tensorflow/models

# Copyright 2017 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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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# ==============================================================================

"""Keypoint Head.

Contains Keypoint prediction head classes for different meta architectures.
All the keypoint prediction heads have a predict function that receives the
`features` as the first argument and returns `keypoint_predictions`.
Keypoints could be used to represent the human body joint locations as in
Mask RCNN paper. Or they could be used to represent different part locations of
objects.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from six.moves import range
import tensorflow.compat.v1 as tf
import tf_slim as slim

from object_detection.predictors.heads import head


class MaskRCNNKeypointHead(head.Head):
  """Mask RCNN keypoint prediction head.

  Please refer to Mask RCNN paper:
  https://arxiv.org/abs/1703.06870
  """

  def __init__(self,
               num_keypoints=17,
               conv_hyperparams_fn=None,
               keypoint_heatmap_height=56,
               keypoint_heatmap_width=56,
               keypoint_prediction_num_conv_layers=8,
               keypoint_prediction_conv_depth=512):
    """Constructor.

    Args:
      num_keypoints: (int scalar) number of keypoints.
      conv_hyperparams_fn: A function to generate tf-slim arg_scope with
        hyperparameters for convolution ops.
      keypoint_heatmap_height: Desired output mask height. The default value
        is 14.
      keypoint_heatmap_width: Desired output mask width. The default value
        is 14.
      keypoint_prediction_num_conv_layers: Number of convolution layers applied
        to the image_features in mask prediction branch.
      keypoint_prediction_conv_depth: The depth for the first conv2d_transpose
        op applied to the image_features in the mask prediction branch. If set
        to 0, the depth of the convolution layers will be automatically chosen
        based on the number of object classes and the number of channels in the
        image features.
    """
    super(MaskRCNNKeypointHead, self).__init__()
    self._num_keypoints = num_keypoints
    self._conv_hyperparams_fn = conv_hyperparams_fn
    self._keypoint_heatmap_height = keypoint_heatmap_height
    self._keypoint_heatmap_width = keypoint_heatmap_width
    self._keypoint_prediction_num_conv_layers = (
        keypoint_prediction_num_conv_layers)
    self._keypoint_prediction_conv_depth = keypoint_prediction_conv_depth

  def predict(self, features, num_predictions_per_location=1):
    """Performs keypoint prediction.

    Args:
      features: A float tensor of shape [batch_size, height, width,
        channels] containing features for a batch of images.
      num_predictions_per_location: Int containing number of predictions per
        location.

    Returns:
      instance_masks: A float tensor of shape
          [batch_size, 1, num_keypoints, heatmap_height, heatmap_width].

    Raises:
      ValueError: If num_predictions_per_location is not 1.
    """
    if num_predictions_per_location != 1:
      raise ValueError('Only num_predictions_per_location=1 is supported')
    with slim.arg_scope(self._conv_hyperparams_fn()):
      net = slim.conv2d(
          features,
          self._keypoint_prediction_conv_depth, [3, 3],
          scope='conv_1')
      for i in range(1, self._keypoint_prediction_num_conv_layers):
        net = slim.conv2d(
            net,
            self._keypoint_prediction_conv_depth, [3, 3],
            scope='conv_%d' % (i + 1))
      net = slim.conv2d_transpose(
          net, self._num_keypoints, [2, 2], scope='deconv1')
      heatmaps_mask = tf.image.resize_bilinear(
          net, [self._keypoint_heatmap_height, self._keypoint_heatmap_width],
          align_corners=True,
          name='upsample')
      return tf.expand_dims(
          tf.transpose(heatmaps_mask, perm=[0, 3, 1, 2]),
          axis=1,
          name='KeypointPredictor')