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train.py
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train.py
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import os
import argparse
import re
import glob
import numpy as np
import scipy.io as sio
from vis_tools import Visualizer
import torch
import torch.nn as nn
import torch.optim as optim
from models import hyperfed_LEARN
import copy
from datasets import trainset_loader
from datasets import testset_loader
from torch.utils.data import DataLoader
from torch.autograd import Variable
# from skimage.metrics import peak_signal_noise_ratio as compare_psnr
# from skimage.metrics import structural_similarity as compare_ssim
# from skimage.measure import compare_psnr
# from skimage.measure import compare_ssim
import time
parser = argparse.ArgumentParser()
###PDF paras
#parser.add_argument("--epochs", type=int, default=200, help="number of epochs of training")
parser.add_argument("--batch_size", type=int, default=2, help="size of the batches")
parser.add_argument("--lr", type=float, default=1e-4, help="adam: learning rate")
parser.add_argument("--n_block", type=int, default=50)
parser.add_argument("--n_cpu", type=int, default=4)
parser.add_argument("--model_save_path", type=str, default="saved_models/1st")
parser.add_argument('--checkpoint_interval', type=int, default=1000000)
###federated paras
parser.add_argument("--num_clients", type = int, default = 1,help='Number of local clients')
parser.add_argument("--communication", type = int, default = 1000, help = 'Number of communications')
parser.add_argument("--epochs", type=int, default=1000, help="Number of local training")
parser.add_argument("--mode", type=str, default='hyperfed')
parser.add_argument("--mu", type=float, default=1e-6, help="the weight of fedprox")
opt = parser.parse_args()
cuda = True if torch.cuda.is_available() else False
# visdom.Visdom(use_incoming_socket=False)
train_vis = Visualizer(env='ass')
def communication(opt, server_model, models, client_weights):
with torch.no_grad():
if opt.mode.lower() == 'hyperfed':
for key in server_model.state_dict().keys():
## if 'weight_fed' not in key and 'MLP' not in key :
# if 'keys' not in key:
if 'Hyper' not in key:
temp = torch.zeros_like(server_model.state_dict()[key], dtype=torch.float32)
for client_idx in range(len(client_weights)):
temp += client_weights[client_idx] * models[client_idx].state_dict()[key]
server_model.state_dict()[key].data.copy_(temp)
for client_idx in range(len(client_weights)):
models[client_idx].state_dict()[key].data.copy_(server_model.state_dict()[key])
else:
for key in server_model.state_dict().keys():
# num_batches_tracked is a non trainable LongTensor and
# num_batches_tracked are the same for all clients for the given datasets
if 'num_batches_tracked' in key:
server_model.state_dict()[key].data.copy_(models[0].state_dict()[key])
else:
temp = torch.zeros_like(server_model.state_dict()[key])
for client_idx in range(len(client_weights)):
temp += client_weights[client_idx] * models[client_idx].state_dict()[key]
server_model.state_dict()[key].data.copy_(temp)
for client_idx in range(len(client_weights)):
models[client_idx].state_dict()[key].data.copy_(server_model.state_dict()[key])
return server_model, models
def my_collate(batch):
input_data = torch.stack([item[0] for item in batch], 0)
label_data = torch.stack([item[1] for item in batch], 0)
prj_data = [item[2] for item in batch]
option = torch.stack([item[3] for item in batch], 0)
feature = torch.stack([item[4] for item in batch], 0)
return input_data, label_data, prj_data, option, feature
def Dataset():
src_dataset_1 = DataLoader(trainset_loader("../dataset/meta_learning/train2/geometry_1"),
batch_size=opt.batch_size, shuffle=True, num_workers=opt.n_cpu, collate_fn=my_collate)
src_dataset_2 = DataLoader(trainset_loader("../dataset/meta_learning/train2/geometry_2"),
batch_size=opt.batch_size, shuffle=True, num_workers=opt.n_cpu, collate_fn=my_collate)
src_dataset_3 = DataLoader(trainset_loader("../dataset/meta_learning/train2/geometry_3"),
batch_size=opt.batch_size, shuffle=True, num_workers=opt.n_cpu, collate_fn=my_collate)
src_dataset_4 = DataLoader(trainset_loader("../dataset/meta_learning/train2/geometry_4"),
batch_size=opt.batch_size, shuffle=True, num_workers=opt.n_cpu, collate_fn=my_collate)
src_dataset_5 = DataLoader(trainset_loader("../dataset/meta_learning/train2/geometry_5"),
batch_size=opt.batch_size, shuffle=True, num_workers=opt.n_cpu, collate_fn=my_collate)
dataloaders = []
dataloaders.append(src_dataset_1)
dataloaders.append(src_dataset_2)
dataloaders.append(src_dataset_3)
dataloaders.append(src_dataset_4)
dataloaders.append(src_dataset_5)
return dataloaders
class net():
def __init__(self):
# self.model = fed_model.Learn(opt.n_block)
self.loss = nn.MSELoss()
self.path = opt.model_save_path
self.train_datas = Dataset()
self.start = 0
self.epoch = opt.epochs
self.com = opt.communication
self.client_num = opt.num_clients
self.server_model = fed_model.Learn(opt.n_block)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.server_model.to(device)
self.models = [copy.deepcopy(self.server_model) for idx in range(self.client_num)]
self.check_saved_model()
#self.optimizer = optim.Adam(self.models[0].parameters(), lr=opt.lr, weight_decay=1e-8)
self.optimizers = [torch.optim.Adam(self.models[idx].parameters(), lr=opt.lr, weight_decay=1e-8) for idx in
range(self.client_num)]
def check_saved_model(self):
if not os.path.exists(self.path):
os.makedirs(self.path)
self.initialize_weights()
else:
model_list = glob.glob(self.path + '/model_commu_*.pth')
if len(model_list) == 0:
self.initialize_weights()
else:
last_epoch = 0
for model in model_list:
epoch_num = int(re.findall(r'model_commu_(-?[0-9]\d*).pth', model)[0])
if epoch_num > last_epoch:
last_epoch = epoch_num
self.start = last_epoch
self.server_model.load_state_dict(torch.load(
'%s/model_commu_%04d.pth' % (self.path, last_epoch), map_location='cpu'))
for wk_iter in range(self.client_num):
self.models[wk_iter].load_state_dict(torch.load(
'%s/model_worker_id(%04d)_commu_%04d.pth' % (self.path, wk_iter, last_epoch), map_location='cpu'))
#print(wk_iter)
def displaywin(self, img, low=0.42, high=0.62):
img[img<low] = low
img[img>high] = high
img = (img - low)/(high - low) * 255
return img
def initialize_weights(self):
for module in self.server_model.modules():
if isinstance(module, fed_model.prj_module):
nn.init.normal_(module.weight_fed, mean=0.02, std=0.001)
if isinstance(module, nn.Conv2d):
nn.init.normal_(module.weight, mean=0.0, std=0.01)
if module.bias is not None:
module.bias.data.zero_()
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=0.001)
if isinstance(module, nn.BatchNorm2d):
module.weight.data.fill_(1)
module.bias.data.zero_()
def train(self):
# self.model.train(mode=True)
for com_iter in range(self.start, self.com):
for i_wkr in range(self.client_num):
for epoch in range(self.epoch):
for batch_index, data in enumerate(self.train_datas[i_wkr]):
#print('456')
input_data, label_data, prj_data, options, feature_vec = data
temp = []
if cuda:
input_data = input_data.cuda()
label_data = label_data.cuda()
options = options.cuda()
feature_vec = feature_vec.cuda()
for i in range(len(prj_data)):
temp.append(torch.FloatTensor(prj_data[i]).cuda())
prj_data = temp
self.optimizers[i_wkr].zero_grad()
#self.optimizer.zero_grad()
output = self.models[i_wkr](input_data, prj_data, options, feature_vec)
loss = self.loss(output, label_data)
loss.backward()
self.optimizers[i_wkr].step()
#self.optimizer.step()
print(
"Com Round: %d | Worker id: %d | [Epoch %d/%d] [Batch %d/%d]: [loss: %f]"
% (com_iter, i_wkr, epoch + 1, self.epoch, batch_index + 1, len(self.train_datas[i_wkr]), loss.item())
)
train_vis.plot('Loss_' + str(i_wkr), loss.item())
train_vis.img('Ground Truth_' + str(i_wkr), self.displaywin(label_data.detach()).cpu())
train_vis.img('Result_' + str(i_wkr), self.displaywin(output.detach()).cpu())
train_vis.img('Input_' + str(i_wkr), self.displaywin(input_data.detach()).cpu())
client_weights = [1 / self.client_num for i in range(self.client_num)]
self.server_model, self.models = communication(opt, self.server_model, self.models, client_weights)
if opt.checkpoint_interval != -1 and (com_iter + 1) % opt.checkpoint_interval == 0:
# torch.save(self.models[i_wkr].state_dict(), '%s/model_com(%04d)_worker_id(%04d)_epoch_%04d.pth' % (self.path, com_iter, i_wkr, epoch + 1))
torch.save(self.server_model.state_dict(), '%s/model_commu_%04d.pth' % (self.path, com_iter + 1))
for check_id in range(self.client_num):
torch.save(self.models[check_id].state_dict(),
'%s/model_worker_id(%04d)_commu_%04d.pth' % (self.path, check_id, com_iter + 1))
if __name__ == "__main__":
network = net()
#print('4567')
network.train()