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train.py
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train.py
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import os
import torch
import random
import argparse
import numpy as np
import scipy.io as sio
import torch.optim as optim
from torch.autograd import Variable
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from evidential_fusion import EFN
from dataset import Multi_view_data
import warnings
warnings.filterwarnings("ignore")
def parse_option():
parser = argparse.ArgumentParser('argument for training')
parser.add_argument('--batch_size', type=int, default=200, metavar='N',
help='input batch size for training [default: 100]')
# epoch=500
parser.add_argument('--epochs', type=int, default=10, metavar='N',
help='number of epochs to train [default: 200]')
parser.add_argument('--view', type=int, default=2,
help='number of sense view')
parser.add_argument('--classes', type=int, default=11,
help='number of class')
parser.add_argument('--seed', default=None, type=int,
help='seed for initializing training. ')
# optimization
parser.add_argument('--lr', type=float, default=0.001, help='learning rate')
parser.add_argument('--lr_decay_epochs', type=str, default='40,80,120,160,200',
help='where to decay lr, can be a list,if is None,no lr_dacey!')
parser.add_argument('--lr_decay_rate', type=float, default=0.2, help='decay rate for learning rate')
parser.add_argument('--weight_decay', type=float, default=0.0001, help='decay for weight_decay')
# model dataset
opt = parser.parse_args(args=[])
if opt.lr_decay_epochs is not None:
iterations = opt.lr_decay_epochs.split(',')
opt.lr_decay_epochs = list([])
for it in iterations:
opt.lr_decay_epochs.append(int(it))
if opt.seed is not None:
seed_it(opt.seed)
warnings.warn(
'You have chosen to seed training.This will turn on the CUDNN deterministic setting,which can slow down your training considerably! You may see unexpected behavior when restarting from checkpoints.')
return opt
def seed_it(seed):
random.seed(seed)
os.environ["PYTHONSEED"] = str(seed)
np.random.seed(seed) #numpy
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.enabled = True
class AverageMeter(object):
"""Computes and stores the average and current value
"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def train(model, epoch, optimizer, train_loader):
model.train()
loss_meter = AverageMeter()
sum_loss = 0.0
data_num, correct_num = 0, 0
for batch_idx, (data, target) in enumerate(train_loader):
for v_num in range(len(data)):
data[v_num] = Variable(data[v_num].cuda())
data_num += target.size(0)
target = Variable(target.long().cuda())
optimizer.zero_grad()
evidences, evidence_a, loss = model(data, target)
# compute gradients and take step
loss.backward()
optimizer.step()
loss_meter.update(loss.item())
sum_loss += loss.item()
train_loss = sum_loss / (batch_idx + 1)
_, predicted = torch.max(evidence_a.data, 1)
correct_num += (predicted == target).sum().item()
train_acc = correct_num / data_num
print(f"epoch{epoch + 1}")
print(f"train: loss:{'%.2f'%train_loss} acc:{'%.4f'%train_acc}")
return train_loss, train_acc
def evalute(model, loader):
model.eval()
loss_meter = AverageMeter()
correct_num, data_num = 0, 0
sum_loss = 0
with torch.no_grad():
for batch_idx, (data, target) in enumerate(loader):
for v_num in range(len(data)):
data[v_num] = Variable(data[v_num].cuda())
data_num += target.size(0)
target = Variable(target.long().cuda())
evidences, evidence_a, loss = model(data, target)
loss_meter.update(loss.item())
sum_loss += loss.item()
val_loss = sum_loss / (batch_idx + 1)
_, predicted = torch.max(evidence_a.data, 1)
correct_num += (predicted == target).sum().item()
val_acc = correct_num / data_num
return val_loss, val_acc
def main():
opt = parse_option()
if torch.cuda.is_available():
print('gpu个数:', torch.cuda.device_count())
idx = torch.cuda.current_device()
print('gpu名称:', torch.cuda.get_device_name(idx))
print(opt)
# data loading
data_name = 'vgg-airound'
data_path = 'datasets/' + data_name
dims = [[4096], [4096]]
view_num = opt.view
classes = opt.classes
train_loader = torch.utils.data.DataLoader(
Multi_view_data(data_path,'train',view_num), batch_size = opt.batch_size,shuffle=True)
val_loader = torch.utils.data.DataLoader(
Multi_view_data(data_path, 'val', view_num), batch_size=opt.batch_size, shuffle=False)
test_loader = torch.utils.data.DataLoader(
Multi_view_data(data_path, 'test', view_num), batch_size=opt.batch_size, shuffle=False)
# model loading
model = EFN(classes, dims, view_num)
optimizer = optim.Adam(model.parameters(), lr=opt.lr, weight_decay=opt.weight_decay)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
# training
best_acc = 0.0
for epoch in range(opt.epochs):
train_loss, train_acc = train(model, epoch, optimizer, train_loader)
val_loss, val_acc = evalute(model, val_loader)
print(f"val: loss:{'%.2f' % val_loss} acc:{'%.4f' % val_acc}")
if val_acc > best_acc:
best_epoch = epoch
best_acc = val_acc
torch.save(model, 'EFN_best.pth')
print('best acc:', best_acc, 'best epoch:', best_epoch)
model1 = (torch.load('EFN_best.pth'))
print('loaded from ckpt!')
test_loss, test_acc = evalute(model1, test_loader)
print(f"test: loss:{'%.2f' % test_loss} acc:{'%.4f' % test_acc}")
if __name__=="__main__":
main()