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:
raise Exception('Check your pretrained {:s}'.format(ckpt_path))
# calculate validation accuracy and display results
recalls, accs_strict, accs_soft = [], [], []
num_test = len(data_loader.validation_docs)
for i in range(num_test):
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]
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