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modules.py
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4563 lines (4195 loc) · 246 KB
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import copy
import datetime
import numpy as np
import sys
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import models
from itertools import compress
from config import cfg
from data import make_data_loader, make_batchnorm_stats, FixTransform, MixDataset
from .utils import init_param, make_batchnorm, loss_fn ,info_nce_loss, SimCLR_Loss,elr_loss
from utils import to_device, make_optimizer, collate, to_device
from train_centralDA_target import op_copy
from metrics import Accuracy
from net_utils import set_random_seed
from net_utils import init_multi_cent_psd_label,init_psd_label_shot_icml,init_psd_label_shot_icml_up
from net_utils import EMA_update_multi_feat_cent_with_feat_simi,get_final_centroids
from data import make_dataset_normal
import gc
from utils import save
import json
from sklearn.metrics import silhouette_score
from sklearn.metrics import adjusted_rand_score
import pickle
from scipy.cluster import hierarchy
import numpy as np
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import fcluster
import timm
class Server:
def __init__(self, model):
# if cfg['pretrained_source']:
# print('loading pretrained resnet50 model ')
# path_source = '/home/sampathkoti/Downloads/A-20231219T043936Z-001/A/'
# F = torch.load(path_source + 'source_F.pt')
# B = torch.load(path_source + 'source_B.pt')
# C = torch.load(path_source + 'source_C.pt')
# # print(F.keys())
# # exit()
# # model.backbone_layer.load_state_dict(torch.load(path_source + 'source_F.pt'))
# # model.feat_embed_layer.load_state_dict(torch.load(path_source + 'source_B.pt'))
# # model.class_layer.load_state_dict(torch.load(path_source + 'source_C.pt'))
# model.backbone_layer.load_state_dict(F)
# model.feat_embed_layer.load_state_dict(B)
# model.class_layer.load_state_dict(C)
self.target_domains = len(list(cfg['unsup_doms'].split('-')))
self.num_clusters = None
self.cluster_labels = []
# print(self.target_domains)
# exit()
if cfg['multi_model']:
print('creating multiple models')
self.model_state_dict = {}
for i in range(self.target_domains):
# print(i)
self.model_state_dict[i] = save_model_state_dict(model.state_dict())
self.global_model_state_dict = save_model_state_dict(model.state_dict())
# print(self.model_state_dict.keys())
else:
self.model_state_dict = save_model_state_dict(model.state_dict())
self.global_model_state_dict = save_model_state_dict(model.state_dict())
self.avg_cent = None
self.avg_cent_ = None
self.var_cent = None
# self.decay = 0.9
# self.model_state_dict = save_model_state_dict(model.module.state_dict() if cfg['world_size'] > 1 else model.state_dict())
if 'fmatch' in cfg['loss_mode']:
optimizer = make_optimizer(model.make_sigma_parameters(), 'local')
global_optimizer = make_optimizer(model.make_phi_parameters(), 'global')
else:
optimizer = make_optimizer(model.parameters(), 'local')
global_optimizer = make_optimizer(model.parameters(), 'global')
self.optimizer_state_dict = save_optimizer_state_dict(optimizer.state_dict())
self.global_optimizer_state_dict = save_optimizer_state_dict(global_optimizer.state_dict())
# print(self.model_state_dict.keys())
del model
del optimizer
del global_optimizer
def compute_dist(self,w1_in,w2_in,crit):
if crit == 'mse':
# return torch.mean((w1_in.reshape(-1).detach() - w2_in.reshape(-1).detach())**2)
return torch.norm((w1_in.reshape(-1) - w2_in.reshape(-1)),0.9)
elif crit == 'mae':
return torch.mean(torch.abs(w1_in.reshape(-1).detach() - w2_in.reshape(-1).detach()))
#num = torch.sum((prev_global_p - client_p)**2)
def compute_l2d_ratio(self,prev_g,valid_client,avg_model,crit='mse'):
ll_div_weights = {}
for k, _ in prev_g.named_parameters():
ll_div_weights[k] = []
param_prev_g = {}
for k,v in prev_g.named_parameters():
param_prev_g[k] = v
param_avg = {}
for k,v in avg_model.named_parameters():
param_avg[k] = v
for k, v in prev_g.named_parameters():
parameter_type = k.split('.')[-1]
if 'weight' in parameter_type or 'bias' in parameter_type:
for m in range(len(valid_client)):
num = self.compute_dist(valid_client[m].model_state_dict[k],param_prev_g[k],crit)
den = self.compute_dist(valid_client[m].model_state_dict[k],param_avg[k],crit)
ll_div_weights[k].append(torch.exp(cfg['tau']*(num-den)))
# ll_div_weights[k].append(torch.exp(0.1*num/(den+1e-5)))
#print("ll_div_weights[k]:",ll_div_weights[k])
ll_div_weights[k] = torch.div(torch.Tensor(ll_div_weights[k]),sum(ll_div_weights[k]))
# ll_div_weights[k] = torch.div(torch.Tensor(ll_div_weights[k]),sum(ll_div_weights[k]))
#print("ll_div_weights[k]:",ll_div_weights[k])
return ll_div_weights
def cluster(self,client,epoch,init_model=None):
client_ids =[]
domain_ids = []
feat = []
model = eval('models.{}()'.format(cfg['model_name']))
for client_i in client:
# print(client.client_id,client.domain_id)
client_ids.append(client_i.client_id.item())
domain_ids.append(client_i.domain_id)
model.load_state_dict(client_i.model_state_dict)
feat.append(np.array(model.state_dict()['feat_embed_layer.bn.running_mean']))
# exit()
# print(client_ids)
# print(domain_ids)
# exit()
feat = np.array(feat)
feat = feat/(1e-9+np.linalg.norm(feat,axis=1,keepdims = True))
print(feat.shape)
# if epoch == 1:
# Define the range of cluster numbers to evaluate
min_clusters = 2
max_clusters = 5
# Initialize lists to store silhouette scores
silhouette_scores = []
# Compute hierarchical clustering and silhouette score for each number of clusters
for n_clusters in range(min_clusters, max_clusters + 1):
Z = hierarchy.linkage(feat, method='ward')
cluster_labels = hierarchy.cut_tree(Z, n_clusters=n_clusters).flatten()
silhouette_avg = silhouette_score(feat, cluster_labels)
silhouette_scores.append(silhouette_avg)
Z = hierarchy.linkage(feat, method='ward')
# # Determine the number of clusters
print(silhouette_scores)
print(max(silhouette_scores))
k_ = silhouette_scores.index(max(silhouette_scores)) # Example: Number of clusters
k = list(range(2,6))[k_]
print('number of clusters',k)
self.num_clusters = k
# Assign cluster labels
cluster_labels = fcluster(Z, k, criterion='maxclust')
#####################################################################
print('creating multiple models')
self.model_state_dict_cluster = {}
for i in range(k):
# print(i)
self.model_state_dict_cluster[i] = self.model_state_dict
# print(self.model_state_dict.keys())
# else:
# Z = hierarchy.linkage(feat, method='ward')
# # # Determine the number of clusters
# k = self.num_clusters
# # Assign cluster labels
# cluster_labels = fcluster(Z, k, criterion='maxclust')
self.global_model_state_dict = self.model_state_dict
cluster_labels = list(cluster_labels)
self.cluster_labels = cluster_labels
# Print cluster labels
print("Cluster Labels:", cluster_labels)
print('GT Labels',domain_ids)
# Initialize a dictionary to store indices for each cluster label
indices_by_label = {}
# Iterate over data points and cluster labels
for idx, label in enumerate(cluster_labels):
if label not in indices_by_label:
indices_by_label[label] = []
indices_by_label[label].append(idx)
# Print indices for each cluster label
for label, indices in indices_by_label.items():
print(f"Cluster Label {label}: Indices {indices}")
og_indices_by_label = {}
for idx, label in enumerate(domain_ids):
if label not in og_indices_by_label:
og_indices_by_label[label] = []
og_indices_by_label[label].append(idx)
# Print indices for each cluster label ground truth
for label, indices in og_indices_by_label.items():
print(f"Cluster Label {label} GT: Indices {indices}")
for i, client_i in enumerate(client):
id = client_i.client_id
if id == client_ids[i]:
client_i.cluster_id = int(cluster_labels[i]-1)
del model
# del init_model
gc.collect()
torch.cuda.empty_cache()
def distribute_cluster(self, client,epoch,BN_stats=False, batchnorm_dataset=None):
# model = eval('models.{}().to(cfg["device"])'.format(cfg['model_name']))
if cfg['world_size']==1:
model = eval('models.{}()'.format(cfg['model_name']))
# model = eval('models.{}().to(cfg["device"])'.format(cfg['model_name']))
elif cfg['world_size']>1:
cfg["device"] = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = eval('models.{}()'.format(cfg['model_name']))
model = torch.nn.DataParallel(model,device_ids = [0, 1])
model.to(cfg["device"])
if epoch == 2:
BN_stats = True
else:
BN_stats = False
# model_state_dict = save_model_state_dict(model.module.state_dict() if cfg['world_size'] > 1 else model.state_dict())
for m in range(len(client)):
if client[m].active:
domain_id = client[m].domain_id
cluster_id = client[m].cluster_id
# print(cluster_id,self.model_state_dict_cluster.keys())
print(f'client{client[m].client_id} with domain {domain_id} and cluster id {cluster_id}')
print('cluster labels',self.cluster_labels)
# exit()
model.load_state_dict(self.model_state_dict_cluster[cluster_id])
model_state_dict = save_model_state_dict(model.state_dict())
client[m].model_state_dict = copy.deepcopy(model_state_dict)
model.load_state_dict(self.global_model_state_dict)
model_state_dict = save_model_state_dict(model.state_dict())
client[m].global_model_state_dict = copy.deepcopy(model_state_dict)
# if BN_stats == False:
# print('distributing without bn stats')
# for key in model_state_dict.keys():
# if 'bn' not in key or 'running' not in key:
# client[m].model_state_dict[key].data.copy_(model_state_dict[key])
# elif BN_stats == True:
# print('distributing with bn stats')
# client[m].model_state_dict = copy.deepcopy(model_state_dict)
if cfg['avg_cent']:
if self.avg_cent is not None:
client[m].avg_cent = self.avg_cent
else:
client[m].avg_cent = None
print('Warning:server.avg_cent is None')
del model
# del init_model
gc.collect()
torch.cuda.empty_cache()
return
def distribute_multi(self, client,epoch,BN_stats=False, batchnorm_dataset=None):
# model = eval('models.{}().to(cfg["device"])'.format(cfg['model_name']))
if cfg['world_size']==1:
model = eval('models.{}()'.format(cfg['model_name']))
# model = eval('models.{}().to(cfg["device"])'.format(cfg['model_name']))
elif cfg['world_size']>1:
cfg["device"] = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = eval('models.{}()'.format(cfg['model_name']))
model = torch.nn.DataParallel(model,device_ids = [0, 1])
model.to(cfg["device"])
# model_state_dict = save_model_state_dict(model.module.state_dict() if cfg['world_size'] > 1 else model.state_dict())
if epoch ==1:
BN_stats = True
for m in range(len(client)):
# if client[m].active:
print('distributing to all clients at epch 1')
domain_id = client[m].domain_id
client[m].cluster_id = domain_id
# print(domain_id,self.model_state_dict.keys())
# exit()
model.load_state_dict(self.model_state_dict[domain_id])
model_state_dict = save_model_state_dict(model.state_dict())
client[m].model_state_dict = copy.deepcopy(model_state_dict)
if cfg['global_reg'] == 1:
model.load_state_dict(self.global_model_state_dict)
model_state_dict = save_model_state_dict(model.state_dict())
client[m].global_model_state_dict = copy.deepcopy(model_state_dict)
# if BN_stats == False:
# print('distributing without bn stats')
# for key in model_state_dict.keys():
# if 'bn' not in key or 'running' not in key:
# client[m].model_state_dict[key].data.copy_(model_state_dict[key])
# elif BN_stats == True:
# print('distributing with bn stats')
# client[m].model_state_dict = copy.deepcopy(model_state_dict)
if cfg['avg_cent']:
if self.avg_cent is not None:
client[m].avg_cent = self.avg_cent
else:
client[m].avg_cent = None
print('Warning:server.avg_cent is None')
else:
BN_stats = False
for m in range(len(client)):
if client[m].active:
domain_id = client[m].domain_id
# print(domain_id,self.model_state_dict.keys())
# exit()
model.load_state_dict(self.model_state_dict[domain_id])
model_state_dict = save_model_state_dict(model.state_dict())
# client[m].model_state_dict = copy.deepcopy(model_state_dict)
if BN_stats == False:
print('distributing without bn stats')
for key in model_state_dict.keys():
if 'bn' not in key or 'running' not in key:
client[m].model_state_dict[key].data.copy_(model_state_dict[key])
elif BN_stats == True:
print('distributing with bn stats')
client[m].model_state_dict = copy.deepcopy(model_state_dict)
if cfg['avg_cent']:
if self.avg_cent is not None:
client[m].avg_cent = self.avg_cent
else:
client[m].avg_cent = None
print('Warning:server.avg_cent is None')
del model
# del init_model
gc.collect()
torch.cuda.empty_cache()
return
def distribute(self, client, batchnorm_dataset=None,BN_stats=False):
# model = eval('models.{}().to(cfg["device"])'.format(cfg['model_name']))
if cfg['world_size']==1:
# model = eval('models.{}().to(cfg["device"])'.format(cfg['model_name']))
model = eval('models.{}()'.format(cfg['model_name']))
elif cfg['world_size']>1:
cfg["device"] = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = eval('models.{}()'.format(cfg['model_name']))
model = torch.nn.DataParallel(model,device_ids = [0, 1])
model.to(cfg["device"])
model.load_state_dict(self.model_state_dict)
# if batchnorm_dataset is not None:
# model = make_batchnorm_stats(batchnorm_dataset, model, 'global')
model_state_dict = save_model_state_dict(model.state_dict())
# model_state_dict = save_model_state_dict(model.module.state_dict() if cfg['world_size'] > 1 else model.state_dict())
for m in range(len(client)):
if client[m].active:
if BN_stats == False:
print('distributing without bn stats')
for key in model_state_dict.keys():
if 'bn' not in key or 'running' not in key:
client[m].model_state_dict[key].data.copy_(model_state_dict[key])
elif BN_stats == True:
print('distributing with bn stats')
client[m].model_state_dict = copy.deepcopy(model_state_dict)
if cfg['avg_cent']:
if self.avg_cent is not None:
client[m].avg_cent = self.avg_cent
else:
client[m].avg_cent = None
print('Warning:server.avg_cent is None')
elif BN_stats == True:
# elif BN_stats == True:
print('distributing with bn stats')
client[m].model_state_dict = copy.deepcopy(model_state_dict)
del model
gc.collect()
torch.cuda.empty_cache()
return
def distribute_fix_model(self, client, batchnorm_dataset=None):
# model = eval('models.{}().to(cfg["device"])'.format(cfg['model_name']))
if cfg['world_size']==1:
model = eval('models.{}().to(cfg["device"])'.format(cfg['model_name']))
elif cfg['world_size']>1:
cfg["device"] = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = eval('models.{}()'.format(cfg['model_name']))
model = torch.nn.DataParallel(model,device_ids = [0, 1])
model.to(cfg["device"])
model.load_state_dict(self.model_state_dict,strict= False)
# if batchnorm_dataset is not None:
# model = make_batchnorm_stats(batchnorm_dataset, model, 'global')
model_state_dict = save_model_state_dict(model.state_dict())
# model_state_dict = save_model_state_dict(model.module.state_dict() if cfg['world_size'] > 1 else model.state_dict())
for m in range(len(client)):
client[m].fix_model_state_dict = copy.deepcopy(model_state_dict)
return
def update(self, client):
if ('fmatch' not in cfg['loss_mode'] and cfg['adapt_wt'] == 0 and cfg['with_BN'] == 1):
print('FedAvg with BN params')
with torch.no_grad():
valid_client = [client[i] for i in range(len(client)) if client[i].active]
if len(valid_client) > 0:
# model = eval('models.{}()'.format(cfg['model_name']))
if cfg['world_size']==1:
model = eval('models.{}()'.format(cfg['model_name']))
elif cfg['world_size']>1:
cfg["device"] = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = eval('models.{}()'.format(cfg['model_name']))
model = torch.nn.DataParallel(model,device_ids = [0, 1])
model.to(cfg["device"])
# model.load_state_dict(self.model_state_dict)
model.load_state_dict(self.model_state_dict)
global_optimizer = make_optimizer(model.parameters(), 'global')
global_optimizer.load_state_dict(self.global_optimizer_state_dict)
global_optimizer.zero_grad()
weight = torch.ones(len(valid_client))
# weight = weight / weight.sum()
if cfg['wt_avg']:
for i in range(len(valid_client)):
weight[i] = valid_client[i].data_len
# print(weight.sum())
weight = weight / weight.sum()
# # Store the averaged batchnorm parameters
# bn_parameters = {k: None for k, v in model.named_parameters() if isinstance(v, torch.nn.BatchNorm2d)}
# print()
for k, v in model.named_parameters():
isBatchNorm = True if '.bn' in k else False
parameter_type = k.split('.')[-1]
# print(f'{k} with parameter type {parameter_type},is batchnorm {isBatchNorm}')
if 'weight' in parameter_type or 'bias' in parameter_type:
tmp_v = v.data.new_zeros(v.size())
for m in range(len(valid_client)):
# print(valid_client[m].model_state_dict.keys())
if cfg['world_size']==1:
tmp_v += weight[m] * valid_client[m].model_state_dict[k]
elif cfg['world_size']>1:
tmp_v += weight[m] * valid_client[m].model_state_dict[k].to(cfg["device"])
v.grad = (v.data - tmp_v).detach()
global_optimizer.step()
self.global_optimizer_state_dict = save_optimizer_state_dict(global_optimizer.state_dict())
self.model_state_dict = save_model_state_dict(model.state_dict())
# self.model_state_dict = save_model_state_dict(model.module.state_dict() if cfg['world_size'] > 1 else model.state_dict())
elif ('fmatch' not in cfg['loss_mode'] and cfg['adapt_wt'] == 0 and cfg['with_BN'] == 0):
print('FedAvg with out BN params')
with torch.no_grad():
valid_client = [client[i] for i in range(len(client)) if client[i].active]
if len(valid_client) > 0:
# model = eval('models.{}()'.format(cfg['model_name']))
if cfg['world_size']==1:
model = eval('models.{}()'.format(cfg['model_name']))
elif cfg['world_size']>1:
cfg["device"] = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = eval('models.{}()'.format(cfg['model_name']))
model = torch.nn.DataParallel(model,device_ids = [0, 1])
model.to(cfg["device"])
# model.load_state_dict(self.model_state_dict)
model.load_state_dict(self.model_state_dict)
global_optimizer = make_optimizer(model.parameters(), 'global')
global_optimizer.load_state_dict(self.global_optimizer_state_dict)
global_optimizer.zero_grad()
weight = torch.ones(len(valid_client))
# weight = weight / weight.sum()
if cfg['wt_avg']:
for i in range(len(valid_client)):
weight[i] = valid_client[i].data_len
# print(weight.sum())
weight = weight / weight.sum()
# # Store the averaged batchnorm parameters
# bn_parameters = {k: None for k, v in model.named_parameters() if isinstance(v, torch.nn.BatchNorm2d)}
for k, v in model.named_parameters():
isBatchNorm = True if '.bn' in k else False
parameter_type = k.split('.')[-1]
# print(f'{k} with parameter type {parameter_type},is batchnorm {isBatchNorm}')
if not isBatchNorm and ('weight' in parameter_type or 'bias' in parameter_type):
print(f'{k} with parameter type {parameter_type},is batchnorm {isBatchNorm}')
tmp_v = v.data.new_zeros(v.size())
for m in range(len(valid_client)):
if cfg['world_size']==1:
tmp_v += weight[m] * valid_client[m].model_state_dict[k]
elif cfg['world_size']>1:
tmp_v += weight[m] * valid_client[m].model_state_dict[k].to(cfg["device"])
v.grad = (v.data - tmp_v).detach()
global_optimizer.step()
self.global_optimizer_state_dict = save_optimizer_state_dict(global_optimizer.state_dict())
self.model_state_dict = save_model_state_dict(model.state_dict())
# self.model_state_dict = save_model_state_dict(model.module.state_dict() if cfg['world_size'] > 1 else model.state_dict())
elif ('fmatch' in cfg['loss_mode'] and cfg['adapt_wt'] == 0):
with torch.no_grad():
valid_client = [client[i] for i in range(len(client)) if client[i].active]
if len(valid_client) > 0:
model = eval('models.{}()'.format(cfg['model_name']))
model.load_state_dict(self.model_state_dict)
global_optimizer = make_optimizer(model.make_phi_parameters(), 'global')
global_optimizer.load_state_dict(self.global_optimizer_state_dict)
global_optimizer.zero_grad()
weight = torch.ones(len(valid_client))
# weight = weight / weight.sum()
if cfg['wt_avg']:
for i in range(len(valid_client)):
weight[i] = valid_client[i].data_len
# print(weight.sum())
weight = weight / weight.sum()
for k, v in model.named_parameters():
# print(k)
parameter_type = k.split('.')[-1]
if 'weight' in parameter_type or 'bias' in parameter_type:
tmp_v = v.data.new_zeros(v.size())
for m in range(len(valid_client)):
tmp_v += weight[m] * valid_client[m].model_state_dict[k]
v.grad = (v.data - tmp_v).detach()
global_optimizer.step()
self.global_optimizer_state_dict = save_optimizer_state_dict(global_optimizer.state_dict())
# self.model_state_dict = save_model_state_dict(model.state_dict())
self.model_state_dict = save_model_state_dict(model.module.state_dict() if cfg['world_size'] > 1 else model.state_dict())
elif cfg['adapt_wt'] == 1:
print('dynamic aggregation')
with torch.no_grad():
valid_client = [client[i] for i in range(len(client)) if client[i].active]
if len(valid_client) > 0:
model = eval('models.{}()'.format(cfg['model_name']))
model.load_state_dict(self.model_state_dict)
prev_model = copy.deepcopy(model)
global_optimizer = make_optimizer(model.parameters(), 'global')
global_optimizer.load_state_dict(self.global_optimizer_state_dict)
global_optimizer.zero_grad()
weight = torch.ones(len(valid_client))
# weight = weight / weight.sum()
if cfg['wt_avg']:
for i in range(len(valid_client)):
weight[i] = valid_client[i].data_len
# print(weight.sum())
weight = weight / weight.sum()
for k, v in model.named_parameters():
parameter_type = k.split('.')[-1]
if 'weight' in parameter_type or 'bias' in parameter_type:
tmp_v = v.data.new_zeros(v.size())
for m in range(len(valid_client)):
tmp_v += weight[m] * valid_client[m].model_state_dict[k]
v.grad = (v.data - tmp_v).detach()
global_optimizer.step()
self.global_optimizer_state_dict = save_optimizer_state_dict(global_optimizer.state_dict())
self.model_state_dict = save_model_state_dict(model.state_dict())
avg_model = copy.deepcopy(model)
###### compute the adaptive weights ######
weight_dict = self.compute_l2d_ratio(prev_model,valid_client,avg_model,'mae')
# weight_dict = self.compute_l2d_ratio(prev_model,valid_client,avg_model,'mse')
global_optimizer = make_optimizer(prev_model.parameters(), 'global')
global_optimizer.load_state_dict(self.global_optimizer_state_dict)
global_optimizer.zero_grad()
for k, v in model.named_parameters():
parameter_type = k.split('.')[-1]
if 'weight' in parameter_type or 'bias' in parameter_type:
tmp_v = v.data.new_zeros(v.size())
for m in range(len(valid_client)):
tmp_v += weight_dict[k][m] * valid_client[m].model_state_dict[k]
v.grad = (v.data - tmp_v).detach()
global_optimizer.step()
self.global_optimizer_state_dict = save_optimizer_state_dict(global_optimizer.state_dict())
self.model_state_dict = save_model_state_dict(model.state_dict())
else:
raise ValueError('Not valid loss mode')
# for i in range(len(client)):
# client[i].active = False
# return
if cfg['avg_cent'] == 1:
with torch.no_grad():
count=0
for i in range(len(client)):
# print(i)
# print(self.avg_cent,client[i].cent)
if client[i].active == True and client[i].cent is not None:
if count==0:
self.avg_cent_=client[i].cent/(1e-9+client[i].var_cent)
count+=1
else:
self.avg_cent_+=client[i].cent/(1e-9+client[i].var_cent)
elif client[i].active == True and client[i].cent is None:
print('Warning:client centntroid is None')
valid_client = [client[i] for i in range(len(client)) if client[i].active]
# sum_var = torch.zeros_like()
for i in range(len(valid_client)):
if i == 0:
sum_var = torch.zeros_like(valid_client[i].var_cent)
# prod_var = torch.zeros_like(valid_client[i].var_cent)
sum_var += 1.0/(1e-9+valid_client[i].var_cent)
# sum_var+= valid_client[i].var_cent
# prod_var*= valid_client[i].var_cent
# sum_var = sum_var/(1e-9+prod_var)
# print(sum_var)
if self.avg_cent_ is not None:
print('averaging centroids')
# exit()
self.avg_cent_=self.avg_cent_/(1e-9+sum_var)
# self.avg_cent_=self.avg_cent_/len(valid_client)
if self.avg_cent is None:
self.avg_cent = self.avg_cent_
# self.avg_cent = cfg['decay']*self.avg_cent+(1-cfg['decay'])*self.avg_cent_
if cfg['save_cent'] == 1:
with torch.no_grad():
# count=0
# cent_list=[]
cent_info = {}
for i in range(len(client)):
# print(client[i].client_id.item())
# print(client[i].domain)
# print(client[i].domain_id)
cent_info[client[i].client_id.item()] = [client[i].domain_id,client[i].domain,client[i].cent.cpu(),client[i].var_cent.cpu()]
print('saving_centroids')
save(cent_info, './output/cent_info10_{}.pt'.format(cfg['cent_log']))
cfg['cent_log']+=1
# print(cent_info)
# exit()
# print('avg_cent',self.avg_cent.shape)
# # print(torch.isnan(self.avg_cent).any())
# exit()
return
def update_multi(self, client):
if ('fmatch' not in cfg['loss_mode'] and cfg['adapt_wt'] == 0 and cfg['with_BN'] == 1):
print('FedAvg with BN params')
with torch.no_grad():
for d in range(self.target_domains):
valid_client = [client[i] for i in range(len(client))
if (client[i].active and client[i].domain_id==d) ]
# for client in valid_client:
# print('domain:',client)
if len(valid_client) > 0:
# model = eval('models.{}()'.format(cfg['model_name']))
if cfg['world_size']==1:
model = eval('models.{}()'.format(cfg['model_name']))
elif cfg['world_size']>1:
cfg["device"] = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = eval('models.{}()'.format(cfg['model_name']))
model = torch.nn.DataParallel(model,device_ids = [0, 1])
model.to(cfg["device"])
# model.load_state_dict(self.model_state_dict)
model.load_state_dict(self.model_state_dict[d])
global_optimizer = make_optimizer(model.parameters(), 'global')
global_optimizer.load_state_dict(self.global_optimizer_state_dict)
global_optimizer.zero_grad()
weight = torch.ones(len(valid_client))
# weight = weight / weight.sum()
if cfg['wt_avg']:
for i in range(len(valid_client)):
weight[i] = valid_client[i].data_len
# print(weight.sum())
weight = weight / weight.sum()
# # Store the averaged batchnorm parameters
# bn_parameters = {k: None for k, v in model.named_parameters() if isinstance(v, torch.nn.BatchNorm2d)}
# print()
for m in range(len(valid_client)):
# print(valid_client[m].model_state_dict.keys())
print(f'domain:{valid_client[m].domain},id:{valid_client[m].domain_id},d:{d}')
for k, v in model.named_parameters():
isBatchNorm = True if '.bn' in k else False
parameter_type = k.split('.')[-1]
# print(f'{k} with parameter type {parameter_type},is batchnorm {isBatchNorm}')
if 'weight' in parameter_type or 'bias' in parameter_type:
tmp_v = v.data.new_zeros(v.size())
for m in range(len(valid_client)):
# print(valid_client[m].model_state_dict.keys())
# print(f'domain:{valid_client[m].domain},id:{valid_client[m].domain_id}')
if cfg['world_size']==1:
tmp_v += weight[m] * valid_client[m].model_state_dict[k]
elif cfg['world_size']>1:
tmp_v += weight[m] * valid_client[m].model_state_dict[k].to(cfg["device"])
v.grad = (v.data - tmp_v).detach()
global_optimizer.step()
self.global_optimizer_state_dict = save_optimizer_state_dict(global_optimizer.state_dict())
self.model_state_dict[d] = save_model_state_dict(model.state_dict())
# self.model_state_dict = save_model_state_dict(model.module.state_dict() if cfg['world_size'] > 1 else model.state_dict())
elif ('fmatch' not in cfg['loss_mode'] and cfg['adapt_wt'] == 0 and cfg['with_BN'] == 0):
print('FedAvg with out BN params')
with torch.no_grad():
for d in range(self.target_domains):
valid_client = [client[i] for i in range(len(client))
if (client[i].active and client[i].domain_id == d)]
if len(valid_client) > 0:
# model = eval('models.{}()'.format(cfg['model_name']))
if cfg['world_size']==1:
model = eval('models.{}()'.format(cfg['model_name']))
elif cfg['world_size']>1:
cfg["device"] = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = eval('models.{}()'.format(cfg['model_name']))
model = torch.nn.DataParallel(model,device_ids = [0, 1])
model.to(cfg["device"])
# model.load_state_dict(self.model_state_dict)
model.load_state_dict(self.model_state_dict[d])
global_optimizer = make_optimizer(model.parameters(), 'global')
global_optimizer.load_state_dict(self.global_optimizer_state_dict)
global_optimizer.zero_grad()
weight = torch.ones(len(valid_client))
# weight = weight / weight.sum()
if cfg['wt_avg']:
for i in range(len(valid_client)):
weight[i] = valid_client[i].data_len
# print(weight.sum())
weight = weight / weight.sum()
# # Store the averaged batchnorm parameters
# bn_parameters = {k: None for k, v in model.named_parameters() if isinstance(v, torch.nn.BatchNorm2d)}
for k, v in model.named_parameters():
isBatchNorm = True if '.bn' in k else False
parameter_type = k.split('.')[-1]
# print(f'{k} with parameter type {parameter_type},is batchnorm {isBatchNorm}')
if not isBatchNorm and ('weight' in parameter_type or 'bias' in parameter_type):
print(f'{k} with parameter type {parameter_type},is batchnorm {isBatchNorm}')
tmp_v = v.data.new_zeros(v.size())
for m in range(len(valid_client)):
if cfg['world_size']==1:
tmp_v += weight[m] * valid_client[m].model_state_dict[k]
elif cfg['world_size']>1:
tmp_v += weight[m] * valid_client[m].model_state_dict[k].to(cfg["device"])
v.grad = (v.data - tmp_v).detach()
global_optimizer.step()
self.global_optimizer_state_dict = save_optimizer_state_dict(global_optimizer.state_dict())
self.model_state_dict[d] = save_model_state_dict(model.state_dict())
# self.model_state_dict = save_model_state_dict(model.module.state_dict() if cfg['world_size'] > 1 else model.state_dict())
elif ('fmatch' in cfg['loss_mode'] and cfg['adapt_wt'] == 0):
with torch.no_grad():
for d in range(self.target_domains):
valid_client = [client[i] for i in range(len(client))
if (client[i].active and client[i].domain_id == d)]
if len(valid_client) > 0:
model = eval('models.{}()'.format(cfg['model_name']))
model.load_state_dict(self.model_state_dict[d])
global_optimizer = make_optimizer(model.make_phi_parameters(), 'global')
global_optimizer.load_state_dict(self.global_optimizer_state_dict)
global_optimizer.zero_grad()
weight = torch.ones(len(valid_client))
# weight = weight / weight.sum()
if cfg['wt_avg']:
for i in range(len(valid_client)):
weight[i] = valid_client[i].data_len
# print(weight.sum())
weight = weight / weight.sum()
for k, v in model.named_parameters():
# print(k)
parameter_type = k.split('.')[-1]
if 'weight' in parameter_type or 'bias' in parameter_type:
tmp_v = v.data.new_zeros(v.size())
for m in range(len(valid_client)):
tmp_v += weight[m] * valid_client[m].model_state_dict[k]
v.grad = (v.data - tmp_v).detach()
global_optimizer.step()
self.global_optimizer_state_dict = save_optimizer_state_dict(global_optimizer.state_dict())
# self.model_state_dict = save_model_state_dict(model.state_dict())
self.model_state_dict[d] = save_model_state_dict(model.module.state_dict() if cfg['world_size'] > 1 else model.state_dict())
elif cfg['adapt_wt'] == 1:
print('dynamic aggregation')
with torch.no_grad():
valid_client = [client[i] for i in range(len(client)) if client[i].active]
if len(valid_client) > 0:
model = eval('models.{}()'.format(cfg['model_name']))
model.load_state_dict(self.model_state_dict)
prev_model = copy.deepcopy(model)
global_optimizer = make_optimizer(model.parameters(), 'global')
global_optimizer.load_state_dict(self.global_optimizer_state_dict)
global_optimizer.zero_grad()
weight = torch.ones(len(valid_client))
# weight = weight / weight.sum()
if cfg['wt_avg']:
for i in range(len(valid_client)):
weight[i] = valid_client[i].data_len
# print(weight.sum())
weight = weight / weight.sum()
for k, v in model.named_parameters():
parameter_type = k.split('.')[-1]
if 'weight' in parameter_type or 'bias' in parameter_type:
tmp_v = v.data.new_zeros(v.size())
for m in range(len(valid_client)):
tmp_v += weight[m] * valid_client[m].model_state_dict[k]
v.grad = (v.data - tmp_v).detach()
global_optimizer.step()
self.global_optimizer_state_dict = save_optimizer_state_dict(global_optimizer.state_dict())
self.model_state_dict = save_model_state_dict(model.state_dict())
avg_model = copy.deepcopy(model)
###### compute the adaptive weights ######
weight_dict = self.compute_l2d_ratio(prev_model,valid_client,avg_model,'mae')
# weight_dict = self.compute_l2d_ratio(prev_model,valid_client,avg_model,'mse')
global_optimizer = make_optimizer(prev_model.parameters(), 'global')
global_optimizer.load_state_dict(self.global_optimizer_state_dict)
global_optimizer.zero_grad()
for k, v in model.named_parameters():
parameter_type = k.split('.')[-1]
if 'weight' in parameter_type or 'bias' in parameter_type:
tmp_v = v.data.new_zeros(v.size())
for m in range(len(valid_client)):
tmp_v += weight_dict[k][m] * valid_client[m].model_state_dict[k]
v.grad = (v.data - tmp_v).detach()
global_optimizer.step()
self.global_optimizer_state_dict = save_optimizer_state_dict(global_optimizer.state_dict())
self.model_state_dict = save_model_state_dict(model.state_dict())
else:
raise ValueError('Not valid loss mode')
if cfg['avg_cent'] == 1:
count=0
for i in range(len(client)):
# print(i)
# print(self.avg_cent,client[i].cent)
if client[i].active == True and client[i].cent is not None:
if count==0:
self.avg_cent_=client[i].cent
count+=1
else:
self.avg_cent_+=client[i].cent
elif client[i].active == True and client[i].cent is None:
print('Warning:client centntroid is None')
if self.avg_cent_ is not None:
print('averaging centroids')
# exit()
self.avg_cent_=self.avg_cent_/len(client)
if self.avg_cent is None:
self.avg_cent = self.avg_cent_
self.avg_cent = cfg['decay']*self.avg_cent+(1-cfg['decay'])*self.avg_cent_
# for i in range(len(client)):
# client[i].active = False
del model
# del init_model
gc.collect()
torch.cuda.empty_cache()
return
def update_global_model(self,client):
if ('fmatch' not in cfg['loss_mode'] and cfg['adapt_wt'] == 0 and cfg['with_BN'] == 1):
print('FedAvg with BN params for global model')
with torch.no_grad():
valid_client = [client[i] for i in range(len(client)) if client[i].active]
if len(valid_client) > 0:
# model = eval('models.{}()'.format(cfg['model_name']))
if cfg['world_size']==1:
model = eval('models.{}()'.format(cfg['model_name']))
elif cfg['world_size']>1:
cfg["device"] = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = eval('models.{}()'.format(cfg['model_name']))
model = torch.nn.DataParallel(model,device_ids = [0, 1])
model.to(cfg["device"])
# model.load_state_dict(self.model_state_dict)
model.load_state_dict(self.global_model_state_dict)
global_optimizer = make_optimizer(model.parameters(), 'global')
global_optimizer.load_state_dict(self.global_optimizer_state_dict)
global_optimizer.zero_grad()
weight = torch.ones(len(valid_client))
# weight = weight / weight.sum()
if cfg['wt_avg']:
for i in range(len(valid_client)):
weight[i] = valid_client[i].data_len
# print(weight.sum())
weight = weight / weight.sum()
# # Store the averaged batchnorm parameters
# bn_parameters = {k: None for k, v in model.named_parameters() if isinstance(v, torch.nn.BatchNorm2d)}
# print()
for k, v in model.named_parameters():
isBatchNorm = True if '.bn' in k else False
parameter_type = k.split('.')[-1]
# print(f'{k} with parameter type {parameter_type},is batchnorm {isBatchNorm}')
if 'weight' in parameter_type or 'bias' in parameter_type:
tmp_v = v.data.new_zeros(v.size())
for m in range(len(valid_client)):
# print(valid_client[m].model_state_dict.keys())
if cfg['world_size']==1:
tmp_v += weight[m] * valid_client[m].model_state_dict[k]
elif cfg['world_size']>1:
tmp_v += weight[m] * valid_client[m].model_state_dict[k].to(cfg["device"])
v.grad = (v.data - tmp_v).detach()
global_optimizer.step()
self.global_optimizer_state_dict = save_optimizer_state_dict(global_optimizer.state_dict())
self.global_model_state_dict = save_model_state_dict(model.state_dict())
# weight = torch.ones(self.num_clusters)
# weight = weight / weight.sum()
# with torch.no_grad():
# for d in range(self.num_clusters):
# for k, v in model.named_parameters():
# isBatchNorm = True if '.bn' in k else False
# parameter_type = k.split('.')[-1]
# # print(f'{k} with parameter type {parameter_type},is batchnorm {isBatchNorm}')
# if 'weight' in parameter_type or 'bias' in parameter_type:
# tmp_v = v.data.new_zeros(v.size())
# for m in range(len(valid_client)):
# # print(valid_client[m].model_state_dict.keys())
# # print(f'domain:{valid_client[m].domain},id:{valid_client[m].domain_id}')
# if cfg['world_size']==1:
# tmp_v += weight[m] * valid_client[m].model_state_dict[k]
# elif cfg['world_size']>1:
# tmp_v += weight[m] * valid_client[m].model_state_dict[k].to(cfg["device"])
# v.grad = (v.data - tmp_v).detach()
# global_optimizer.step()
# self.global_optimizer_state_dict = save_optimizer_state_dict(global_optimizer.state_dict())
# self.model_state_dict_cluster[d] = save_model_state_dict(model.state_dict())
def update_cluster(self, client):
if ('fmatch' not in cfg['loss_mode'] and cfg['adapt_wt'] == 0 and cfg['with_BN'] == 1):
print('FedAvg with BN params')
with torch.no_grad():
for d in range(self.num_clusters):
print(f'FedAvg for cluster {d}')
valid_client = [client[i] for i in range(len(client))
if (client[i].active and client[i].cluster_id==d) ]
# for client in valid_client:
# print('domain:',client)
if len(valid_client) > 0:
# model = eval('models.{}()'.format(cfg['model_name']))
if cfg['world_size']==1:
model = eval('models.{}()'.format(cfg['model_name']))
elif cfg['world_size']>1:
cfg["device"] = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = eval('models.{}()'.format(cfg['model_name']))
model = torch.nn.DataParallel(model,device_ids = [0, 1])
model.to(cfg["device"])
# model.load_state_dict(self.model_state_dict)
model.load_state_dict(self.model_state_dict_cluster[d])
global_optimizer = make_optimizer(model.parameters(), 'global')
global_optimizer.load_state_dict(self.global_optimizer_state_dict)
global_optimizer.zero_grad()
weight = torch.ones(len(valid_client))
# weight = weight / weight.sum()
if cfg['wt_avg']:
for i in range(len(valid_client)):
weight[i] = valid_client[i].data_len
# print(weight.sum())
weight = weight / weight.sum()
# # Store the averaged batchnorm parameters
# bn_parameters = {k: None for k, v in model.named_parameters() if isinstance(v, torch.nn.BatchNorm2d)}
# print()
for m in range(len(valid_client)):
# print(valid_client[m].model_state_dict.keys())
print(f'domain:{valid_client[m].domain},cluster id:{valid_client[m].cluster_id},client id {valid_client[m].client_id}')
for k, v in model.named_parameters():
isBatchNorm = True if '.bn' in k else False
parameter_type = k.split('.')[-1]
# print(f'{k} with parameter type {parameter_type},is batchnorm {isBatchNorm}')
if 'weight' in parameter_type or 'bias' in parameter_type: