segmentation_losses.py

# Copyright 2022 The TensorFlow Authors. 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Losses used for segmentation models."""

import tensorflow as tf

from official.modeling import tf_utils
from official.vision.dataloaders import utils

EPSILON = 1e-5


class SegmentationLoss:
  """Semantic segmentation loss."""

  def __init__(self,
               label_smoothing,
               class_weights,
               ignore_label,
               use_groundtruth_dimension,
               top_k_percent_pixels=1.0,
               gt_is_matting_map=False
               ):
    """Initializes `SegmentationLoss`.

    Args:
      label_smoothing: A float, if > 0., smooth out one-hot probability by
        spreading the amount of probability to all other label classes.
      class_weights: A float list containing the weight of each class.
      ignore_label: An integer specifying the ignore label.

      use_groundtruth_dimension: A boolean, whether to resize the output to
        match the dimension of the ground truth.
      top_k_percent_pixels: A float, the value lies in [0.0, 1.0]. When its
        value < 1., only compute the loss for the top k percent pixels. This is
        useful for hard pixel mining.
      gt_is_matting_map: If or not the groundtruth mask is a matting map. Note
        that the matting map is only supported for 2 class segmentation.
    """
    self._label_smoothing = label_smoothing
    self._class_weights = class_weights
    self._ignore_label = ignore_label
    self._use_groundtruth_dimension = use_groundtruth_dimension
    self._top_k_percent_pixels = top_k_percent_pixels
    self._gt_is_matting_map = gt_is_matting_map

  def __call__(self, logits, labels, **kwargs):
    """Computes `SegmentationLoss`.

    Args:
      logits: A float tensor in shape (batch_size, height, width, num_classes)
        which is the output of the network.
      labels: A tensor in shape (batch_size, height, width, 1), which is the
        label mask of the ground truth.
      **kwargs: additional keyword arguments.

    Returns:
       A 0-D float which stores the overall loss of the batch.
    """
    _, height, width, _ = logits.get_shape().as_list()

    if self._use_groundtruth_dimension:
      # TODO(arashwan): Test using align corners to match deeplab alignment.
      logits = tf.image.resize(
          logits, tf.shape(labels)[1:3], method=tf.image.ResizeMethod.BILINEAR)
    else:
      labels = tf.image.resize(
          labels, (height, width),
          method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)

    # Do not need to cast into int32 if it is a matting map
    if not self._gt_is_matting_map:
      labels = tf.cast(labels, tf.int32)

    valid_mask = tf.not_equal(labels, self._ignore_label)

    cross_entropy_loss = self.compute_pixelwise_loss(labels, logits, valid_mask,
                                                     **kwargs)

    if self._top_k_percent_pixels < 1.0:
      return self.aggregate_loss_top_k(cross_entropy_loss)
    else:
      return self.aggregate_loss(cross_entropy_loss, valid_mask)

  def compute_pixelwise_loss(self, labels, logits, valid_mask, **kwargs):
    """Computes the loss for each pixel.

    Args:
      labels: An int32 tensor in shape (batch_size, height, width, 1), which is
        the label mask of the ground truth.
      logits: A float tensor in shape (batch_size, height, width, num_classes)
        which is the output of the network.
      valid_mask: A bool tensor in shape (batch_size, height, width, 1) which
        masks out ignored pixels.
      **kwargs: additional keyword arguments.

    Returns:
       A float tensor in shape (batch_size, height, width) which stores the loss
       value for each pixel.
    """
    num_classes = logits.get_shape().as_list()[-1]

    # Assign pixel with ignore label to class 0 (background). The loss on the
    # pixel will later be masked out.
    labels = tf.where(valid_mask, labels, tf.zeros_like(labels))

    cross_entropy_loss = tf.nn.softmax_cross_entropy_with_logits(
        labels=self.get_labels_with_prob(labels, logits, **kwargs),
        logits=logits)

    if not self._class_weights:
      class_weights = [1] * num_classes
    else:
      class_weights = self._class_weights

    if num_classes != len(class_weights):
      raise ValueError(
          'Length of class_weights should be {}'.format(num_classes))

    valid_mask = tf.squeeze(tf.cast(valid_mask, tf.float32), axis=-1)

    # If groundtruth is matting map, binarize the value to create the weight
    # mask
    if self._gt_is_matting_map:
      labels = tf.cast(utils.binarize_matting_map(labels), tf.int32)

    weight_mask = tf.einsum(
        '...y,y->...',
        tf.one_hot(tf.squeeze(labels, axis=-1), num_classes, dtype=tf.float32),
        tf.constant(class_weights, tf.float32))
    return cross_entropy_loss * valid_mask * weight_mask

  def get_labels_with_prob(self, labels, logits, **unused_kwargs):
    """Get a tensor representing the probability of each class for each pixel.

    This method can be overridden in subclasses for customizing loss function.

    Args:
      labels: If groundtruth mask is not matting map, an int32 tensor which is
      the label map of the groundtruth. If groundtruth mask is matting map,
      an float32 tensor. The shape is always (batch_size, height, width, 1).
      logits: A float tensor in shape (batch_size, height, width, num_classes)
        which is the output of the network.
      **unused_kwargs: Unused keyword arguments.

    Returns:
       A float tensor in shape (batch_size, height, width, num_classes).
    """
    num_classes = logits.get_shape().as_list()[-1]

    if self._gt_is_matting_map:
      train_labels = tf.concat([1 - labels, labels], axis=-1)
    else:
      labels = tf.squeeze(labels, axis=-1)
      train_labels = tf.one_hot(labels, num_classes)
    return train_labels * (
        1 - self._label_smoothing) + self._label_smoothing / num_classes

  def aggregate_loss(self, pixelwise_loss, valid_mask):
    """Aggregate the pixelwise loss.

    Args:
      pixelwise_loss: A float tensor in shape (batch_size, height, width) which
        stores the loss of each pixel.
      valid_mask: A bool tensor in shape (batch_size, height, width, 1) which
        masks out ignored pixels.

    Returns:
       A 0-D float which stores the overall loss of the batch.
    """
    normalizer = tf.reduce_sum(tf.cast(valid_mask, tf.float32)) + EPSILON
    return tf.reduce_sum(pixelwise_loss) / normalizer

  def aggregate_loss_top_k(self, pixelwise_loss):
    """Aggregate the top-k greatest pixelwise loss.

    Args:
      pixelwise_loss: A float tensor in shape (batch_size, height, width) which
        stores the loss of each pixel.

    Returns:
       A 0-D float which stores the overall loss of the batch.
    """
    pixelwise_loss = tf.reshape(pixelwise_loss, shape=[-1])
    top_k_pixels = tf.cast(
        self._top_k_percent_pixels *
        tf.cast(tf.size(pixelwise_loss), tf.float32), tf.int32)
    top_k_losses, _ = tf.math.top_k(pixelwise_loss, k=top_k_pixels, sorted=True)
    normalizer = tf.reduce_sum(
        tf.cast(tf.not_equal(top_k_losses, 0.0), tf.float32)) + EPSILON
    return tf.reduce_sum(top_k_losses) / normalizer


def get_actual_mask_scores(logits, labels, ignore_label):
  """Gets actual mask scores."""
  _, height, width, num_classes = logits.get_shape().as_list()
  batch_size = tf.shape(logits)[0]
  logits = tf.stop_gradient(logits)
  labels = tf.image.resize(
      labels, (height, width), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
  predicted_labels = tf.argmax(logits, -1, output_type=tf.int32)
  flat_predictions = tf.reshape(predicted_labels, [batch_size, -1])
  flat_labels = tf.cast(tf.reshape(labels, [batch_size, -1]), tf.int32)

  one_hot_predictions = tf.one_hot(
      flat_predictions, num_classes, on_value=True, off_value=False)
  one_hot_labels = tf.one_hot(
      flat_labels, num_classes, on_value=True, off_value=False)
  keep_mask = tf.not_equal(flat_labels, ignore_label)
  keep_mask = tf.expand_dims(keep_mask, 2)

  overlap = tf.logical_and(one_hot_predictions, one_hot_labels)
  overlap = tf.logical_and(overlap, keep_mask)
  overlap = tf.reduce_sum(tf.cast(overlap, tf.float32), axis=1)
  union = tf.logical_or(one_hot_predictions, one_hot_labels)
  union = tf.logical_and(union, keep_mask)
  union = tf.reduce_sum(tf.cast(union, tf.float32), axis=1)
  actual_scores = tf.divide(overlap, tf.maximum(union, EPSILON))
  return actual_scores


class MaskScoringLoss:
  """Mask Scoring loss."""

  def __init__(self, ignore_label):
    self._ignore_label = ignore_label
    self._mse_loss = tf.keras.losses.MeanSquaredError(
        reduction=tf.keras.losses.Reduction.NONE)

  def __call__(self, predicted_scores, logits, labels):
    actual_scores = get_actual_mask_scores(logits, labels, self._ignore_label)
    loss = tf_utils.safe_mean(self._mse_loss(actual_scores, predicted_scores))
    return loss