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ctpn_train.py
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ctpn_train.py
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#-*- coding:utf-8 -*-
#'''
# Created on 18-12-27 上午10:31
#
# @Author: Greg Gao(laygin)
#'''
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import torch
from torch.utils.data import DataLoader
from torch import optim
import numpy as np
import argparse
import config
from ctpn_model import CTPN_Model, RPN_CLS_Loss, RPN_REGR_Loss
from data import VOCDataset
random_seed = 2019
torch.random.manual_seed(random_seed)
np.random.seed(random_seed)
num_workers = 8
epochs = 20
lr = 1e-3
resume_epoch = 0
pre_weights = os.path.join(config.checkpoints_dir, 'ctpn_keras_weights.pth.tar')
def get_arguments():
parser = argparse.ArgumentParser(description='Pytorch CTPN For TexT Detection')
parser.add_argument('--num-workers', type=int, default=num_workers)
parser.add_argument('--image-dir', type=str, default=config.img_dir)
parser.add_argument('--labels-dir', type=str, default=config.xml_dir)
parser.add_argument('--pretrained-weights', type=str,default=pre_weights)
return parser.parse_args()
def save_checkpoint(state, epoch, loss_cls, loss_regr, loss, ext='pth.tar'):
check_path = os.path.join(config.checkpoints_dir,
f'ctpn_ep{epoch:02d}_'
f'{loss_cls:.4f}_{loss_regr:.4f}_{loss:.4f}.{ext}')
torch.save(state, check_path)
print('saving to {}'.format(check_path))
args = vars(get_arguments())
if __name__ == '__main__':
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
checkpoints_weight = args['pretrained_weights']
if os.path.exists(checkpoints_weight):
pretrained = False
dataset = VOCDataset(args['image_dir'], args['labels_dir'])
dataloader = DataLoader(dataset, batch_size=1, shuffle=True, num_workers=args['num_workers'])
model = CTPN_Model()
model.to(device)
if os.path.exists(checkpoints_weight):
print('using pretrained weight: {}'.format(checkpoints_weight))
cc = torch.load(checkpoints_weight, map_location=device)
model.load_state_dict(cc['model_state_dict'])
resume_epoch = cc['epoch']
params_to_uodate = model.parameters()
optimizer = optim.SGD(params_to_uodate, lr=lr, momentum=0.9)
critetion_cls = RPN_CLS_Loss(device)
critetion_regr = RPN_REGR_Loss(device)
best_loss_cls = 100
best_loss_regr = 100
best_loss = 100
best_model = None
epochs += resume_epoch
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)
for epoch in range(resume_epoch+1, epochs):
print(f'Epoch {epoch}/{epochs}')
print('#'*50)
epoch_size = len(dataset) // 1
model.train()
epoch_loss_cls = 0
epoch_loss_regr = 0
epoch_loss = 0
scheduler.step(epoch)
for batch_i, (imgs, clss, regrs) in enumerate(dataloader):
imgs = imgs.to(device)
clss = clss.to(device)
regrs = regrs.to(device)
optimizer.zero_grad()
out_cls, out_regr = model(imgs)
loss_cls = critetion_cls(out_cls, clss)
loss_regr = critetion_regr(out_regr, regrs)
loss = loss_cls + loss_regr # total loss
loss.backward()
optimizer.step()
epoch_loss_cls += loss_cls.item()
epoch_loss_regr += loss_regr.item()
epoch_loss += loss.item()
mmp = batch_i+1
print(f'Ep:{epoch}/{epochs-1}--'
f'Batch:{batch_i}/{epoch_size}\n'
f'batch: loss_cls:{loss_cls.item():.4f}--loss_regr:{loss_regr.item():.4f}--loss:{loss.item():.4f}\n'
f'Epoch: loss_cls:{epoch_loss_cls/mmp:.4f}--loss_regr:{epoch_loss_regr/mmp:.4f}--'
f'loss:{epoch_loss/mmp:.4f}\n')
epoch_loss_cls /= epoch_size
epoch_loss_regr /= epoch_size
epoch_loss /= epoch_size
print(f'Epoch:{epoch}--{epoch_loss_cls:.4f}--{epoch_loss_regr:.4f}--{epoch_loss:.4f}')
if best_loss_cls > epoch_loss_cls or best_loss_regr > epoch_loss_regr or best_loss > epoch_loss:
best_loss = epoch_loss
best_loss_regr = epoch_loss_regr
best_loss_cls = epoch_loss_cls
best_model = model
save_checkpoint({'model_state_dict': best_model.state_dict(),
'epoch': epoch},
epoch,
best_loss_cls,
best_loss_regr,
best_loss)
if torch.cuda.is_available():
torch.cuda.empty_cache()