test.py

import tensorflow as tf
import numpy as np
import C3D_model
import data_processing
TRAIN_LOG_DIR = 'Log/train/'
TRAIN_CHECK_POINT = 'check_point/train.ckpt-36'
TEST_LIST_PATH = 'test.list'
BATCH_SIZE = 10
NUM_CLASSES = 101
CROP_SZIE = 112
CHANNEL_NUM = 3
CLIP_LENGTH = 16
EPOCH_NUM = 50
test_num = data_processing.get_test_num(TEST_LIST_PATH)

test_video_indices = range(test_num)

with tf.Graph().as_default():
    batch_clips = tf.placeholder(tf.float32, [BATCH_SIZE, CLIP_LENGTH, CROP_SZIE, CROP_SZIE, CHANNEL_NUM], name='X')
    batch_labels = tf.placeholder(tf.int32, [BATCH_SIZE, NUM_CLASSES], name='Y')
    keep_prob = tf.placeholder(tf.float32)
    logits = C3D_model.C3D(batch_clips, NUM_CLASSES, keep_prob)
    accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits, 1), tf.argmax(batch_labels, 1)), np.float32))

    restorer = tf.train.Saver()
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    with tf.Session(config=config) as sess:
        sess.run(tf.global_variables_initializer())
        sess.run(tf.local_variables_initializer())
        restorer.restore(sess, TRAIN_CHECK_POINT)
        accuracy_epoch = 0
        batch_index = 0
        for i in range(test_num // BATCH_SIZE):
            if i % 10 == 0:
               print('Testing %d of %d'%(i + 1, test_num // BATCH_SIZE))
            batch_data, batch_index = data_processing.get_batches(TEST_LIST_PATH, NUM_CLASSES, batch_index,
                                                              test_video_indices, BATCH_SIZE)
            accuracy_out = sess.run(accuracy,feed_dict={batch_clips: batch_data['clips'],
                                                     batch_labels: batch_data['labels'],
                                                       keep_prob: 1.0})
            accuracy_epoch += accuracy_out

    print('Test accuracy is %.5f' % (accuracy_epoch / (test_num // BATCH_SIZE)))