Merge pull request #1 from LDOUBLEV/upload

Upload PaddleOCR code 

tools/tmp/test_rec_benchmark.py

+# 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 time
+import multiprocessing
+import numpy as np
+
+
+def set_paddle_flags(**kwargs):
+    for key, value in kwargs.items():
+        if os.environ.get(key, None) is None:
+            os.environ[key] = str(value)
+
+
+# NOTE(paddle-dev): All of these flags should be
+# set before `import paddle`. Otherwise, it would
+# not take any effect. 
+set_paddle_flags(
+    FLAGS_eager_delete_tensor_gb=0,  # enable GC to save memory
+)
+
+from paddle import fluid
+
+from ppocr.utils.utility import load_config, merge_config
+import ppocr.data.rec.reader_main as reader
+
+from ppocr.utils.utility import ArgsParser
+from ppocr.utils.character import CharacterOps, cal_predicts_accuracy
+from ppocr.utils.check import check_gpu
+from ppocr.utils.utility import create_module
+
+from ppocr.utils.eval_utils import eval_run
+
+from ppocr.utils.utility import initial_logger
+logger = initial_logger()
+
+
+def main():
+    config = load_config(FLAGS.config)
+    merge_config(FLAGS.opt)
+    char_ops = CharacterOps(config['Global'])
+    config['Global']['char_num'] = char_ops.get_char_num()
+
+    # check if set use_gpu=True in paddlepaddle cpu version
+    use_gpu = config['Global']['use_gpu']
+    check_gpu(use_gpu)
+
+    if use_gpu:
+        devices_num = fluid.core.get_cuda_device_count()
+    else:
+        devices_num = int(
+            os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
+
+    place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    rec_model = create_module(config['Architecture']['function'])(params=config)
+
+    startup_prog = fluid.Program()
+    eval_prog = fluid.Program()
+    with fluid.program_guard(eval_prog, startup_prog):
+        with fluid.unique_name.guard():
+            eval_loader, eval_outputs = rec_model(mode="eval")
+            eval_fetch_list = [v.name for v in eval_outputs]
+    eval_prog = eval_prog.clone(for_test=True)
+
+    exe.run(startup_prog)
+    pretrain_weights = config['Global']['pretrain_weights']
+    if pretrain_weights is not None:
+        fluid.load(eval_prog, pretrain_weights)
+
+    eval_data_list = ['IIIT5k_3000', 'SVT', 'IC03_860', 'IC03_867',\
+        'IC13_857', 'IC13_1015', 'IC15_1811', 'IC15_2077', 'SVTP', 'CUTE80']
+    eval_data_dir = config['TestReader']['lmdb_sets_dir']
+    total_forward_time = 0
+    total_evaluation_data_number = 0
+    total_correct_number = 0
+    eval_data_acc_info = {}
+    for eval_data in eval_data_list:
+        config['TestReader']['lmdb_sets_dir'] = \
+            eval_data_dir + "/" + eval_data
+        eval_reader = reader.train_eval_reader(
+            config=config, char_ops=char_ops, mode="test")
+        eval_loader.set_sample_list_generator(eval_reader, places=place)
+
+        start_time = time.time()
+        outs = eval_run(exe, eval_prog, eval_loader, eval_fetch_list, char_ops,
+                        "best", "test")
+        infer_time = time.time() - start_time
+        eval_acc, acc_num, sample_num = outs
+        total_forward_time += infer_time
+        total_evaluation_data_number += sample_num
+        total_correct_number += acc_num
+        eval_data_acc_info[eval_data] = outs
+
+    avg_forward_time = total_forward_time / total_evaluation_data_number
+    avg_acc = total_correct_number * 1.0 / total_evaluation_data_number
+    logger.info('-' * 50)
+    strs = ""
+    for eval_data in eval_data_list:
+        eval_acc, acc_num, sample_num = eval_data_acc_info[eval_data]
+        strs += "\n {}, accuracy:{:.6f}".format(eval_data, eval_acc)
+    strs += "\n average, accuracy:{:.6f}, time:{:.6f}".format(avg_acc,
+                                                              avg_forward_time)
+    logger.info(strs)
+    logger.info('-' * 50)
+
+
+if __name__ == '__main__':
+    parser = ArgsParser()
+    FLAGS = parser.parse_args()
+    main()

tools/tmp/train_det.py

+# 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
+import time
+import multiprocessing
+import numpy as np
+
+# from paddle.fluid.contrib.model_stat import summary
+
+
+def set_paddle_flags(**kwargs):
+    for key, value in kwargs.items():
+        if os.environ.get(key, None) is None:
+            os.environ[key] = str(value)
+
+
+# NOTE(paddle-dev): All of these flags should be
+# set before `import paddle`. Otherwise, it would
+# not take any effect. 
+set_paddle_flags(
+    FLAGS_eager_delete_tensor_gb=0,  # enable GC to save memory
+)
+
+from paddle import fluid
+from ppocr.utils.utility import create_module
+from ppocr.utils.utility import load_config, merge_config
+import ppocr.data.det.reader_main as reader
+from ppocr.utils.utility import ArgsParser
+from ppocr.utils.character import CharacterOps, cal_predicts_accuracy
+from ppocr.utils.check import check_gpu
+from ppocr.utils.stats import TrainingStats
+from ppocr.utils.checkpoint import load_pretrain, load_checkpoint, save, save_model
+from ppocr.utils.eval_utils import eval_run
+from ppocr.utils.eval_utils import eval_det_run
+
+from ppocr.utils.utility import initial_logger
+logger = initial_logger()
+from ppocr.utils.utility import create_multi_devices_program
+
+
+def main():
+    config = load_config(FLAGS.config)
+    merge_config(FLAGS.opt)
+    print(config)
+
+    alg = config['Global']['algorithm']
+    assert alg in ['EAST', 'DB']
+
+    # check if set use_gpu=True in paddlepaddle cpu version
+    use_gpu = config['Global']['use_gpu']
+    check_gpu(use_gpu)
+
+    place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    det_model = create_module(config['Architecture']['function'])(params=config)
+
+    startup_prog = fluid.Program()
+    train_prog = fluid.Program()
+    with fluid.program_guard(train_prog, startup_prog):
+        with fluid.unique_name.guard():
+            train_loader, train_outputs = det_model(mode="train")
+            train_fetch_list = [v.name for v in train_outputs]
+            train_loss = train_outputs[0]
+            opt_params = config['Optimizer']
+            optimizer = create_module(opt_params['function'])(opt_params)
+            optimizer.minimize(train_loss)
+            global_lr = optimizer._global_learning_rate()
+            global_lr.persistable = True
+            train_fetch_list.append(global_lr.name)
+
+    eval_prog = fluid.Program()
+    with fluid.program_guard(eval_prog, startup_prog):
+        with fluid.unique_name.guard():
+            eval_loader, eval_outputs = det_model(mode="eval")
+            eval_fetch_list = [v.name for v in eval_outputs]
+    eval_prog = eval_prog.clone(for_test=True)
+
+    train_reader = reader.train_reader(config=config)
+    train_loader.set_sample_list_generator(train_reader, places=place)
+
+    exe.run(startup_prog)
+
+    # compile program for multi-devices
+    train_compile_program = create_multi_devices_program(train_prog,
+                                                         train_loss.name)
+
+    pretrain_weights = config['Global']['pretrain_weights']
+    if pretrain_weights is not None:
+        load_pretrain(exe, train_prog, pretrain_weights)
+        print("pretrain weights loaded!")
+
+    train_batch_id = 0
+    if alg == 'EAST':
+        train_log_keys = ['loss_total', 'loss_cls', 'loss_offset']
+    elif alg == 'DB':
+        train_log_keys = [
+            'loss_total', 'loss_shrink', 'loss_threshold', 'loss_binary'
+        ]
+    log_smooth_window = config['Global']['log_smooth_window']
+    epoch_num = config['Global']['epoch_num']
+    print_step = config['Global']['print_step']
+    eval_step = config['Global']['eval_step']
+    save_epoch_step = config['Global']['save_epoch_step']
+    save_dir = config['Global']['save_dir']
+    train_stats = TrainingStats(log_smooth_window, train_log_keys)
+    best_eval_hmean = -1
+    best_batch_id = 0
+    best_epoch = 0
+    for epoch in range(epoch_num):
+        train_loader.start()
+        try:
+            while True:
+                t1 = time.time()
+                train_outs = exe.run(program=train_compile_program,
+                                     fetch_list=train_fetch_list,
+                                     return_numpy=False)
+                loss_total = np.mean(np.array(train_outs[0]))
+                if alg == 'EAST':
+                    loss_cls = np.mean(np.array(train_outs[1]))
+                    loss_offset = np.mean(np.array(train_outs[2]))
+                    stats = {'loss_total':loss_total, 'loss_cls':loss_cls,\
+                        'loss_offset':loss_offset}
+                elif alg == 'DB':
+                    loss_shrink_maps = np.mean(np.array(train_outs[1]))
+                    loss_threshold_maps = np.mean(np.array(train_outs[2]))
+                    loss_binary_maps = np.mean(np.array(train_outs[3]))
+                    stats = {'loss_total':loss_total, 'loss_shrink':loss_shrink_maps, \
+                        'loss_threshold':loss_threshold_maps, 'loss_binary':loss_binary_maps}
+                lr = np.mean(np.array(train_outs[-1]))
+                t2 = time.time()
+                train_batch_elapse = t2 - t1
+
+                # stats = {'loss_total':loss_total, 'loss_cls':loss_cls,\
+                #     'loss_offset':loss_offset}
+                train_stats.update(stats)
+                if train_batch_id > 0 and train_batch_id % print_step == 0:
+                    logs = train_stats.log()
+                    strs = 'epoch: {}, iter: {}, lr: {:.6f}, {}, time: {:.3f}'.format(
+                        epoch, train_batch_id, lr, logs, train_batch_elapse)
+                    logger.info(strs)
+
+                if train_batch_id > 0 and\
+                    train_batch_id % eval_step == 0:
+                    metrics = eval_det_run(exe, eval_prog, eval_fetch_list,
+                                           config, "eval")
+                    hmean = metrics['hmean']
+                    if hmean >= best_eval_hmean:
+                        best_eval_hmean = hmean
+                        best_batch_id = train_batch_id
+                        best_epoch = epoch
+                        save_path = save_dir + "/best_accuracy"
+                        save_model(train_prog, save_path)
+                    strs = 'Test iter: {}, metrics:{}, best_hmean:{:.6f}, best_epoch:{}, best_batch_id:{}'.format(
+                        train_batch_id, metrics, best_eval_hmean, best_epoch,
+                        best_batch_id)
+                    logger.info(strs)
+                train_batch_id += 1
+
+        except fluid.core.EOFException:
+            train_loader.reset()
+
+        if epoch > 0 and epoch % save_epoch_step == 0:
+            save_path = save_dir + "/iter_epoch_%d" % (epoch)
+            save_model(train_prog, save_path)
+
+
+def test_reader():
+    config = load_config(FLAGS.config)
+    merge_config(FLAGS.opt)
+    print(config)
+    tmp_reader = reader.train_reader(config=config)
+    count = 0
+    print_count = 0
+    import time
+    while True:
+        starttime = time.time()
+        count = 0
+        for data in tmp_reader():
+            count += 1
+            if print_count % 1 == 0:
+                batch_time = time.time() - starttime
+                starttime = time.time()
+                print("reader:", count, len(data), batch_time)
+    print("finish reader:", count)
+    print("success")
+
+
+if __name__ == '__main__':
+    parser = ArgsParser()
+    parser.add_argument(
+        "-r",
+        "--resume_checkpoint",
+        default=None,
+        type=str,
+        help="Checkpoint path for resuming training.")
+    FLAGS = parser.parse_args()
+    main()
+    # test_reader()

tools/tmp/train_rec.py

+# 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
+import time
+import multiprocessing
+import numpy as np
+
+# from paddle.fluid.contrib.model_stat import summary
+
+
+def set_paddle_flags(**kwargs):
+    for key, value in kwargs.items():
+        if os.environ.get(key, None) is None:
+            os.environ[key] = str(value)
+
+
+# NOTE(paddle-dev): All of these flags should be
+# set before `import paddle`. Otherwise, it would
+# not take any effect. 
+set_paddle_flags(
+    FLAGS_eager_delete_tensor_gb=0,  # enable GC to save memory
+)
+
+from paddle import fluid
+from ppocr.utils.utility import create_module
+from ppocr.utils.utility import load_config, merge_config
+import ppocr.data.rec.reader_main as reader
+from ppocr.utils.utility import ArgsParser
+from ppocr.utils.character import CharacterOps, cal_predicts_accuracy
+from ppocr.utils.check import check_gpu
+from ppocr.utils.stats import TrainingStats
+from ppocr.utils.checkpoint import load_pretrain, load_checkpoint, save, save_model
+from ppocr.utils.eval_utils import eval_run
+
+from ppocr.utils.utility import initial_logger
+logger = initial_logger()
+from ppocr.utils.utility import create_multi_devices_program
+
+
+def main():
+    config = load_config(FLAGS.config)
+    merge_config(FLAGS.opt)
+    char_ops = CharacterOps(config['Global'])
+    config['Global']['char_num'] = char_ops.get_char_num()
+    print(config)
+
+    # check if set use_gpu=True in paddlepaddle cpu version
+    use_gpu = config['Global']['use_gpu']
+    check_gpu(use_gpu)
+
+    place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    rec_model = create_module(config['Architecture']['function'])(params=config)
+
+    startup_prog = fluid.Program()
+    train_prog = fluid.Program()
+    with fluid.program_guard(train_prog, startup_prog):
+        with fluid.unique_name.guard():
+            train_loader, train_outputs = rec_model(mode="train")
+            save_var = train_outputs[1]
+
+            if "gradient_clip" in config['Global']:
+                gradient_clip = config['Global']['gradient_clip']
+                clip = fluid.clip.GradientClipByGlobalNorm(gradient_clip)
+                fluid.clip.set_gradient_clip(clip, program=train_prog)
+
+            train_fetch_list = [v.name for v in train_outputs]
+            train_loss = train_outputs[0]
+            opt_params = config['Optimizer']
+            optimizer = create_module(opt_params['function'])(opt_params)
+            optimizer.minimize(train_loss)
+            global_lr = optimizer._global_learning_rate()
+            global_lr.persistable = True
+            train_fetch_list.append(global_lr.name)
+
+    train_reader = reader.train_eval_reader(
+        config=config, char_ops=char_ops, mode="train")
+    train_loader.set_sample_list_generator(train_reader, places=place)
+
+    eval_prog = fluid.Program()
+    with fluid.program_guard(eval_prog, startup_prog):
+        with fluid.unique_name.guard():
+            eval_loader, eval_outputs = rec_model(mode="eval")
+            eval_fetch_list = [v.name for v in eval_outputs]
+
+    eval_prog = eval_prog.clone(for_test=True)
+    exe.run(startup_prog)
+
+    eval_reader = reader.train_eval_reader(
+        config=config, char_ops=char_ops, mode="eval")
+    eval_loader.set_sample_list_generator(eval_reader, places=place)
+
+    # compile program for multi-devices
+    train_compile_program = create_multi_devices_program(train_prog,
+                                                         train_loss.name)
+
+    pretrain_weights = config['Global']['pretrain_weights']
+    if pretrain_weights is not None:
+        load_pretrain(exe, train_prog, pretrain_weights)
+
+    train_batch_id = 0
+    train_log_keys = ['loss', 'acc']
+    log_smooth_window = config['Global']['log_smooth_window']
+    epoch_num = config['Global']['epoch_num']
+    loss_type = config['Global']['loss_type']
+    print_step = config['Global']['print_step']
+    eval_step = config['Global']['eval_step']
+    save_epoch_step = config['Global']['save_epoch_step']
+    save_dir = config['Global']['save_dir']
+    train_stats = TrainingStats(log_smooth_window, train_log_keys)
+    best_eval_acc = -1
+    best_batch_id = 0
+    best_epoch = 0
+    for epoch in range(epoch_num):
+        train_loader.start()
+        try:
+            while True:
+                t1 = time.time()
+                train_outs = exe.run(program=train_compile_program,
+                                     fetch_list=train_fetch_list,
+                                     return_numpy=False)
+                loss = np.mean(np.array(train_outs[0]))
+                lr = np.mean(np.array(train_outs[-1]))
+
+                preds = np.array(train_outs[1])
+                preds_lod = train_outs[1].lod()[0]
+                labels = np.array(train_outs[2])
+                labels_lod = train_outs[2].lod()[0]
+
+                acc, acc_num, img_num = cal_predicts_accuracy(
+                    char_ops, preds, preds_lod, labels, labels_lod)
+
+                t2 = time.time()
+                train_batch_elapse = t2 - t1
+
+                stats = {'loss': loss, 'acc': acc}
+                train_stats.update(stats)
+                if train_batch_id > 0 and train_batch_id % print_step == 0:
+                    logs = train_stats.log()
+                    strs = 'epoch: {}, iter: {}, lr: {:.6f}, {}, time: {:.3f}'.format(
+                        epoch, train_batch_id, lr, logs, train_batch_elapse)
+                    logger.info(strs)
+
+                if train_batch_id > 0 and train_batch_id % eval_step == 0:
+                    outs = eval_run(exe, eval_prog, eval_loader,
+                                    eval_fetch_list, char_ops, train_batch_id,
+                                    "eval")
+                    eval_acc, acc_num, sample_num = outs
+                    if eval_acc > best_eval_acc:
+                        best_eval_acc = eval_acc
+                        best_batch_id = train_batch_id
+                        best_epoch = epoch
+                        save_path = save_dir + "/best_accuracy"
+                        save_model(train_prog, save_path)
+
+                    strs = 'Test iter: {}, acc:{:.6f}, best_acc:{:.6f}, best_epoch:{}, best_batch_id:{}, sample_num:{}'.format(
+                        train_batch_id, eval_acc, best_eval_acc, best_epoch,
+                        best_batch_id, sample_num)
+                    logger.info(strs)
+                train_batch_id += 1
+
+        except fluid.core.EOFException:
+            train_loader.reset()
+
+        if epoch > 0 and epoch % save_epoch_step == 0:
+            save_path = save_dir + "/iter_epoch_%d" % (epoch)
+            save_model(train_prog, save_path)
+
+
+def test_reader():
+    config = load_config(FLAGS.config)
+    merge_config(FLAGS.opt)
+    char_ops = CharacterOps(config['Global'])
+    config['Global']['char_num'] = char_ops.get_char_num()
+    print(config)
+    #     tmp_reader = reader.train_eval_reader(
+    #         config=cfg, char_ops=char_ops, mode="train")
+    tmp_reader = reader.train_eval_reader(
+        config=config, char_ops=char_ops, mode="eval")
+    count = 0
+    print_count = 0
+    import time
+    starttime = time.time()
+    for data in tmp_reader():
+        count += len(data)
+        print_count += 1
+        if print_count % 10 == 0:
+            batch_time = (time.time() - starttime) / print_count
+            print("reader:", count, len(data), batch_time)
+    print("finish reader:", count)
+    print("success")
+
+
+if __name__ == '__main__':
+    parser = ArgsParser()
+    parser.add_argument(
+        "-r",
+        "--resume_checkpoint",
+        default=None,
+        type=str,
+        help="Checkpoint path for resuming training.")
+    FLAGS = parser.parse_args()
+    main()
+#     test_reader()

tools/train.py

+# 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
+import time
+import multiprocessing
+import numpy as np
+
+
+def set_paddle_flags(**kwargs):
+    for key, value in kwargs.items():
+        if os.environ.get(key, None) is None:
+            os.environ[key] = str(value)
+
+
+# NOTE(paddle-dev): All of these flags should be
+# set before `import paddle`. Otherwise, it would
+# not take any effect. 
+set_paddle_flags(
+    FLAGS_eager_delete_tensor_gb=0,  # enable GC to save memory
+)
+
+import program
+from paddle import fluid
+from ppocr.utils.utility import initial_logger
+logger = initial_logger()
+from ppocr.data.reader_main import reader_main
+from ppocr.utils.save_load import init_model
+from ppocr.utils.character import CharacterOps
+
+
+def main():
+    config = program.load_config(FLAGS.config)
+    program.merge_config(FLAGS.opt)
+    logger.info(config)
+
+    # check if set use_gpu=True in paddlepaddle cpu version
+    use_gpu = config['Global']['use_gpu']
+    program.check_gpu(True)
+
+    alg = config['Global']['algorithm']
+    assert alg in ['EAST', 'DB', 'Rosetta', 'CRNN', 'STARNet', 'RARE']
+    if alg in ['Rosetta', 'CRNN', 'STARNet', 'RARE']:
+        config['Global']['char_ops'] = CharacterOps(config['Global'])
+
+    place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
+    startup_program = fluid.Program()
+    train_program = fluid.Program()
+    train_build_outputs = program.build(
+        config, train_program, startup_program, mode='train')
+    train_loader = train_build_outputs[0]
+    train_fetch_name_list = train_build_outputs[1]
+    train_fetch_varname_list = train_build_outputs[2]
+    train_opt_loss_name = train_build_outputs[3]
+
+    eval_program = fluid.Program()
+    eval_build_outputs = program.build(
+        config, eval_program, startup_program, mode='eval')
+    eval_fetch_name_list = eval_build_outputs[1]
+    eval_fetch_varname_list = eval_build_outputs[2]
+    eval_program = eval_program.clone(for_test=True)
+
+    train_reader = reader_main(config=config, mode="train")
+    train_loader.set_sample_list_generator(train_reader, places=place)
+
+    eval_reader = reader_main(config=config, mode="eval")
+
+    exe = fluid.Executor(place)
+    exe.run(startup_program)
+
+    # compile program for multi-devices
+    train_compile_program = program.create_multi_devices_program(
+        train_program, train_opt_loss_name)
+    init_model(config, train_program, exe)
+
+    train_info_dict = {'compile_program':train_compile_program,\
+        'train_program':train_program,\
+        'reader':train_loader,\
+        'fetch_name_list':train_fetch_name_list,\
+        'fetch_varname_list':train_fetch_varname_list}
+
+    eval_info_dict = {'program':eval_program,\
+        'reader':eval_reader,\
+        'fetch_name_list':eval_fetch_name_list,\
+        'fetch_varname_list':eval_fetch_varname_list}
+
+    if alg in ['EAST', 'DB']:
+        program.train_eval_det_run(config, exe, train_info_dict, eval_info_dict)
+    else:
+        program.train_eval_rec_run(config, exe, train_info_dict, eval_info_dict)
+
+
+if __name__ == '__main__':
+    parser = program.ArgsParser()
+    FLAGS = parser.parse_args()
+    main()
+#     test_reader()