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# 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.insert(0, __dir__)
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..')))
import paddle
from ppocr.data import build_dataloader
from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
from ppocr.metrics import build_metric
from ppocr.utils.save_load import load_model
import tools.program as program
def main():
global_config = config['Global']
# build dataloader
valid_dataloader = build_dataloader(config, 'Eval', device, logger)
# 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
out_channels_list = {}
if config['PostProcess'][
'name'] == 'DistillationSARLabelDecode':
char_num = char_num - 2
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
out_channels_list = {}
if config['PostProcess']['name'] == 'SARLabelDecode':
char_num = char_num - 2
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
model = build_model(config['Architecture'])
extra_input_models = [
"SRN", "NRTR", "SAR", "SEED", "SVTR", "VisionLAN", "RobustScanner"
]
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
if "model_type" in config['Architecture'].keys():
if config['Architecture']['algorithm'] == 'CAN':
model_type = 'can'
else:
model_type = config['Architecture']['model_type']
else:
model_type = None
# build metric
eval_class = build_metric(config['Metric'])
# amp
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_cudnn_batchnorm_spatial_persistent': 1,
'FLAGS_max_inplace_grad_add': 8,
}
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 = paddle.amp.decorate(
models=model, level=amp_level, master_weight=True)
else:
scaler = None
best_model_dict = load_model(
config, model, model_type=config['Architecture']["model_type"])
if len(best_model_dict):
logger.info('metric in ckpt ***************')
for k, v in best_model_dict.items():
logger.info('{}:{}'.format(k, v))
# start eval
metric = program.eval(model, valid_dataloader, post_process_class,
eval_class, model_type, extra_input, scaler,
amp_level, amp_custom_black_list)
logger.info('metric eval ***************')
for k, v in metric.items():
logger.info('{}:{}'.format(k, v))
if __name__ == '__main__':
config, device, logger, vdl_writer = program.preprocess()
main()