eval1.py

# -*- coding: utf-8 -*-
# /usr/bin/python2



import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 

import argparse

import tensorflow as tf
import hparams as hp
from data_load import get_batch
from models import Model


def eval(logdir, hparams):
    # Load graph
    model = Model(mode="test1", hparams=hparams)

    # Accuracy
    acc_op = model.acc_net1()

    # Loss
    loss_op = model.loss_net1()

    # Summary
    summ_op = summaries(acc_op, loss_op)

    #session_conf = tf.ConfigProto(
    #    allow_soft_placement=True,
    #    device_count={'CPU': 1, 'GPU': 0},
    #)

    session_conf=tf.ConfigProto()
    session_conf.gpu_options.per_process_gpu_memory_fraction=0.9

    with tf.Session(config=session_conf) as sess:
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(coord=coord)

        writer = tf.summary.FileWriter(logdir, sess.graph)

        # Load trained model
        sess.run(tf.global_variables_initializer())
        model.load(sess, 'train1', logdir=logdir)

        mfcc, ppg = get_batch(model.mode, model.batch_size)
        summ, acc, loss = sess.run([summ_op, acc_op, loss_op], feed_dict={model.x_mfcc: mfcc, model.y_ppgs: ppg})

        writer.add_summary(summ)

        print("acc:", acc)
        print("loss:", loss)
        print('\n')

        writer.close()

        coord.request_stop()
        coord.join(threads)


def summaries(acc, loss):
    tf.summary.scalar('net1/eval/acc', acc)
    tf.summary.scalar('net1/eval/loss', loss)
    return tf.summary.merge_all()


def get_arguments():
    parser = argparse.ArgumentParser()
    parser.add_argument('-case', type=str, default='default' ,help='experiment case name')
    parser.add_argument('-logdir', type=str, default='./logdir' ,help='tensorflow logdir, default: ./logdir')
    arguments = parser.parse_args()
    return arguments


if __name__ == '__main__':
    args = get_arguments()
    logdir = '{}/{}/train1'.format(args.logdir, args.case)
    
    eval(logdir, hp)

    print("Done")