prior_box

paddle.fluid.layers.prior_box(input, image, min_sizes, max_sizes=None, aspect_ratios=[1.0], variance=[0.1, 0.1, 0.2, 0.2], flip=False, clip=False, steps=[0.0, 0.0], offset=0.5, name=None, min_max_aspect_ratios_order=False)[source]

This op generates prior boxes for SSD(Single Shot MultiBox Detector) algorithm. Each position of the input produce N prior boxes, N is determined by the count of min_sizes, max_sizes and aspect_ratios, The size of the box is in range(min_size, max_size) interval, which is generated in sequence according to the aspect_ratios.

Parameters
  • input (Variable) – 4-D tensor(NCHW), the data type should be float32 or float64.

  • image (Variable) – 4-D tensor(NCHW), the input image data of PriorBoxOp, the data type should be float32 or float64.

  • min_sizes (list|tuple|float) – the min sizes of generated prior boxes.

  • max_sizes (list|tuple|None) – the max sizes of generated prior boxes. Default: None.

  • aspect_ratios (list|tuple|float) – the aspect ratios of generated prior boxes. Default: [1.].

  • variance (list|tuple) – the variances to be encoded in prior boxes. Default:[0.1, 0.1, 0.2, 0.2].

  • flip (bool) – Whether to flip aspect ratios. Default:False.

  • clip (bool) – Whether to clip out-of-boundary boxes. Default: False.

  • step (list|tuple) – Prior boxes step across width and height, If step[0] equals to 0.0 or step[1] equals to 0.0, the prior boxes step across height or weight of the input will be automatically calculated. Default: [0., 0.]

  • offset (float) – Prior boxes center offset. Default: 0.5

  • min_max_aspect_ratios_order (bool) – If set True, the output prior box is in order of [min, max, aspect_ratios], which is consistent with Caffe. Please note, this order affects the weights order of convolution layer followed by and does not affect the final detection results. Default: False.

  • name (str, optional) – The default value is None. Normally there is no need for user to set this property. For more information, please refer to Name

Returns

A tuple with two Variable (boxes, variances)

boxes(Variable): the output prior boxes of PriorBox. 4-D tensor, the layout is [H, W, num_priors, 4]. H is the height of input, W is the width of input, num_priors is the total box count of each position of input.

variances(Variable): the expanded variances of PriorBox. 4-D tensor, the layput is [H, W, num_priors, 4]. H is the height of input, W is the width of input num_priors is the total box count of each position of input

Return type

Tuple

Examples

#declarative mode
import paddle.fluid as fluid
import numpy as np
input = fluid.data(name="input", shape=[None,3,6,9])
image = fluid.data(name="image", shape=[None,3,9,12])
box, var = fluid.layers.prior_box(
     input=input,
     image=image,
     min_sizes=[100.],
     clip=True,
     flip=True)

place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())

# prepare a batch of data
input_data = np.random.rand(1,3,6,9).astype("float32")
image_data = np.random.rand(1,3,9,12).astype("float32")

box_out, var_out = exe.run(fluid.default_main_program(),
    feed={"input":input_data,"image":image_data},
    fetch_list=[box,var],
    return_numpy=True)

# print(box_out.shape)
# (6, 9, 1, 4)
# print(var_out.shape)
# (6, 9, 1, 4)

# imperative mode
import paddle.fluid.dygraph as dg

with dg.guard(place) as g:
    input = dg.to_variable(input_data)
    image = dg.to_variable(image_data)
    box, var = fluid.layers.prior_box(
        input=input,
        image=image,
        min_sizes=[100.],
        clip=True,
        flip=True)
    # print(box.shape)
    # [6L, 9L, 1L, 4L]
    # print(var.shape)
    # [6L, 9L, 1L, 4L]