def __init__(self,
num_classes=80,
confidence_threshold=0.05,
nms_iou_threshold=0.5,
max_detections_per_class=100,
max_detections=100,
box_variance=[0.1, 0.1, 0.2, 0.2],
**kwargs):
super(DecodePredictions, self).__init__(**kwargs)
self.num_classes = num_classes
self.confidence_threshold = confidence_threshold
self.nms_iou_threshold = nms_iou_threshold
self.max_detections_per_class = max_detections_per_class
self.max_detections = max_detections
self._anchor_box = AnchorBox()
self._box_variance = tf.convert_to_tensor([0.1, 0.1, 0.2, 0.2],
dtype=tf.float32)
def build_decoding_layer(
num_classes=2,
confidence_threshold=0.05,
nms_iou_threshold=0.5,
max_detections_per_class=100,
max_detections=100,
box_variance=[0.1, 0.1, 0.2, 0.2],
):
"""Decodes predictions of the RetinaNet model.
Args:
num_classes: Number of classes in the dataset
confidence_threshold: Minimum class probability, below which detections
are pruned.
nms_iou_threshold: IOU threshold for the NMS operation
max_detections_per_class: Maximum number of detections to retain per
class.
max_detections: Maximum number of detections to retain across all
classes.
box_variance: The scaling factors used to scale the bounding box
predictions.
"""
_anchor_box = AnchorBox()
_box_variance = tf.convert_to_tensor([0.1, 0.1, 0.2, 0.2],
dtype=tf.float32)
def _decode_box_predictions(anchor_boxes, box_predictions):
boxes = box_predictions * _box_variance
boxes = tf.concat([
boxes[:, :, :2] * anchor_boxes[:, :, 2:] + anchor_boxes[:, :, :2],
tf.math.exp(boxes[:, :, 2:]) * anchor_boxes[:, :, 2:]
],
axis=-1)
boxes_transformed = convert_to_corners(boxes)
return boxes_transformed
def forward(images, predictions):
image_shape = tf.cast(tf.shape(images), dtype=tf.float32)
anchor_boxes = _anchor_box.get_anchors(image_shape[1], image_shape[2])
box_predictions = predictions[:, :, :4]
cls_predictions = tf.nn.sigmoid(predictions[:, :, 4:])
boxes = _decode_box_predictions(anchor_boxes[None, ...],
box_predictions)
return tf.image.combined_non_max_suppression(tf.expand_dims(boxes,
axis=2),
cls_predictions,
max_detections_per_class,
max_detections,
nms_iou_threshold,
confidence_threshold,
clip_boxes=False)
return forward
def __init__(self):
self._anchor_box = AnchorBox()
self._box_variance = tf.convert_to_tensor([0.1, 0.1, 0.2, 0.2],
dtype=tf.float32)
class LabelEncoder:
"""Transforms the raw labels into targets for training.
This class has operations to generate targets for a batch of samples which
is made up of the input images, bounding boxes for the objects present and
their class ids.
Attributes:
anchor_box: Anchor box generator to encode the bounding boxes.
box_variance: The scaling factors used to scale the bounding box targets.
"""
def __init__(self):
self._anchor_box = AnchorBox()
self._box_variance = tf.convert_to_tensor([0.1, 0.1, 0.2, 0.2],
dtype=tf.float32)
def _match_anchor_boxes(self,
anchor_boxes,
gt_boxes,
match_iou=0.5,
ignore_iou=0.4):
"""Matches ground truth boxes to anchor boxes based on IOU.
1. Calculates the pairwise IOU for the M `anchor_boxes` and N `gt_boxes`
to get a `(M, N)` shaped matrix.
2. The ground truth box with the maximum IOU in each row is assigned to
the anchor box provided the IOU is greater than `match_iou`.
3. If the maximum IOU in a row is less than `ignore_iou`, the anchor
box is assigned with the background class.
4. The remaining anchor boxes that do not have any class assigned are
ignored during training.
Arguments:
anchor_boxes: A float tensor with the shape `(total_anchors, 4)`
representing all the anchor boxes for a given input image shape,
where each anchor box is of the format `[x, y, width, height]`.
gt_boxes: A float tensor with shape `(num_objects, 4)` representing
the ground truth boxes, where each box is of the format
`[x, y, width, height]`.
match_iou: A float value representing the minimum IOU threshold for
determining if a ground truth box can be assigned to an anchor box.
ignore_iou: A float value representing the IOU threshold under which
an anchor box is assigned to the background class.
Returns:
matched_gt_idx: Index of the matched object
positive_mask: A mask for anchor boxes that have been assigned ground
truth boxes.
ignore_mask: A mask for anchor boxes that need to by ignored during
training
"""
iou_matrix = compute_iou(anchor_boxes, gt_boxes)
max_iou = tf.reduce_max(iou_matrix, axis=1)
matched_gt_idx = tf.argmax(iou_matrix, axis=1)
positive_mask = tf.greater_equal(max_iou, match_iou)
negative_mask = tf.less(max_iou, ignore_iou)
ignore_mask = tf.logical_not(
tf.logical_or(positive_mask, negative_mask))
return (matched_gt_idx, tf.cast(positive_mask, dtype=tf.float32),
tf.cast(ignore_mask, dtype=tf.float32))
def _compute_box_target(self, anchor_boxes, matched_gt_boxes):
"""Transforms the ground truth boxes into targets for training"""
box_target = tf.concat([
(matched_gt_boxes[:, :2] - anchor_boxes[:, :2]) /
anchor_boxes[:, 2:],
tf.math.log(matched_gt_boxes[:, 2:] / anchor_boxes[:, 2:]),
],
axis=-1)
box_target = box_target / self._box_variance
return box_target
def _encode_sample(self, image_shape, gt_boxes, cls_ids):
"""Creates box and classification targets for a single sample"""
anchor_boxes = self._anchor_box.get_anchors(image_shape[1],
image_shape[2])
cls_ids = tf.cast(cls_ids, dtype=tf.float32)
matched_gt_idx, positive_mask, ignore_mask = self._match_anchor_boxes(
anchor_boxes, gt_boxes)
matched_gt_boxes = tf.gather(gt_boxes, matched_gt_idx)
box_target = self._compute_box_target(anchor_boxes, matched_gt_boxes)
matched_gt_cls_ids = tf.gather(cls_ids, matched_gt_idx)
cls_target = tf.where(tf.not_equal(positive_mask, 1.0), -1.0,
matched_gt_cls_ids)
cls_target = tf.where(tf.equal(ignore_mask, 1.0), -2.0, cls_target)
cls_target = tf.expand_dims(cls_target, axis=-1)
label = tf.concat([box_target, cls_target], axis=-1)
return label
def encode_batch(self, batch_images, gt_boxes, cls_ids):
"""Creates box and classification targets for a batch"""
images_shape = tf.shape(batch_images)
batch_size = images_shape[0]
labels = tf.TensorArray(dtype=tf.float32,
size=batch_size,
dynamic_size=True)
for i in range(batch_size):
label = self._encode_sample(images_shape, gt_boxes[i], cls_ids[i])
labels = labels.write(i, label)
batch_images = tf.keras.applications.resnet.preprocess_input(
batch_images)
return batch_images, labels.stack()
class DecodePredictions(tf.keras.layers.Layer):
"""A Keras layer that decodes predictions of the RetinaNet model.
Attributes:
num_classes: Number of classes in the dataset
confidence_threshold: Minimum class probability, below which detections
are pruned.
nms_iou_threshold: IOU threshold for the NMS operation
max_detections_per_class: Maximum number of detections to retain per
class.
max_detections: Maximum number of detections to retain across all
classes.
box_variance: The scaling factors used to scale the bounding box
predictions.
"""
def __init__(self,
num_classes=80,
confidence_threshold=0.05,
nms_iou_threshold=0.5,
max_detections_per_class=100,
max_detections=100,
box_variance=[0.1, 0.1, 0.2, 0.2],
**kwargs):
super(DecodePredictions, self).__init__(**kwargs)
self.num_classes = num_classes
self.confidence_threshold = confidence_threshold
self.nms_iou_threshold = nms_iou_threshold
self.max_detections_per_class = max_detections_per_class
self.max_detections = max_detections
self._anchor_box = AnchorBox()
self._box_variance = tf.convert_to_tensor([0.1, 0.1, 0.2, 0.2],
dtype=tf.float32)
def _decode_box_predictions(self, anchor_boxes, box_predictions):
boxes = box_predictions * self._box_variance
boxes = tf.concat(
[
boxes[:, :, :2] * anchor_boxes[:, :, 2:] +
anchor_boxes[:, :, :2],
tf.math.exp(boxes[:, :, 2:]) * anchor_boxes[:, :, 2:],
],
axis=-1,
)
boxes_transformed = convert_to_corners(boxes)
return boxes_transformed
def call(self, images, predictions):
image_shape = tf.cast(tf.shape(images), dtype=tf.float32)
anchor_boxes = self._anchor_box.get_anchors(image_shape[1],
image_shape[2])
box_predictions = predictions[:, :, :4]
cls_predictions = tf.nn.sigmoid(predictions[:, :, 4:])
boxes = self._decode_box_predictions(anchor_boxes[None, ...],
box_predictions)
return tf.image.combined_non_max_suppression(
tf.expand_dims(boxes, axis=2),
cls_predictions,
self.max_detections_per_class,
self.max_detections,
self.nms_iou_threshold,
self.confidence_threshold,
clip_boxes=False,
)