223 lines
8.2 KiB
Python
Executable File
223 lines
8.2 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 sys
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import time
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import multiprocessing
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import numpy as np
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# from paddle.fluid.contrib.model_stat import summary
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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 create_module
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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.stats import TrainingStats
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from ppocr.utils.checkpoint import load_pretrain, load_checkpoint, save, save_model
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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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from ppocr.utils.utility import create_multi_devices_program
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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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print(config)
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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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train_prog = fluid.Program()
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with fluid.program_guard(train_prog, startup_prog):
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with fluid.unique_name.guard():
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train_loader, train_outputs = rec_model(mode="train")
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save_var = train_outputs[1]
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if "gradient_clip" in config['Global']:
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gradient_clip = config['Global']['gradient_clip']
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clip = fluid.clip.GradientClipByGlobalNorm(gradient_clip)
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fluid.clip.set_gradient_clip(clip, program=train_prog)
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train_fetch_list = [v.name for v in train_outputs]
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train_loss = train_outputs[0]
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opt_params = config['Optimizer']
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optimizer = create_module(opt_params['function'])(opt_params)
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optimizer.minimize(train_loss)
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global_lr = optimizer._global_learning_rate()
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global_lr.persistable = True
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train_fetch_list.append(global_lr.name)
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train_reader = reader.train_eval_reader(
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config=config, char_ops=char_ops, mode="train")
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train_loader.set_sample_list_generator(train_reader, places=place)
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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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eval_reader = reader.train_eval_reader(
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config=config, char_ops=char_ops, mode="eval")
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eval_loader.set_sample_list_generator(eval_reader, places=place)
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# compile program for multi-devices
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train_compile_program = create_multi_devices_program(train_prog,
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train_loss.name)
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pretrain_weights = config['Global']['pretrain_weights']
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if pretrain_weights is not None:
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load_pretrain(exe, train_prog, pretrain_weights)
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train_batch_id = 0
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train_log_keys = ['loss', 'acc']
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log_smooth_window = config['Global']['log_smooth_window']
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epoch_num = config['Global']['epoch_num']
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loss_type = config['Global']['loss_type']
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print_step = config['Global']['print_step']
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eval_step = config['Global']['eval_step']
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save_epoch_step = config['Global']['save_epoch_step']
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save_dir = config['Global']['save_dir']
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train_stats = TrainingStats(log_smooth_window, train_log_keys)
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best_eval_acc = -1
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best_batch_id = 0
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best_epoch = 0
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for epoch in range(epoch_num):
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train_loader.start()
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try:
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while True:
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t1 = time.time()
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train_outs = exe.run(program=train_compile_program,
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fetch_list=train_fetch_list,
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return_numpy=False)
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loss = np.mean(np.array(train_outs[0]))
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lr = np.mean(np.array(train_outs[-1]))
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preds = np.array(train_outs[1])
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preds_lod = train_outs[1].lod()[0]
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labels = np.array(train_outs[2])
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labels_lod = train_outs[2].lod()[0]
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acc, acc_num, img_num = cal_predicts_accuracy(
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char_ops, preds, preds_lod, labels, labels_lod)
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t2 = time.time()
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train_batch_elapse = t2 - t1
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stats = {'loss': loss, 'acc': acc}
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train_stats.update(stats)
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if train_batch_id > 0 and train_batch_id % print_step == 0:
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logs = train_stats.log()
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strs = 'epoch: {}, iter: {}, lr: {:.6f}, {}, time: {:.3f}'.format(
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epoch, train_batch_id, lr, logs, train_batch_elapse)
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logger.info(strs)
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if train_batch_id > 0 and train_batch_id % eval_step == 0:
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outs = eval_run(exe, eval_prog, eval_loader,
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eval_fetch_list, char_ops, train_batch_id,
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"eval")
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eval_acc, acc_num, sample_num = outs
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if eval_acc > best_eval_acc:
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best_eval_acc = eval_acc
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best_batch_id = train_batch_id
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best_epoch = epoch
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save_path = save_dir + "/best_accuracy"
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save_model(train_prog, save_path)
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strs = 'Test iter: {}, acc:{:.6f}, best_acc:{:.6f}, best_epoch:{}, best_batch_id:{}, sample_num:{}'.format(
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train_batch_id, eval_acc, best_eval_acc, best_epoch,
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best_batch_id, sample_num)
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logger.info(strs)
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train_batch_id += 1
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except fluid.core.EOFException:
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train_loader.reset()
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if epoch > 0 and epoch % save_epoch_step == 0:
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save_path = save_dir + "/iter_epoch_%d" % (epoch)
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save_model(train_prog, save_path)
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def test_reader():
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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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print(config)
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# tmp_reader = reader.train_eval_reader(
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# config=cfg, char_ops=char_ops, mode="train")
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tmp_reader = reader.train_eval_reader(
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config=config, char_ops=char_ops, mode="eval")
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count = 0
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print_count = 0
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import time
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starttime = time.time()
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for data in tmp_reader():
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count += len(data)
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print_count += 1
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if print_count % 10 == 0:
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batch_time = (time.time() - starttime) / print_count
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print("reader:", count, len(data), batch_time)
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print("finish reader:", count)
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print("success")
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if __name__ == '__main__':
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parser = ArgsParser()
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parser.add_argument(
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"-r",
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"--resume_checkpoint",
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default=None,
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type=str,
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help="Checkpoint path for resuming training.")
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FLAGS = parser.parse_args()
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main()
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# test_reader()
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