Double_V
GitHub
commit
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<paddle.fluid.core_avx.ProgramDesc object at 0x10d15fab0>
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# 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 numpy as np
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from copy import deepcopy
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import json
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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.det.reader_main as reader
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from ppocr.utils.utility import ArgsParser
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from ppocr.utils.check import check_gpu
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from ppocr.utils.checkpoint import load_pretrain, load_checkpoint, save, save_model
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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.eval_utils import eval_det_run
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def draw_det_res(dt_boxes, config, img_name, ino):
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if len(dt_boxes) > 0:
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img_set_path = config['TestReader']['img_set_dir']
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img_path = img_set_path + img_name
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import cv2
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src_im = cv2.imread(img_path)
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for box in dt_boxes:
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box = box.astype(np.int32).reshape((-1, 1, 2))
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cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
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cv2.imwrite("tmp%d.jpg" % ino, src_im)
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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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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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det_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 = det_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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load_pretrain(exe, eval_prog, pretrain_weights)
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# fluid.load(eval_prog, pretrain_weights)
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# def if_exist(var):
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# return os.path.exists(os.path.join(pretrain_weights, var.name))
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# fluid.io.load_vars(exe, pretrain_weights, predicate=if_exist, main_program=eval_prog)
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else:
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logger.info("Not find pretrain_weights:%s" % pretrain_weights)
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sys.exit(0)
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# fluid.io.save_inference_model("./output/", feeded_var_names=['image'],
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# target_vars=eval_outputs, executor=exe, main_program=eval_prog,
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# model_filename="model", params_filename="params")
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# sys.exit(-1)
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metrics = eval_det_run(exe, eval_prog, eval_fetch_list, config, "test")
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logger.info("metrics:{}".format(metrics))
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logger.info("success!")
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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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print(config)
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tmp_reader = reader.test_reader(config=config)
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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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FLAGS = parser.parse_args()
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main()
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# test_reader()
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# 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 numpy as np
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from copy import deepcopy
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import json
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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.det.reader_main as reader
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from ppocr.utils.utility import ArgsParser
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from ppocr.utils.check import check_gpu
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from ppocr.utils.checkpoint import load_pretrain, load_checkpoint, save, save_model
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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.eval_utils import eval_det_run
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def draw_det_res(dt_boxes, config, img_name, ino):
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if len(dt_boxes) > 0:
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img_set_path = config['TestReader']['img_set_dir']
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img_path = img_set_path + img_name
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import cv2
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src_im = cv2.imread(img_path)
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for box in dt_boxes:
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box = box.astype(np.int32).reshape((-1, 1, 2))
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cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
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cv2.imwrite("tmp%d.jpg" % ino, src_im)
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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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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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det_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 = det_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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else:
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logger.info("Not find pretrain_weights:%s" % pretrain_weights)
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sys.exit(0)
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save_res_path = config['Global']['save_res_path']
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with open(save_res_path, "wb") as fout:
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test_reader = reader.test_reader(config=config)
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tackling_num = 0
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for data in test_reader():
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img_num = len(data)
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tackling_num = tackling_num + img_num
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logger.info("tackling_num:%d", tackling_num)
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img_list = []
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ratio_list = []
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img_name_list = []
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for ino in range(img_num):
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img_list.append(data[ino][0])
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ratio_list.append(data[ino][1])
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img_name_list.append(data[ino][2])
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img_list = np.concatenate(img_list, axis=0)
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outs = exe.run(eval_prog,\
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feed={'image': img_list},\
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fetch_list=eval_fetch_list)
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global_params = config['Global']
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postprocess_params = deepcopy(config["PostProcess"])
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postprocess_params.update(global_params)
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postprocess = create_module(postprocess_params['function'])\
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(params=postprocess_params)
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dt_boxes_list = postprocess(outs, ratio_list)
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for ino in range(img_num):
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dt_boxes = dt_boxes_list[ino]
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img_name = img_name_list[ino]
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dt_boxes_json = []
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for box in dt_boxes:
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tmp_json = {"transcription": ""}
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tmp_json['points'] = box.tolist()
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dt_boxes_json.append(tmp_json)
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otstr = img_name + "\t" + json.dumps(dt_boxes_json) + "\n"
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fout.write(otstr.encode())
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#draw_det_res(dt_boxes, config, img_name, ino)
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logger.info("success!")
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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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print(config)
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tmp_reader = reader.test_reader(config=config)
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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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FLAGS = parser.parse_args()
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main()
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# test_reader()
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# 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,
|
|
||||||
# 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.
|
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||||||
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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)
|
|
||||||
predict = exe.run(program=eval_prog,
|
|
||||||
feed={"image": blobs},
|
|
||||||
fetch_list=eval_fetch_list,
|
|
||||||
return_numpy=False)
|
|
||||||
preds = np.array(predict[0])
|
|
||||||
if preds.shape[1] == 1:
|
|
||||||
preds = preds.reshape(-1)
|
|
||||||
preds_lod = predict[0].lod()[0]
|
|
||||||
preds_text = char_ops.decode(preds)
|
|
||||||
else:
|
|
||||||
end_pos = np.where(preds[0, :] == 1)[0]
|
|
||||||
if len(end_pos) <= 1:
|
|
||||||
preds_text = preds[0, 1:]
|
|
||||||
else:
|
|
||||||
preds_text = preds[0, 1:end_pos[1]]
|
|
||||||
preds_text = preds_text.reshape(-1)
|
|
||||||
preds_text = char_ops.decode(preds_text)
|
|
||||||
|
|
||||||
fluid.io.save_inference_model(
|
|
||||||
"./output/",
|
|
||||||
feeded_var_names=['image'],
|
|
||||||
target_vars=eval_outputs,
|
|
||||||
executor=exe,
|
|
||||||
main_program=eval_prog,
|
|
||||||
model_filename="model",
|
|
||||||
params_filename="params")
|
|
||||||
print(preds)
|
|
||||||
print(preds_text)
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
|
||||||
parser = ArgsParser()
|
|
||||||
FLAGS = parser.parse_args()
|
|
||||||
main()
|
|
||||||
|
|
@ -1,128 +0,0 @@
|
||||||
# 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()
|
|
||||||
|
|
@ -1,216 +0,0 @@
|
||||||
# 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()
|
|
||||||
|
|
@ -1,222 +0,0 @@
|
||||||
# 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()
|
|
||||||
Loading…
Reference in New Issue