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import tensorflow as tf
import pandas as pd
import numpy as np
from tensorflow.python.platform import app
from core.Initializer import Initializer
from core.Model import create_model
from core.development_op import train, validate
from core.production_op import feed, create_batch_and_feed
from settings import FLAGS
from core.Memory import Memory
from utils.io_handler import create_session_dir, create_subfolder, write_metainfo, generate_batch_from_dir
def main(argv):
# train model is required in every case
train_model = create_model(mode='train')
if FLAGS.mode is "train_mode" or FLAGS.mode is "valid_mode":
val_model = create_model(mode='valid', train_model_scope=train_model.scope)
elif FLAGS.mode is "feeding_mode":
feeding_model = create_model(mode='feeding', train_model_scope=train_model.scope)
initializer = Initializer()
initializer.start_session()
initializer.start_saver()
# ---- training ----- #
if FLAGS.mode is "train_mode":
"""either create new directory or reuse old one"""
if not FLAGS.pretrained_model:
output_dir = create_session_dir(FLAGS.output_dir)
else:
output_dir = FLAGS.pretrained_model
print('Reusing provided session directory:', output_dir)
tf.logging.info(' --- ' + FLAGS.mode.capitalize() + ' --- ')
write_metainfo(output_dir, train_model.model_name, FLAGS)
train(output_dir, initializer, train_model, val_model)
# ---- validation ----- #
if FLAGS.mode is "valid_mode":
assert FLAGS.pretrained_model
if FLAGS.dump_dir:
output_dir = FLAGS.dump_dir
else:
output_dir = create_subfolder(output_dir, 'valid_run')
print('Storing validation data in:', output_dir)
tf.logging.info(' --- ' + FLAGS.mode.capitalize() + ' --- ')
validate(output_dir, initializer, val_model)
# ---- feeding ----- #
if FLAGS.mode is "feeding_mode":
tf.logging.info(' --- ' + FLAGS.mode.capitalize() + ' --- ')
""" scenario 1: feed input from a directory to create hidden representations of query """
hidden_repr = create_batch_and_feed(initializer, feeding_model)
print("output of model has shape: " + str(np.shape(hidden_repr)))
""" scenario 2: load the memory and query it with the hidden reps to get nearest neighbours """
# alternatively, run a validation with the 'memory_prep' VALID MODE (settings) and set memory path in settings
assert FLAGS.memory_path
memory_df = pd.read_pickle(FLAGS.memory_path)
memory = Memory(memory_df, '/common/homes/students/rothfuss/Documents/example/base_dir')
# choose e.g. first hidden representation
query = hidden_repr[0]
_, cos_distances, _, paths = memory.matching(query)
print(cos_distances, paths)
if __name__ == '__main__':
app.run()