# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import yaml
import time
import shutil
import paddle
import paddle.distributed as dist
from tqdm import tqdm
from argparse import ArgumentParser, RawDescriptionHelpFormatter
from ppocr.utils.stats import TrainingStats
from ppocr.utils.save_load import save_model
from ppocr.utils.utility import print_dict
from ppocr.utils.logging import get_logger
from ppocr.data import build_dataloader
import numpy as np
class ArgsParser(ArgumentParser):
def __init__(self):
super(ArgsParser, self).__init__(
formatter_class=RawDescriptionHelpFormatter)
self.add_argument("-c", "--config", help="configuration file to use")
self.add_argument(
"-o", "--opt", nargs='+', help="set configuration options")
def parse_args(self, argv=None):
args = super(ArgsParser, self).parse_args(argv)
assert args.config is not None, \
"Please specify --config=configure_file_path."
args.opt = self._parse_opt(args.opt)
return args
def _parse_opt(self, opts):
config = {}
if not opts:
return config
for s in opts:
s = s.strip()
k, v = s.split('=')
config[k] = yaml.load(v, Loader=yaml.Loader)
return config
class AttrDict(dict):
"""Single level attribute dict, NOT recursive"""
def __init__(self, **kwargs):
super(AttrDict, self).__init__()
super(AttrDict, self).update(kwargs)
def __getattr__(self, key):
if key in self:
return self[key]
raise AttributeError("object has no attribute '{}'".format(key))
global_config = AttrDict()
default_config = {'Global': {'debug': False, }}
def load_config(file_path):
"""
Load config from yml/yaml file.
Args:
file_path (str): Path of the config file to be loaded.
Returns: global config
"""
merge_config(default_config)
_, ext = os.path.splitext(file_path)
assert ext in ['.yml', '.yaml'], "only support yaml files for now"
merge_config(yaml.load(open(file_path, 'rb'), Loader=yaml.Loader))
return global_config
def merge_config(config):
"""
Merge config into global config.
Args:
config (dict): Config to be merged.
Returns: global config
"""
for key, value in config.items():
if "." not in key:
if isinstance(value, dict) and key in global_config:
global_config[key].update(value)
else:
global_config[key] = value
else:
sub_keys = key.split('.')
assert (
sub_keys[0] in global_config
), "the sub_keys can only be one of global_config: {}, but get: {}, please check your running command".format(
global_config.keys(), sub_keys[0])
cur = global_config[sub_keys[0]]
for idx, sub_key in enumerate(sub_keys[1:]):
if idx == len(sub_keys) - 2:
cur[sub_key] = value
else:
cur = cur[sub_key]
def check_gpu(use_gpu):
"""
Log error and exit when set use_gpu=true in paddlepaddle
cpu version.
"""
err = "Config use_gpu cannot be set as true while you are " \
"using paddlepaddle cpu version ! \nPlease try: \n" \
"\t1. Install paddlepaddle-gpu to run model on GPU \n" \
"\t2. Set use_gpu as false in config file to run " \
"model on CPU"
try:
if use_gpu and not paddle.is_compiled_with_cuda():
print(err)
sys.exit(1)
except Exception as e:
pass
def train(config,
train_dataloader,
valid_dataloader,
device,
model,
loss_class,
optimizer,
lr_scheduler,
post_process_class,
eval_class,
pre_best_model_dict,
logger,
vdl_writer=None):
cal_metric_during_train = config['Global'].get('cal_metric_during_train',
False)
log_smooth_window = config['Global']['log_smooth_window']
epoch_num = config['Global']['epoch_num']
print_batch_step = config['Global']['print_batch_step']
eval_batch_step = config['Global']['eval_batch_step']
global_step = 0
start_eval_step = 0
if type(eval_batch_step) == list and len(eval_batch_step) >= 2:
start_eval_step = eval_batch_step[0]
eval_batch_step = eval_batch_step[1]
if len(valid_dataloader) == 0:
logger.info(
'No Images in eval dataset, evaluation during training will be disabled'
)
start_eval_step = 1e111
logger.info(
"During the training process, after the {}th iteration, an evaluation is run every {} iterations".
format(start_eval_step, eval_batch_step))
save_epoch_step = config['Global']['save_epoch_step']
save_model_dir = config['Global']['save_model_dir']
if not os.path.exists(save_model_dir):
os.makedirs(save_model_dir)
main_indicator = eval_class.main_indicator
best_model_dict = {main_indicator: 0}
best_model_dict.update(pre_best_model_dict)
train_stats = TrainingStats(log_smooth_window, ['lr'])
model_average = False
model.train()
use_srn = config['Architecture']['algorithm'] == "SRN"
if 'start_epoch' in best_model_dict:
start_epoch = best_model_dict['start_epoch']
else:
start_epoch = 1
for epoch in range(start_epoch, epoch_num + 1):
train_dataloader = build_dataloader(
config, 'Train', device, logger, seed=epoch)
train_batch_cost = 0.0
train_reader_cost = 0.0
batch_sum = 0
batch_start = time.time()
for idx, batch in enumerate(train_dataloader):
train_reader_cost += time.time() - batch_start
if idx >= len(train_dataloader):
break
lr = optimizer.get_lr()
images = batch[0]
if use_srn:
others = batch[-4:]
preds = model(images, others)
model_average = True
else:
preds = model(images)
loss = loss_class(preds, batch)
avg_loss = loss['loss']
avg_loss.backward()
optimizer.step()
optimizer.clear_grad()
train_batch_cost += time.time() - batch_start
batch_sum += len(images)
if not isinstance(lr_scheduler, float):
lr_scheduler.step()
# logger and visualdl
stats = {k: v.numpy().mean() for k, v in loss.items()}
stats['lr'] = lr
train_stats.update(stats)
if cal_metric_during_train: # only rec and cls need
batch = [item.numpy() for item in batch]
post_result = post_process_class(preds, batch[1])
eval_class(post_result, batch)
metric = eval_class.get_metric()
train_stats.update(metric)
if vdl_writer is not None and dist.get_rank() == 0:
for k, v in train_stats.get().items():
vdl_writer.add_scalar('TRAIN/{}'.format(k), v, global_step)
vdl_writer.add_scalar('TRAIN/lr', lr, global_step)
if dist.get_rank(
) == 0 and global_step > 0 and global_step % print_batch_step == 0:
logs = train_stats.log()
strs = 'epoch: [{}/{}], iter: {}, {}, reader_cost: {:.5f} s, batch_cost: {:.5f} s, samples: {}, ips: {:.5f}'.format(
epoch, epoch_num, global_step, logs, train_reader_cost /
print_batch_step, train_batch_cost / print_batch_step,
batch_sum, batch_sum / train_batch_cost)
logger.info(strs)
train_batch_cost = 0.0
train_reader_cost = 0.0
batch_sum = 0
# eval
if global_step > start_eval_step and \
(global_step - start_eval_step) % eval_batch_step == 0 and dist.get_rank() == 0:
if model_average:
Model_Average = paddle.incubate.optimizer.ModelAverage(
0.15,
parameters=model.parameters(),
min_average_window=10000,
max_average_window=15625)
Model_Average.apply()
cur_metric = eval(
model,
valid_dataloader,
post_process_class,
eval_class,
use_srn=use_srn)
cur_metric_str = 'cur metric, {}'.format(', '.join(
['{}: {}'.format(k, v) for k, v in cur_metric.items()]))
logger.info(cur_metric_str)
# logger metric
if vdl_writer is not None:
for k, v in cur_metric.items():
if isinstance(v, (float, int)):
vdl_writer.add_scalar('EVAL/{}'.format(k),
cur_metric[k], global_step)
if cur_metric[main_indicator] >= best_model_dict[
main_indicator]:
best_model_dict.update(cur_metric)
best_model_dict['best_epoch'] = epoch
save_model(
model,
optimizer,
save_model_dir,
logger,
is_best=True,
prefix='best_accuracy',
best_model_dict=best_model_dict,
epoch=epoch)
best_str = 'best metric, {}'.format(', '.join([
'{}: {}'.format(k, v) for k, v in best_model_dict.items()
]))
logger.info(best_str)
# logger best metric
if vdl_writer is not None:
vdl_writer.add_scalar('EVAL/best_{}'.format(main_indicator),
best_model_dict[main_indicator],
global_step)
global_step += 1
optimizer.clear_grad()
batch_start = time.time()
if dist.get_rank() == 0:
save_model(
model,
optimizer,
save_model_dir,
logger,
is_best=False,
prefix='latest',
best_model_dict=best_model_dict,
epoch=epoch)
if dist.get_rank() == 0 and epoch > 0 and epoch % save_epoch_step == 0:
save_model(
model,
optimizer,
save_model_dir,
logger,
is_best=False,
prefix='iter_epoch_{}'.format(epoch),
best_model_dict=best_model_dict,
epoch=epoch)
best_str = 'best metric, {}'.format(', '.join(
['{}: {}'.format(k, v) for k, v in best_model_dict.items()]))
logger.info(best_str)
if dist.get_rank() == 0 and vdl_writer is not None:
vdl_writer.close()
return
def eval(model, valid_dataloader, post_process_class, eval_class,
use_srn=False):
model.eval()
with paddle.no_grad():
total_frame = 0.0
total_time = 0.0
pbar = tqdm(total=len(valid_dataloader), desc='eval model:')
for idx, batch in enumerate(valid_dataloader):
if idx >= len(valid_dataloader):
break
images = batch[0]
start = time.time()
if use_srn:
others = batch[-4:]
preds = model(images, others)
else:
preds = model(images)
batch = [item.numpy() for item in batch]
# Obtain usable results from post-processing methods
post_result = post_process_class(preds, batch[1])
total_time += time.time() - start
# Evaluate the results of the current batch
eval_class(post_result, batch)
pbar.update(1)
total_frame += len(images)
# Get final metric，eg. acc or hmean
metric = eval_class.get_metric()
pbar.close()
model.train()
metric['fps'] = total_frame / total_time
return metric
def preprocess(is_train=False):
FLAGS = ArgsParser().parse_args()
config = load_config(FLAGS.config)
merge_config(FLAGS.opt)
# check if set use_gpu=True in paddlepaddle cpu version
use_gpu = config['Global']['use_gpu']
check_gpu(use_gpu)
alg = config['Architecture']['algorithm']
assert alg in [
'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', 'CLS'
]
device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'
device = paddle.set_device(device)
config['Global']['distributed'] = dist.get_world_size() != 1
if is_train:
# save_config
save_model_dir = config['Global']['save_model_dir']
os.makedirs(save_model_dir, exist_ok=True)
with open(os.path.join(save_model_dir, 'config.yml'), 'w') as f:
yaml.dump(
dict(config), f, default_flow_style=False, sort_keys=False)
log_file = '{}/train.log'.format(save_model_dir)
else:
log_file = None
logger = get_logger(name='root', log_file=log_file)
if config['Global']['use_visualdl']:
from visualdl import LogWriter
save_model_dir = config['Global']['save_model_dir']
vdl_writer_path = '{}/vdl/'.format(save_model_dir)
os.makedirs(vdl_writer_path, exist_ok=True)
vdl_writer = LogWriter(logdir=vdl_writer_path)
else:
vdl_writer = None
print_dict(config, logger)
logger.info('train with paddle {} and device {}'.format(paddle.__version__,
device))
return config, device, logger, vdl_writer