117 lines
3.6 KiB
Python
Executable File
117 lines
3.6 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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from ppocr.data.rec.reader_main import test_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.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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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_outputs = rec_model(mode="test")
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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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test_img_path = config['test_img_path']
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image_shape = config['Global']['image_shape']
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blobs = test_reader(image_shape, test_img_path)
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predict = exe.run(program=eval_prog,
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feed={"image": blobs},
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fetch_list=eval_fetch_list,
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return_numpy=False)
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preds = np.array(predict[0])
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if preds.shape[1] == 1:
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preds = preds.reshape(-1)
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preds_lod = predict[0].lod()[0]
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preds_text = char_ops.decode(preds)
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else:
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end_pos = np.where(preds[0, :] == 1)[0]
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if len(end_pos) <= 1:
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preds_text = preds[0, 1:]
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else:
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preds_text = preds[0, 1:end_pos[1]]
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preds_text = preds_text.reshape(-1)
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preds_text = char_ops.decode(preds_text)
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fluid.io.save_inference_model(
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"./output/",
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feeded_var_names=['image'],
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target_vars=eval_outputs,
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executor=exe,
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main_program=eval_prog,
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model_filename="model",
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params_filename="params")
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print(preds)
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print(preds_text)
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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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