official/legacy/detection/modeling/maskrcnn_model.py

# Copyright 2023 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.

"""Model defination for the Mask R-CNN Model."""

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

import tensorflow as tf, tf_keras

from official.legacy.detection.dataloader import anchor
from official.legacy.detection.dataloader import mode_keys
from official.legacy.detection.evaluation import factory as eval_factory
from official.legacy.detection.modeling import base_model
from official.legacy.detection.modeling import losses
from official.legacy.detection.modeling.architecture import factory
from official.legacy.detection.ops import postprocess_ops
from official.legacy.detection.ops import roi_ops
from official.legacy.detection.ops import spatial_transform_ops
from official.legacy.detection.ops import target_ops
from official.legacy.detection.utils import box_utils


class MaskrcnnModel(base_model.Model):
  """Mask R-CNN model function."""

  def __init__(self, params):
    super(MaskrcnnModel, self).__init__(params)

    # For eval metrics.
    self._params = params
    self._keras_model = None

    self._include_mask = params.architecture.include_mask

    # Architecture generators.
    self._backbone_fn = factory.backbone_generator(params)
    self._fpn_fn = factory.multilevel_features_generator(params)
    self._rpn_head_fn = factory.rpn_head_generator(params)
    self._generate_rois_fn = roi_ops.ROIGenerator(params.roi_proposal)
    self._sample_rois_fn = target_ops.ROISampler(params.roi_sampling)
    self._sample_masks_fn = target_ops.MaskSampler(
        params.architecture.mask_target_size,
        params.mask_sampling.num_mask_samples_per_image)

    self._frcnn_head_fn = factory.fast_rcnn_head_generator(params)
    if self._include_mask:
      self._mrcnn_head_fn = factory.mask_rcnn_head_generator(params)

    # Loss function.
    self._rpn_score_loss_fn = losses.RpnScoreLoss(params.rpn_score_loss)
    self._rpn_box_loss_fn = losses.RpnBoxLoss(params.rpn_box_loss)
    self._frcnn_class_loss_fn = losses.FastrcnnClassLoss()
    self._frcnn_box_loss_fn = losses.FastrcnnBoxLoss(params.frcnn_box_loss)
    if self._include_mask:
      self._mask_loss_fn = losses.MaskrcnnLoss()

    self._generate_detections_fn = postprocess_ops.GenericDetectionGenerator(
        params.postprocess)

    self._transpose_input = params.train.transpose_input
    assert not self._transpose_input, 'Transpose input is not supportted.'

  def build_outputs(self, inputs, mode):
    is_training = mode == mode_keys.TRAIN
    model_outputs = {}

    image = inputs['image']
    _, image_height, image_width, _ = image.get_shape().as_list()
    backbone_features = self._backbone_fn(image, is_training)
    fpn_features = self._fpn_fn(backbone_features, is_training)

    rpn_score_outputs, rpn_box_outputs = self._rpn_head_fn(
        fpn_features, is_training)
    model_outputs.update({
        'rpn_score_outputs':
            tf.nest.map_structure(lambda x: tf.cast(x, tf.float32),
                                  rpn_score_outputs),
        'rpn_box_outputs':
            tf.nest.map_structure(lambda x: tf.cast(x, tf.float32),
                                  rpn_box_outputs),
    })
    input_anchor = anchor.Anchor(self._params.architecture.min_level,
                                 self._params.architecture.max_level,
                                 self._params.anchor.num_scales,
                                 self._params.anchor.aspect_ratios,
                                 self._params.anchor.anchor_size,
                                 (image_height, image_width))
    rpn_rois, _ = self._generate_rois_fn(rpn_box_outputs, rpn_score_outputs,
                                         input_anchor.multilevel_boxes,
                                         inputs['image_info'][:, 1, :],
                                         is_training)
    if is_training:
      rpn_rois = tf.stop_gradient(rpn_rois)

      # Sample proposals.
      rpn_rois, matched_gt_boxes, matched_gt_classes, matched_gt_indices = (
          self._sample_rois_fn(rpn_rois, inputs['gt_boxes'],
                               inputs['gt_classes']))

      # Create bounding box training targets.
      box_targets = box_utils.encode_boxes(
          matched_gt_boxes, rpn_rois, weights=[10.0, 10.0, 5.0, 5.0])
      # If the target is background, the box target is set to all 0s.
      box_targets = tf.where(
          tf.tile(
              tf.expand_dims(tf.equal(matched_gt_classes, 0), axis=-1),
              [1, 1, 4]), tf.zeros_like(box_targets), box_targets)
      model_outputs.update({
          'class_targets': matched_gt_classes,
          'box_targets': box_targets,
      })

    roi_features = spatial_transform_ops.multilevel_crop_and_resize(
        fpn_features, rpn_rois, output_size=7)

    class_outputs, box_outputs = self._frcnn_head_fn(roi_features, is_training)

    model_outputs.update({
        'class_outputs':
            tf.nest.map_structure(lambda x: tf.cast(x, tf.float32),
                                  class_outputs),
        'box_outputs':
            tf.nest.map_structure(lambda x: tf.cast(x, tf.float32),
                                  box_outputs),
    })

    # Add this output to train to make the checkpoint loadable in predict mode.
    # If we skip it in train mode, the heads will be out-of-order and checkpoint
    # loading will fail.
    boxes, scores, classes, valid_detections = self._generate_detections_fn(
        box_outputs, class_outputs, rpn_rois, inputs['image_info'][:, 1:2, :])
    model_outputs.update({
        'num_detections': valid_detections,
        'detection_boxes': boxes,
        'detection_classes': classes,
        'detection_scores': scores,
    })

    if not self._include_mask:
      return model_outputs

    if is_training:
      rpn_rois, classes, mask_targets = self._sample_masks_fn(
          rpn_rois, matched_gt_boxes, matched_gt_classes, matched_gt_indices,
          inputs['gt_masks'])
      mask_targets = tf.stop_gradient(mask_targets)

      classes = tf.cast(classes, dtype=tf.int32)

      model_outputs.update({
          'mask_targets': mask_targets,
          'sampled_class_targets': classes,
      })
    else:
      rpn_rois = boxes
      classes = tf.cast(classes, dtype=tf.int32)

    mask_roi_features = spatial_transform_ops.multilevel_crop_and_resize(
        fpn_features, rpn_rois, output_size=14)

    mask_outputs = self._mrcnn_head_fn(mask_roi_features, classes, is_training)

    if is_training:
      model_outputs.update({
          'mask_outputs':
              tf.nest.map_structure(lambda x: tf.cast(x, tf.float32),
                                    mask_outputs),
      })
    else:
      model_outputs.update({'detection_masks': tf.nn.sigmoid(mask_outputs)})

    return model_outputs

  def build_loss_fn(self):
    if self._keras_model is None:
      raise ValueError('build_loss_fn() must be called after build_model().')

    filter_fn = self.make_filter_trainable_variables_fn()
    trainable_variables = filter_fn(self._keras_model.trainable_variables)

    def _total_loss_fn(labels, outputs):
      rpn_score_loss = self._rpn_score_loss_fn(outputs['rpn_score_outputs'],
                                               labels['rpn_score_targets'])
      rpn_box_loss = self._rpn_box_loss_fn(outputs['rpn_box_outputs'],
                                           labels['rpn_box_targets'])

      frcnn_class_loss = self._frcnn_class_loss_fn(outputs['class_outputs'],
                                                   outputs['class_targets'])
      frcnn_box_loss = self._frcnn_box_loss_fn(outputs['box_outputs'],
                                               outputs['class_targets'],
                                               outputs['box_targets'])

      if self._include_mask:
        mask_loss = self._mask_loss_fn(outputs['mask_outputs'],
                                       outputs['mask_targets'],
                                       outputs['sampled_class_targets'])
      else:
        mask_loss = 0.0

      model_loss = (
          rpn_score_loss + rpn_box_loss + frcnn_class_loss + frcnn_box_loss +
          mask_loss)

      l2_regularization_loss = self.weight_decay_loss(trainable_variables)
      total_loss = model_loss + l2_regularization_loss
      return {
          'total_loss': total_loss,
          'loss': total_loss,
          'fast_rcnn_class_loss': frcnn_class_loss,
          'fast_rcnn_box_loss': frcnn_box_loss,
          'mask_loss': mask_loss,
          'model_loss': model_loss,
          'l2_regularization_loss': l2_regularization_loss,
          'rpn_score_loss': rpn_score_loss,
          'rpn_box_loss': rpn_box_loss,
      }

    return _total_loss_fn

  def build_input_layers(self, params, mode):
    is_training = mode == mode_keys.TRAIN
    input_shape = (
        params.maskrcnn_parser.output_size +
        [params.maskrcnn_parser.num_channels])
    if is_training:
      batch_size = params.train.batch_size
      input_layer = {
          'image':
              tf_keras.layers.Input(
                  shape=input_shape,
                  batch_size=batch_size,
                  name='image',
                  dtype=tf.bfloat16 if self._use_bfloat16 else tf.float32),
          'image_info':
              tf_keras.layers.Input(
                  shape=[4, 2],
                  batch_size=batch_size,
                  name='image_info',
              ),
          'gt_boxes':
              tf_keras.layers.Input(
                  shape=[params.maskrcnn_parser.max_num_instances, 4],
                  batch_size=batch_size,
                  name='gt_boxes'),
          'gt_classes':
              tf_keras.layers.Input(
                  shape=[params.maskrcnn_parser.max_num_instances],
                  batch_size=batch_size,
                  name='gt_classes',
                  dtype=tf.int64),
      }
      if self._include_mask:
        input_layer['gt_masks'] = tf_keras.layers.Input(
            shape=[
                params.maskrcnn_parser.max_num_instances,
                params.maskrcnn_parser.mask_crop_size,
                params.maskrcnn_parser.mask_crop_size
            ],
            batch_size=batch_size,
            name='gt_masks')
    else:
      batch_size = params.eval.batch_size
      input_layer = {
          'image':
              tf_keras.layers.Input(
                  shape=input_shape,
                  batch_size=batch_size,
                  name='image',
                  dtype=tf.bfloat16 if self._use_bfloat16 else tf.float32),
          'image_info':
              tf_keras.layers.Input(
                  shape=[4, 2],
                  batch_size=batch_size,
                  name='image_info',
              ),
      }
    return input_layer

  def build_model(self, params, mode):
    if self._keras_model is None:
      input_layers = self.build_input_layers(self._params, mode)
      outputs = self.model_outputs(input_layers, mode)

      model = tf_keras.models.Model(
          inputs=input_layers, outputs=outputs, name='maskrcnn')
      assert model is not None, 'Fail to build tf_keras.Model.'
      model.optimizer = self.build_optimizer()
      self._keras_model = model

    return self._keras_model

  def post_processing(self, labels, outputs):
    required_output_fields = ['class_outputs', 'box_outputs']
    for field in required_output_fields:
      if field not in outputs:
        raise ValueError('"%s" is missing in outputs, requried %s found %s' %
                         (field, required_output_fields, outputs.keys()))
    predictions = {
        'image_info': labels['image_info'],
        'num_detections': outputs['num_detections'],
        'detection_boxes': outputs['detection_boxes'],
        'detection_classes': outputs['detection_classes'],
        'detection_scores': outputs['detection_scores'],
    }
    if self._include_mask:
      predictions.update({
          'detection_masks': outputs['detection_masks'],
      })

    if 'groundtruths' in labels:
      predictions['source_id'] = labels['groundtruths']['source_id']
      predictions['gt_source_id'] = labels['groundtruths']['source_id']
      predictions['gt_height'] = labels['groundtruths']['height']
      predictions['gt_width'] = labels['groundtruths']['width']
      predictions['gt_image_info'] = labels['image_info']
      predictions['gt_num_detections'] = (
          labels['groundtruths']['num_detections'])
      predictions['gt_boxes'] = labels['groundtruths']['boxes']
      predictions['gt_classes'] = labels['groundtruths']['classes']
      predictions['gt_areas'] = labels['groundtruths']['areas']
      predictions['gt_is_crowds'] = labels['groundtruths']['is_crowds']
    return labels, predictions

  def eval_metrics(self):
    return eval_factory.evaluator_generator(self._params.eval)