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train.py
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train.py
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"""Train the model"""
import argparse
import logging
import os
import numpy as np
import tensorflow as tf
from model.input_fn import input_fn
from model.model_fn import model_fn
from model.training import train_and_evaluate
from model.utils import Params
from model.utils import set_logger
parser = argparse.ArgumentParser()
parser.add_argument('--model_dir', default='experiments/test',
help="Experiment directory containing params.json")
parser.add_argument('--data_dir', default='data/300x300_MUMU',
help="Directory containing the dataset")
parser.add_argument('--restore_from', default=None,
help="Optional, directory or file containing weights to reload before training")
if __name__ == '__main__':
# Set the random seed for the whole graph for reproducible experiments
tf.set_random_seed(230)
# Load the parameters from json file
args = parser.parse_args()
json_path = os.path.join(args.model_dir, 'params.json')
assert os.path.isfile(json_path), "No json configuration file found at {}".format(json_path)
params = Params(json_path)
# Check that we are not overwriting some previous experiment (comment out if developing model)
# model_dir_has_best_weights = os.path.isdir(os.path.join(args.model_dir, "best_weights"))
# overwritting = model_dir_has_best_weights and args.restore_from is None
# assert not overwritting, "Weights found in model_dir, aborting to avoid overwrite"
# Set the logger
set_logger(os.path.join(args.model_dir, 'train.log'))
# Create the input data pipeline
logging.info("Creating the dataset...")
data_dir = args.data_dir
train_images_dir = os.path.join(data_dir, "train/images")
dev_images_dir = os.path.join(data_dir, "dev/images")
train_genres_file = os.path.join(data_dir, "train/genres/y_train.npy")
dev_genres_file = os.path.join(data_dir, "dev/genres/y_dev.npy")
# Get the filenames from the train and dev sets
train_filenames = [os.path.join(train_images_dir, f) for f in os.listdir(train_images_dir) if f.endswith('.jpg')]
eval_filenames = [os.path.join(dev_images_dir, f) for f in os.listdir(dev_images_dir) if f.endswith('.jpg')]
# Labels will be binary vector representing all genres
train_labels = np.load(train_genres_file)
eval_labels = np.load(dev_genres_file)
# Specify the sizes of the dataset we train on and evaluate on
params.train_size = len(train_filenames)
params.eval_size = len(eval_filenames)
# Create the two iterators over the two datasets
train_inputs = input_fn(True, train_filenames, train_labels, params)
eval_inputs = input_fn(False, eval_filenames, eval_labels, params)
# Define the model
logging.info("Creating the model...")
train_model_spec = model_fn('train', train_inputs, params)
eval_model_spec = model_fn('eval', eval_inputs, params, reuse=True)
# Train the model
logging.info("Starting training for {} epoch(s)".format(params.num_epochs))
train_and_evaluate(train_model_spec, eval_model_spec, args.model_dir, params, args.restore_from)