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f3507ef | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 | # Copyright 2017 The TensorFlow 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.
# ==============================================================================
"""Define flags are common for both train.py and eval.py scripts."""
import logging
import sys
from tensorflow.compat.v1 import flags
import datasets
import model
FLAGS = flags.FLAGS
logging.basicConfig(
level=logging.DEBUG,
stream=sys.stderr,
format='%(levelname)s '
'%(asctime)s.%(msecs)06d: '
'%(filename)s: '
'%(lineno)d '
'%(message)s',
datefmt='%Y-%m-%d %H:%M:%S')
_common_flags_defined = False
def define():
"""Define common flags."""
# yapf: disable
# common_flags.define() may be called multiple times in unit tests.
global _common_flags_defined
if _common_flags_defined:
return
_common_flags_defined = True
flags.DEFINE_integer('batch_size', 32,
'Batch size.')
flags.DEFINE_integer('crop_width', None,
'Width of the central crop for images.')
flags.DEFINE_integer('crop_height', None,
'Height of the central crop for images.')
flags.DEFINE_string('train_log_dir', '/tmp/attention_ocr/train',
'Directory where to write event logs.')
flags.DEFINE_string('dataset_name', 'fsns',
'Name of the dataset. Supported: fsns')
flags.DEFINE_string('split_name', 'train',
'Dataset split name to run evaluation for: test,train.')
flags.DEFINE_string('dataset_dir', None,
'Dataset root folder.')
flags.DEFINE_string('checkpoint', '',
'Path for checkpoint to restore weights from.')
flags.DEFINE_string('master',
'',
'BNS name of the TensorFlow master to use.')
# Model hyper parameters
flags.DEFINE_float('learning_rate', 0.004,
'learning rate')
flags.DEFINE_string('optimizer', 'momentum',
'the optimizer to use')
flags.DEFINE_float('momentum', 0.9,
'momentum value for the momentum optimizer if used')
flags.DEFINE_bool('use_augment_input', True,
'If True will use image augmentation')
# Method hyper parameters
# conv_tower_fn
flags.DEFINE_string('final_endpoint', 'Mixed_5d',
'Endpoint to cut inception tower')
# sequence_logit_fn
flags.DEFINE_bool('use_attention', True,
'If True will use the attention mechanism')
flags.DEFINE_bool('use_autoregression', True,
'If True will use autoregression (a feedback link)')
flags.DEFINE_integer('num_lstm_units', 256,
'number of LSTM units for sequence LSTM')
flags.DEFINE_float('weight_decay', 0.00004,
'weight decay for char prediction FC layers')
flags.DEFINE_float('lstm_state_clip_value', 10.0,
'cell state is clipped by this value prior to the cell'
' output activation')
# 'sequence_loss_fn'
flags.DEFINE_float('label_smoothing', 0.1,
'weight for label smoothing')
flags.DEFINE_bool('ignore_nulls', True,
'ignore null characters for computing the loss')
flags.DEFINE_bool('average_across_timesteps', False,
'divide the returned cost by the total label weight')
# yapf: enable
def get_crop_size():
if FLAGS.crop_width and FLAGS.crop_height:
return (FLAGS.crop_width, FLAGS.crop_height)
else:
return None
def create_dataset(split_name):
ds_module = getattr(datasets, FLAGS.dataset_name)
return ds_module.get_split(split_name, dataset_dir=FLAGS.dataset_dir)
def create_mparams():
return {
'conv_tower_fn':
model.ConvTowerParams(final_endpoint=FLAGS.final_endpoint),
'sequence_logit_fn':
model.SequenceLogitsParams(
use_attention=FLAGS.use_attention,
use_autoregression=FLAGS.use_autoregression,
num_lstm_units=FLAGS.num_lstm_units,
weight_decay=FLAGS.weight_decay,
lstm_state_clip_value=FLAGS.lstm_state_clip_value),
'sequence_loss_fn':
model.SequenceLossParams(
label_smoothing=FLAGS.label_smoothing,
ignore_nulls=FLAGS.ignore_nulls,
average_across_timesteps=FLAGS.average_across_timesteps)
}
def create_model(*args, **kwargs):
ocr_model = model.Model(mparams=create_mparams(), *args, **kwargs)
return ocr_model
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