anchor_generator

paddle.fluid.layers.anchor_generator(input, anchor_sizes=None, aspect_ratios=None, variance=[0.1, 0.1, 0.2, 0.2], stride=None, offset=0.5, name=None)[source]

Anchor generator operator

Generate anchors for Faster RCNN algorithm. Each position of the input produce N anchors, N = size(anchor_sizes) * size(aspect_ratios). The order of generated anchors is firstly aspect_ratios loop then anchor_sizes loop.

Parameters
  • input (Variable) – 4-D Tensor with shape [N,C,H,W]. The input feature map.

  • anchor_sizes (float32|list|tuple, optional) – The anchor sizes of generated anchors, given in absolute pixels e.g. [64., 128., 256., 512.]. For instance, the anchor size of 64 means the area of this anchor equals to 64**2. None by default.

  • aspect_ratios (float32|list|tuple, optional) – The height / width ratios of generated anchors, e.g. [0.5, 1.0, 2.0]. None by default.

  • variance (list|tuple, optional) – The variances to be used in box regression deltas. The data type is float32, [0.1, 0.1, 0.2, 0.2] by default.

  • stride (list|tuple, optional) – The anchors stride across width and height. The data type is float32. e.g. [16.0, 16.0]. None by default.

  • offset (float32, optional) – Prior boxes center offset. 0.5 by default.

  • name (str, optional) – For detailed information, please refer to Name. Usually name is no need to set and None by default.

Returns

Anchors(Variable): The output anchors with a layout of [H, W, num_anchors, 4]. H is the height of input, W is the width of input, num_anchors is the box count of each position. Each anchor is in (xmin, ymin, xmax, ymax) format an unnormalized.

Variances(Variable): The expanded variances of anchors with a layout of [H, W, num_priors, 4]. H is the height of input, W is the width of input num_anchors is the box count of each position. Each variance is in (xcenter, ycenter, w, h) format.

Return type

Tuple

Examples

import paddle.fluid as fluid
conv1 = fluid.data(name='conv1', shape=[None, 48, 16, 16], dtype='float32')
anchor, var = fluid.layers.anchor_generator(
    input=conv1,
    anchor_sizes=[64, 128, 256, 512],
    aspect_ratios=[0.5, 1.0, 2.0],
    variance=[0.1, 0.1, 0.2, 0.2],
    stride=[16.0, 16.0],
    offset=0.5)