# Copyright 2018 Google Inc., Juhan Bae All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#      http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

"""Provides definitions for non-regularized training or test losses."""
# noinspection PyUnresolvedReferences
import pathmagic
import tensorflow as tf

class BaseLoss(object):
    """Inherit from this class when implementing new losses."""

    def calculate_loss(self, unused_predictions, unused_labels, **unused_params):
        """Calculates the average loss of the examples in a mini-batch.

          unused_predictions: a 2-d tensor storing the prediction scores, in which
            each row represents a sample in the mini-batch and each column
            represents a class.
          unused_labels: a 2-d tensor storing the labels, which has the same shape
            as the unused_predictions. The labels must be in the range of 0 and 1.
          unused_params: loss specific parameters.

          A scalar loss tensor.
        raise NotImplementedError()

class CrossEntropyLoss(BaseLoss):
    """Calculate the cross entropy loss between the predictions and labels.
    def calculate_loss(self, predictions, labels, **unused_params):
        with tf.name_scope("loss_xent"):
            epsilon = 10e-6
            float_labels = tf.cast(labels, tf.float32)
            cross_entropy_loss = float_labels * tf.log(predictions + epsilon) + \
                                 (1 - float_labels) * tf.log(1 - predictions + epsilon)
            cross_entropy_loss = tf.negative(cross_entropy_loss)
        return tf.reduce_mean(tf.reduce_sum(cross_entropy_loss, 1))

class HingeLoss(BaseLoss):
    """Calculate the hinge loss between the predictions and labels.

    Note the subgradient is used in the backpropagation, and thus the optimization
    may converge slower. The predictions trained by the hinge loss are between -1
    and +1.
    def calculate_loss(self, predictions, labels, b=1.0, **unused_params):
        with tf.name_scope("loss_hinge"):
            float_labels = tf.cast(labels, tf.float32)
            all_zeros = tf.zeros(tf.shape(float_labels), dtype=tf.float32)
            all_ones = tf.ones(tf.shape(float_labels), dtype=tf.float32)
            sign_labels = tf.subtract(tf.scalar_mul(2, float_labels), all_ones)
            hinge_loss = tf.maximum(
              all_zeros, tf.scalar_mul(b, all_ones) - sign_labels * predictions)
            return tf.reduce_mean(tf.reduce_sum(hinge_loss, 1))

class SoftmaxLoss(BaseLoss):
    """Calculate the softmax loss between the predictions and labels.

    The function calculates the loss in the following way: first we feed the
    predictions to the softmax activation function and then we calculate
    the minus linear dot product between the logged softmax activations and the
    normalized ground truth label.

    It is an extension to the one-hot label. It allows for more than one positive
    labels for each sample.

    def calculate_loss(self, predictions, labels, **unused_params):
        with tf.name_scope("loss_softmax"):
            epsilon = 10e-8
            float_labels = tf.cast(labels, tf.float32)
            # l1 normalization (labels are no less than 0)
            label_rowsum = tf.maximum(
              tf.reduce_sum(float_labels, 1, keep_dims=True),
            norm_float_labels = tf.div(float_labels, label_rowsum)
            softmax_outputs = tf.nn.softmax(predictions)
            softmax_loss = tf.negative(tf.reduce_sum(
              tf.multiply(norm_float_labels, tf.log(softmax_outputs)), 1))
        return tf.reduce_mean(softmax_loss)