def test_label():
stride = 16
feat_h, feat_w = (7, 7)
img_info = keras.backend.variable([[112, 112, 3]])
gt_boxes = keras.backend.variable(100 * numpy.random.random((91, 4)))
gt_boxes = tensorflow.convert_to_tensor(gt_boxes, dtype=tensorflow.float32)
all_anchors = keras_rcnn.backend.shift((feat_h, feat_w), stride)
inds_inside, all_inside_anchors = anchor_target.inside_image(
all_anchors, img_info[0], allowed_border=1)
all_inside_anchors = keras_rcnn.backend.clip(all_inside_anchors,
img_info[0][:2])
argmax_overlaps_inds, anchor_labels = anchor_target.label(
gt_boxes, all_inside_anchors, inds_inside)
result1 = keras.backend.eval(argmax_overlaps_inds)
result2 = keras.backend.eval(anchor_labels)
assert result1.shape == (32, ), keras.backend.eval(inds_inside).shape
assert result2.shape == (32, )
assert numpy.max(result2) <= 1
assert numpy.min(result2) >= -1
argmax_overlaps_inds, anchor_labels = anchor_target.label(
gt_boxes, all_inside_anchors, inds_inside, clobber_positives=False)
result1 = keras.backend.eval(argmax_overlaps_inds)
result2 = keras.backend.eval(anchor_labels)
assert result1.shape == (32, )
assert result2.shape == (32, )
assert numpy.max(result2) <= 1
assert numpy.min(result2) >= -1
gt_boxes = keras.backend.variable(224 * numpy.random.random((55, 4)))
gt_boxes = tensorflow.convert_to_tensor(gt_boxes, dtype=tensorflow.float32)
argmax_overlaps_inds, anchor_labels = anchor_target.label(
gt_boxes, all_inside_anchors, inds_inside, clobber_positives=False)
result1 = keras.backend.eval(argmax_overlaps_inds)
result2 = keras.backend.eval(anchor_labels)
assert result1.shape == (32, )
assert result2.shape == (32, )
assert numpy.max(result2) <= 1
assert numpy.min(result2) >= -1
def test_label():
stride = 16
feat_h, feat_w = (14, 14)
img_info = keras.backend.variable([[224, 224, 3]])
gt_boxes = keras.backend.variable(100 * numpy.random.random((91, 4)))
gt_boxes = tensorflow.convert_to_tensor(gt_boxes, dtype=tensorflow.float32)
all_bbox = keras_rcnn.backend.shift((feat_h, feat_w), stride)
inds_inside, all_inside_bbox = anchor_target.inside_image(
all_bbox, img_info[0])
argmax_overlaps_inds, bbox_labels = anchor_target.label(
gt_boxes, all_inside_bbox, inds_inside)
result1 = keras.backend.eval(argmax_overlaps_inds)
result2 = keras.backend.eval(bbox_labels)
assert result1.shape == (376,)
assert result2.shape == (376,)
assert numpy.max(result2) <= 1
assert numpy.min(result2) >= -1
argmax_overlaps_inds, bbox_labels = anchor_target.label(
gt_boxes, all_inside_bbox, inds_inside, clobber_positives=False)
result1 = keras.backend.eval(argmax_overlaps_inds)
result2 = keras.backend.eval(bbox_labels)
assert result1.shape == (376,)
assert result2.shape == (376,)
assert numpy.max(result2) <= 1
assert numpy.min(result2) >= -1
gt_boxes = keras.backend.variable(224 * numpy.random.random((55, 4)))
gt_boxes = tensorflow.convert_to_tensor(gt_boxes, dtype=tensorflow.float32)
argmax_overlaps_inds, bbox_labels = anchor_target.label(
gt_boxes, all_inside_bbox, inds_inside, clobber_positives=False)
result1 = keras.backend.eval(argmax_overlaps_inds)
result2 = keras.backend.eval(bbox_labels)
assert result1.shape == (376,)
assert result2.shape == (376,)
assert numpy.max(result2) <= 1
assert numpy.min(result2) >= -1
def test_unmap():
stride = 16
features = (14, 14)
anchors = 9
total_anchors = features[0] * features[1] * anchors
img_info = keras.backend.variable([[224, 224, 3]])
gt_boxes = numpy.zeros((91, 4))
gt_boxes = keras.backend.variable(gt_boxes)
all_anchors = keras_rcnn.backend.shift(features, stride)
inds_inside, all_inside_anchors = anchor_target.inside_image(
all_anchors, img_info[0])
argmax_overlaps_indices, labels = anchor_target.label(
gt_boxes, all_inside_anchors, inds_inside)
bbox_reg_targets = keras_rcnn.backend.bbox_transform(
all_inside_anchors,
keras.backend.gather(gt_boxes, argmax_overlaps_indices)
)
labels = anchor_target.unmap(labels,
total_anchors,
inds_inside,
fill=-1)
bbox_reg_targets = anchor_target.unmap(
bbox_reg_targets, total_anchors, inds_inside, fill=0)
assert keras.backend.eval(labels).shape == (total_anchors,)
assert keras.backend.eval(bbox_reg_targets).shape == (total_anchors, 4)