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# Licensed under the Apache License, Version 2.0 (the "License");
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"""Metrics for segmentation."""
import tensorflow as tf

from official.vision.evaluation import iou


class MeanIoU(tf.keras.metrics.MeanIoU):
  """Mean IoU metric for semantic segmentation.

  This class utilizes tf.keras.metrics.MeanIoU to perform batched mean iou when
  both input images and groundtruth masks are resized to the same size
  (rescale_predictions=False). It also computes mean iou on groundtruth original
  sizes, in which case, each prediction is rescaled back to the original image
  size.
  """

  def __init__(
      self, num_classes, rescale_predictions=False, name=None, dtype=None):
    """Constructs Segmentation evaluator class.

    Args:
      num_classes: `int`, number of classes.
      rescale_predictions: `bool`, whether to scale back prediction to original
        image sizes. If True, y_true['image_info'] is used to rescale
        predictions.
      name: `str`, name of the metric instance..
      dtype: data type of the metric result.
    """
    self._rescale_predictions = rescale_predictions
    super().__init__(num_classes=num_classes, name=name, dtype=dtype)

  def update_state(self, y_true, y_pred):
    """Updates metric state.

    Args:
      y_true: `dict`, dictionary with the following name, and key values.
        - masks: [batch, width, height, 1], groundtruth masks.
        - valid_masks: [batch, width, height, 1], valid elements in the mask.
        - image_info: [batch, 4, 2], a tensor that holds information about
          original and preprocessed images. Each entry is in the format of
          [[original_height, original_width], [input_height, input_width],
          [y_scale, x_scale], [y_offset, x_offset]], where [desired_height,
          desired_width] is the actual scaled image size, and [y_scale, x_scale]
          is the scaling factor, which is the ratio of scaled dimension /
          original dimension.
      y_pred: Tensor [batch, width_p, height_p, num_classes], predicated masks.
    """
    predictions = y_pred
    masks = y_true['masks']
    valid_masks = y_true['valid_masks']
    images_info = y_true['image_info']

    if isinstance(predictions, tuple) or isinstance(predictions, list):
      predictions = tf.concat(predictions, axis=0)
      masks = tf.concat(masks, axis=0)
      valid_masks = tf.concat(valid_masks, axis=0)
      images_info = tf.concat(images_info, axis=0)

    # Ignore mask elements is set to zero for argmax op.
    masks = tf.where(valid_masks, masks, tf.zeros_like(masks))

    if self._rescale_predictions:
      # This part can only run on cpu/gpu due to dynamic image resizing.
      for i in range(tf.shape(predictions)[0]):
        mask = masks[i]
        valid_mask = valid_masks[i]
        predicted_mask = predictions[i]
        image_info = images_info[i]

        rescale_size = tf.cast(
            tf.math.ceil(image_info[1, :] / image_info[2, :]), tf.int32)
        image_shape = tf.cast(image_info[0, :], tf.int32)
        offsets = tf.cast(image_info[3, :], tf.int32)

        predicted_mask = tf.image.resize(
            predicted_mask,
            rescale_size,
            method=tf.image.ResizeMethod.BILINEAR)

        predicted_mask = tf.image.crop_to_bounding_box(predicted_mask,
                                                       offsets[0], offsets[1],
                                                       image_shape[0],
                                                       image_shape[1])
        mask = tf.image.crop_to_bounding_box(mask, 0, 0, image_shape[0],
                                             image_shape[1])
        valid_mask = tf.image.crop_to_bounding_box(valid_mask, 0, 0,
                                                   image_shape[0],
                                                   image_shape[1])

        predicted_mask = tf.argmax(predicted_mask, axis=2)
        flatten_predictions = tf.reshape(predicted_mask, shape=[1, -1])
        flatten_masks = tf.reshape(mask, shape=[1, -1])
        flatten_valid_masks = tf.reshape(valid_mask, shape=[1, -1])
        super(MeanIoU, self).update_state(
            flatten_masks, flatten_predictions,
            tf.cast(flatten_valid_masks, tf.float32))

    else:
      predictions = tf.image.resize(
          predictions,
          tf.shape(masks)[1:3],
          method=tf.image.ResizeMethod.BILINEAR)
      predictions = tf.argmax(predictions, axis=3)
      flatten_predictions = tf.reshape(predictions, shape=[-1])
      flatten_masks = tf.reshape(masks, shape=[-1])
      flatten_valid_masks = tf.reshape(valid_masks, shape=[-1])

      super().update_state(flatten_masks, flatten_predictions,
                           tf.cast(flatten_valid_masks, tf.float32))


class PerClassIoU(iou.PerClassIoU):
  """Per Class IoU metric for semantic segmentation.

  This class utilizes iou.PerClassIoU to perform batched per class
  iou when both input images and groundtruth masks are resized to the same size
  (rescale_predictions=False). It also computes per class iou on groundtruth
  original sizes, in which case, each prediction is rescaled back to the
  original image size.
  """

  def __init__(
      self, num_classes, rescale_predictions=False, name=None, dtype=None):
    """Constructs Segmentation evaluator class.

    Args:
      num_classes: `int`, number of classes.
      rescale_predictions: `bool`, whether to scale back prediction to original
        image sizes. If True, y_true['image_info'] is used to rescale
        predictions.
      name: `str`, name of the metric instance..
      dtype: data type of the metric result.
    """
    self._rescale_predictions = rescale_predictions
    super().__init__(num_classes=num_classes, name=name, dtype=dtype)

  def update_state(self, y_true, y_pred):
    """Updates metric state.

    Args:
      y_true: `dict`, dictionary with the following name, and key values.
        - masks: [batch, width, height, 1], groundtruth masks.
        - valid_masks: [batch, width, height, 1], valid elements in the mask.
        - image_info: [batch, 4, 2], a tensor that holds information about
          original and preprocessed images. Each entry is in the format of
          [[original_height, original_width], [input_height, input_width],
          [y_scale, x_scale], [y_offset, x_offset]], where [desired_height,
          desired_width] is the actual scaled image size, and [y_scale, x_scale]
          is the scaling factor, which is the ratio of scaled dimension /
          original dimension.
      y_pred: Tensor [batch, width_p, height_p, num_classes], predicated masks.
    """
    predictions = y_pred
    masks = y_true['masks']
    valid_masks = y_true['valid_masks']
    images_info = y_true['image_info']

    if isinstance(predictions, tuple) or isinstance(predictions, list):
      predictions = tf.concat(predictions, axis=0)
      masks = tf.concat(masks, axis=0)
      valid_masks = tf.concat(valid_masks, axis=0)
      images_info = tf.concat(images_info, axis=0)

    # Ignore mask elements is set to zero for argmax op.
    masks = tf.where(valid_masks, masks, tf.zeros_like(masks))

    if self._rescale_predictions:
      # This part can only run on cpu/gpu due to dynamic image resizing.
      for i in range(tf.shape(predictions)[0]):
        mask = masks[i]
        valid_mask = valid_masks[i]
        predicted_mask = predictions[i]
        image_info = images_info[i]

        rescale_size = tf.cast(
            tf.math.ceil(image_info[1, :] / image_info[2, :]), tf.int32)
        image_shape = tf.cast(image_info[0, :], tf.int32)
        offsets = tf.cast(image_info[3, :], tf.int32)

        predicted_mask = tf.image.resize(
            predicted_mask,
            rescale_size,
            method=tf.image.ResizeMethod.BILINEAR)

        predicted_mask = tf.image.crop_to_bounding_box(predicted_mask,
                                                       offsets[0], offsets[1],
                                                       image_shape[0],
                                                       image_shape[1])
        mask = tf.image.crop_to_bounding_box(mask, 0, 0, image_shape[0],
                                             image_shape[1])
        valid_mask = tf.image.crop_to_bounding_box(valid_mask, 0, 0,
                                                   image_shape[0],
                                                   image_shape[1])

        predicted_mask = tf.argmax(predicted_mask, axis=2)
        flatten_predictions = tf.reshape(predicted_mask, shape=[1, -1])
        flatten_masks = tf.reshape(mask, shape=[1, -1])
        flatten_valid_masks = tf.reshape(valid_mask, shape=[1, -1])
        super().update_state(flatten_masks, flatten_predictions,
                             tf.cast(flatten_valid_masks, tf.float32))

    else:
      predictions = tf.image.resize(
          predictions,
          tf.shape(masks)[1:3],
          method=tf.image.ResizeMethod.BILINEAR)
      predictions = tf.argmax(predictions, axis=3)
      flatten_predictions = tf.reshape(predictions, shape=[-1])
      flatten_masks = tf.reshape(masks, shape=[-1])
      flatten_valid_masks = tf.reshape(valid_masks, shape=[-1])

      super().update_state(flatten_masks, flatten_predictions,
                           tf.cast(flatten_valid_masks, tf.float32))
