prior_box¶
- paddle.fluid.layers.detection. 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]
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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
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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
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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
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Tuple
Examples
#declarative mode import paddle.fluid as fluid import numpy as npimport paddle paddle.enable_static()
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(),
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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:
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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]