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train_language_dist.py
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import os
import sys
import time
import random
import string
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
from torch.optim.lr_scheduler import CosineAnnealingLR
from utils import Averager, TokenLabelConverter
from dataset import TextDataset
from models import LevOCRModel
from utils import get_args
import utils_dist as utils
from levt import utils as utils_levt
from abinet.utils import CharsetMapper
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# def fix_bn(m):
# classname = m.__class__.__name__
# if classname.find("BatchNorm") !=-1:
# m.eval()
def train(opt):
""" character configuration """
charset = CharsetMapper(opt.dataset_charset_path, max_length=opt.batch_max_length)
opt.num_class = charset.num_classes
print('num_class:', opt.num_class)
indices = charset.char_to_label
src_dict = utils_levt.build_dict(indices)
if opt.rgb:
opt.input_channel = 3
model = LevOCRModel(opt, src_dict)
print(model)
""" dataset preparation """
if not opt.data_filtering_off:
print('Filtering the images containing characters which are not in opt.character')
print('Filtering the images whose label is longer than opt.batch_max_length')
# see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L130
opt.eval = False
train_dataset = TextDataset(opt.train_data, opt=opt)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=opt.batch_size,
shuffle=True,
num_workers=int(opt.workers), pin_memory=True, drop_last=True)
log = open(f'{opt.saved_path}/{opt.exp_name}/log_dataset.txt', 'a')
print('-' * 80)
log.write('-' * 80 + '\n')
log.close()
""" model configuration """
converter = TokenLabelConverter(src_dict.indices)
# data parallel for multi-GPU
model.to(device)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[opt.gpu], find_unused_parameters=True)
model.train()
# filter that only require gradient decent
filtered_parameters = []
params_num = []
for p in filter(lambda p: p.requires_grad, model.parameters()):
filtered_parameters.append(p)
params_num.append(np.prod(p.size()))
""" setup loss """
criterion = torch.nn.CrossEntropyLoss().to(device) # ignore [GO] token = ignore index 0
# loss averager
loss_avg = Averager()
# setup optimizer
optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps)
scheduler = CosineAnnealingLR(optimizer, T_max=int(opt.num_iter))
# scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=2000000)
""" final options """
# print(opt)
with open(f'{opt.saved_path}/{opt.exp_name}/opt.txt', 'a') as opt_file:
opt_log = '------------ Options -------------\n'
args = vars(opt)
for k, v in args.items():
opt_log += f'{str(k)}: {str(v)}\n'
opt_log += '---------------------------------------\n'
#print(opt_log)
opt_file.write(opt_log)
total_params = int(sum(params_num))
total_params = f'Trainable network params num : {total_params:,}'
print(total_params)
opt_file.write(total_params)
""" start training """
start_iter = 0
iteration = start_iter
while(True):
# train part
for labels, labels_noise in train_loader:
tgt_tokens, _ = converter.encode_levt(labels, src_dict, device=device, batch_max_length=opt.batch_max_length)
text_levt_noise, _ = converter.encode_levt(labels_noise, src_dict, device=device, batch_max_length=opt.batch_max_length)
loss_levt, _, _, preds, logging_output = model(None, text_levt_noise, None, tgt_tokens, criterion)
cost = loss_levt
model.zero_grad()
cost.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip) # gradient clipping with 5 (Default)
optimizer.step()
loss_avg.add(cost)
if utils.is_main_process() and ((iteration + 1) % opt.valInterval == 0 or iteration == 0): # To see training progress, we also conduct validation when 'iteration == 0'
# for log
with open(f'{opt.saved_path}/{opt.exp_name}/log_train.txt', 'a') as log:
loss_log = f'[{iteration+1}/{opt.num_iter}] Train loss: {loss_avg.val():0.5f}'
loss_avg.reset()
print(loss_log)
log.write(loss_log + '\n')
# save model per 1e+5 iter.
if utils.is_main_process() and (iteration + 1) % 5e+3 == 0:
torch.save(
model.state_dict(), f'{opt.saved_path}/{opt.exp_name}/iter_{iteration+1}.pth')
if (iteration + 1) == opt.num_iter:
print('end the training')
sys.exit()
iteration += 1
if scheduler is not None:
scheduler.step()
if __name__ == '__main__':
opt = get_args()
if not opt.exp_name:
opt.exp_name = f'{opt.TransformerModel}' if opt.Transformer else f'{opt.Transformation}-{opt.FeatureExtraction}-{opt.SequenceModeling}-{opt.Prediction}'
opt.exp_name += f'-Seed{opt.manualSeed}'
os.makedirs(f'{opt.saved_path}/{opt.exp_name}', exist_ok=True)
""" vocab / character number configuration """
if opt.sensitive:
opt.character = string.printable[:-6] # same with ASTER setting (use 94 char).
utils.init_distributed_mode(opt)
print(opt)
""" Seed and GPU setting """
seed = opt.manualSeed + utils.get_rank()
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
cudnn.benchmark = True
cudnn.deterministic = True
opt.num_gpu = torch.cuda.device_count()
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
train(opt)