train.py 文件源码

def do_eval(sess,
            eval_loss,
            images_placeholder,
            labels_placeholder,
            training_time_placeholder,
            images,
            labels,
            batch_size):

    '''
    Function for running the evaluations every X iterations on the training and validation sets. 
    :param sess: The current tf session 
    :param eval_loss: The placeholder containing the eval loss
    :param images_placeholder: Placeholder for the images
    :param labels_placeholder: Placeholder for the masks
    :param training_time_placeholder: Placeholder toggling the training/testing mode. 
    :param images: A numpy array or h5py dataset containing the images
    :param labels: A numpy array or h45py dataset containing the corresponding labels 
    :param batch_size: The batch_size to use. 
    :return: The average loss (as defined in the experiment), and the average dice over all `images`. 
    '''

    loss_ii = 0
    dice_ii = 0
    num_batches = 0

    for batch in BackgroundGenerator(iterate_minibatches(images, labels, batch_size=batch_size, augment_batch=False)):  # No aug in evaluation
    # As before you can wrap the iterate_minibatches function in the BackgroundGenerator class for speed improvements
    # but at the risk of not catching exceptions

        x, y = batch

        if y.shape[0] < batch_size:
            continue

        feed_dict = { images_placeholder: x,
                      labels_placeholder: y,
                      training_time_placeholder: False}

        closs, cdice = sess.run(eval_loss, feed_dict=feed_dict)
        loss_ii += closs
        dice_ii += cdice
        num_batches += 1

    avg_loss = loss_ii / num_batches
    avg_dice = dice_ii / num_batches

    logging.info('  Average loss: %0.04f, average dice: %0.04f' % (avg_loss, avg_dice))

    return avg_loss, avg_dice