# 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
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..')))
import yaml
import paddle
import paddle.distributed as dist
from ppocr.data import build_dataloader
from ppocr.modeling.architectures import build_model
from ppocr.losses import build_loss
from ppocr.optimizer import build_optimizer
from ppocr.postprocess import build_post_process
from ppocr.metrics import build_metric
from ppocr.utils.save_load import load_model
from ppocr.utils.utility import set_seed
from ppocr.modeling.architectures import apply_to_static
import tools.program as program
dist.get_world_size()
def main(config, device, logger, vdl_writer):
# init dist environment
if config['Global']['distributed']:
dist.init_parallel_env()
global_config = config['Global']
# build dataloader
train_dataloader = build_dataloader(config, 'Train', device, logger)
if len(train_dataloader) == 0:
logger.error(
"No Images in train dataset, please ensure\n" +
"\t1. The images num in the train label_file_list should be larger than or equal with batch size.\n"
+
"\t2. The annotation file and path in the configuration file are provided normally."
)
return
if config['Eval']:
valid_dataloader = build_dataloader(config, 'Eval', device, logger)
else:
valid_dataloader = None
# build post process
post_process_class = build_post_process(config['PostProcess'],
global_config)
# build model
# for rec algorithm
if hasattr(post_process_class, 'character'):
char_num = len(getattr(post_process_class, 'character'))
if config['Architecture']["algorithm"] in ["Distillation",
]: # distillation model
for key in config['Architecture']["Models"]:
if config['Architecture']['Models'][key]['Head'][
'name'] == 'MultiHead': # for multi head
if config['PostProcess'][
'name'] == 'DistillationSARLabelDecode':
char_num = char_num - 2
# update SARLoss params
assert list(config['Loss']['loss_config_list'][-1].keys())[
0] == 'DistillationSARLoss'
config['Loss']['loss_config_list'][-1][
'DistillationSARLoss']['ignore_index'] = char_num + 1
out_channels_list = {}
out_channels_list['CTCLabelDecode'] = char_num
out_channels_list['SARLabelDecode'] = char_num + 2
config['Architecture']['Models'][key]['Head'][
'out_channels_list'] = out_channels_list
else:
config['Architecture']["Models"][key]["Head"][
'out_channels'] = char_num
elif config['Architecture']['Head'][
'name'] == 'MultiHead': # for multi head
if config['PostProcess']['name'] == 'SARLabelDecode':
char_num = char_num - 2
# update SARLoss params
assert list(config['Loss']['loss_config_list'][1].keys())[
0] == 'SARLoss'
if config['Loss']['loss_config_list'][1]['SARLoss'] is None:
config['Loss']['loss_config_list'][1]['SARLoss'] = {
'ignore_index': char_num + 1
}
else:
config['Loss']['loss_config_list'][1]['SARLoss'][
'ignore_index'] = char_num + 1
out_channels_list = {}
out_channels_list['CTCLabelDecode'] = char_num
out_channels_list['SARLabelDecode'] = char_num + 2
config['Architecture']['Head'][
'out_channels_list'] = out_channels_list
else: # base rec model
config['Architecture']["Head"]['out_channels'] = char_num
if config['PostProcess']['name'] == 'SARLabelDecode': # for SAR model
config['Loss']['ignore_index'] = char_num - 1
model = build_model(config['Architecture'])
use_sync_bn = config["Global"].get("use_sync_bn", False)
if use_sync_bn:
model = paddle.nn.SyncBatchNorm.convert_sync_batchnorm(model)
logger.info('convert_sync_batchnorm')
model = apply_to_static(model, config, logger)
# build loss
loss_class = build_loss(config['Loss'])
# build optim
optimizer, lr_scheduler = build_optimizer(
config['Optimizer'],
epochs=config['Global']['epoch_num'],
step_each_epoch=len(train_dataloader),
model=model)
# build metric
eval_class = build_metric(config['Metric'])
logger.info('train dataloader has {} iters'.format(len(train_dataloader)))
if valid_dataloader is not None:
logger.info('valid dataloader has {} iters'.format(
len(valid_dataloader)))
use_amp = config["Global"].get("use_amp", False)
amp_level = config["Global"].get("amp_level", 'O2')
amp_custom_black_list = config['Global'].get('amp_custom_black_list', [])
if use_amp:
AMP_RELATED_FLAGS_SETTING = {'FLAGS_max_inplace_grad_add': 8, }
if paddle.is_compiled_with_cuda():
AMP_RELATED_FLAGS_SETTING.update({
'FLAGS_cudnn_batchnorm_spatial_persistent': 1
})
paddle.fluid.set_flags(AMP_RELATED_FLAGS_SETTING)
scale_loss = config["Global"].get("scale_loss", 1.0)
use_dynamic_loss_scaling = config["Global"].get(
"use_dynamic_loss_scaling", False)
scaler = paddle.amp.GradScaler(
init_loss_scaling=scale_loss,
use_dynamic_loss_scaling=use_dynamic_loss_scaling)
if amp_level == "O2":
model, optimizer = paddle.amp.decorate(
models=model,
optimizers=optimizer,
level=amp_level,
master_weight=True)
else:
scaler = None
# load pretrain model
pre_best_model_dict = load_model(config, model, optimizer,
config['Architecture']["model_type"])
if config['Global']['distributed']:
model = paddle.DataParallel(model)
# start train
program.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, scaler,
amp_level, amp_custom_black_list)
def test_reader(config, device, logger):
loader = build_dataloader(config, 'Train', device, logger)
import time
starttime = time.time()
count = 0
try:
for data in loader():
count += 1
if count % 1 == 0:
batch_time = time.time() - starttime
starttime = time.time()
logger.info("reader: {}, {}, {}".format(
count, len(data[0]), batch_time))
except Exception as e:
logger.info(e)
logger.info("finish reader: {}, Success!".format(count))
if __name__ == '__main__':
config, device, logger, vdl_writer = program.preprocess(is_train=True)
seed = config['Global']['seed'] if 'seed' in config['Global'] else 1024
set_seed(seed)
main(config, device, logger, vdl_writer)
# test_reader(config, device, logger)