def main():
global total_iter
data_root = os.path.join('/home/shf/fudan_ocr_system/datasets/',
cfg.dataset)
trainset = TotalText(data_root=data_root,
ignore_list=os.path.join(data_root,
'ignore_list.txt'),
is_training=True,
transform=NewAugmentation(size=cfg.input_size,
mean=cfg.means,
std=cfg.stds,
maxlen=1280,
minlen=512))
train_loader = data.DataLoader(trainset,
batch_size=cfg.batch_size,
shuffle=True,
num_workers=cfg.num_workers)
# Model
model = TextNet(backbone=cfg.backbone, output_channel=7)
model = model.to(cfg.device)
if cfg.cuda:
cudnn.benchmark = True
criterion = TextLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=cfg.lr)
# scheduler = lr_scheduler.StepLR(optimizer, step_size=10000, gamma=0.94)
# if cfg.dataset == 'ArT_train':
# scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[10000, 50000], gamma=0.1)
# elif cfg.dataset == 'LSVT_full_train':
# scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[10000, 50000], gamma=0.1)
# load model if resume
if cfg.resume is not None:
global total_iter
checkpoint = load_model(cfg.resume)
cfg.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optim'])
# scheduler.load_state_dict(checkpoint['scheduler'])
total_iter = checkpoint['epoch'] * len(train_loader)
if not os.path.exists(os.path.join(cfg.summary_dir, cfg.exp_name)):
os.mkdir(os.path.join(cfg.summary_dir, cfg.exp_name))
summary_writer = SummaryWriter(
log_dir=os.path.join(cfg.summary_dir, cfg.exp_name))
print('Start training TextSnake.')
for epoch in range(cfg.start_epoch, cfg.max_epoch):
adjust_learning_rate(optimizer, total_iter)
train(model, train_loader, criterion, None, optimizer, epoch,
summary_writer)
print('End.')
def train_TextSnake(config_file):
import sys
sys.path.append('./detection_model/TextSnake_pytorch')
import os
import time
import torch
import torch.backends.cudnn as cudnn
import torch.utils.data as data
from torch.optim import lr_scheduler
import torchvision.utils as vutils
from tensorboardX import SummaryWriter
from dataset.total_text import TotalText
from network.loss import TextLoss
from network.textnet import TextNet
from util.augmentation import EvalTransform, NewAugmentation
from util.config import config as cfg, update_config, print_config, init_config
from util.misc import AverageMeter
from util.misc import mkdirs, to_device
from util.option import BaseOptions
from util.visualize import visualize_network_output
from yacs.config import CfgNode as CN
global total_iter
total_iter = 0
def read_config_file(config_file):
"""
read config information form yaml file
"""
f = open(config_file)
opt = CN.load_cfg(f)
return opt
opt = read_config_file(config_file)
def adjust_learning_rate(optimizer, i):
if 0 <= i * opt.batch_size < 100000:
lr = opt.lr
elif 100000 <= i * opt.batch_size < 400000:
lr = opt.lr * 0.1
else:
lr = opt.lr * 0.1 * (0.94**(
(i * opt.batch_size - 300000) // 100000))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def adjust_requires_grad(model, i):
if 0 <= i < 4000:
for name, param in model.named_parameters():
if name == 'conv1.0.weight' or name == 'conv1.0.bias' or \
name == 'conv1.1.weight' or name == 'conv1.1.bias':
param.requires_grad = False
else:
for name, param in model.named_parameters():
if name == 'conv1.0.weight' or name == 'conv1.0.bias' or \
name == 'conv1.1.weight' or name == 'conv1.1.bias':
param.requires_grad = True
def save_model(model, optimizer, scheduler, epoch):
save_dir = os.path.join(opt.save_dir, opt.exp_name)
if not os.path.exists(save_dir):
mkdirs(save_dir)
save_path = os.path.join(
save_dir, 'textsnake_{}_{}.pth'.format(model.backbone_name, epoch))
print('Saving to {}.'.format(save_path))
state_dict = {
'epoch': epoch,
'model': model.state_dict(),
'optim': optimizer.state_dict()
# 'scheduler': scheduler.state_dict()
}
torch.save(state_dict, save_path)
def load_model(save_path):
print('Loading from {}.'.format(save_path))
checkpoint = torch.load(save_path)
return checkpoint
def train(model, train_loader, criterion, scheduler, optimizer, epoch,
summary_writer):
start = time.time()
losses = AverageMeter()
batch_time = AverageMeter()
data_time = AverageMeter()
end = time.time()
model.train()
global total_iter
for i, (img, train_mask, tr_mask, tcl_mask, radius_map, sin_map,
cos_map, meta) in enumerate(train_loader):
data_time.update(time.time() - end)
img, train_mask, tr_mask, tcl_mask, radius_map, sin_map, cos_map = to_device(
img, train_mask, tr_mask, tcl_mask, radius_map, sin_map,
cos_map)
output = model(img)
tr_loss, tcl_loss, sin_loss, cos_loss, radii_loss = \
criterion(output, tr_mask, tcl_mask, sin_map, cos_map, radius_map, train_mask, total_iter)
loss = tr_loss + tcl_loss + sin_loss + cos_loss + radii_loss
# backward
# scheduler.step()
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.update(loss.item())
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if opt.viz and i < opt.vis_num:
visualize_network_output(output,
tr_mask,
tcl_mask,
prefix='train_{}'.format(i))
if i % opt.display_freq == 0:
print(
'Epoch: [ {} ][ {:03d} / {:03d} ] - Loss: {:.4f} - tr_loss: {:.4f} - tcl_loss: {:.4f} - sin_loss: {:.4f} - cos_loss: {:.4f} - radii_loss: {:.4f} - {:.2f}s/step'
.format(epoch, i, len(train_loader), loss.item(),
tr_loss.item(), tcl_loss.item(), sin_loss.item(),
cos_loss.item(), radii_loss.item(),
batch_time.avg))
# write summary
if total_iter % opt.summary_freq == 0:
print('Summary in {}'.format(
os.path.join(opt.summary_dir, opt.exp_name)))
tr_pred = output[:, 0:2].softmax(dim=1)[:, 1:2]
tcl_pred = output[:, 2:4].softmax(dim=1)[:, 1:2]
summary_writer.add_image('input_image',
vutils.make_grid(img, normalize=True),
total_iter)
summary_writer.add_image(
'tr/tr_pred',
vutils.make_grid(tr_pred * 255, normalize=True),
total_iter)
summary_writer.add_image(
'tr/tr_mask',
vutils.make_grid(
torch.unsqueeze(tr_mask * train_mask, 1) * 255),
total_iter)
summary_writer.add_image(
'tcl/tcl_pred',
vutils.make_grid(tcl_pred * 255, normalize=True),
total_iter)
summary_writer.add_image(
'tcl/tcl_mask',
vutils.make_grid(
torch.unsqueeze(tcl_mask * train_mask, 1) * 255),
total_iter)
summary_writer.add_scalar('learning_rate',
optimizer.param_groups[0]['lr'],
total_iter)
summary_writer.add_scalar('model/tr_loss', tr_loss.item(),
total_iter)
summary_writer.add_scalar('model/tcl_loss', tcl_loss.item(),
total_iter)
summary_writer.add_scalar('model/sin_loss', sin_loss.item(),
total_iter)
summary_writer.add_scalar('model/cos_loss', cos_loss.item(),
total_iter)
summary_writer.add_scalar('model/radii_loss',
radii_loss.item(), total_iter)
summary_writer.add_scalar('model/loss', loss.item(),
total_iter)
total_iter += 1
print('Speed: {}s /step, {}s /epoch'.format(batch_time.avg,
time.time() - start))
if epoch % opt.save_freq == 0:
save_model(model, optimizer, scheduler, epoch)
print('Training Loss: {}'.format(losses.avg))
def validation(model, valid_loader, criterion):
"""
print a series of loss information
"""
model.eval()
losses = AverageMeter()
for i, (img, train_mask, tr_mask, tcl_mask, radius_map, sin_map,
cos_map, meta) in enumerate(valid_loader):
print(meta['image_id'])
img, train_mask, tr_mask, tcl_mask, radius_map, sin_map, cos_map = to_device(
img, train_mask, tr_mask, tcl_mask, radius_map, sin_map,
cos_map)
output = model(img)
tr_loss, tcl_loss, sin_loss, cos_loss, radii_loss = \
criterion(output, tr_mask, tcl_mask, sin_map, cos_map, radius_map, train_mask)
loss = tr_loss + tcl_loss + sin_loss + cos_loss + radii_loss
losses.update(loss.item())
if opt.viz and i < opt.vis_num:
visualize_network_output(output,
tr_mask,
tcl_mask,
prefix='val_{}'.format(i))
if i % opt.display_freq == 0:
print(
'Validation: - Loss: {:.4f} - tr_loss: {:.4f} - tcl_loss: {:.4f} - sin_loss: {:.4f} - cos_loss: {:.4f} - radii_loss: {:.4f}'
.format(loss.item(), tr_loss.item(), tcl_loss.item(),
sin_loss.item(), cos_loss.item(),
radii_loss.item()))
print('Validation Loss: {}'.format(losses.avg))
# parse arguments
torch.cuda.set_device(opt.num_device)
option = BaseOptions(config_file)
args = option.initialize()
init_config(opt, config_file)
update_config(opt, args)
print_config(opt)
data_root = os.path.join(opt.data_root, opt.dataset)
trainset = TotalText(data_root=data_root,
ignore_list=os.path.join(data_root,
'ignore_list.txt'),
is_training=True,
transform=NewAugmentation(size=opt.input_size,
mean=opt.means,
std=opt.stds,
maxlen=1280,
minlen=512))
train_loader = data.DataLoader(trainset,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers)
# Model
model = TextNet(backbone=opt.backbone, output_channel=7)
model = model.to(opt.device)
if opt.cuda:
cudnn.benchmark = True
criterion = TextLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=opt.lr)
# scheduler = lr_scheduler.StepLR(optimizer, step_size=10000, gamma=0.94)
# if opt.dataset == 'ArT_train':
# scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[10000, 50000], gamma=0.1)
# elif opt.dataset == 'LSVT_full_train':
# scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[10000, 50000], gamma=0.1)
# load model if resume
if opt.resume is not False:
checkpoint = load_model(opt.resume)
opt.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optim'])
# scheduler.load_state_dict(checkpoint['scheduler'])
total_iter = checkpoint['epoch'] * len(train_loader)
if not os.path.exists(os.path.join(opt.summary_dir, opt.exp_name)):
os.mkdir(os.path.join(opt.summary_dir, opt.exp_name))
summary_writer = SummaryWriter(
log_dir=os.path.join(opt.summary_dir, opt.exp_name))
print('Start training TextSnake.')
for epoch in range(opt.start_epoch, opt.max_epoch):
adjust_learning_rate(optimizer, total_iter)
train(model, train_loader, criterion, None, optimizer, epoch,
summary_writer)
print('End.')