model.py 文件源码

def evaluation(logits, labels, images, nlabels, loss_type):
    '''
    A function for evaluating the performance of the netwrok on a minibatch. This function returns the loss and the 
    current foreground Dice score, and also writes example segmentations and imges to to tensorboard.
    :param logits: Output of network before softmax
    :param labels: Ground-truth label mask
    :param images: Input image mini batch
    :param nlabels: Number of labels in the dataset
    :param loss_type: Which loss should be evaluated
    :return: The loss without weight decay, the foreground dice of a minibatch
    '''

    mask = tf.arg_max(tf.nn.softmax(logits, dim=-1), dimension=-1)  # was 3
    mask_gt = labels

    tf.summary.image('example_gt', prepare_tensor_for_summary(mask_gt, mode='mask', nlabels=nlabels))
    tf.summary.image('example_pred', prepare_tensor_for_summary(mask, mode='mask', nlabels=nlabels))
    tf.summary.image('example_zimg', prepare_tensor_for_summary(images, mode='image'))

    total_loss, nowd_loss, weights_norm = loss(logits, labels, nlabels=nlabels, loss_type=loss_type)

    cdice_structures = losses.per_structure_dice(logits, tf.one_hot(labels, depth=nlabels))
    cdice_foreground = cdice_structures[:,1:]

    cdice = tf.reduce_mean(cdice_foreground)

    return nowd_loss, cdice