def encode_annos(image, labels, bboxes, anchors, num_classes):
"""Encode annotations for losses computations.
All the output tensors have a fix shape(none dynamic dimention).
Args:
image: 4-D with shape `[H, W, C]`.
b_labels: 2-D with shape `[num_bounding_boxes]`.
b_bboxes: 3-D with shape `[num_bounding_boxes, 4]`. Scaled.
anchors: 4-D tensor with shape `[fea_h, fea_w, num_anchors, 4]`
Returns:
input_mask: 2-D with shape `[num_anchors, 1]`, indicate which anchor to be used to cal loss.
labels_input: 2-D with shape `[num_anchors, num_classes]`, one hot encode for every anchor.
box_delta_input: 2-D with shape `[num_anchors, 4]`.
box_input: 2-D with shape '[num_anchors, 4]'.
"""
anchors_shape = anchors.get_shape().as_list()
fea_h = anchors_shape[0]
fea_w = anchors_shape[1]
num_anchors = anchors_shape[2] * fea_h * fea_w
anchors = tf.reshape(anchors, [num_anchors, 4]) # reshape anchors
# Cal iou, find the target anchor
_anchors = xywh_to_yxyx(anchors)
ious, indices = batch_iou_fast(_anchors, bboxes)
indices = tf.reshape(indices, shape=[-1, 1])
target_anchors = tf.gather(anchors, indices)
target_anchors = tf.squeeze(target_anchors, axis=1)
delta = batch_delta(yxyx_to_xywh_(bboxes), target_anchors)
# bbox
box_input = tf.scatter_nd(
indices,
bboxes,
shape=[num_anchors, 4]
)
# label
labels_input = tf.scatter_nd(
indices,
tf.one_hot(labels, num_classes),
shape=[num_anchors, num_classes]
)
# anchor mask
onehot_anchor = tf.one_hot(indices, num_anchors)
onehot_anchor = tf.squeeze(onehot_anchor, axis=1)
print("indices shape:", indices.get_shape().as_list())
print("one hot anchors shape:", onehot_anchor.get_shape().as_list())
input_mask = tf.reduce_sum(onehot_anchor, axis=0)
input_mask = tf.reshape(input_mask, shape=[-1, 1])
# delta
box_delta_input = tf.scatter_nd(
indices,
delta,
shape=[num_anchors, 4]
)
return input_mask, labels_input, box_delta_input, box_input
# TODO(shizehao): align anchor center to the grid
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