPool2d(2),
nn.ReLU(),
nn.Conv2d(20, 50, kernel_size=5),
nn.Dropout2d(p=0.5),
nn.MaxPool2d(2),
nn.ReLU(),
)
self.bn = nn.BatchNorm1d(bottleneck_dim, affine=True)
self.dropout = nn.Dropout(p=0.5)
self.bottleneck = nn.Linear(feature_dim, bottleneck_dim)
self.bottleneck.apply(init_weights)
self.fc = nn.Linear(bottleneck_dim, num_classes)
if iswn == "wn":
self.fc = nn.utils.weight_norm(self.fc, name="weight")
self.fc.apply(init_weights)
def forward(self, x):
x = self.conv_params(x)
x = x.view(x.size(0), -1)
x = self.bottleneck(x)
x = self.bn(x)
x = self.dropout(x)
x = self.fc(x)
x = F.log_softmax(x, dim=1)
return x
# ====================================================================================================================
# class CNNCifar(nn.Module):
# def __init__(self, num_classes=10):
# super(CNNCifar, self).__init__()
# self.conv1 = nn.Conv2d(3, 6, 5)
# self.pool = nn.MaxPool2d(2, 2)
# self.conv2 = nn.Conv2d(6, batch_size, 5)
# self.fc1 = nn.Linear(batch_size * 5 * 5, 120)
# self.fc2 = nn.Linear(120, 100)
# self.fc3 = nn.Linear(100, num_classes)
# # self.weight_keys = [['fc1.weight', 'fc1.bias'],
# # ['fc2.weight', 'fc2.bias'],
# # ['fc3.weight', 'fc3.bias'],
# # ['conv2.weight', 'conv2.bias'],
# # ['conv1.weight', 'conv1.bias'],
# # ]
# def forward(self, x):
# x = self.pool(F.relu(self.conv1(x)))
# x = self.pool(F.relu(self.conv2(x)))
# x = x.view(-1, batch_size * 5 * 5)
# x = F.relu(self.fc1(x))
# x = F.relu(self.fc2(x))
# x = self.fc3(x)
# x = F.log_softmax(x, dim=1)
# return x
# ====================================================================================================================
class LSTMNet(nn.Module):
def __init__(self, hidden_dim, num_layers=2, bidirectional=False, dropout=0.2,
padding_idx=0, vocab_size=98635, num_classes=10):
super().__init__()
self.dropout = nn.Dropout(dropout)
self.embedding = nn.Embedding(vocab_size, hidden_dim, padding_idx)
self.lstm = nn.LSTM(input_size=hidden_dim,
hidden_size=hidden_dim,
num_layers=num_layers,
bidirectional=bidirectional,
dropout=dropout,
batch_first=True)
dims = hidden_dim*2 if bidirectional else hidden_dim
self.fc = nn.Linear(dims, num_classes)
def forward(self, x):
text, text_lengths = x
embedded = self.embedding(text)
#pack sequence
packed_embedded = nn.utils.rnn.pack_padded_sequence(embedded, text_lengths, batch_first=True, enforce_sorted=False)
packed_output, (hidden, cell) = self.lstm(packed_embedded)
#unpack sequence
out, out_lengths = nn.utils.rnn.pad_packed_sequence(packed_output, batch_first=True)
out = torch.relu_(out[:,-1,:])
out = self.dropout(out)
out = self.fc(out)
out = F.log_softmax(out, dim=1)
return out
# ====================================================================================================================
class fastText(nn.Module):
def __init__(self, hidden_dim, padding_idx=0, vocab_size=98635, num_classes=10):
super(fastText, self).__init__()
# Embedding Layer
self.embedding = nn.Embedding(vocab_size, hidden_dim, padding_idx)
# Hidden Layer
self.fc1 = nn.Linear(hidden_dim, hidden_dim)
# Output Layer
self.fc = nn.Linear(hidden_dim, num_classes)
def forward(self, x):
text, text_lengths = x
embedded_sent = self.embedding(text)
h = self.fc1(embedded_sent.mean(1))
z = self.fc(h)
out = F.log_softmax(z, dim=1)
return out
# ====================================================================================================================
class TextCNN(nn.Module):
def __init__(self, hidden_dim, num_channels=100, kernel_size=[3,4,5], max_len=200, dropout=0.8,
padding_idx=0, vocab_size=98635, num_classes=10):
super(TextCNN, self).__init__()
# Embedding Layer
self.embedding = nn.Embedding(vocab_size, hidden_dim, padding_idx)
# This stackoverflow thread clarifies how conv1d works
# https://stackoverflow.com/questions/46503816/keras-conv1d-layer-parameters-filters-and-kernel-size/46504997
self.conv1 = nn.Sequential(
nn.Conv1d(in_channels=hidden_dim, out_channels=num_channels, kernel_size=kernel_size[0]),
nn.ReLU(),
nn.MaxPool1d(max_len - kernel_size[0]+1)
)
self.conv2 = nn.Sequential(
nn.Conv1d(in_channels=hidden_dim, out_channels=num_channels, kernel_size=kernel_size[1]),
nn.ReLU(),
nn.MaxPool1d(max_len - kernel_size[1]+1)
)
self.conv3 = nn.Sequential(
nn.Conv1d(in_channels=hidden_dim, out_channels=num_channels, kernel_size=kernel_size[2]),
nn.ReLU(),
nn.MaxPool1d(max_len - kernel_size[2]+1)
)
self.dropout = nn.Dropout(dropout)
# Fully-Connected Layer
self.fc = nn.Linear(num_channels*len(kernel_size), num_classes)
def forward(self, x):
text, text_lengths = x
embedded_sent = self.embedding(text).permute(0,2,1)
conv_out1 = self.conv1(embedded_sent).squeeze(2)
conv_out2 = self.conv2(embedded_sent).squeeze(2)
conv_out3 = self.conv3(embedded_sent).squeeze(2)
all_out = torch.cat((conv_out1, conv_out2, conv_out3), 1)
final_feature_map = self.dropout(all_out)
out = self.fc(final_feature_map)
out = F.log_softmax(out, dim=1)
return out
# ====================================================================================================================
class PatchEmbed(nn.Module): # PatchEmbed from timm
"""
Image to Patch Embedding
CNN_proj + Rearrange: [B, C, W, H] -> [B, D, Pn_W, Pn_H] -> [B, D, Pn] -> [B, Pn, D]
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = num_patches
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, x):
B, C, H, W = x.shape
# FIXME look at relaxing size constraints
assert H == self.img_size[0] and W == self.img_size[1], \
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
# CNN_proj + Rearrange: [B, C, W, H] -> [B, D, Pn_W, Pn_H] -> [B, D, Pn] -> [B, Pn, D]
x = self.proj(x).flatten(2).transpose(1, 2)
# x: (B, 14*14, 768)
return x
class FFN(nn.Module): # Mlp from timm
"""
FFN (from timm)
:param in_features:
:param hidden_features:
:param out_features:
:param act_layer:
:param drop:
"""
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
# MHSA
class Attention(nn.Module): # qkv Transform + MSA(MHSA) (Attention from timm)
"""
qkv Transform + MSA(MHSA) (from timm)
# input x.shape = batch, patch_number, patch_dim
# output x.shape = batch, patch_number, patch_dim
:param dim: dim=CNN feature dim, because the patch size is 1x1
:param num_heads:
:param qkv_bias:
:param qk_scale: by default head_dim ** -0.5 (squre root)
:param attn_drop: dropout rate after MHSA
:param proj_drop:
"""
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
self.scale = qk_scale or head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
# input x.shape = batch, patch_number, patch_dim
batch, patch_number, patch_dim = x.shape
# mlp transform + head split [B, Pn, D] -> [B, Pn, 3D] -> [B, Pn, 3, H, D/H] -> [3, B, H, Pn, D/H]
qkv = self.qkv(x).reshape(batch, patch_number, 3, self.num_heads, patch_dim //
self.num_heads).permute(2, 0, 3, 1, 4)
# 3 [B, H, Pn, D/H]
q, k, v = qkv[0], qkv[1], qkv[2]
# [B, H, Pn, D/H] -> [B, H, Pn, D/H]
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn) # Dropout
# head fusion: [B, H, Pn, D/H] -> [B, Pn, H, D/H] -> [B, Pn, D]
x = (attn @ v).transpose(1, 2).reshape(batch, patch_number, patch_dim)
x = self.proj(x)
x = self.proj_drop(x) # mlp
# output x.shape = batch, patch_number, patch_dim
return x
# Encoder_Block
class Block(nn.Module): # teansformer Block from timm
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
"""
# input x.shape = batch, patch_number, patch_dim
# output x.shape = batch, patch_number, patch_dim
:param dim: dim
:param num_heads:
:param mlp_ratio: FFN
:param qkv_bias:
:param qk_scale: by default head_dim ** -0.5 (squre root)
:param drop:
:param attn_drop: dropout rate after Attention
:param drop_path: dropout rate after sd
:param act_layer: FFN act
:param norm_layer: Pre Norm
"""
super().__init__()
# Pre Norm
self.norm1 = norm_layer(dim) # Transformer used the nn.LayerNorm
self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop,
proj_drop=drop)
# NOTE from timm: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() # stochastic depth
# Add & Norm
self.norm2 = norm_layer(dim)
# FFN
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = FFN(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
def forward(self, x):
# MHSA + Res_connection
x = x + self.drop_path(self.attn(self.norm1(x)))
# FFN + Res_connection
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class VisionTransformer(nn.Module): # From timm
"""
Vision Transformer
A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
- https://arxiv.org/abs/2010.11929
"""
def __init__(self, img_size=32, patch_size=16, in_chans=3, num_classes=10, embed_dim=128, depth=8,
num_heads=8, mlp_ratio=2., qkv_bias=True, representation_size=None, drop_rate=0., attn_drop_rate=0.,
drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None, act_layer=None):
"""
Args:
img_size (int, tuple): input image size
patch_size (int, tuple): patch size
in_chans (int): number of input channels
num_classes (int): number of classes for classification head
embed_dim (int): embedding dimension
depth (int): depth of transformer
num_heads (int): number of attention heads
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
qkv_bias (bool): enable bias for qkv if True
representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
drop_rate (float): dropout rate
attn_drop_rate (float): attention dropout rate
drop_path_rate (float): stochastic depth rate
embed_layer (nn.Module): patch embedding layer
norm_layer: (nn.Module): normalization layer
"""
super().__init__()
self.num_classes = num_classes
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
self.num_tokens = 1
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
act_layer = act_layer or nn.GELU
self.patch_embed = embed_layer(
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
num_patches = self.patch_embed.num_patches
# Embedding tokens
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
self.pos_drop = nn.Dropout(p=drop_rate)
# Stochastic Depth Decay rule
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
# Encoders
self.blocks = nn.Sequential(*[
Block(dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate,
attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer)
for i in range(depth)])
# last norm
self.norm = norm_layer(embed_dim)
# Representation layer
if representation_size:
self.num_features = representation_size
self.pre_logits = nn.Sequential(OrderedDict([
('fc', nn.Linear(embed_dim, representation_size)),
('act', nn.Tanh())
]))
else:
self.pre_logits = nn.Identity()
# Classifier head(s)
self.fc = nn.Linear(self.num_features, self.num_classes) if self.num_classes > 0 else nn.Identity()
self.head_dist = None
def forward_features(self, x):
x = self.patch_embed(x)
# print(x.shape,self.pos_embed.shape)
cls_token = self.cls_token.expand(x.shape[0], -1, -1)
x = torch.cat((cls_token, x), dim=1)
x = self.pos_drop(x + self.pos_embed)
x = self.blocks(x)
x = self.norm(x)
return x
def forward(self, x):
x = self.forward_features(x)
x = self.pre_logits(x[:, 0]) # use cls token for cls head [B,1,768]
x = self.fc(x)
return x
# class linear(Function):
# @staticmethod
# def forward(ctx, input):
# return input
# @staticmethod
# def backward(ctx, grad_output):
# return grad_output | 26,452 | Python | .py | 595 | 36.364706 | 123 | 0.531646 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,751 | serverpfedpt.py | hkgdifyu_pFedPT/system/flcore/servers/serverpfedpt.py | from flcore.clients.clientpt import clientPT
from flcore.servers.serverbase import Server
from threading import Thread
import time
import torch
import os
import h5py
import copy
import numpy as np
class PFedPT(Server):
def __init__(self, args, times):
super().__init__(args, times)
# select slow clients
self.set_slow_clients()
self.args = args
self.set_clients(args, clientPT)
self.global_model = copy.deepcopy(args.model.base)
self.diff_pro = []
self.clients_diverge = []
print(f"\nJoin ratio / total clients: {self.join_ratio} / {self.num_clients}")
print("Finished creating server and clients.")
# self.load_model()
self.Budget = []
def train(self):
local_acc = []
for i in range(self.global_rounds+1):
s_t = time.time()
self.selected_clients = self.select_clients()
self.send_models()
if i%self.eval_gap == 0:
print(f"\n-------------Round number: {i}-------------")
print("\nEvaluate global model")
self.evaluate()
temp_diff_pro = 0
for client in self.selected_clients:
temp_diff_pro_client = client.train()
temp_diff_pro = temp_diff_pro +temp_diff_pro_client.item()
print("Averaged prompr difference: {:.4f}".format(temp_diff_pro))
self.diff_pro.append(temp_diff_pro)
diverge_clents =0
for new_param, old_param in zip(self.clients[0].model.generator.parameters(), self.clients[1].model.generator.parameters()):
diff_pro = new_param - old_param
diff_pro = torch.where(diff_pro > 0, diff_pro, torch.zeros_like(diff_pro) - diff_pro)
diverge_clents = diverge_clents+torch.sum(diff_pro)
print("0 and 1 clients difference: {:.4f}".format(diverge_clents.item()))
self.clients_diverge.append(diverge_clents.item())
if i%self.eval_gap == 0:
print("\nEvaluate local model")
self.evaluate(acc=local_acc)
# threads = [Thread(target=client.train)
# for client in self.selected_clients]
# [t.start() for t in threads]
# [t.join() for t in threads]
self.receive_models()
self.aggregate_parameters()
self.Budget.append(time.time() - s_t)
print('-'*25, 'time cost', '-'*25, self.Budget[-1])
print("\nBest global accuracy.")
# self.print_(max(self.rs_test_acc), max(
# self.rs_train_acc), min(self.rs_train_loss))
print(max(self.rs_test_acc))
print("\nAverage time cost per round.")
print(sum(self.Budget[1:])/len(self.Budget[1:]))
print("\nBest local accuracy.")
print(max(local_acc))
self.save_results()
self.save_global_model()
self.save_client_model()
def receive_models(self):
assert (len(self.selected_clients) > 0)
active_train_samples = 0
for client in self.selected_clients:
active_train_samples += client.train_samples
self.uploaded_weights = []
self.uploaded_ids = []
self.uploaded_models = []
for client in self.selected_clients:
self.uploaded_weights.append(client.train_samples / active_train_samples)
self.uploaded_ids.append(client.id)
self.uploaded_models.append(copy.deepcopy(client.model.base))
def save_client_model(self):
model_path = os.path.join("models", self.dataset,"client",self.args.arv1+"*"+self.args.arv2+"*"+self.args.arv3+"*"+self.args.arv4+"*"+self.args.arv5+"*"+self.args.arv6 )
if not os.path.exists(model_path):
os.makedirs(model_path)
for c_idx,c in enumerate(self.clients):
model_path_save = os.path.join(model_path, self.algorithm + "_client" +str(c_idx)+ "_" + str(self.args.num_prompt) + "_" + str(self.args.join_ratio) + "_" + str(self.args.num_clients)+ "_" + str(self.args.plocal_steps) + "_" + str(self.args.global_rounds)+ ".pt")
torch.save(c.model, model_path_save)
def save_results(self):
algo = self.dataset + "_" + self.algorithm
result_path = "../results/"+ self.args.arv1+"*"+self.args.arv2+"*"+self.args.arv3+"*"+self.args.arv4+"*"+self.args.arv5+"*"+self.args.arv6 +"/"
if not os.path.exists(result_path):
os.makedirs(result_path)
if (len(self.rs_test_acc)):
algo =algo + "_" + self.goal + "_" + str(self.times)+ "_" + str(self.args.num_prompt) + "_" + str(self.args.join_ratio) + "_" + str(self.args.num_clients)+ "_" + str(self.args.plocal_steps) + "_" + str(self.args.global_rounds)
file_path = result_path + "{}.h5".format(algo)
print("File path: " + file_path)
with h5py.File(file_path, 'w') as hf:
hf.create_dataset('rs_test_acc', data=self.rs_test_acc)
hf.create_dataset('rs_test_acc_std', data=self.rs_test_acc_std)
hf.create_dataset('rs_test_auc', data=self.rs_test_auc)
hf.create_dataset('rs_train_loss', data=self.rs_train_loss)
hf.create_dataset('diff_pro', data=self.diff_pro)
hf.create_dataset('clients_diverge', data=self.clients_diverge)
def evaluate(self, acc=None, loss=None):
stats = self.test_metrics()
stats_train = self.train_metrics()
test_acc = sum(stats[2]) * 1.0 / sum(stats[1])
test_acc2 = sum(stats[4]) * 1.0 / sum(stats[1])
test_auc = sum(stats[3]) * 1.0 / sum(stats[1])
train_loss = sum(stats_train[2]) * 1.0 / sum(stats_train[1])
accs = [a / n for a, n in zip(stats[2], stats[1])]
aucs = [a / n for a, n in zip(stats[3], stats[1])]
if acc == None:
self.rs_test_acc.append(test_acc)
else:
acc.append(test_acc)
self.rs_test_auc.append(test_auc)
self.rs_test_acc_std.append(np.std(accs))
if loss == None:
self.rs_train_loss.append(train_loss)
else:
loss.append(train_loss)
print("Averaged Train Loss: {:.4f}".format(train_loss))
print("Averaged Test Accurancy: {:.4f}".format(test_acc))
print("Averaged Test oral Accurancy: {:.4f}".format(test_acc2))
print("Averaged Test AUC: {:.4f}".format(test_auc))
# self.print_(test_acc, train_acc, train_loss)
print("Std Test Accurancy: {:.4f}".format(np.std(accs)))
print("Std Test AUC: {:.4f}".format(np.std(aucs)))
def test_metrics(self):
num_samples = []
tot_correct = []
tot_correct2 = []
tot_auc = []
for c in self.clients:
ct, ct2, ns, auc = c.test_metrics()
tot_correct.append(ct * 1.0)
tot_correct2.append(ct2 * 1.0)
tot_auc.append(auc * ns)
num_samples.append(ns)
ids = [c.id for c in self.clients]
return ids, num_samples, tot_correct, tot_auc, tot_correct2 | 7,153 | Python | .py | 142 | 39.802817 | 275 | 0.580849 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,752 | serverbase.py | hkgdifyu_pFedPT/system/flcore/servers/serverbase.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.global_rounds = args.global_rounds
self.local_steps = args.local_steps
self.batch_size = args.batch_size
self.learning_rate = args.local_learning_rate
self.global_model = copy.deepcopy(args.model)
self.num_clients = args.num_clients
self.join_ratio = args.join_ratio
self.join_clients = int(self.num_clients * self.join_ratio)
self.algorithm = args.algorithm
self.time_select = args.time_select
self.goal = args.goal
self.time_threthold = args.time_threthold
self.save_folder_name = args.save_folder_name
self.num_prompt = args.num_prompt
self.args = args
self.plocal_steps = args.plocal_steps
self.top_cnt = 100
self.clients = []
self.selected_clients = []
self.train_slow_clients = []
self.send_slow_clients = []
self.uploaded_weights = []
self.uploaded_ids = []
self.uploaded_models = []
self.rs_test_acc = []
self.rs_test_acc_std = []
self.rs_test_auc = []
self.rs_train_loss = []
self.times = times
self.eval_gap = args.eval_gap
self.client_drop_rate = args.client_drop_rate
self.train_slow_rate = args.train_slow_rate
self.send_slow_rate = args.send_slow_rate
def set_clients(self, args, clientObj):
for i, train_slow, send_slow in zip(range(self.num_clients), self.train_slow_clients, self.send_slow_clients):
train_data = read_client_data(self.dataset, i, args,is_train=True)
test_data = read_client_data(self.dataset, i, args,is_train=False)
client = clientObj(args,
id=i,
train_samples=len(train_data),
test_samples=len(test_data),
train_slow=train_slow,
send_slow=send_slow)
self.clients.append(client)
# random select slow clients
def select_slow_clients(self, slow_rate):
slow_clients = [False for i in range(self.num_clients)]
idx = [i for i in range(self.num_clients)]
idx_ = np.random.choice(idx, int(slow_rate * self.num_clients))
for i in idx_:
slow_clients[i] = True
return slow_clients
def set_slow_clients(self):
self.train_slow_clients = self.select_slow_clients(
self.train_slow_rate)
self.send_slow_clients = self.select_slow_clients(
self.send_slow_rate)
def select_clients(self):
selected_clients = list(np.random.choice(self.clients, self.join_clients, replace=False))
return selected_clients
def send_models(self):
assert (len(self.clients) > 0)
for client in self.clients:
client.set_parameters(self.global_model)
def receive_models(self):
assert (len(self.selected_clients) > 0)
self.uploaded_weights = []
tot_samples = 0
self.uploaded_ids = []
self.uploaded_models = []
for client in self.selected_clients:
self.uploaded_weights.append(client.train_samples)
tot_samples += client.train_samples
self.uploaded_ids.append(client.id)
self.uploaded_models.append(client.model)
for i, w in enumerate(self.uploaded_weights):
self.uploaded_weights[i] = w / tot_samples
def aggregate_parameters(self):
assert (len(self.uploaded_models) > 0)
self.global_model = copy.deepcopy(self.uploaded_models[0])
for param in self.global_model.parameters():
param.data.zero_()
for w, client_model in zip(self.uploaded_weights, self.uploaded_models):
self.add_parameters(w, client_model)
def add_parameters(self, w, client_model):
for server_param, client_param in zip(self.global_model.parameters(), client_model.parameters()):
server_param.data += client_param.data.clone() * w
def save_global_model(self):
model_path = os.path.join("models", self.dataset, self.args.arv1+"*"+self.args.arv2+"*"+self.args.arv3+"*"+self.args.arv4+"*"+self.args.arv5+"*"+self.args.arv6)
if not os.path.exists(model_path):
os.makedirs(model_path)
model_path = os.path.join(model_path, self.algorithm + "_server"+ "_" + str(self.num_prompt) + "_" + str(self.join_ratio) + "_" + str(self.num_clients)+ "_" + str(self.plocal_steps) + "_" + str(self.global_rounds) + ".pt")
torch.save(self.global_model, model_path)
def load_model(self):
model_path = os.path.join("models", self.dataset, self.args.arv1+"*"+self.args.arv2+"*"+self.args.arv3+"*"+self.args.arv4+"*"+self.args.arv5+"*"+self.args.arv6)
model_path = os.path.join(model_path, self.algorithm + "_server" + "_" + str(self.num_prompt) + "_" + str(self.join_ratio) + "_" + str(self.num_clients)+ "_" + str(self.plocal_steps) + "_" + str(self.global_rounds) +".pt")
assert (os.path.exists(model_path))
self.global_model = torch.load(model_path)
def model_exists(self):
model_path = os.path.join("models", self.dataset, self.args.arv1+"*"+self.args.arv2+"*"+self.args.arv3+"*"+self.args.arv4+"*"+self.args.arv5+"*"+self.args.arv6)
model_path = os.path.join(model_path, self.algorithm + "_" + str(self.num_prompt) + "_" + str(self.join_ratio) + "_" + str(self.num_clients)+ "_" + str(self.plocal_steps) + "_" + str(self.global_rounds)+".pt")
return os.path.exists(model_path)
def save_results(self):
algo = self.dataset + "_" + self.algorithm
result_path = "../results/"+ self.args.arv1+"*"+self.args.arv2+"*"+self.args.arv3+"*"+self.args.arv4+"*"+self.args.arv5+"*"+self.args.arv6 +"/"
if not os.path.exists(result_path):
os.makedirs(result_path)
if (len(self.rs_test_acc)):
algo = algo + "_" + self.goal + "_" + str(self.times)+ "_" + str(self.num_prompt) + "_" + str(self.join_ratio) + "_" + str(self.num_clients)+ "_" + str(self.plocal_steps) + "_" + str(self.global_rounds)
file_path = result_path + "{}.h5".format(algo)
print("File path: " + file_path)
with h5py.File(file_path, 'w') as hf:
hf.create_dataset('rs_test_acc', data=self.rs_test_acc)
hf.create_dataset('rs_test_acc_std', data=self.rs_test_acc_std)
hf.create_dataset('rs_test_auc', data=self.rs_test_auc)
hf.create_dataset('rs_train_loss', data=self.rs_train_loss)
def save_item(self, item, item_name):
if not os.path.exists(self.save_folder_name):
os.makedirs(self.save_folder_name)
torch.save(item, os.path.join(self.save_folder_name, "server_" + item_name + "_" + str(self.num_prompt) + "_" + str(self.join_ratio) + "_" + str(self.num_clients)+ "_" + str(self.plocal_steps) + "_" + str(self.global_rounds) + ".pt"))
def load_item(self, item_name):
return torch.load(os.path.join(self.save_folder_name, "server_" + item_name + "_" + str(self.num_prompt) + "_" + str(self.join_ratio) + "_" + str(self.num_clients)+ "_" + str(self.plocal_steps) + "_" + str(self.global_rounds) + ".pt"))
def test_metrics(self):
num_samples = []
tot_correct = []
tot_auc = []
for c in self.clients:
ct, ns, auc = c.test_metrics()
tot_correct.append(ct*1.0)
tot_auc.append(auc*ns)
num_samples.append(ns)
ids = [c.id for c in self.clients]
return ids, num_samples, tot_correct, tot_auc
def test_metrics2(self):
num_samples = []
tot_correct = []
tot_auc = []
for c in self.selected_clients:
ct, ns, auc = c.test_metrics()
tot_correct.append(ct*1.0)
tot_auc.append(auc*ns)
num_samples.append(ns)
ids = [c.id for c in self.clients]
return ids, num_samples, tot_correct, tot_auc
def train_metrics(self):
num_samples = []
losses = []
for c in self.clients:
cl, ns = c.train_metrics()
num_samples.append(ns)
losses.append(cl*1.0)
ids = [c.id for c in self.clients]
return ids, num_samples, losses
# evaluate selected clients
def evaluate(self, acc=None, loss=None):
if acc == None:
stats = self.test_metrics()
else:
stats = self.test_metrics()
stats_train = self.train_metrics()
test_acc = sum(stats[2])*1.0 / sum(stats[1])
test_auc = sum(stats[3])*1.0 / sum(stats[1])
train_loss = sum(stats_train[2])*1.0 / sum(stats_train[1])
accs = [a / n for a, n in zip(stats[2], stats[1])]
aucs = [a / n for a, n in zip(stats[3], stats[1])]
if acc == None:
self.rs_test_acc.append(test_acc)
else:
acc.append(test_acc)
self.rs_test_auc.append(test_auc)
self.rs_test_acc_std.append(np.std(accs))
if loss == None:
self.rs_train_loss.append(train_loss)
else:
loss.append(train_loss)
print("Averaged Train Loss: {:.4f}".format(train_loss))
print("Averaged Test Accurancy: {:.4f}".format(test_acc))
print("Averaged Test AUC: {:.4f}".format(test_auc))
# self.print_(test_acc, train_acc, train_loss)
print("Std Test Accurancy: {:.4f}".format(np.std(accs)))
print("Std Test AUC: {:.4f}".format(np.std(aucs)))
def print_(self, test_acc, test_auc, train_loss):
print("Average Test Accurancy: {:.4f}".format(test_acc))
print("Average Test AUC: {:.4f}".format(test_auc))
print("Average Train Loss: {:.4f}".format(train_loss))
def check_done(self, acc_lss, top_cnt=None, div_value=None):
for acc_ls in acc_lss:
if top_cnt != None and div_value != None:
find_top = len(acc_ls) - torch.topk(torch.tensor(acc_ls), 1).indices[0] > top_cnt
find_div = len(acc_ls) > 1 and np.std(acc_ls[-top_cnt:]) < div_value
if find_top and find_div:
pass
else:
return False
elif top_cnt != None:
find_top = len(acc_ls) - torch.topk(torch.tensor(acc_ls), 1).indices[0] > top_cnt
if find_top:
pass
else:
return False
elif div_value != None:
find_div = len(acc_ls) > 1 and np.std(acc_ls[-top_cnt:]) < div_value
if find_div:
pass
else:
return False
else:
raise NotImplementedError
return True
| 11,178 | Python | .py | 222 | 39.572072 | 243 | 0.583571 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,753 | serveravg.py | hkgdifyu_pFedPT/system/flcore/servers/serveravg.py | import time
import torch
from flcore.clients.clientavg import clientAVG
from flcore.servers.serverbase import Server
from threading import Thread
class FedAvg(Server):
def __init__(self, args, times):
super().__init__(args, times)
# select slow clients
self.set_slow_clients()
self.set_clients(args, clientAVG)
print(f"\nJoin ratio / total clients: {self.join_ratio} / {self.num_clients}")
print("Finished creating server and clients.")
# self.load_model()
self.Budget = []
def train(self):
local_acc = []
for i in range(self.global_rounds+1):
s_t = time.time()
self.selected_clients = self.select_clients()
self.send_models()
if i%self.eval_gap == 0:
print(f"\n-------------Round number: {i}-------------")
print("\nEvaluate global model")
self.evaluate()
for client in self.selected_clients:
client.train()
# threads = [Thread(target=client.train)
# for client in self.selected_clients]
# [t.start() for t in threads]
# [t.join() for t in threads]
if i%self.eval_gap == 0:
print("\nEvaluate local model")
self.evaluate(acc=local_acc)
self.receive_models()
self.aggregate_parameters()
self.Budget.append(time.time() - s_t)
print('-'*25, 'time cost', '-'*25, self.Budget[-1])
# i= i+1
#
# s_t = time.time()
# self.selected_clients = self.clients
# self.send_models()
#
# if i % self.eval_gap == 0:
# print(f"\n-------------Round number: {i}-------------")
# print("\nEvaluate global model")
# self.evaluate()
#
# for client in self.selected_clients:
# client.local_steps = 50
# client.train()
#
# # threads = [Thread(target=client.train)
# # for client in self.selected_clients]
# # [t.start() for t in threads]
# # [t.join() for t in threads]
# if i % self.eval_gap == 0:
# print("\nEvaluate local model")
# self.evaluate(acc=local_acc)
# self.receive_models()
# self.aggregate_parameters()
#
# self.Budget.append(time.time() - s_t)
# print('-' * 25, 'time cost', '-' * 25, self.Budget[-1])
print("\nBest global accuracy.")
# self.print_(max(self.rs_test_acc), max(
# self.rs_train_acc), min(self.rs_train_loss))
print(max(self.rs_test_acc))
print("\nBest local accuracy.")
print(max(local_acc))
print("\nAverage time cost per round.")
print(sum(self.Budget[1:])/len(self.Budget[1:]))
self.save_results()
self.save_global_model()
| 2,949 | Python | .py | 75 | 29.466667 | 86 | 0.528691 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,754 | serverrep.cpython-37.pyc | hkgdifyu_pFedPT/system/flcore/servers/__pycache__/serverrep.cpython-37.pyc | B
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2,286,755 | servermtl.cpython-39.pyc | hkgdifyu_pFedPT/system/flcore/servers/__pycache__/servermtl.cpython-39.pyc | a
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Join clients / total clients: z / z%Finished creating server and clients.)ÚsuperÚ__init__ÚlenÚflattenÚ global_modelÚdimÚtorchÚzerosÚ join_clientsr ÚW_globÚonesÚmmÚTÚtoÚomegaÚset_slow_clientsÚ set_clientsr ÚprintÚ num_clients)ÚselfÚargsÚtimesÚIÚir ©Ú __class__© úr/media/sim812/391e55df-b6f2-4fe9-a920-53434a8506fa/lgh/pdept/PFL-Non-IID-master/system/flcore/servers/servermtl.pyr s zFedMTL.__init__c C s¬ t | jd ƒD ]r}| ¡ | _| ¡ || j dkrRtd|› d�ƒ tdƒ | ¡ t| jƒD ]"\}}| | j
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2,286,757 | serverperavg.cpython-38.pyc | hkgdifyu_pFedPT/system/flcore/servers/__pycache__/serverperavg.cpython-38.pyc | U
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| 2,024 | Python | .py | 22 | 90.727273 | 347 | 0.482776 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |
2,286,758 | serverpFedMe.cpython-39.pyc | hkgdifyu_pFedPT/system/flcore/servers/__pycache__/serverpFedMe.cpython-39.pyc | a
f¾`cE ã @ sP d dl Z d dlZd dlZd dlmZ d dlmZ d dlmZ G dd„ deƒZ dS )é N)Ú clientpFedMe)ÚServer)ÚThreadc sL e Zd Z‡ fdd„Zdd„ Zdd„ Zdd„ Zd d
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2,286,759 | serverphp.cpython-39.pyc | hkgdifyu_pFedPT/system/flcore/servers/__pycache__/serverphp.cpython-39.pyc | a
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Join ratio / total clients: z / z%Finished creating server and clients.) ÚsuperÚ__init__Úset_slow_clientsÚ set_clientsr ÚprintÚ
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-------------Round number: z
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2,286,760 | serveravg.cpython-37.pyc | hkgdifyu_pFedPT/system/flcore/servers/__pycache__/serveravg.cpython-37.pyc | B
¿:ccç ã @ sH d dl Z d dlZd dlmZ d dlmZ d dlmZ G dd„ deƒZdS )é N)Ú clientAVG)ÚServer)ÚThreadc s$ e Zd Z‡ fdd„Zdd„ Z‡ ZS )ÚFedAvgc sL t ƒ ||¡ | ¡ | |t¡ td| j› d| j› �ƒ tdƒ g | _d S )Nz
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-------------Round number: z
-------------z
Evaluate global modelz-------------------------z time costéÿÿÿÿz
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$r )r3 r- r? ÚnumpyrB rP Zflcore.clients.clientampr r Úflcore.servers.serverbaser Ú threadingr r r r r r Ú<module> s | 2,696 | Python | .py | 26 | 102.5 | 373 | 0.469861 | hkgdifyu/pFedPT | 8 | 3 | 0 | GPL-2.0 | 9/5/2024, 10:48:09 PM (Europe/Amsterdam) |