def main(unused_argv):
# 输出路径不存在就创建
if not os.path.exists(FLAGS.output_model_dir):
os.makedirs(FLAGS.output_model_dir)
if FLAGS.params_file:
dict_params = import_params_from_json(json_filename=FLAGS.params_file)
parameters = Params(**dict_params)
else:
parameters = Params(
train_batch_size=128,
eval_batch_size=59,
learning_rate=0.001, # 1e-3 recommended
learning_decay_rate=0.5,
learning_decay_steps=23438 * 2,
evaluate_every_epoch=1,
save_interval=5e3,
input_shape=(32, 304),
image_channels=3,
optimizer='adam',
digits_only=False,
alphabet=Alphabet.CHINESECHAR_LETTERS_DIGITS_EXTENDED,
alphabet_decoding='same',
csv_delimiter=' ',
csv_files_train=FLAGS.csv_files_train,
csv_files_eval=FLAGS.csv_files_eval,
output_model_dir=FLAGS.output_model_dir,
n_epochs=FLAGS.nb_epochs,
gpu=FLAGS.gpu)
model_params = {
'Params': parameters,
}
# 保存配置
parameters.export_experiment_params()
os.environ['CUDA_VISIBLE_DEVICES'] = parameters.gpu
config_sess = tf.ConfigProto()
config_sess.gpu_options.per_process_gpu_memory_fraction = 0.4
# Count number of image filenames in csv
n_samples = 0
for file in parameters.csv_files_train:
with open(file, mode='r', encoding='utf8') as csvfile:
n_samples += len(csvfile.readlines())
save_checkpoints_steps = int(
np.ceil(n_samples / parameters.train_batch_size))
keep_checkpoint_max = parameters.n_epochs
print(n_samples, 'save_checkpoints_steps', save_checkpoints_steps,
' keep_checkpoint_max', keep_checkpoint_max)
# Config estimator
est_config = tf.estimator.RunConfig()
est_config = est_config.replace(
keep_checkpoint_max=keep_checkpoint_max,
save_checkpoints_steps=save_checkpoints_steps,
session_config=config_sess,
save_summary_steps=100,
model_dir=parameters.output_model_dir)
estimator = tf.estimator.Estimator(model_fn=crnn_fn,
params=model_params,
model_dir=parameters.output_model_dir,
config=est_config)
try:
tensors_to_log = {'train_accuracy': 'train_accuracy'}
logging_hook = tf.train.LoggingTensorHook(tensors=tensors_to_log,
every_n_iter=100)
for e in range(0, parameters.n_epochs,
parameters.evaluate_every_epoch):
estimator.train(input_fn=data_loader(
csv_filename=parameters.csv_files_train,
params=parameters,
batch_size=parameters.train_batch_size,
num_epochs=parameters.evaluate_every_epoch,
data_augmentation=True,
image_summaries=True),
hooks=[logging_hook])
eval_results = estimator.evaluate(
input_fn=data_loader(csv_filename=parameters.csv_files_eval,
params=parameters,
batch_size=parameters.eval_batch_size,
num_epochs=1),
steps=np.floor(n_samples / parameters.eval_batch_size),
)
print('Evaluation results: %s' % (str(eval_results)))
# for tensorflow1.4
# estimator.train(input_fn=input_fn(filename=parameters.csv_files_train,
# is_training=True
# params=parameters,
# batch_size=parameters.train_batch_size,
# num_epochs=parameters.n_epochs),
# hooks=[logging_hook])
except KeyboardInterrupt:
print('Interrupted')
estimator.export_savedmodel(
os.path.join(parameters.output_model_dir, 'export'),
preprocess_image_for_prediction(min_width=10))
print('Exported model to {}'.format(
os.path.join(parameters.output_model_dir, 'export')))
estimator.export_savedmodel(
os.path.join(parameters.output_model_dir, 'export'),
preprocess_image_for_prediction(min_width=10))
print('Exported model to {}'.format(
os.path.join(parameters.output_model_dir, 'export')))
config=est_config)
# Count number of image filenames in csv
n_samples = 0
for file in parameters.csv_files_eval:
with open(file, 'r', encoding='utf-8') as csvfile:
reader = csv.reader(csvfile, delimiter=parameters.csv_delimiter)
n_samples += len(list(reader))
try:
for e in trange(0, parameters.n_epochs,
parameters.evaluate_every_epoch):
estimator.train(input_fn=data_loader(
csv_filename=parameters.csv_files_train,
params=parameters,
batch_size=parameters.train_batch_size,
num_epochs=parameters.evaluate_every_epoch,
data_augmentation=True,
image_summaries=True))
estimator.evaluate(
input_fn=data_loader(csv_filename=parameters.csv_files_eval,
params=parameters,
batch_size=parameters.eval_batch_size,
num_epochs=1),
steps=np.floor(n_samples / parameters.eval_batch_size))
except KeyboardInterrupt:
print('Interrupted')
estimator.export_savedmodel(
os.path.join(parameters.output_model_dir, 'export'),
preprocess_image_for_prediction(min_width=10))
save_checkpoints_secs=None,
save_summary_steps=1000)
model_params = {
'Params': params,
}
estimator = tf.estimator.Estimator(
model_fn=crnn_fn,
params=model_params,
model_dir=args.get('model_dir'),
config=est_config,
)
estimator.train(input_fn=data_loader(csv_filename=params.csv_files_train,
params=params,
batch_size=1,
num_epochs=1,
data_augmentation=True,
image_summaries=True))
estimator.export_savedmodel(
args.get('export_dir'),
serving_input_receiver_fn=preprocess_image_for_prediction(
min_width=10))
#
# def _signature_def_to_tensors(signature_def):
# g = tf.get_default_graph()
# return {k: g.get_tensor_by_name(v.name) for k,v in signature_def.inputs.items()}, \
# {k: g.get_tensor_by_name(v.name) for k,v in signature_def.outputs.items()}
#
# with tf.Session(graph=tf.Graph()) as sess:
save_checkpoints_steps=parameters.save_interval,
session_config=config_sess,
save_checkpoints_secs=None,
save_summary_steps=1000,
model_dir=parameters.output_model_dir)
estimator = tf.estimator.Estimator(model_fn=crnn_fn,
params=model_params,
model_dir=parameters.output_model_dir,
config=est_config
)
predictResults=estimator.predict(input_fn=data_loader(csv_filename='/Users/samueltin/Projects/sf/sf-image-generator/output/Test/sample.csv',
params=parameters,
batch_size=1,
num_epochs=1,
data_augmentation=False,
image_summaries=False
))
is_vis=False
ans_dict = {}
ans_file = open('/Users/samueltin/Projects/sf/sf-image-generator/output/Test/sample.csv', mode='r', encoding='utf-8')
content = ans_file.read()
ans_file.close()
lines = content.split('\n')
for line in lines:
items = line.split('\t')
if len(items)>1:
chats = items[1].split('{')
label = ''.join(chats)