def _parse_train_data(self, data): """Parse data for ShapeMask training.""" classes = data['groundtruth_classes'] boxes = data['groundtruth_boxes'] masks = data['groundtruth_instance_masks'] is_crowds = data['groundtruth_is_crowd'] # Skips annotations with `is_crowd` = True. if self._skip_crowd_during_training and self._is_training: num_groundtrtuhs = tf.shape(classes)[0] with tf.control_dependencies([num_groundtrtuhs, is_crowds]): indices = tf.cond( tf.greater(tf.size(is_crowds), 0), lambda: tf.where(tf.logical_not(is_crowds))[:, 0], lambda: tf.cast(tf.range(num_groundtrtuhs), tf.int64)) classes = tf.gather(classes, indices) boxes = tf.gather(boxes, indices) masks = tf.gather(masks, indices) # Gets original image and its size. image = data['image'] image_shape = tf.shape(image)[0:2] # If not using category, makes all categories with id = 0. if not self._use_category: classes = tf.cast(tf.greater(classes, 0), dtype=tf.float32) # Normalizes image with mean and std pixel values. image = input_utils.normalize_image(image) # Flips image randomly during training. if self._aug_rand_hflip: image, boxes, masks = input_utils.random_horizontal_flip( image, boxes, masks) # Converts boxes from normalized coordinates to pixel coordinates. boxes = box_utils.denormalize_boxes(boxes, image_shape) # Resizes and crops image. image, image_info = input_utils.resize_and_crop_image( image, self._output_size, self._output_size, aug_scale_min=self._aug_scale_min, aug_scale_max=self._aug_scale_max) image_scale = image_info[2, :] offset = image_info[3, :] # Resizes and crops boxes and masks. boxes = input_utils.resize_and_crop_boxes(boxes, image_scale, self._output_size, offset) # Filters out ground truth boxes that are all zeros. indices = input_utils.get_non_empty_box_indices(boxes) boxes = tf.gather(boxes, indices) classes = tf.gather(classes, indices) masks = tf.gather(masks, indices) # Assigns anchors. input_anchor = anchor.Anchor(self._min_level, self._max_level, self._num_scales, self._aspect_ratios, self._anchor_size, self._output_size) anchor_labeler = anchor.AnchorLabeler(input_anchor, self._match_threshold, self._unmatched_threshold) (cls_targets, box_targets, num_positives) = anchor_labeler.label_anchors( boxes, tf.cast(tf.expand_dims(classes, axis=1), tf.float32)) # Sample groundtruth masks/boxes/classes for mask branch. num_masks = tf.shape(masks)[0] mask_shape = tf.shape(masks)[1:3] # Pad sampled boxes/masks/classes to a constant batch size. padded_boxes = input_utils.pad_to_fixed_size(boxes, self._num_sampled_masks) padded_classes = input_utils.pad_to_fixed_size(classes, self._num_sampled_masks) padded_masks = input_utils.pad_to_fixed_size(masks, self._num_sampled_masks) # Randomly sample groundtruth masks for mask branch training. For the image # without groundtruth masks, it will sample the dummy padded tensors. rand_indices = tf.random.shuffle( tf.range(tf.maximum(num_masks, self._num_sampled_masks))) rand_indices = tf.math.mod(rand_indices, tf.maximum(num_masks, 1)) rand_indices = rand_indices[0:self._num_sampled_masks] rand_indices = tf.reshape(rand_indices, [self._num_sampled_masks]) sampled_boxes = tf.gather(padded_boxes, rand_indices) sampled_classes = tf.gather(padded_classes, rand_indices) sampled_masks = tf.gather(padded_masks, rand_indices) # Jitter the sampled boxes to mimic the noisy detections. sampled_boxes = box_utils.jitter_boxes( sampled_boxes, noise_scale=self._box_jitter_scale) sampled_boxes = box_utils.clip_boxes(sampled_boxes, self._output_size) # Compute mask targets in feature crop. A feature crop fully contains a # sampled box. mask_outer_boxes = box_utils.compute_outer_boxes( sampled_boxes, tf.shape(image)[0:2], scale=self._outer_box_scale) mask_outer_boxes = box_utils.clip_boxes(mask_outer_boxes, self._output_size) # Compensate the offset of mask_outer_boxes to map it back to original image # scale. mask_outer_boxes_ori = mask_outer_boxes mask_outer_boxes_ori += tf.tile(tf.expand_dims(offset, axis=0), [1, 2]) mask_outer_boxes_ori /= tf.tile(tf.expand_dims(image_scale, axis=0), [1, 2]) norm_mask_outer_boxes_ori = box_utils.normalize_boxes( mask_outer_boxes_ori, mask_shape) # Set sampled_masks shape to [batch_size, height, width, 1]. sampled_masks = tf.cast(tf.expand_dims(sampled_masks, axis=-1), tf.float32) mask_targets = tf.image.crop_and_resize( sampled_masks, norm_mask_outer_boxes_ori, box_indices=tf.range(self._num_sampled_masks), crop_size=[self._mask_crop_size, self._mask_crop_size], method='bilinear', extrapolation_value=0, name='train_mask_targets') mask_targets = tf.where(tf.greater_equal(mask_targets, 0.5), tf.ones_like(mask_targets), tf.zeros_like(mask_targets)) mask_targets = tf.squeeze(mask_targets, axis=-1) if self._up_sample_factor > 1: fine_mask_targets = tf.image.crop_and_resize( sampled_masks, norm_mask_outer_boxes_ori, box_indices=tf.range(self._num_sampled_masks), crop_size=[ self._mask_crop_size * self._up_sample_factor, self._mask_crop_size * self._up_sample_factor ], method='bilinear', extrapolation_value=0, name='train_mask_targets') fine_mask_targets = tf.where( tf.greater_equal(fine_mask_targets, 0.5), tf.ones_like(fine_mask_targets), tf.zeros_like(fine_mask_targets)) fine_mask_targets = tf.squeeze(fine_mask_targets, axis=-1) else: fine_mask_targets = mask_targets # If bfloat16 is used, casts input image to tf.bfloat16. if self._use_bfloat16: image = tf.cast(image, dtype=tf.bfloat16) valid_image = tf.cast(tf.not_equal(num_masks, 0), tf.int32) if self._mask_train_class == 'all': mask_is_valid = valid_image * tf.ones_like(sampled_classes, tf.int32) else: # Get the intersection of sampled classes with training splits. mask_valid_classes = tf.cast( tf.expand_dims( class_utils.coco_split_class_ids(self._mask_train_class), 1), sampled_classes.dtype) match = tf.reduce_any( tf.equal(tf.expand_dims(sampled_classes, 0), mask_valid_classes), 0) mask_is_valid = valid_image * tf.cast(match, tf.int32) # Packs labels for model_fn outputs. labels = { 'cls_targets': cls_targets, 'box_targets': box_targets, 'anchor_boxes': input_anchor.multilevel_boxes, 'num_positives': num_positives, 'image_info': image_info, # For ShapeMask. 'mask_boxes': sampled_boxes, 'mask_outer_boxes': mask_outer_boxes, 'mask_targets': mask_targets, 'fine_mask_targets': fine_mask_targets, 'mask_classes': sampled_classes, 'mask_is_valid': mask_is_valid, } return image, labels
def build_outputs(self, inputs, mode): is_training = mode == mode_keys.TRAIN images = inputs['image'] if 'anchor_boxes' in inputs: anchor_boxes = inputs['anchor_boxes'] else: anchor_boxes = 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, images.get_shape().as_list()[1:3]).multilevel_boxes batch_size = tf.shape(images)[0] for level in anchor_boxes: anchor_boxes[level] = tf.tile( tf.expand_dims(anchor_boxes[level], 0), [batch_size, 1, 1, 1]) backbone_features = self._backbone_fn(images, is_training=is_training) fpn_features = self._fpn_fn(backbone_features, is_training=is_training) cls_outputs, box_outputs = self._retinanet_head_fn( fpn_features, is_training=is_training) valid_boxes, valid_scores, valid_classes, valid_detections = ( self._generate_detections_fn(box_outputs, cls_outputs, anchor_boxes, inputs['image_info'][:, 1:2, :])) image_size = images.get_shape().as_list()[1:3] valid_outer_boxes = box_utils.compute_outer_boxes( tf.reshape(valid_boxes, [-1, 4]), image_size, scale=self._params.shapemask_parser.outer_box_scale) valid_outer_boxes = tf.reshape(valid_outer_boxes, tf.shape(valid_boxes)) # Wrapping if else code paths into a layer to make the checkpoint loadable # in prediction mode. class SampledBoxesLayer(tf.keras.layers.Layer): """ShapeMask model function.""" def call(self, inputs, val_boxes, val_classes, val_outer_boxes, training): if training: boxes = inputs['mask_boxes'] outer_boxes = inputs['mask_outer_boxes'] classes = inputs['mask_classes'] else: boxes = val_boxes classes = val_classes outer_boxes = val_outer_boxes return boxes, classes, outer_boxes boxes, classes, outer_boxes = SampledBoxesLayer()(inputs, valid_boxes, valid_classes, valid_outer_boxes, training=is_training) instance_features, prior_masks = self._shape_prior_head_fn( fpn_features, boxes, outer_boxes, classes, is_training) coarse_mask_logits = self._coarse_mask_fn(instance_features, prior_masks, classes, is_training) fine_mask_logits = self._fine_mask_fn(instance_features, coarse_mask_logits, classes, is_training) model_outputs = { 'cls_outputs': cls_outputs, 'box_outputs': box_outputs, 'fine_mask_logits': fine_mask_logits, 'coarse_mask_logits': coarse_mask_logits, 'prior_masks': prior_masks, } if not is_training: model_outputs.update({ 'num_detections': valid_detections, 'detection_boxes': valid_boxes, 'detection_outer_boxes': valid_outer_boxes, 'detection_masks': fine_mask_logits, 'detection_classes': valid_classes, 'detection_scores': valid_scores, }) return model_outputs