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b_train_cls_conv.py
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b_train_cls_conv.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Author: Wenxuan Wu, Zhongang Qi, Li Fuxin.
@Contact: wuwen@oregonstate.edu
@File: train_cls_conv.py
Modified by
@Author: Jiawei Chen, Linlin Li
@Contact: jc762@duke.edu
@File: b_train_cls_conv.py
"""
import argparse
import os
import torch
import torch.nn.parallel
import torch.utils.data
import torch.nn.functional as F
from data_utils.b_ModelNetDataLoader import ModelNetDataLoader, load_data
import datetime
import logging
from pathlib import Path
from tqdm import tqdm
from utils.utils import test, save_checkpoint
from model.pointconv import PointConvDensityClsSsg as PointConvClsSsg
def parse_args():
'''PARAMETERS'''
parser = argparse.ArgumentParser('PointConv')
parser.add_argument('--batchsize', type=int, default=16, help='batch size in training')
parser.add_argument('--epoch', default=400, type=int, help='number of epoch in training')
parser.add_argument('--learning_rate', default=0.001, type=float, help='learning rate in training')
parser.add_argument('--gpu', type=str, default='0', help='specify gpu device')
parser.add_argument('--train_metric', type=str, default=False, help='whether evaluate on training dataset')
parser.add_argument('--optimizer', type=str, default='SGD', help='optimizer for training')
parser.add_argument('--pretrain', type=str, default=None,help='whether use pretrain model')
parser.add_argument('--decay_rate', type=float, default=1e-4, help='decay rate of learning rate')
parser.add_argument('--model_name', default='pointconv', help='model name')
return parser.parse_args()
def main(args):
'''HYPER PARAMETER'''
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
datapath = './data/ModelNet/'
'''CREATE DIR'''
experiment_dir = Path('./experiment/')
experiment_dir.mkdir(exist_ok=True)
file_dir = Path(str(experiment_dir) + '/%sModelNet40-'%args.model_name + str(datetime.datetime.now().strftime('%Y-%m-%d_%H-%M')))
file_dir.mkdir(exist_ok=True)
checkpoints_dir = file_dir.joinpath('checkpoints/')
checkpoints_dir.mkdir(exist_ok=True)
log_dir = file_dir.joinpath('logs/')
log_dir.mkdir(exist_ok=True)
'''LOG'''
args = parse_args()
logger = logging.getLogger(args.model_name)
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
file_handler = logging.FileHandler(str(log_dir) + 'train_%s_cls.txt'%args.model_name)
file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
logger.info('---------------------------------------------------TRANING---------------------------------------------------')
logger.info('PARAMETER ...')
logger.info(args)
'''DATA LOADING'''
logger.info('Load dataset ...')
train_data, train_label, test_data, test_label = load_data(datapath, classification=True)
logger.info("The number of training data is: %d",train_data.shape[0])
logger.info("The number of test data is: %d", test_data.shape[0])
trainDataset = ModelNetDataLoader(train_data, train_label)
testDataset = ModelNetDataLoader(test_data, test_label)
trainDataLoader = torch.utils.data.DataLoader(trainDataset, batch_size=args.batchsize, shuffle=True)
testDataLoader = torch.utils.data.DataLoader(testDataset, batch_size=args.batchsize, shuffle=False)
'''MODEL LOADING'''
num_class = 2
classifier = PointConvClsSsg(num_class).cuda()
if args.pretrain is not None:
print('Use pretrain model...')
logger.info('Use pretrain model')
checkpoint = torch.load(args.pretrain)
start_epoch = checkpoint['epoch']
classifier.load_state_dict(checkpoint['model_state_dict'])
else:
print('No existing model, starting training from scratch...')
start_epoch = 0
if args.optimizer == 'SGD':
optimizer = torch.optim.SGD(classifier.parameters(), lr=0.001, momentum=0.9)
elif args.optimizer == 'Adam':
optimizer = torch.optim.Adam(
classifier.parameters(),
lr=args.learning_rate,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=args.decay_rate
)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.5)
global_epoch = 0
global_step = 0
best_tst_accuracy = 0.0
blue = lambda x: '\033[94m' + x + '\033[0m'
'''TRANING'''
logger.info('Start training...')
for epoch in range(start_epoch,args.epoch):
print('Epoch %d (%d/%s):' % (global_epoch + 1, epoch + 1, args.epoch))
logger.info('Epoch %d (%d/%s):' ,global_epoch + 1, epoch + 1, args.epoch)
scheduler.step()
for batch_id, data in tqdm(enumerate(trainDataLoader, 0), total=len(trainDataLoader), smoothing=0.9):
points, target = data
target = target[:, 0]
points = points.transpose(2, 1)
points, target = points.cuda(), target.cuda()
#print(target)
optimizer.zero_grad()
classifier = classifier.train()
pred = classifier(points)
#print(pred)
loss = F.nll_loss(pred, target.long())
#print('hhh')
loss.backward()
optimizer.step()
global_step += 1
train_acc = test(classifier.eval(), trainDataLoader) if args.train_metric else None
acc = test(classifier, testDataLoader)
print('\r Loss: %f' % loss.data)
logger.info('Loss: %.2f', loss.data)
if args.train_metric:
print('Train Accuracy: %f' % train_acc)
logger.info('Train Accuracy: %f', (train_acc))
print('\r Test %s: %f *** %s: %f' % (blue('Accuracy'),acc, blue('Best Accuracy'),best_tst_accuracy))
logger.info('Test Accuracy: %f *** Best Test Accuracy: %f', acc, best_tst_accuracy)
if (acc >= best_tst_accuracy) and epoch > 5:
best_tst_accuracy = acc
logger.info('Save model...')
save_checkpoint(
global_epoch + 1,
train_acc if args.train_metric else 0.0,
acc,
classifier,
optimizer,
str(checkpoints_dir),
args.model_name)
print('Saving model....')
global_epoch += 1
print('Best Accuracy: %f'%best_tst_accuracy)
logger.info('End of training...')
if __name__ == '__main__':
args = parse_args()
main(args)