def main():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = EAST()
model = model.eval()
model = model.to(device)
model.load_state_dict(torch.load(args.trained_model))
if os.path.exists(args.save_folder):
shutil.rmtree(args.save_folder)
os.mkdir(args.save_folder)
test_process = tqdm(os.listdir(args.img_path), ascii=True)
for img_file in test_process:
test_process.set_description("Processing")
img = Image.open(os.path.join(args.img_path, img_file))
boxes = detect(img, model, device)
#绘制boxes到图片上
plot_img = plot_boxes(img, boxes)
plot_img.save(os.path.join(args.save_folder, img_file))
if args.show_image:
plot_img.show()
def eval_model(model_name, test_img_path, submit_path, save_flag=True):
if os.path.exists(submit_path):
shutil.rmtree(submit_path)
os.mkdir(submit_path)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = EAST(False).to(device)
model.load_state_dict(torch.load(model_name))
model.eval()
start_time = time.time()
detect_dataset(model, device, test_img_path, submit_path)
os.chdir(submit_path)
res = subprocess.getoutput('zip -q submit.zip *.txt')
res = subprocess.getoutput('mv submit.zip ../')
os.chdir('../')
res = subprocess.getoutput(
'python ./evaluate/script.py –g=./evaluate/gt.zip –s=./submit.zip')
print(res)
os.remove('./submit.zip')
print('eval time is {}'.format(time.time() - start_time))
if not save_flag:
shutil.rmtree(submit_path)
'''
img_files = os.listdir(test_img_path)
img_files = sorted([os.path.join(test_img_path, img_file) for img_file in img_files])
for i, img_file in enumerate(img_files):
try:
print('evaluating {} image'.format(i))
boxes = detect(Image.open(img_file), model, device)
seq = []
if boxes is not None:
seq.extend([','.join([str(int(b)) for b in box[:-1]]) + '\n' for box in boxes])
with open(os.path.join(submit_path, os.path.basename(img_file).replace('.jpg','.txt')), 'w') as f:
f.writelines(seq)
except:
print('overload ram')
if __name__ == '__main__':
img_path = '/content/test/'
submit_path = '/content/res/'
model_path = './pths/east_vgg16.pth'
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = EAST().to(device)
model.load_state_dict(torch.load(model_path))
model.eval()
detect_dataset(model, device, img_path, submit_path)
def train(train_ds_path,
val_ds_path,
pths_path,
results_path,
batch_size,
lr,
num_workers,
train_iter,
interval,
opt_level=0,
checkpoint_path=None,
val_freq=10):
torch.cuda.set_device(rank)
tensorboard_dir = os.path.join(results_path, 'logs')
checkpoints_dir = os.path.join(results_path, 'checkpoints')
if rank == 0:
os.makedirs(tensorboard_dir, exist_ok=True)
os.makedirs(checkpoints_dir, exist_ok=True)
barrier()
try:
logger.info('Importing AutoResume lib...')
from userlib.auto_resume import AutoResume as auto_resume
auto_resume.init()
logger.info('Success!')
except:
logger.info('Failed!')
auto_resume = None
trainset = custom_dataset(
os.path.join(train_ds_path, 'images'),
os.path.join(train_ds_path, 'gt'),
)
valset = custom_dataset(os.path.join(val_ds_path, 'images'),
os.path.join(val_ds_path, 'gt'),
is_val=True)
logger.info(f'World Size: {world_size}, Rank: {rank}')
if world_size > 1:
train_sampler = torch.utils.data.distributed.DistributedSampler(
trainset)
val_sampler = torch.utils.data.distributed.DistributedSampler(
valset, shuffle=False)
else:
train_sampler = None
val_sampler = None
worker_init = LoaderWorkerProcessInit(rank, 43)
train_loader = DataLoader(trainset,
batch_size=batch_size,
shuffle=train_sampler is None,
sampler=train_sampler,
num_workers=num_workers,
pin_memory=True,
drop_last=True,
worker_init_fn=worker_init)
val_loader = DataLoader(valset,
batch_size=batch_size,
shuffle=False,
sampler=val_sampler,
num_workers=num_workers,
pin_memory=True,
drop_last=True,
worker_init_fn=worker_init)
criterion = Loss()
device = torch.device(
f"cuda:{rank}" if torch.cuda.is_available() else "cpu")
model = EAST()
model.to(device)
model = apex.parallel.convert_syncbn_model(model)
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=f'O{opt_level}')
start_iter = 0
if auto_resume is not None:
auto_resume_details = auto_resume.get_resume_details()
if auto_resume_details is not None:
logger.info(
'Detected that this is a resumption of a previous job!')
checkpoint_path = auto_resume_details['CHECKPOINT_PATH']
if checkpoint_path:
logger.info(f'Loading checkpoint at path "{checkpoint_path}"...')
checkpoint = torch.load(checkpoint_path, map_location=f'cuda:{rank}')
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
amp.load_state_dict(checkpoint['amp_state'])
start_iter = checkpoint['iter']
logger.info('Done')
data_parallel = False
main_model = model
if torch.distributed.is_initialized():
logger.info(
f'DataParallel: Using {torch.cuda.device_count()} devices!')
model = DDP(model)
data_parallel = True
for param_group in optimizer.param_groups:
param_group.setdefault('initial_lr', lr)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=[train_iter // 2],
gamma=0.1,
last_epoch=start_iter)
# This allows us to change dataset size without affecting things such as validation frequency
steps_per_epoch = 1000 // (world_size * batch_size)
step = start_iter
start_epoch = step // steps_per_epoch
epoch_iter = int(math.ceil(train_iter / steps_per_epoch))
if rank == 0:
logger.info('Initializing Tensorboard')
writer = SummaryWriter(tensorboard_dir, purge_step=step)
loss_meters = MeterDict(reset_on_value=True)
val_loss_meters = MeterDict(reset_on_value=True)
time_meters = MeterDict(reset_on_value=True)
logger.info('Training')
model.train()
train_start_time = time.time()
best_loss = 100
train_iter = [iter(train_loader)]
def get_batch():
try:
return next(train_iter[0])
except:
train_iter[0] = iter(train_loader)
return get_batch()
for epoch in range(start_epoch, epoch_iter):
if train_sampler is not None:
train_sampler.set_epoch(epoch)
epoch_loss = 0
epoch_time = time.time()
start_time = time.time()
model.train()
for i in range(steps_per_epoch):
batch = get_batch()
optimizer.zero_grad()
batch = [b.cuda(rank, non_blocking=True) for b in batch]
img, gt_score, gt_geo, ignored_map = batch
barrier()
time_meters['batch_time'].add_sample(time.time() - start_time)
pred_score, pred_geo = model(img)
loss, details = criterion(gt_score, pred_score, gt_geo, pred_geo,
ignored_map)
epoch_loss += loss.detach().item()
with amp.scale_loss(loss, optimizer) as loss_scaled:
loss_scaled.backward()
optimizer.step()
barrier()
time_meters['step_time'].add_sample(time.time() - start_time)
details['global'] = loss.detach().item()
for k, v in details.items():
loss_meters[k].add_sample(v)
if i % 10 == 0:
logger.info(f'\tStep [{i+1}/{steps_per_epoch}]')
start_time = time.time()
step += 1
scheduler.step()
if step == train_iter:
break
term_requested = auto_resume is not None and auto_resume.termination_requested(
)
checkpoint_path = None
if rank == 0:
times = {k: m.value() for k, m in time_meters.items()}
losses = {k: m.value() for k, m in loss_meters.items()}
times['epoch'] = time.time() - epoch_time
logger.info(
f'Epoch is [{epoch+1}/{epoch_iter}], time consumption is {times}, batch_loss is {losses}'
)
for k, v in times.items():
writer.add_scalar(f'performance/{k}', v, step)
for k, v in losses.items():
writer.add_scalar(f'loss/{k}', v, step)
writer.add_scalar('learning_rate', optimizer.param_groups[0]['lr'],
step)
if term_requested or (epoch + 1) % interval == 0:
state_dict = main_model.state_dict()
optim_state = optimizer.state_dict()
checkpoint_path = os.path.join(
checkpoints_dir, 'model_epoch_{}.pth'.format(epoch + 1))
logger.info(f'Saving checkpoint to "{checkpoint_path}"...')
torch.save(
{
'model': state_dict,
'optimizer': optim_state,
'amp_state': amp.state_dict(),
'epoch': epoch + 1,
'iter': step
}, checkpoint_path)
logger.info(f'Done')
if (epoch + 1) % val_freq == 0 or step == train_iter:
logger.info(f'Validating epoch {epoch+1}...')
model.eval()
val_loader.dataset.reset_random()
with torch.no_grad():
for i, batch in enumerate(val_loader):
batch = [b.cuda(rank, non_blocking=True) for b in batch]
img, gt_score, gt_geo, ignored_map = batch
barrier()
pred_score, pred_geo = model(img)
loss, details = criterion(gt_score, pred_score, gt_geo,
pred_geo, ignored_map)
details['global'] = loss.detach().item()
barrier()
for k, v in details.items():
val_loss_meters[k].add_sample(v)
print_dict = dict()
for k, m in val_loss_meters.items():
t = torch.tensor(m.value(),
device=f'cuda:{rank}',
dtype=torch.float32)
if world_size > 1:
torch.distributed.reduce(t, 0)
t /= world_size
if rank == 0:
writer.add_scalar(f'val/loss/{k}', t.item(), step)
print_dict[k] = t.item()
logger.info(f'\tLoss: {print_dict}')
val_loss = print_dict['global']
if rank == 0 and val_loss < best_loss:
logger.info(
f'This is the best model so far. New loss: {val_loss}, previous: {best_loss}'
)
best_loss = val_loss
shutil.copyfile(checkpoint_path,
os.path.join(checkpoints_dir, 'best.pth'))
logger.info('Training')
if term_requested:
logger.warning('Termination requested! Exiting...')
if rank == 0:
auto_resume.request_resume(user_dict={
'CHECKPOINT_PATH': save_path,
'EPOCH': epoch
})
break
logger.info(
f'Finished training!!! Took {time.time()-train_start_time:0.3f} seconds!'
)
submit_path : submit result for evaluation
'''
img_files = os.listdir(test_img_path)
img_files = sorted([os.path.join(test_img_path, img_file) for img_file in img_files])
for i, img_file in enumerate(img_files):
print('evaluating {} image'.format(i), end='\r')
boxes = detect(Image.open(img_file), model, device)
seq = []
if boxes is not None:
seq.extend([','.join([str(int(b)) for b in box[:-1]]) + '\n' for box in boxes])
with open(os.path.join(submit_path, 'res_' + os.path.basename(img_file).replace('.jpg','.txt')), 'w') as f:
f.writelines(seq)
if __name__ == '__main__':
img_path = '../ICDAR_2015/test_img/img_2.jpg' # 测试图片路径
model_path = './pths/east_vgg16.pth' # 训练好的模型
res_img = './res.bmp' # 保存的图片
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = EAST().to(device) # 模型分配给cpu或gpu
model.load_state_dict(torch.load(model_path)) # 加载模型参数
model.eval() # 将模型设置为评估模式，相当于self.train(False).
img = Image.open(img_path) # 打开图片
boxes = detect(img, model, device) # 进行图片测试
plot_img = plot_boxes(img, boxes) # 将结果在图片上显示
plot_img.save(res_img) # 保存图片
