提交 d641d5ac 编写于 作者: C chengduoZH

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上级 74f7aff3
......@@ -17,11 +17,8 @@ All layers just related to the detection neural network.
from ..layer_helper import LayerHelper
from ..framework import Variable
from ..param_attr import ParamAttr
from ..framework import Variable
from layer_function_generator import autodoc
from tensor import concat
from nn import flatten
from ops import reshape
import math
__all__ = [
......@@ -30,91 +27,6 @@ __all__ = [
]
def prior_box(input,
image,
min_sizes,
max_sizes,
aspect_ratios,
variance,
flip=False,
clip=False,
step_w=0.0,
step_h=0.0,
offset=0.5,
name=None):
"""
**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 PriorBox, 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, optional, default=False): Whether to flip aspect ratios.
clip(bool, optional, default=False)): Whether to clip
out-of-boundary boxes.
step_w(int, optional, default=0.0): Prior boxes step across
width, 0.0 for auto calculation.
step_h(int, optional, default=0.0): Prior boxes step across
height, 0.0 for auto calculation.
offset(float, optional, default=0.5): Prior boxes center offset.
name(str, optional, default=None): 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. Where num_priors =
len(aspect_ratios) * len(min_sizes) + len(max_sizes)
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. Where num_priors =
len(aspect_ratios) * len(min_sizes) + len(max_sizes)
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()
box = helper.create_tmp_variable(dtype)
var = helper.create_tmp_variable(dtype)
helper.append_op(
type="prior_box",
inputs={"Input": input,
"Image": image},
outputs={"Boxes": box,
"Variances": var},
attrs={
'min_sizes': min_sizes,
'max_sizes': max_sizes,
'aspect_ratios': aspect_ratios,
'variances': variance,
'flip': flip,
'clip': clip,
'step_w': step_w,
'step_h': step_h,
'offset': offset
})
return box, var
def prior_boxes(inputs,
image,
min_ratio,
......@@ -128,20 +40,19 @@ def prior_boxes(inputs,
variance=[0.1, 0.1, 0.1, 0.1],
flip=False,
clip=False,
min_sizes=None,
max_sizes=None,
name=None):
"""
**Prior_boxes**
Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
Each position of the inputs produces many prior boxes respectly, the number
of prior boxes which is produced by inputs respectly is determined by
the count of min_ratio, max_ratio and aspect_ratios, The size of the
box is in range(min_ratio, max_ratio) interval, which is generated in
sequence according to the aspect_ratios.
The details of this algorithm, please refer the section 2.2 of SSD paper
(SSD: Single Shot MultiBox Detector)<https://arxiv.org/abs/1512.02325>`_ .
Args:
inputs(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.
inputs(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(int): the min ratio of generated prior boxes.
max_ratio(int): the max ratio of generated prior boxes.
aspect_ratios(list): the aspect ratios of generated prior boxes.
......@@ -159,13 +70,17 @@ def prior_boxes(inputs,
to be encoded in prior boxes.
flip(bool, optional, default=False): Whether to flip aspect ratios.
clip(bool, optional, default=False): Whether to clip out-of-boundary boxes.
min_sizes(list, optional, default=None): If `len(inputs) <=2`, min_sizes must
be set up, and the length of min_sizes should equal to the length of inputs.
max_sizes(list, optional, default=None): If `len(inputs) <=2`, max_sizes must
be set up, and the length of min_sizes should equal to the length of inputs.
name(str, optional, None): Name of the prior box layer.
Returns:
boxes(variable): the output prior boxes of PriorBoxOp. The layout is
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 inputs.
Variances(variable): the expanded variances of PriorBoxOp. The layout
Variances(Variable): the expanded variances of PriorBoxOp. The layout
is [num_priors, 4]. num_priors is the total box count of each
position of inputs
......@@ -185,13 +100,60 @@ def prior_boxes(inputs,
flip=True,
clip=True)
"""
def _prior_box_(input,
image,
min_sizes,
max_sizes,
aspect_ratios,
variance,
flip=False,
clip=False,
step_w=0.0,
step_h=0.0,
offset=0.5,
name=None):
helper = LayerHelper("prior_box", **locals())
dtype = helper.input_dtype()
box = helper.create_tmp_variable(dtype)
var = helper.create_tmp_variable(dtype)
helper.append_op(
type="prior_box",
inputs={"Input": input,
"Image": image},
outputs={"Boxes": box,
"Variances": var},
attrs={
'min_sizes': min_sizes,
'max_sizes': max_sizes,
'aspect_ratios': aspect_ratios,
'variances': variance,
'flip': flip,
'clip': clip,
'step_w': step_w,
'step_h': step_h,
'offset': offset
})
return box, var
def _reshape_with_axis_(input, axis=1):
if not (axis > 0 and axis < len(input.shape)):
raise ValueError(
"The axis should be smaller than the arity of input's shape.")
new_shape = [-1, reduce(mul, input.shape[axis:len(input.shape)], 1)]
out = reshape([input], shape=new_shape)
return out
assert isinstance(inputs, list), 'inputs should be a list.'
num_layer = len(inputs)
assert num_layer > 2 # TODO(zcd): currently, num_layer must be bigger than two.
if num_layer <= 2:
assert min_sizes is not None and max_sizes is not None
assert len(min_sizes) == num_layer and len(max_sizes) == num_layer
else:
min_sizes = []
max_sizes = []
if num_layer > 2:
step = int(math.floor(((max_ratio - min_ratio)) / (num_layer - 2)))
for ratio in xrange(min_ratio, max_ratio + 1, step):
min_sizes.append(base_size * ratio / 100.)
......@@ -199,21 +161,29 @@ def prior_boxes(inputs,
min_sizes = [base_size * .10] + min_sizes
max_sizes = [base_size * .20] + max_sizes
if aspect_ratios:
if not (isinstance(aspect_ratios, list) and
len(aspect_ratios) == num_layer):
raise ValueError(
'aspect_ratios should be list and the length of inputs '
'and aspect_ratios should be the same.')
if step_h:
assert isinstance(step_h,list) and len(step_h) == num_layer, \
'step_h should be list and inputs and step_h should have same length'
if not (isinstance(step_h, list) and len(step_h) == num_layer):
raise ValueError(
'step_h should be list and the length of inputs and '
'step_h should be the same.')
if step_w:
assert isinstance(step_w,list) and len(step_w) == num_layer, \
'step_w should be list and inputs and step_w should have same length'
if not (isinstance(step_w, list) and len(step_w) == num_layer):
raise ValueError(
'step_w should be list and the length of inputs and '
'step_w should be the same.')
if steps:
assert isinstance(steps,list) and len(steps) == num_layer, \
'steps should be list and inputs and step_w should have same length'
if not (isinstance(steps, list) and len(steps) == num_layer):
raise ValueError(
'steps should be list and the length of inputs and '
'step_w should be the same.')
step_w = steps
step_h = steps
if aspect_ratios:
assert isinstance(aspect_ratios, list) and len(aspect_ratios) == num_layer, \
'aspect_ratios should be list and inputs and aspect_ratios should ' \
'have same length'
box_results = []
var_results = []
......@@ -230,7 +200,7 @@ def prior_boxes(inputs,
if not isinstance(aspect_ratio, list):
aspect_ratio = [aspect_ratio]
box, var = prior_box(input, image, min_size, max_size, aspect_ratio,
box, var = _prior_box_(input, image, min_size, max_size, aspect_ratio,
variance, flip, clip, step_w[i]
if step_w else 0.0, step_h[i]
if step_w else 0.0, offset)
......@@ -242,17 +212,11 @@ def prior_boxes(inputs,
box = box_results[0]
var = var_results[0]
else:
axis = 3
reshaped_boxes = []
reshaped_vars = []
for i in range(len(box_results)):
reshaped_boxes += [flatten(box_results[i], axis=3)]
reshaped_vars += [flatten(var_results[i], axis=3)]
helper = LayerHelper("concat", **locals())
dtype = helper.input_dtype()
box = helper.create_tmp_variable(dtype)
var = helper.create_tmp_variable(dtype)
reshaped_boxes.append(_reshape_with_axis_(box_results[i], axis=3))
reshaped_vars.append(_reshape_with_axis_(var_results[i], axis=3))
box = concat(reshaped_boxes)
var = concat(reshaped_vars)
......
......@@ -21,8 +21,6 @@ from ..framework import Variable
from ..param_attr import ParamAttr
from layer_function_generator import autodoc
from tensor import concat
import math
from operator import mul
__all__ = [
'fc',
......@@ -66,8 +64,6 @@ __all__ = [
'nce',
'beam_search',
'row_conv',
'reshape_with_axis',
'flatten',
'multiplex',
'layer_norm',
]
......@@ -3095,86 +3091,3 @@ def multiplex(inputs, index):
'Ids': index},
outputs={'Out': [out]})
return out
def reshape_with_axis(input, axis):
"""
**ReshapeWithAxis Layer**
ReshapeWithAxis is used to merge adjacent dimensions according to axis.
Args:
input(variable): The input tensor.
axis(list): The axis which is used to merge the adjacent dimensions.
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
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())
helper.append_op(
type='reshape',
inputs={'X': [input]},
outputs={'Out': [out]},
attrs={'shape': new_shape})
return out
def flatten(input, axis=1):
"""
**Flatten Layer**
ReshapeWithAxis is used to merge adjacent dimensions according to axis.
Args:
input(variable): The input tensor.
axis(int):
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]
"""
assert len(input.shape) > axis and axis > 0, \
"the axis should be litter than input.shape's."
input_shape = input.shape
new_shape = [-1, reduce(mul, input_shape[axis:len(input_shape)], 1)]
helper = LayerHelper('reshape', **locals())
out = helper.create_tmp_variable(helper.input_dtype())
helper.append_op(
type='reshape',
inputs={'X': [input]},
outputs={'Out': [out]},
attrs={'shape': new_shape})
return out
......@@ -51,15 +51,15 @@ def main(use_cuda):
if use_cuda: # prior_box only support CPU.
return
box, var = prior_box_output(data_shape=[3, 224, 224])
data_shape = [3, 224, 224]
box, var = prior_box_output(data_shape)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
batch = [128]
for i in range(1):
# print("iteration : %d" % i)
for _ in range(1):
x = np.random.random(batch + data_shape).astype("float32")
tensor_x = core.LoDTensor()
tensor_x.set(x, place)
......@@ -75,11 +75,9 @@ def main(use_cuda):
class TestFitALine(unittest.TestCase):
def test_cpu(self):
with self.program_scope_guard():
main(use_cuda=False)
def test_cuda(self):
with self.program_scope_guard():
main(use_cuda=True)
......
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