File size: 26,962 Bytes
a89d9fd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 |
# Copyright (c) 2021 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 platform
import yaml
import time
import datetime
import paddle
import paddle.distributed as dist
from tqdm import tqdm
import cv2
import numpy as np
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, AverageMeter
from ppocr.utils.logging import get_logger
from ppocr.utils.loggers import VDLLogger, WandbLogger, Loggers
from ppocr.utils import profiler
from ppocr.data import build_dataloader
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")
self.add_argument(
'-p',
'--profiler_options',
type=str,
default=None,
help='The option of profiler, which should be in format ' \
'\"key1=value1;key2=value2;key3=value3\".'
)
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
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
"""
_, ext = os.path.splitext(file_path)
assert ext in ['.yml', '.yaml'], "only support yaml files for now"
config = yaml.load(open(file_path, 'rb'), Loader=yaml.Loader)
return config
def merge_config(config, opts):
"""
Merge config into global config.
Args:
config (dict): Config to be merged.
Returns: global config
"""
for key, value in opts.items():
if "." not in key:
if isinstance(value, dict) and key in config:
config[key].update(value)
else:
config[key] = value
else:
sub_keys = key.split('.')
assert (
sub_keys[0] in config
), "the sub_keys can only be one of global_config: {}, but get: " \
"{}, please check your running command".format(
config.keys(), sub_keys[0])
cur = 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]
return config
def check_device(use_gpu, use_xpu=False, use_npu=False, use_mlu=False):
"""
Log error and exit when set use_gpu=true in paddlepaddle
cpu version.
"""
err = "Config {} cannot be set as true while your paddle " \
"is not compiled with {} ! \nPlease try: \n" \
"\t1. Install paddlepaddle to run model on {} \n" \
"\t2. Set {} as false in config file to run " \
"model on CPU"
try:
if use_gpu and use_xpu:
print("use_xpu and use_gpu can not both be ture.")
if use_gpu and not paddle.is_compiled_with_cuda():
print(err.format("use_gpu", "cuda", "gpu", "use_gpu"))
sys.exit(1)
if use_xpu and not paddle.device.is_compiled_with_xpu():
print(err.format("use_xpu", "xpu", "xpu", "use_xpu"))
sys.exit(1)
if use_npu:
if int(paddle.version.major) != 0 and int(
paddle.version.major) <= 2 and int(
paddle.version.minor) <= 4:
if not paddle.device.is_compiled_with_npu():
print(err.format("use_npu", "npu", "npu", "use_npu"))
sys.exit(1)
# is_compiled_with_npu() has been updated after paddle-2.4
else:
if not paddle.device.is_compiled_with_custom_device("npu"):
print(err.format("use_npu", "npu", "npu", "use_npu"))
sys.exit(1)
if use_mlu and not paddle.device.is_compiled_with_mlu():
print(err.format("use_mlu", "mlu", "mlu", "use_mlu"))
sys.exit(1)
except Exception as e:
pass
def to_float32(preds):
if isinstance(preds, dict):
for k in preds:
if isinstance(preds[k], dict) or isinstance(preds[k], list):
preds[k] = to_float32(preds[k])
elif isinstance(preds[k], paddle.Tensor):
preds[k] = preds[k].astype(paddle.float32)
elif isinstance(preds, list):
for k in range(len(preds)):
if isinstance(preds[k], dict):
preds[k] = to_float32(preds[k])
elif isinstance(preds[k], list):
preds[k] = to_float32(preds[k])
elif isinstance(preds[k], paddle.Tensor):
preds[k] = preds[k].astype(paddle.float32)
elif isinstance(preds, paddle.Tensor):
preds = preds.astype(paddle.float32)
return preds
def train(config,
train_dataloader,
valid_dataloader,
device,
model,
loss_class,
optimizer,
lr_scheduler,
post_process_class,
eval_class,
pre_best_model_dict,
logger,
log_writer=None,
scaler=None,
amp_level='O2',
amp_custom_black_list=[]):
cal_metric_during_train = config['Global'].get('cal_metric_during_train',
False)
calc_epoch_interval = config['Global'].get('calc_epoch_interval', 1)
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']
profiler_options = config['profiler_options']
global_step = 0
if 'global_step' in pre_best_model_dict:
global_step = pre_best_model_dict['global_step']
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"
extra_input_models = [
"SRN", "NRTR", "SAR", "SEED", "SVTR", "SPIN", "VisionLAN",
"RobustScanner", "RFL", 'DRRG'
]
extra_input = False
if config['Architecture']['algorithm'] == 'Distillation':
for key in config['Architecture']["Models"]:
extra_input = extra_input or config['Architecture']['Models'][key][
'algorithm'] in extra_input_models
else:
extra_input = config['Architecture']['algorithm'] in extra_input_models
try:
model_type = config['Architecture']['model_type']
except:
model_type = None
algorithm = config['Architecture']['algorithm']
start_epoch = best_model_dict[
'start_epoch'] if 'start_epoch' in best_model_dict else 1
total_samples = 0
train_reader_cost = 0.0
train_batch_cost = 0.0
reader_start = time.time()
eta_meter = AverageMeter()
max_iter = len(train_dataloader) - 1 if platform.system(
) == "Windows" else len(train_dataloader)
for epoch in range(start_epoch, epoch_num + 1):
if train_dataloader.dataset.need_reset:
train_dataloader = build_dataloader(
config, 'Train', device, logger, seed=epoch)
max_iter = len(train_dataloader) - 1 if platform.system(
) == "Windows" else len(train_dataloader)
for idx, batch in enumerate(train_dataloader):
profiler.add_profiler_step(profiler_options)
train_reader_cost += time.time() - reader_start
if idx >= max_iter:
break
lr = optimizer.get_lr()
images = batch[0]
if use_srn:
model_average = True
# use amp
if scaler:
with paddle.amp.auto_cast(
level=amp_level,
custom_black_list=amp_custom_black_list):
if model_type == 'table' or extra_input:
preds = model(images, data=batch[1:])
elif model_type in ["kie"]:
preds = model(batch)
elif algorithm in ['CAN']:
preds = model(batch[:3])
else:
preds = model(images)
preds = to_float32(preds)
loss = loss_class(preds, batch)
avg_loss = loss['loss']
scaled_avg_loss = scaler.scale(avg_loss)
scaled_avg_loss.backward()
scaler.minimize(optimizer, scaled_avg_loss)
else:
if model_type == 'table' or extra_input:
preds = model(images, data=batch[1:])
elif model_type in ["kie", 'sr']:
preds = model(batch)
elif algorithm in ['CAN']:
preds = model(batch[:3])
else:
preds = model(images)
loss = loss_class(preds, batch)
avg_loss = loss['loss']
avg_loss.backward()
optimizer.step()
optimizer.clear_grad()
if cal_metric_during_train and epoch % calc_epoch_interval == 0: # only rec and cls need
batch = [item.numpy() for item in batch]
if model_type in ['kie', 'sr']:
eval_class(preds, batch)
elif model_type in ['table']:
post_result = post_process_class(preds, batch)
eval_class(post_result, batch)
elif algorithm in ['CAN']:
model_type = 'can'
eval_class(preds[0], batch[2:], epoch_reset=(idx == 0))
else:
if config['Loss']['name'] in ['MultiLoss', 'MultiLoss_v2'
]: # for multi head loss
post_result = post_process_class(
preds['ctc'], batch[1]) # for CTC head out
elif config['Loss']['name'] in ['VLLoss']:
post_result = post_process_class(preds, batch[1],
batch[-1])
else:
post_result = post_process_class(preds, batch[1])
eval_class(post_result, batch)
metric = eval_class.get_metric()
train_stats.update(metric)
train_batch_time = time.time() - reader_start
train_batch_cost += train_batch_time
eta_meter.update(train_batch_time)
global_step += 1
total_samples += 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 log_writer is not None and dist.get_rank() == 0:
log_writer.log_metrics(
metrics=train_stats.get(), prefix="TRAIN", step=global_step)
if dist.get_rank() == 0 and (
(global_step > 0 and global_step % print_batch_step == 0) or
(idx >= len(train_dataloader) - 1)):
logs = train_stats.log()
eta_sec = ((epoch_num + 1 - epoch) * \
len(train_dataloader) - idx - 1) * eta_meter.avg
eta_sec_format = str(datetime.timedelta(seconds=int(eta_sec)))
strs = 'epoch: [{}/{}], global_step: {}, {}, avg_reader_cost: ' \
'{:.5f} s, avg_batch_cost: {:.5f} s, avg_samples: {}, ' \
'ips: {:.5f} samples/s, eta: {}'.format(
epoch, epoch_num, global_step, logs,
train_reader_cost / print_batch_step,
train_batch_cost / print_batch_step,
total_samples / print_batch_step,
total_samples / train_batch_cost, eta_sec_format)
logger.info(strs)
total_samples = 0
train_reader_cost = 0.0
train_batch_cost = 0.0
# 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,
model_type,
extra_input=extra_input,
scaler=scaler,
amp_level=amp_level,
amp_custom_black_list=amp_custom_black_list)
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 log_writer is not None:
log_writer.log_metrics(
metrics=cur_metric, prefix="EVAL", step=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,
config,
is_best=True,
prefix='best_accuracy',
best_model_dict=best_model_dict,
epoch=epoch,
global_step=global_step)
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 log_writer is not None:
log_writer.log_metrics(
metrics={
"best_{}".format(main_indicator):
best_model_dict[main_indicator]
},
prefix="EVAL",
step=global_step)
log_writer.log_model(
is_best=True,
prefix="best_accuracy",
metadata=best_model_dict)
reader_start = time.time()
if dist.get_rank() == 0:
save_model(
model,
optimizer,
save_model_dir,
logger,
config,
is_best=False,
prefix='latest',
best_model_dict=best_model_dict,
epoch=epoch,
global_step=global_step)
if log_writer is not None:
log_writer.log_model(is_best=False, prefix="latest")
if dist.get_rank() == 0 and epoch > 0 and epoch % save_epoch_step == 0:
save_model(
model,
optimizer,
save_model_dir,
logger,
config,
is_best=False,
prefix='iter_epoch_{}'.format(epoch),
best_model_dict=best_model_dict,
epoch=epoch,
global_step=global_step)
if log_writer is not None:
log_writer.log_model(
is_best=False, prefix='iter_epoch_{}'.format(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 log_writer is not None:
log_writer.close()
return
def eval(model,
valid_dataloader,
post_process_class,
eval_class,
model_type=None,
extra_input=False,
scaler=None,
amp_level='O2',
amp_custom_black_list=[]):
model.eval()
with paddle.no_grad():
total_frame = 0.0
total_time = 0.0
pbar = tqdm(
total=len(valid_dataloader),
desc='eval model:',
position=0,
leave=True)
max_iter = len(valid_dataloader) - 1 if platform.system(
) == "Windows" else len(valid_dataloader)
sum_images = 0
for idx, batch in enumerate(valid_dataloader):
if idx >= max_iter:
break
images = batch[0]
start = time.time()
# use amp
if scaler:
with paddle.amp.auto_cast(
level=amp_level,
custom_black_list=amp_custom_black_list):
if model_type == 'table' or extra_input:
preds = model(images, data=batch[1:])
elif model_type in ["kie"]:
preds = model(batch)
elif model_type in ['can']:
preds = model(batch[:3])
elif model_type in ['sr']:
preds = model(batch)
sr_img = preds["sr_img"]
lr_img = preds["lr_img"]
else:
preds = model(images)
preds = to_float32(preds)
else:
if model_type == 'table' or extra_input:
preds = model(images, data=batch[1:])
elif model_type in ["kie"]:
preds = model(batch)
elif model_type in ['can']:
preds = model(batch[:3])
elif model_type in ['sr']:
preds = model(batch)
sr_img = preds["sr_img"]
lr_img = preds["lr_img"]
else:
preds = model(images)
batch_numpy = []
for item in batch:
if isinstance(item, paddle.Tensor):
batch_numpy.append(item.numpy())
else:
batch_numpy.append(item)
# Obtain usable results from post-processing methods
total_time += time.time() - start
# Evaluate the results of the current batch
if model_type in ['table', 'kie']:
if post_process_class is None:
eval_class(preds, batch_numpy)
else:
post_result = post_process_class(preds, batch_numpy)
eval_class(post_result, batch_numpy)
elif model_type in ['sr']:
eval_class(preds, batch_numpy)
elif model_type in ['can']:
eval_class(preds[0], batch_numpy[2:], epoch_reset=(idx == 0))
else:
post_result = post_process_class(preds, batch_numpy[1])
eval_class(post_result, batch_numpy)
pbar.update(1)
total_frame += len(images)
sum_images += 1
# 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 update_center(char_center, post_result, preds):
result, label = post_result
feats, logits = preds
logits = paddle.argmax(logits, axis=-1)
feats = feats.numpy()
logits = logits.numpy()
for idx_sample in range(len(label)):
if result[idx_sample][0] == label[idx_sample][0]:
feat = feats[idx_sample]
logit = logits[idx_sample]
for idx_time in range(len(logit)):
index = logit[idx_time]
if index in char_center.keys():
char_center[index][0] = (
char_center[index][0] * char_center[index][1] +
feat[idx_time]) / (char_center[index][1] + 1)
char_center[index][1] += 1
else:
char_center[index] = [feat[idx_time], 1]
return char_center
def get_center(model, eval_dataloader, post_process_class):
pbar = tqdm(total=len(eval_dataloader), desc='get center:')
max_iter = len(eval_dataloader) - 1 if platform.system(
) == "Windows" else len(eval_dataloader)
char_center = dict()
for idx, batch in enumerate(eval_dataloader):
if idx >= max_iter:
break
images = batch[0]
start = time.time()
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])
#update char_center
char_center = update_center(char_center, post_result, preds)
pbar.update(1)
pbar.close()
for key in char_center.keys():
char_center[key] = char_center[key][0]
return char_center
def preprocess(is_train=False):
FLAGS = ArgsParser().parse_args()
profiler_options = FLAGS.profiler_options
config = load_config(FLAGS.config)
config = merge_config(config, FLAGS.opt)
profile_dic = {"profiler_options": FLAGS.profiler_options}
config = merge_config(config, profile_dic)
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(log_file=log_file)
# check if set use_gpu=True in paddlepaddle cpu version
use_gpu = config['Global'].get('use_gpu', False)
use_xpu = config['Global'].get('use_xpu', False)
use_npu = config['Global'].get('use_npu', False)
use_mlu = config['Global'].get('use_mlu', False)
alg = config['Architecture']['algorithm']
assert alg in [
'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE',
'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'LayoutLMv2', 'PREN', 'FCE',
'SVTR', 'ViTSTR', 'ABINet', 'DB++', 'TableMaster', 'SPIN', 'VisionLAN',
'Gestalt', 'SLANet', 'RobustScanner', 'CT', 'RFL', 'DRRG', 'CAN',
'Telescope'
]
if use_xpu:
device = 'xpu:{0}'.format(os.getenv('FLAGS_selected_xpus', 0))
elif use_npu:
device = 'npu:{0}'.format(os.getenv('FLAGS_selected_npus', 0))
elif use_mlu:
device = 'mlu:{0}'.format(os.getenv('FLAGS_selected_mlus', 0))
else:
device = 'gpu:{}'.format(dist.ParallelEnv()
.dev_id) if use_gpu else 'cpu'
check_device(use_gpu, use_xpu, use_npu, use_mlu)
device = paddle.set_device(device)
config['Global']['distributed'] = dist.get_world_size() != 1
loggers = []
if 'use_visualdl' in config['Global'] and config['Global']['use_visualdl']:
save_model_dir = config['Global']['save_model_dir']
vdl_writer_path = '{}/vdl/'.format(save_model_dir)
log_writer = VDLLogger(vdl_writer_path)
loggers.append(log_writer)
if ('use_wandb' in config['Global'] and
config['Global']['use_wandb']) or 'wandb' in config:
save_dir = config['Global']['save_model_dir']
wandb_writer_path = "{}/wandb".format(save_dir)
if "wandb" in config:
wandb_params = config['wandb']
else:
wandb_params = dict()
wandb_params.update({'save_dir': save_dir})
log_writer = WandbLogger(**wandb_params, config=config)
loggers.append(log_writer)
else:
log_writer = None
print_dict(config, logger)
if loggers:
log_writer = Loggers(loggers)
else:
log_writer = None
logger.info('train with paddle {} and device {}'.format(paddle.__version__,
device))
return config, device, logger, log_writer
|