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
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())
try:
#ckpt_path = os.path.join(params.e_ckpt_path, params.save_prefix, params.ckpt_file)
ckpt_path = '/content/content/CUTIE/graph/INVOICE/CUTIE_atrousSPP_best.ckpt'
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:
import json
from data_loader_json import DataLoader
# from configs.config import model_params
from model_cutie_aspp import CUTIERes as CUTIEv1
trained_checkpoint_prefix = '/home/jainammm/flipkart-grid2.0/code/CUTIEPI/pretrained_model/CUTIE.ckpt'
export_dir = os.path.join('model_for_serving', 'CUTIE', '1')
num_words = 20000
num_classes = 26
# model
network = CUTIEv1(num_words, num_classes, model_params)
model_input = network.data_grid
model_output = network.get_output('softmax')
ckpt_saver = tf.train.Saver()
config = tf.ConfigProto(allow_soft_placement=True)
graph = tf.Graph()
with tf.Session(graph=graph, config=config) as sess:
# Restore from checkpoint
print('jainam')
loader = tf.train.import_meta_graph(trained_checkpoint_prefix + '.meta')
loader.restore(sess, trained_checkpoint_prefix)
# Export checkpoint to SavedModel