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train.py
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# -*- coding: utf-8 -*-
# @Time : 2018/11/8 9:03
# @Author : Xu HongBin
# @Email : [email protected]
# @github : https://github.com/ToughStoneX
# @blog : https://blog.csdn.net/hongbin_xu
# @File : train.py
# @Software: PyCharm
import os
import torch
import torch.nn as nn
from torch.optim import lr_scheduler
import torch.nn.functional as F
import numpy as np
from tensorboardX import SummaryWriter
from tqdm import tqdm
import logging # 引入logging模块
logging.basicConfig(level = logging.INFO,format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s')
import warnings
warnings.filterwarnings("ignore")
from dataset import ModelNet40DataSet
from Model.dynami_graph_cnn import DGCNNCls_vanilla, DGCNNCls_vanilla_2
from Model.pointnet import PointNet_Vanilla
from Model.l2_reg_loss import L2RegularizationLoss
from params import Args
class AverageMeter(object):
"""Computes and stores the average and current value.
Code imported from https://github.com/pytorch/examples/blob/master/imagenet/main.py#L247-L262
"""
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():
pwd = os.getcwd()
weights_dir = os.path.join(pwd, 'weights')
if not os.path.exists(weights_dir):
os.makedirs(weights_dir)
logging.info('Loading Dataset...')
train_dataset = ModelNet40DataSet(train=True)
test_dataset = ModelNet40DataSet(train=False)
logging.info('train_dataset: {}'.format(len(train_dataset)))
logging.info('test_dataset: {}'.format(len(test_dataset)))
logging.info('Done...\n')
logging.info('Creating DataLoader...')
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=Args.batch_size, shuffle=True, num_workers=2)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=Args.batch_size, shuffle=False, num_workers=2)
logging.info('Done...\n')
logging.info('Checking gpu...')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if torch.cuda.is_available():
logging.info('gpu available: {}'.format(torch.cuda.device_count()))
logging.info('current gpu: {}'.format(torch.cuda.get_device_name(0)))
logging.info('gpu capability: {}'.format(torch.cuda.get_device_capability(0)))
else:
logging.info('gpu not available, running on cpu instead.')
logging.info('Done...\n')
logging.info('Create SummaryWriter in ./summary')
# 创建SummaryWriter
summary_writer = SummaryWriter(comment='PointNet', log_dir='summary')
logging.info('Done...\n')
logging.info('Creating Model...')
# create DGCNN
model = DGCNNCls_vanilla(num_classes=40).to(Args.device)
# model = DGCNNCls_vanilla_2(num_classes=40).to(Args.device)
# create pointnet
# model = PointNet_Vanilla(num_classes=40).to(Args.device)
# add graph
# dummy_input = torch.rand(2, 2048, 3).to(device)
# summary_writer.add_graph(model, dummy_input)
# CrossEntropy Loss
criterion = nn.NLLLoss()
# criterion = nn.CrossEntropyLoss()
# 正则化损失
# criterion_reg_loss = L2RegularizationLoss().to(Args.device)
# Adam optimizer
optimizer = torch.optim.Adam(model.parameters(), weight_decay=Args.weight_reg)
# 学习率衰减
schedular = lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5)
logging.info('Done...\n')
logging.info('Start training...')
for epoch in range(1, Args.num_epochs+1):
logging.info("--------Epoch {}--------".format(epoch))
# 更新学习率
schedular.step()
tqdm_batch = tqdm(train_loader, desc='Epoch-{} training'.format(epoch))
# train
model.train()
# crossentropy_loss_tracker = AverageMeter()
# reg_loss_tracker = AverageMeter()
loss_tracker = AverageMeter()
for batch_idx, (data, label) in enumerate(tqdm_batch):
data, label = data.to(device), label.to(device)
# print(data.size())
# data = data.permute(0, 2, 1)
out = model(data)
# print('out: {}, label: {}'.format(out.size(), label.size()))
loss = criterion(out, label.view(-1))
# reg_loss = Args.weight_reg * criterion_reg_loss(model.parameters())
# loss = crossentropy_loss + reg_loss
# loss = crossentropy_loss
optimizer.zero_grad()
# loss.backward(retain_graph=True)
loss.backward()
optimizer.step()
# crossentropy_loss_tracker.update(crossentropy_loss.item(), label.size(0))
# reg_loss_tracker.update(reg_loss, label.size(0))
loss_tracker.update(loss.item(), label.size(0))
# print('\n')
# print('cross_entropy_loss: {}'.format(crossentropy_loss.item()))
# print('reg_loss: {}'.format(reg_loss))
# print('loss: {}'.format(loss.item()))
# print('\n')
# del data, label
tqdm_batch.close()
# logging.info('Crossentropy Loss: {:.4f} ({:.4f})'.format(crossentropy_loss_tracker.val, crossentropy_loss_tracker.avg))
# logging.info('Reg Loss: {:.4f} ({:.4f})'.format(reg_loss_tracker.val, reg_loss_tracker.avg))
logging.info('Loss: {:.4f} ({:.4f})'.format(loss_tracker.val, loss_tracker.avg))
summary_writer.add_scalar('loss', loss_tracker.avg, epoch)
if epoch % Args.test_freq == 0:
tqdm_batch = tqdm(test_loader, desc='Epoch-{} testing'.format(epoch))
model.eval()
correct_cnt = 0
total_cnt = 0
with torch.no_grad():
for batch_idx, (data, label) in enumerate(tqdm_batch):
data, label = data.to(device), label.to(device)
# data = data.permute(0, 2, 1)
out = model(data)
pred_choice = out.max(1)[1]
correct_cnt += pred_choice.eq(label.view(-1)).sum().item()
total_cnt += label.size(0)
# del data, label
print('correct_cnt: {}, total_cnt: {}'.format(correct_cnt, total_cnt))
acc = correct_cnt / total_cnt
logging.info('Accuracy: {:.4f}'.format(acc))
summary_writer.add_scalar('acc', acc, epoch)
tqdm_batch.close()
if epoch % Args.save_freq == 0:
ckpt_name = os.path.join(weights_dir, 'dgcnn_{0}.pth'.format(epoch))
torch.save(model.state_dict(), ckpt_name)
logging.info('model saved in {}'.format(ckpt_name))
summary_writer.close()
if __name__ == '__main__':
train()