This op generates prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
Each position of the input produce N prior boxes, N is determined by
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
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
box is in range(min_size, max_size) interval, which is generated in
sequence according to the aspect_ratios.
sequence according to the aspect_ratios.
Args:
Parameters:
input(Variable): The Input Variables, the format is NCHW.
input(Variable): 4-D tenosr(NCHW), the data type should be float32 or float64.
image(Variable): The input image data of PriorBoxOp,
image(Variable): 4-D tensor(NCHW), the input image data of PriorBoxOp,
the layout is NCHW.
the data type should be float32 or float64.
min_sizes(list|tuple|float value): min sizes of generated prior boxes.
min_sizes(list|tuple|float): the min sizes of generated prior boxes.
max_sizes(list|tuple|None): max sizes of generated prior boxes.
max_sizes(list|tuple|None): the max sizes of generated prior boxes.
Default: None.
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.].
prior boxes. Default: [1.].
variance(list|tuple): the variances to be encoded in prior boxes.
variance(list|tuple): the variances to be encoded in prior boxes.
Default:[0.1, 0.1, 0.2, 0.2].
Default:[0.1, 0.1, 0.2, 0.2].
flip(bool): Whether to flip aspect ratios. Default:False.
flip(bool): Whether to flip aspect ratios. Default:False.
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(list|tuple): Prior boxes step across width and height, If
step[0] == 0.0/step[1] == 0.0, the prior boxes step across
step[0] equals to 0.0 or step[1] equals to 0.0, the prior boxes step across
height/weight of the input will be automatically calculated.
height or weight of the input will be automatically calculated.
Default: [0., 0.]
Default: [0., 0.]
offset(float): Prior boxes center offset. Default: 0.5
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
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
in order of [min, max, aspect_ratios], which is consistent with
Caffe. Please note, this order affects the weights order of
Caffe. Please note, this order affects the weights order of
convolution layer followed by and does not affect the final
convolution layer followed by and does not affect the final
detection results. Default: False.
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:
Returns:
tuple: A tuple with two Variable (boxes, variances)
Tuple: A tuple with two Variable (boxes, variances)
boxes: the output prior boxes of PriorBox.
boxes(Variable): the output prior boxes of PriorBox.
The layout is [H, W, num_priors, 4].
4-D tensor, the layout is [H, W, num_priors, 4].
H is the height of input, W is the width of input,
H is the height of input, W is the width of input,
num_priors is the total
num_priors is the total box count of each position of input.
box count of each position of input.
variances: the expanded variances of PriorBox.
variances(Variable): the expanded variances of PriorBox.
The layout is [H, W, num_priors, 4].
4-D tensor, the layput is [H, W, num_priors, 4].
H is the height of input, W is the width of input
H is the height of input, W is the width of input
num_priors is the total
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.
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.
boxes, this attribute should be a list or tuple of integers.
Default: None.
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
prior boxes, this attribute should a list or tuple of same
length with :attr:`densities`. Default: None.
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`
prior boxes, if this attribute is not set and :attr:`densities`
and :attr:`fix_sizes` is set, :attr:`aspect_ratios` will be used
and :attr:`fix_sizes` is set, :attr:`aspect_ratios` will be used
to generate density prior boxes.
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].
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(list|tuple): Prior boxes step across width and height, If
step[0] == 0.0/step[1] == 0.0, the density prior boxes step across
step[0] equals 0.0 or step[1] equals 0.0, the density prior boxes step across
height/weight of the input will be automatically calculated.
height or weight of the input will be automatically calculated.
Default: [0., 0.]
Default: [0., 0.]
offset(float): Prior boxes center offset. Default: 0.5
offset(float): Prior boxes center offset. Default: 0.5
flatten_to_2d(bool): Whether to flatten output prior boxes and variance
flatten_to_2d(bool): Whether to flatten output prior boxes and variance
to 2D shape, the second dim is 4. Default: False.
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:
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.
boxes: the output density prior boxes of PriorBox.
The layout is [H, W, num_priors, 4] when flatten_to_2d is False.
4-D tensor, 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.
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,
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.
num_priors is the total box count of each position of input.
variances: the expanded variances of PriorBox.
variances: the expanded variances of PriorBox.
The layout is [H, W, num_priors, 4] when flatten_to_2d is False.
4-D tensor, 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.
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
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.
num_priors is the total box count of each position of input.
x(Variable|list): The input tensor to l2_normalize layer.
x(Variable|list): The input tensor could be N-D tensor, and the input data type could be float32 or float64.
axis(int): The axis on which to apply normalization. If `axis < 0`, \
axis(int): The axis on which to apply normalization. If `axis < 0`, \
the dimension to normalization is rank(X) + axis. -1 is the
the dimension to normalization is rank(X) + axis. -1 is the
last dimension.
last dimension.
epsilon(float): The epsilon value is used to avoid division by zero, \
epsilon(float): The epsilon value is used to avoid division by zero, \
the default value is 1e-12.
the default value is 1e-12.
name(str|None): A name for this layer(optional). If set None, the layer \
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`
will be named automatically.
Returns:
Returns:
Variable: The output tensor variable is the same shape with `x`.
Variable: The output has the same shape and data type with `x`.
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:
Returns:
A 4-D Tensor in shape of (num_batches, channels, out_h, out_w) or
input(${x_type}): 5-D Tensor, its data type is float32, float64, or uint8,
input(${x_type}): 5-D Tensor, its data type is float32, float64, or uint8,
its data format is specified by :attr:`data_format`.
its data format is specified by :attr:`data_format`.
out_shape(list|tuple|Variable|None): Output shape of resize bilinear
out_shape(list|tuple|Variable|None): The output shape of resized tensor, the shape is (out_d, out_h, out_w). Default: None. Every element should be an integer or a Tensor Variable with shape: [1] if it is a list. If it is a Tensor Variable, its dimension size should be 1.
layer, the shape is (out_d, out_h, out_w). Default: None. If a list,
each element can be an integer or a Tensor Variable with shape: [1]. If
a Tensor Variable, its dimension size should be 1.
scale(float|Variable|None): The multiplier for the input depth, height or width.
scale(float|Variable|None): The multiplier for the input depth, height or width.
At least one of :attr:`out_shape` or :attr:`scale` must be set.
At least one of :attr:`out_shape` or :attr:`scale` must be set.
And :attr:`out_shape` has a higher priority than :attr:`scale`.
And :attr:`out_shape` has a higher priority than :attr:`scale`.
Default: None.
Default: None.
name(str|None): The output variable name.
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`
actual_shape(Variable): An optional input to specify output shape
actual_shape(Variable): An optional input to specify output shape
input(${x_type}): 4-D Tensor, its data type is float32, float64, or uint8,
input(${x_type}): 4-D Tensor, its data type is float32, float64, or uint8,
its data format is specified by :attr:`data_format`.
its data format is specified by :attr:`data_format`.
out_shape(list|tuple|Variable|None): Output shape of resize nearest
out_shape(list|tuple|Variable|None): The output shape of resized tensor, the shape is (out_h, out_w). Default: None. Every element should be an integer or a tensor Variable with shape: [1] if it is a list. If it is a tensor Variable, its dimension size should be 1.
layer, the shape is (out_h, out_w). Default: None. If a list, each
element can be integer or a tensor Variable with shape: [1]. If a
tensor Variable, its dimension size should be 1.
scale(float|Variable|None): The multiplier for the input height or width. At
scale(float|Variable|None): The multiplier for the input height or width. At
least one of :attr:`out_shape` or :attr:`scale` must be set.
least one of :attr:`out_shape` or :attr:`scale` must be set.
And :attr:`out_shape` has a higher priority than :attr:`scale`.
And :attr:`out_shape` has a higher priority than :attr:`scale`.
Default: None.
Default: None.
name(str|None): The output variable name.
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`
actual_shape(Variable): An optional input to specify output shape
actual_shape(Variable): An optional input to specify output shape
