129 lines
4.4 KiB
Python
Executable File
129 lines
4.4 KiB
Python
Executable File
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import time
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import multiprocessing
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import numpy as np
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def set_paddle_flags(**kwargs):
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for key, value in kwargs.items():
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if os.environ.get(key, None) is None:
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os.environ[key] = str(value)
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# NOTE(paddle-dev): All of these flags should be
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# set before `import paddle`. Otherwise, it would
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# not take any effect.
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set_paddle_flags(
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FLAGS_eager_delete_tensor_gb=0, # enable GC to save memory
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)
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from paddle import fluid
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from ppocr.utils.utility import load_config, merge_config
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import ppocr.data.rec.reader_main as reader
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from ppocr.utils.utility import ArgsParser
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from ppocr.utils.character import CharacterOps, cal_predicts_accuracy
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from ppocr.utils.check import check_gpu
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from ppocr.utils.utility import create_module
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from ppocr.utils.eval_utils import eval_run
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from ppocr.utils.utility import initial_logger
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logger = initial_logger()
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def main():
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config = load_config(FLAGS.config)
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merge_config(FLAGS.opt)
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char_ops = CharacterOps(config['Global'])
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config['Global']['char_num'] = char_ops.get_char_num()
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# check if set use_gpu=True in paddlepaddle cpu version
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use_gpu = config['Global']['use_gpu']
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check_gpu(use_gpu)
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if use_gpu:
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devices_num = fluid.core.get_cuda_device_count()
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else:
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devices_num = int(
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os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
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place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
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exe = fluid.Executor(place)
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rec_model = create_module(config['Architecture']['function'])(params=config)
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startup_prog = fluid.Program()
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eval_prog = fluid.Program()
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with fluid.program_guard(eval_prog, startup_prog):
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with fluid.unique_name.guard():
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eval_loader, eval_outputs = rec_model(mode="eval")
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eval_fetch_list = [v.name for v in eval_outputs]
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eval_prog = eval_prog.clone(for_test=True)
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exe.run(startup_prog)
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pretrain_weights = config['Global']['pretrain_weights']
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if pretrain_weights is not None:
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fluid.load(eval_prog, pretrain_weights)
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eval_data_list = ['IIIT5k_3000', 'SVT', 'IC03_860', 'IC03_867',\
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'IC13_857', 'IC13_1015', 'IC15_1811', 'IC15_2077', 'SVTP', 'CUTE80']
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eval_data_dir = config['TestReader']['lmdb_sets_dir']
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total_forward_time = 0
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total_evaluation_data_number = 0
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total_correct_number = 0
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eval_data_acc_info = {}
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for eval_data in eval_data_list:
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config['TestReader']['lmdb_sets_dir'] = \
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eval_data_dir + "/" + eval_data
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eval_reader = reader.train_eval_reader(
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config=config, char_ops=char_ops, mode="test")
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eval_loader.set_sample_list_generator(eval_reader, places=place)
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start_time = time.time()
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outs = eval_run(exe, eval_prog, eval_loader, eval_fetch_list, char_ops,
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"best", "test")
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infer_time = time.time() - start_time
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eval_acc, acc_num, sample_num = outs
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total_forward_time += infer_time
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total_evaluation_data_number += sample_num
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total_correct_number += acc_num
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eval_data_acc_info[eval_data] = outs
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avg_forward_time = total_forward_time / total_evaluation_data_number
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avg_acc = total_correct_number * 1.0 / total_evaluation_data_number
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logger.info('-' * 50)
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strs = ""
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for eval_data in eval_data_list:
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eval_acc, acc_num, sample_num = eval_data_acc_info[eval_data]
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strs += "\n {}, accuracy:{:.6f}".format(eval_data, eval_acc)
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strs += "\n average, accuracy:{:.6f}, time:{:.6f}".format(avg_acc,
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avg_forward_time)
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logger.info(strs)
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logger.info('-' * 50)
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if __name__ == '__main__':
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parser = ArgsParser()
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FLAGS = parser.parse_args()
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main()
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