def load_model(doc_path="inference_data", is_savedmodel=False):
params = inference_input(doc_path)
data_loader = DataLoader(
params,
params.classes,
update_dict=False,
load_dictionary=True,
data_split=0.0) # False to provide a path with only test data
num_words = max(20000, data_loader.num_words)
num_classes = data_loader.num_classes
# model
if params.use_cutie2:
network = CUTIEv2(num_words, num_classes, params)
else:
network = CUTIEv1(num_words, num_classes, params)
model_output = network.get_output('softmax')
if is_savedmodel:
sess = load_savedmodel(params.savedmodel_dir)
else:
# evaluation
ckpt_saver = tf.train.Saver()
config = tf.ConfigProto(allow_soft_placement=True)
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
try:
ckpt_path = os.path.join(params.e_ckpt_path, params.save_prefix,
params.ckpt_file)
ckpt = tf.train.get_checkpoint_state(ckpt_path)
print('Restoring from {}...'.format(ckpt_path))
ckpt_saver.restore(sess, ckpt_path)
print('{} restored'.format(ckpt_path))
except:
raise Exception('Check your pretrained {:s}'.format(ckpt_path))
return network, model_output, sess
if dim is None:
non_static_indexes.append(index)
if not non_static_indexes:
return shape
dyn_shape = tf.shape(tensor)
for index in non_static_indexes:
shape[index] = dyn_shape[index]
return shape
if __name__ == '__main__':
# data
data_loader = DataLoader(params,
update_dict=False,
load_dictionary=params.load_dict,
data_split=0.75)
# save bert dictionary
with open(params.bert_dict_file, encoding='utf-8') as f:
vocabs = f.read().split('\n')
num_words = len(vocabs)
dictionary = {vocab: 0 for vocab in vocabs}
word_to_index = dict(list(zip(dictionary.keys(), list(range(num_words)))))
index_to_word = dict(list(zip(list(range(num_words)), dictionary.keys())))
np.save(params.dict_path + '_dictionary.npy', dictionary)
np.save(params.dict_path + '_word_to_index.npy', word_to_index)
np.save(params.dict_path + '_index_to_word.npy', index_to_word)
# model
bert = BertEmbedding()
parser.add_argument('--load_dict_from_path', type=str, default='dict/SROIEnc') # 40000 or table or 20000TC
parser.add_argument('--tokenize', type=bool, default=True) # tokenize input text
parser.add_argument('--text_case', type=bool, default=False) # case sensitive
parser.add_argument('--dict_path', type=str, default='dict/---') # not used if load_dict is True
parser.add_argument('--restore_ckpt', type=bool, default=True)
parser.add_argument('--embedding_size', type=int, default=128)
parser.add_argument('--batch_size', type=int, default=1)
parser.add_argument('--c_threshold', type=float, default=0.5)
params = parser.parse_args()
if __name__ == '__main__':
# data
#data_loader = DataLoader(params, True, True) # True to use 25% training data
data_loader = DataLoader(params, update_dict=False, load_dictionary=True, data_split=0.75) # False to provide a path with only test data
num_words = max(20000, data_loader.num_words)
num_classes = data_loader.num_classes
# model
if params.use_cutie2:
network = CUTIEv2(num_words, num_classes, params)
else:
network = CUTIEv1(num_words, num_classes, params)
model_output = network.get_output('softmax')
# evaluation
ckpt_saver = tf.train.Saver()
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
# not used if load_dict is True
parser.add_argument('--dict_path', type=str, default='dict/---')
parser.add_argument('--restore_ckpt', type=bool, default=True)
parser.add_argument('--embedding_size', type=int, default=128)
parser.add_argument('--batch_size', type=int, default=1)
parser.add_argument('--c_threshold', type=float, default=0.5)
params = parser.parse_args()
if __name__ == '__main__':
# data
# data_loader = DataLoader(params, True, True) # True to use 25% training data
# False to provide a path with only test data
data_loader = DataLoader(params,
update_dict=False,
load_dictionary=True,
data_split=False)
num_words = max(20000, data_loader.num_words)
num_classes = data_loader.num_classes
# model
if params.use_cutie2:
network = CUTIEv2(num_words, num_classes, params)
else:
network = CUTIEv1(num_words, num_classes, params)
model_output = network.get_output('softmax')
# evaluation
ckpt_saver = tf.train.Saver()
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
if not os.path.exists(ckpt_path):
os.makedirs(ckpt_path)
filename = os.path.join(
ckpt_path, network.name + '_d{:d}c{:d}(r{:d}c{:d})_iter_{:d}'.format(
num_words, num_classes, data_loader.rows_ulimit,
data_loader.cols_ulimit, iter) + '.ckpt')
ckpt_saver.save(sess, filename)
print('\nCheckpoint saved to: {:s}\n'.format(filename))
if __name__ == '__main__':
pprint(params)
# data
data_loader = DataLoader(params,
update_dict=params.update_dict,
load_dictionary=params.load_dict,
data_split=0.99)
num_words = max(20000, data_loader.num_words)
num_classes = data_loader.num_classes
for _ in range(2000):
a = data_loader.next_batch()
b = data_loader.fetch_validation_data()
# c = data_loader.fetch_test_data()
# model
if params.use_cutie2:
network = CUTIEv2(num_words, num_classes, params)
else:
network = CUTIEv1(num_words, num_classes, params)
model_loss, regularization_loss, total_loss, model_logits, model_output = network.build_loss(
)
# written by Xiaohui Zhao # 2018-01 # [email protected] import tensorflow as tf import argparse from data_loader_json import DataLoader parser = argparse.ArgumentParser(description='CUTIE parameters') parser.add_argument('--dict_path', type=str, default='dict/SROIE') parser.add_argument('--doc_path', type=str, default='data/SROIE') parser.add_argument('--test_path', type=str, default='') # leave empty if no test data provided parser.add_argument('--text_case', type=bool, default=True) # case sensitive parser.add_argument('--tokenize', type=bool, default=True) # tokenize input text parser.add_argument('--batch_size', type=int, default=32) parser.add_argument('--use_cutie2', type=bool, default=False) params = parser.parse_args() if __name__ == '__main__': ## run this program before training to create a basic dictionary for training data_loader = DataLoader(params, update_dict=True, load_dictionary=False)
def infer(doc_path,
network=network,
model_output=model_output,
sess=sess) -> List[Prediction]:
params = inference_input(doc_path)
data_loader = DataLoader(
params,
params.classes,
update_dict=False,
load_dictionary=True,
data_split=0.0) # False to provide a path with only test data
'''
num_words = max(20000, data_loader.num_words)
num_classes = data_loader.num_classes
# model
if params.use_cutie2:
network = CUTIEv2(num_words, num_classes, params)
else:
network = CUTIEv1(num_words, num_classes, params)
model_output = network.get_output('softmax')
# evaluation
ckpt_saver = tf.train.Saver()
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
try:
ckpt_path = os.path.join(params.e_ckpt_path, params.save_prefix, params.ckpt_file)
ckpt = tf.train.get_checkpoint_state(ckpt_path)
print('Restoring from {}...'.format(ckpt_path))
ckpt_saver.restore(sess, ckpt_path)
print('{} restored'.format(ckpt_path))
except:
raise Exception('Check your pretrained {:s}'.format(ckpt_path))
'''
num_test = len(data_loader.validation_docs)
results = []
result_files = []
for i in range(num_test):
predictions = []
data = data_loader.fetch_validation_data()
print('{:d} samples left to be tested'.format(num_test - i))
# grid_table = data['grid_table']
# gt_classes = data['gt_classes']
feed_dict = {
network.data_grid: data['grid_table'],
}
if params.use_cutie2:
feed_dict = {
network.data_grid: data['grid_table'],
network.data_image: data['data_image'],
network.ps_1d_indices: data['ps_1d_indices']
}
fetches = [model_output]
print(data['file_name'][0])
print(data['grid_table'].shape, data['data_image'].shape,
data['ps_1d_indices'].shape)
timer_start = timeit.default_timer()
[model_output_val] = sess.run(fetches=fetches, feed_dict=feed_dict)
timer_stop = timeit.default_timer()
print('\t >>time per step: %.2fs <<' % (timer_stop - timer_start))
# visualize result
shape = data['shape']
file_name = data['file_name'][0] # use one single file_name
bboxes = data['bboxes'][file_name]
if not params.is_table:
predictions = get_predicted_bboxes(data_loader, params.doc_path,
np.array(data['grid_table'])[0],
np.array(data['gt_classes'])[0],
np.array(model_output_val)[0],
file_name, np.array(bboxes),
shape)
results.append(predictions)
result_files.append(file_name)
return results, result_files