# Path: piecewisecrf/slim/slim.py



import tensorflow as tf
import piecewisecrf.datasets.helpers.pairwise_label_generator as indices
import piecewisecrf.config.prefs as prefs
from piecewisecrf.slim import slim
        one_hot_labels_unary = tf.reshape(one_hot_labels_unary, [num_examples, FLAGS.num_classes])
        one_hot_labels_binary = tf.one_hot(tf.to_int64(labels_binary), FLAGS.num_classes * FLAGS.num_classes, 1, 0)
        one_hot_labels_binary = tf.reshape(one_hot_labels_binary, [num_neighbours,
                                           FLAGS.num_classes * FLAGS.num_classes])
        one_hot_labels_binary_ab = tf.one_hot(tf.to_int64(labels_binary_ab),
                                              FLAGS.num_classes * FLAGS.num_classes, 1, 0)
        one_hot_labels_binary_ab = tf.reshape(one_hot_labels_binary_ab, [num_neighbours_ab,
                                              FLAGS.num_classes * FLAGS.num_classes])

        out_unary_1d = tf.reshape(out_unary, [num_examples, FLAGS.num_classes])
        out_binary_1d = tf.reshape(out_binary, [num_neighbours, FLAGS.num_classes * FLAGS.num_classes])
        out_binary_ab_1d = tf.reshape(out_binary_ab, [num_neighbours_ab, FLAGS.num_classes * FLAGS.num_classes])

        log_softmax_unary = tf.log(tf.clip_by_value(tf.nn.softmax(out_unary_1d), 1.0e-6, 1.0))
        log_softmax_binary = tf.log(tf.clip_by_value(tf.nn.softmax(out_binary_1d), 1.0e-6, 1.0))
        log_softmax_binary_ab = tf.log(tf.clip_by_value(tf.nn.softmax(out_binary_ab_1d), 1.0e-6, 1.0))

        xent = tf.reduce_sum(tf.mul(tf.to_float(one_hot_labels_unary), log_softmax_unary), 1)
        pu = tf.mul(tf.minimum(tf.to_float(100), weights), xent)

        xent_bin = tf.reduce_sum(tf.mul(tf.to_float(one_hot_labels_binary), log_softmax_binary), 1)
        pb = tf.mul(tf.minimum(tf.to_float(100), weights_surr), xent_bin)

        xent_ab = tf.reduce_sum(tf.mul(tf.to_float(one_hot_labels_binary_ab), log_softmax_binary_ab), 1)
        pb_ab = tf.mul(tf.minimum(tf.to_float(100), weights_ab), xent_ab)

        loss_val = (tf.div(tf.reduce_sum(-pu), tf.to_float(num_examples)) +
                    tf.div(tf.reduce_sum(-pb), tf.to_float(num_neighbours)) +
                    tf.div(tf.reduce_sum(-pb_ab), tf.to_float(num_neighbours_ab)))


# Path: piecewisecrf/slim/slim.py



# Path: piecewisecrf/models/losses.py
import tensorflow as tf
import piecewisecrf.datasets.helpers.pairwise_label_generator as indices
import piecewisecrf.config.prefs as prefs
from piecewisecrf.slim import slim

        one_hot_labels_unary = tf.reshape(one_hot_labels_unary, [num_examples, FLAGS.num_classes])
        one_hot_labels_binary = tf.one_hot(tf.to_int64(labels_binary), FLAGS.num_classes * FLAGS.num_classes, 1, 0)
        one_hot_labels_binary = tf.reshape(one_hot_labels_binary, [num_neighbours,
                                           FLAGS.num_classes * FLAGS.num_classes])
        one_hot_labels_binary_ab = tf.one_hot(tf.to_int64(labels_binary_ab),
                                              FLAGS.num_classes * FLAGS.num_classes, 1, 0)
        one_hot_labels_binary_ab = tf.reshape(one_hot_labels_binary_ab, [num_neighbours_ab,
                                              FLAGS.num_classes * FLAGS.num_classes])

        out_unary_1d = tf.reshape(out_unary, [num_examples, FLAGS.num_classes])
        out_binary_1d = tf.reshape(out_binary, [num_neighbours, FLAGS.num_classes * FLAGS.num_classes])
        out_binary_ab_1d = tf.reshape(out_binary_ab, [num_neighbours_ab, FLAGS.num_classes * FLAGS.num_classes])

        log_softmax_unary = tf.log(tf.clip_by_value(tf.nn.softmax(out_unary_1d), 1.0e-6, 1.0))
        log_softmax_binary = tf.log(tf.clip_by_value(tf.nn.softmax(out_binary_1d), 1.0e-6, 1.0))
        log_softmax_binary_ab = tf.log(tf.clip_by_value(tf.nn.softmax(out_binary_ab_1d), 1.0e-6, 1.0))

        xent = tf.reduce_sum(tf.mul(tf.to_float(one_hot_labels_unary), log_softmax_unary), 1)
        pu = tf.mul(tf.minimum(tf.to_float(100), weights), xent)

        xent_bin = tf.reduce_sum(tf.mul(tf.to_float(one_hot_labels_binary), log_softmax_binary), 1)
        pb = tf.mul(tf.minimum(tf.to_float(100), weights_surr), xent_bin)

        xent_ab = tf.reduce_sum(tf.mul(tf.to_float(one_hot_labels_binary_ab), log_softmax_binary_ab), 1)
        pb_ab = tf.mul(tf.minimum(tf.to_float(100), weights_ab), xent_ab)

        loss_val = (tf.div(tf.reduce_sum(-pu), tf.to_float(num_examples)) +
                    tf.div(tf.reduce_sum(-pb), tf.to_float(num_neighbours)) +
                    tf.div(tf.reduce_sum(-pb_ab), tf.to_float(num_neighbours_ab)))


        tf.add_to_collection(slim.losses.LOSSES_COLLECTION, loss_val)