Tensorflow/models/research/deeplab/evaluation/streaming_metrics.py

# Lint as: python2, python3
# Copyright 2019 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.
# ==============================================================================
"""Code to compute segmentation in a "streaming" pattern in Tensorflow.

These aggregate the metric over examples of the evaluation set. Each example is
assumed to be fed in in a stream, and the metric implementation accumulates
across them.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

from deeplab.evaluation import panoptic_quality
from deeplab.evaluation import parsing_covering

_EPSILON = 1e-10


def _realdiv_maybe_zero(x, y):
  """Support tf.realdiv(x, y) where y may contain zeros."""
  return tf.where(tf.less(y, _EPSILON), tf.zeros_like(x), tf.realdiv(x, y))


def _running_total(value, shape, name=None):
  """Maintains a running total of tensor `value` between calls."""
  with tf.variable_scope(name, 'running_total', [value]):
    total_var = tf.get_variable(
        'total',
        shape,
        value.dtype,
        initializer=tf.zeros_initializer(),
        trainable=False,
        collections=[
            tf.GraphKeys.LOCAL_VARIABLES, tf.GraphKeys.METRIC_VARIABLES
        ])
    updated_total = tf.assign_add(total_var, value, use_locking=True)

  return total_var, updated_total


def _panoptic_quality_helper(
    groundtruth_category_array, groundtruth_instance_array,
    predicted_category_array, predicted_instance_array, num_classes,
    max_instances_per_category, ignored_label, offset):
  """Helper function to compute panoptic quality."""
  pq = panoptic_quality.PanopticQuality(num_classes, ignored_label,
                                        max_instances_per_category, offset)
  pq.compare_and_accumulate(groundtruth_category_array,
                            groundtruth_instance_array,
                            predicted_category_array, predicted_instance_array)
  return pq.iou_per_class, pq.tp_per_class, pq.fn_per_class, pq.fp_per_class


def streaming_panoptic_quality(groundtruth_categories,
                               groundtruth_instances,
                               predicted_categories,
                               predicted_instances,
                               num_classes,
                               max_instances_per_category,
                               ignored_label,
                               offset,
                               name=None):
  """Aggregates the panoptic metric across calls with different input tensors.

  See tf.metrics.* functions for comparable functionality and usage.

  Args:
    groundtruth_categories: A 2D uint16 tensor of groundtruth category labels.
    groundtruth_instances: A 2D uint16 tensor of groundtruth instance labels.
    predicted_categories: A 2D uint16 tensor of predicted category labels.
    predicted_instances: A 2D uint16 tensor of predicted instance labels.
    num_classes: Number of classes in the dataset as an integer.
    max_instances_per_category: The maximum number of instances for each class
      as an integer or integer tensor.
    ignored_label: The class id to be ignored in evaluation as an integer or
      integer tensor.
    offset: The maximum number of unique labels as an integer or integer tensor.
    name: An optional variable_scope name.

  Returns:
    qualities: A tensor of shape `[6, num_classes]`, where (1) panoptic quality,
      (2) segmentation quality, (3) recognition quality, (4) total_tp,
      (5) total_fn and (6) total_fp are saved in the respective rows.
    update_ops: List of operations that update the running overall panoptic
      quality.

  Raises:
    RuntimeError: If eager execution is enabled.
  """
  if tf.executing_eagerly():
    raise RuntimeError('Cannot aggregate when eager execution is enabled.')

  input_args = [
      tf.convert_to_tensor(groundtruth_categories, tf.uint16),
      tf.convert_to_tensor(groundtruth_instances, tf.uint16),
      tf.convert_to_tensor(predicted_categories, tf.uint16),
      tf.convert_to_tensor(predicted_instances, tf.uint16),
      tf.convert_to_tensor(num_classes, tf.int32),
      tf.convert_to_tensor(max_instances_per_category, tf.int32),
      tf.convert_to_tensor(ignored_label, tf.int32),
      tf.convert_to_tensor(offset, tf.int32),
  ]
  return_types = [
      tf.float64,
      tf.float64,
      tf.float64,
      tf.float64,
  ]
  with tf.variable_scope(name, 'streaming_panoptic_quality', input_args):
    panoptic_results = tf.py_func(
        _panoptic_quality_helper, input_args, return_types, stateful=False)
    iou, tp, fn, fp = tuple(panoptic_results)

    total_iou, updated_iou = _running_total(
        iou, [num_classes], name='iou_total')
    total_tp, updated_tp = _running_total(tp, [num_classes], name='tp_total')
    total_fn, updated_fn = _running_total(fn, [num_classes], name='fn_total')
    total_fp, updated_fp = _running_total(fp, [num_classes], name='fp_total')
    update_ops = [updated_iou, updated_tp, updated_fn, updated_fp]

    sq = _realdiv_maybe_zero(total_iou, total_tp)
    rq = _realdiv_maybe_zero(total_tp,
                             total_tp + 0.5 * total_fn + 0.5 * total_fp)
    pq = tf.multiply(sq, rq)
    qualities = tf.stack([pq, sq, rq, total_tp, total_fn, total_fp], axis=0)
  return qualities, update_ops


def _parsing_covering_helper(
    groundtruth_category_array, groundtruth_instance_array,
    predicted_category_array, predicted_instance_array, num_classes,
    max_instances_per_category, ignored_label, offset, normalize_by_image_size):
  """Helper function to compute parsing covering."""
  pc = parsing_covering.ParsingCovering(num_classes, ignored_label,
                                        max_instances_per_category, offset,
                                        normalize_by_image_size)
  pc.compare_and_accumulate(groundtruth_category_array,
                            groundtruth_instance_array,
                            predicted_category_array, predicted_instance_array)
  return pc.weighted_iou_per_class, pc.gt_area_per_class


def streaming_parsing_covering(groundtruth_categories,
                               groundtruth_instances,
                               predicted_categories,
                               predicted_instances,
                               num_classes,
                               max_instances_per_category,
                               ignored_label,
                               offset,
                               normalize_by_image_size=True,
                               name=None):
  """Aggregates the covering across calls with different input tensors.

  See tf.metrics.* functions for comparable functionality and usage.

  Args:
    groundtruth_categories: A 2D uint16 tensor of groundtruth category labels.
    groundtruth_instances: A 2D uint16 tensor of groundtruth instance labels.
    predicted_categories: A 2D uint16 tensor of predicted category labels.
    predicted_instances: A 2D uint16 tensor of predicted instance labels.
    num_classes: Number of classes in the dataset as an integer.
    max_instances_per_category: The maximum number of instances for each class
      as an integer or integer tensor.
    ignored_label: The class id to be ignored in evaluation as an integer or
      integer tensor.
    offset: The maximum number of unique labels as an integer or integer tensor.
    normalize_by_image_size: Whether to normalize groundtruth region areas by
      image size. If True, groundtruth instance areas and weighted IoUs will be
      divided by the size of the corresponding image before accumulated across
      the dataset.
    name: An optional variable_scope name.

  Returns:
    coverings: A tensor of shape `[3, num_classes]`, where (1) per class
      coverings, (2) per class sum of weighted IoUs, and (3) per class sum of
      groundtruth region areas are saved in the perspective rows.
    update_ops: List of operations that update the running overall parsing
      covering.

  Raises:
    RuntimeError: If eager execution is enabled.
  """
  if tf.executing_eagerly():
    raise RuntimeError('Cannot aggregate when eager execution is enabled.')

  input_args = [
      tf.convert_to_tensor(groundtruth_categories, tf.uint16),
      tf.convert_to_tensor(groundtruth_instances, tf.uint16),
      tf.convert_to_tensor(predicted_categories, tf.uint16),
      tf.convert_to_tensor(predicted_instances, tf.uint16),
      tf.convert_to_tensor(num_classes, tf.int32),
      tf.convert_to_tensor(max_instances_per_category, tf.int32),
      tf.convert_to_tensor(ignored_label, tf.int32),
      tf.convert_to_tensor(offset, tf.int32),
      tf.convert_to_tensor(normalize_by_image_size, tf.bool),
  ]
  return_types = [
      tf.float64,
      tf.float64,
  ]
  with tf.variable_scope(name, 'streaming_parsing_covering', input_args):
    covering_results = tf.py_func(
        _parsing_covering_helper, input_args, return_types, stateful=False)
    weighted_iou_per_class, gt_area_per_class = tuple(covering_results)

    total_weighted_iou_per_class, updated_weighted_iou_per_class = (
        _running_total(
            weighted_iou_per_class, [num_classes],
            name='weighted_iou_per_class_total'))
    total_gt_area_per_class, updated_gt_area_per_class = _running_total(
        gt_area_per_class, [num_classes], name='gt_area_per_class_total')

    covering_per_class = _realdiv_maybe_zero(total_weighted_iou_per_class,
                                             total_gt_area_per_class)
    coverings = tf.stack([
        covering_per_class,
        total_weighted_iou_per_class,
        total_gt_area_per_class,
    ],
                         axis=0)
    update_ops = [updated_weighted_iou_per_class, updated_gt_area_per_class]

  return coverings, update_ops