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add doc for prior box

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emailweixu-patch-1
chengduoZH 7 years ago
parent 19749d5234
commit 3cdb419b15
1 changed files with 137 additions and 21 deletions
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158
python/paddle/v2/fluid/layers/nn.py
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@ -22,7 +22,6 @@ from ..param_attr import ParamAttr
from layer_function_generator import autodoc
from tensor import concat
import math
import numpy as np
from operator import mul
__all__ = [
@ -3006,10 +3005,43 @@ def reshape_with_axis(input, axis):
"""
**ReshapeWithAxis Layer**
"""
assert len(input.shape) > axis and axis >= 0, ' '
According to the axis to merge the adjacent dim of input. Currently, the axis of
reshape_with_axis must be a scalar.
Args:
input(variable): The input tensor.
axis(list): According to the axis to merge the adjacent dim.
Returns:
Variable: A tensor variable.
Examples:
.. code-block:: python
x = fluid.layers.data(name="data", shape=[3, 32, 32], dtype="float32")
reshaped = fluid.layers.reshape_with_axis(input=x, axis=2)
reshaped.shape
>> [-1, 1024]
reshaped = fluid.layers.reshape_with_axis(input=x, axis=[1,3])
reshaped.shape
>> [-1, 96, 32]
"""
assert isinstance(axis, list), "axis should be list."
assert len(input.shape) > len(
axis), "the length of axis should be litter than input.shape's."
input_shape = input.shape
new_dim = [-1, reduce(mul, input_shape[axis:len(input_shape)], 1)]
temp = 0
for ax in axis:
assert ax < len(input.shape) and ax > 0, \
'The data of Axis should be between 1 and len(input.shape)'
assert ax > temp, 'Axis should be incremented sequence'
temp = ax
axis += [len(input.shape)]
new_shape = []
for i in range(len(axis) - 1):
new_shape += [reduce(mul, input_shape[axis[i]:axis[i + 1]], 1)]
new_shape = [-1] + new_shape
helper = LayerHelper('reshape', **locals())
out = helper.create_tmp_variable(helper.input_dtype())
@ -3017,14 +3049,28 @@ def reshape_with_axis(input, axis):
type='reshape',
inputs={'X': [input]},
outputs={'Out': [out]},
attrs={'shape': new_dim})
attrs={'shape': new_shape})
return out
def reshape(input, new_dim):
def reshape(input, new_shape):
"""
**Reshape Layer**
Reshape the shape of input according to new_dim.
Args:
input(variable): The input tensor.
new_shape(list): The new shape of input.
Returns:
Variable: A tensor variable.
Examples:
.. code-block:: python
x = fluid.layers.data(name="data", shape=[3, 32, 32], dtype="float32")
reshaped = fluid.layers.reshape(input=x, new_shape=[-1, 1024])
"""
helper = LayerHelper('reshape', **locals())
out = helper.create_tmp_variable(helper.input_dtype())
@ -3051,6 +3097,44 @@ def prior_box(input,
"""
**Prior_box**
Generate 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 feature data of PriorBox, the layout is NCHW.
image(variable): The input image data of PriorBoxOp, the layout is NCHW.
min_sizes(list): the min sizes of generated prior boxes.
max_sizes(list): the max sizes of generated prior boxes.
aspect_ratios(list): the aspect ratios of generated prior boxes.
variance(list): the variances to be encoded in prior boxes.
flip(bool): Whether to flip aspect ratios.
clip(bool): Whether to clip out-of-boundary boxes.
step_w(list): Prior boxes step across width, 0 for auto calculation.
step_h(list): Prior boxes step across height, 0 for auto calculation.
offset(float): Prior boxes center offset.
name(str): Name of the prior box layer.
Returns:
boxes(variable): the output prior boxes of PriorBoxOp. The layout is
[H, W, num_priors, 4]. H is the height of input, W is the width
of input, num_priors is the box count of each position.
Variances(variable): the expanded variances of PriorBoxOp. The layout
is [H, W, num_priors, 4]. H is the height of input, W is the width
of input, num_priors is the box count of each position.
Examples:
.. code-block:: python
data = fluid.layers.data(name="data", shape=[3, 32, 32], dtype="float32")
conv2d = fluid.layers.conv2d(
input=data, num_filters=2, filter_size=3)
box, var = fluid.layers.prior_box(conv2d, data,
min_size, max_size, aspect_ratio,
variance, flip, clip,
step_w, step_h, offset)
"""
helper = LayerHelper("prior_box", **locals())
dtype = helper.input_dtype()
@ -3093,19 +3177,51 @@ def prior_boxes(input_layers,
name=None):
"""
**Prior_boxes**
e.g.
prior_boxes(
input_layers = [conv1, conv2, conv3, conv4, conv5, conv6],
image = data,
min_ratio = 0.2,
max_ratio = 0.9,
steps = [8., 16., 32., 64., 100., 300.],
aspect_ratios = [[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
min_dim = 300,
offset = 0.5,
variance = [0.1],
flip=True,
clip=True)
Generate 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(list): The list of input variables, the format of all variables is NCHW.
image(variable): The input image data of PriorBoxOp, the layout is NCHW.
min_ratio(list): the min sizes of generated prior boxes.
max_ratio(list): the max sizes of generated prior boxes.
aspect_ratios(list): the aspect ratios of generated prior boxes.
min_dim(int):
step_w(list): Prior boxes step across width, 0 for auto calculation.
step_h(list): Prior boxes step across height, 0 for auto calculation.
offset(float): Prior boxes center offset.
variance(list): the variances to be encoded in prior boxes.
flip(bool): Whether to flip aspect ratios.
clip(bool): Whether to clip out-of-boundary boxes.
name(str): Name of the prior box layer.
Returns:
boxes(variable): the output prior boxes of PriorBoxOp. The layout is
[num_priors, 4]. num_priors is the total box count of each
position of input_layers.
Variances(variable): the expanded variances of PriorBoxOp. The layout
is [num_priors, 4]. num_priors is the total box count of each
position of input_layers
Examples:
.. code-block:: python
prior_boxes(
input_layers = [conv1, conv2, conv3, conv4, conv5, conv6],
image = data,
min_ratio = 0.2,
max_ratio = 0.9,
steps = [8., 16., 32., 64., 100., 300.],
aspect_ratios = [[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
min_dim = 300,
offset = 0.5,
variance = [0.1,0.1,0.1,0.1],
flip=True,
clip=True)
"""
assert isinstance(input_layers, list), 'input_layer should be a list.'
num_layer = len(input_layers)
@ -3168,8 +3284,8 @@ def prior_boxes(input_layers,
reshaped_boxes = []
reshaped_vars = []
for i in range(len(box_results)):
reshaped_boxes += [reshape_with_axis(box_results[i], axis=axis)]
reshaped_vars += [reshape_with_axis(var_results[i], axis=axis)]
reshaped_boxes += [reshape_with_axis(box_results[i], axis=[axis])]
reshaped_vars += [reshape_with_axis(var_results[i], axis=[axis])]
helper = LayerHelper("concat", **locals())
dtype = helper.input_dtype()

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