Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
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.
Args:
input(Variable): The Input Variables, the format is NCHW.
image(Variable): The input image data of PriorBoxOp,
the layout is NCHW.
min_sizes(list|tuple|float value): min sizes of generated prior boxes.
max_sizes(list|tuple|None): max sizes of generated prior boxes.
Parameters:
input(Variable): 4-D tenosr(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 value): the aspect ratios of generated
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] == 0.0/step[1] == 0.0, the prior boxes step across
height/weight of the input will be automatically calculated.
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
name(str): Name of the prior box op. Default: None.
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 :ref:`api_guide_Name`
Returns:
tuple: A tuple with two Variable (boxes, variances)
Tuple: A tuple with two Variable (boxes, variances)
boxes: the output prior boxes of PriorBox.
The layout is [H, W, num_priors, 4].
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.
num_priors is the total box count of each position of input.
variances: the expanded variances of PriorBox.
The layout is [H, W, num_priors, 4].
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
num_priors is the total box count of each position of input
N_density_prior_box is the number of density_prior_box and N_fixed_ratios is the number of fixed_ratios.
Parameters:
input(Variable): 4-D tensor(NCHW), the data type should be float32 of float64.
image(Variable): 4-D tensor(NCHW), the input image data of PriorBoxOp, the data type should be float32 or float64.
the layout is NCHW.
densities(list|tuple|None): the densities of generated density prior
densities(list|tuple|None): The densities of generated density prior
boxes, this attribute should be a list or tuple of integers.
Default: None.
fixed_sizes(list|tuple|None): the fixed sizes of generated density
fixed_sizes(list|tuple|None): The fixed sizes of generated density
prior boxes, this attribute should a list or tuple of same
length with :attr:`densities`. Default: None.
fixed_ratios(list|tuple|None): the fixed ratios of generated density
fixed_ratios(list|tuple|None): The fixed ratios of generated density
prior boxes, if this attribute is not set and :attr:`densities`
and :attr:`fix_sizes` is set, :attr:`aspect_ratios` will be used
to generate density prior boxes.
variance(list|tuple): the variances to be encoded in density prior boxes.
variance(list|tuple): The variances to be encoded in density prior boxes.
Default:[0.1, 0.1, 0.2, 0.2].
clip(bool): Whether to clip out-of-boundary boxes. 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] == 0.0/step[1] == 0.0, the density prior boxes step across
height/weight of the input will be automatically calculated.
step[0] equals 0.0 or step[1] equals 0.0, the density 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
flatten_to_2d(bool): Whether to flatten output prior boxes and variance
to 2D shape, the second dim is 4. Default: False.
name(str): Name of the density prior box op. Default: None.
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 :ref:`api_guide_Name`
Returns:
tuple: A tuple with two Variable (boxes, variances)
Tuple: A tuple with two Variable (boxes, variances)
boxes: the output density prior boxes of PriorBox.
The layout is [H, W, num_priors, 4] when flatten_to_2d is False.
The layout is [H * W * num_priors, 4] when flatten_to_2d is True.
H is the height of input, W is the width of input,
num_priors is the total box count of each position of input.
4-D tensor, the layout is [H, W, num_priors, 4] when flatten_to_2d is False.
2-D tensor, the layout is [H * W * num_priors, 4] when flatten_to_2d is True.
H is the height of input, W is the width of input, and num_priors is the total box count of each position of input.
variances: the expanded variances of PriorBox.
The layout is [H, W, num_priors, 4] when flatten_to_2d is False.
The layout is [H * W * num_priors, 4] when flatten_to_2d is True.
H is the height of input, W is the width of input
num_priors is the total box count of each position of input.
4-D tensor, the layout is [H, W, num_priors, 4] when flatten_to_2d is False.
2-D tensor, the layout is [H * W * num_priors, 4] when flatten_to_2d is True.
H is the height of input, W is the width of input, and num_priors is the total box count of each position of input.
