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提交 6e79d01b 编写于 作者: C chengduoZH
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merge prior_box and multi_box

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python/paddle/v2/fluid/layers/detection.py
浏览文件 @ 6e79d01b
...@@ -23,7 +23,6 @@ import nn ...@@ -23,7 +23,6 @@ import nn
import math import math
__all__ = [ __all__ = [
'prior_box',
'multi_box_head', 'multi_box_head',
'bipartite_match', 'bipartite_match',
'target_assign', 'target_assign',
...@@ -133,219 +132,6 @@ def detection_output(scores, ...@@ -133,219 +132,6 @@ def detection_output(scores,
return nmsed_outs return nmsed_outs
def prior_box(inputs,
image,
min_ratio,
max_ratio,
aspect_ratios,
base_size,
steps=None,
step_w=None,
step_h=None,
offset=0.5,
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. 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|tuple): 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|tuple): the aspect ratios of generated prior
boxes. The length of input and aspect_ratios must be equal.
base_size(int): the base_size is used to get min_size
and max_size according to min_ratio and max_ratio.
step_w(list|tuple|None): Prior boxes step
across width. If step_w[i] == 0.0, the prior boxes step
across width of the inputs[i] will be automatically calculated.
step_h(list|tuple|None): Prior boxes step
across height, If step_h[i] == 0.0, the prior boxes
step across height of the inputs[i] will be automatically calculated.
offset(float, optional, default=0.5): Prior boxes center offset.
variance(list|tuple|[0.1, 0.1, 0.1, 0.1]): the variances
to be encoded in prior boxes.
flip(bool|False): Whether to flip
aspect ratios.
clip(bool, optional, default=False): Whether to clip
out-of-boundary boxes.
min_sizes(list|tuple|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|tuple|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|None): Name of the prior box layer.
Returns:
boxes(Variable): the output prior boxes of PriorBox.
The layout is [num_priors, 4]. num_priors is the total
box count of each position of inputs.
Variances(Variable): the expanded variances of PriorBox.
The layout is [num_priors, 4]. num_priors is the total
box count of each position of inputs
Examples:
.. code-block:: python
prior_box(
inputs = [conv1, conv2, conv3, conv4, conv5, conv6],
image = data,
min_ratio = 20, # 0.20
max_ratio = 90, # 0.90
offset = 0.5,
base_size = 300,
variance = [0.1,0.1,0.1,0.1],
aspect_ratios = [[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
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 and bigger than 0.")
new_shape = [
-1, reduce(lambda x, y: x * y, input.shape[axis:len(input.shape)])
]
out = ops.reshape(x=input, shape=new_shape)
return out
def _is_list_or_tuple_(data):
return (isinstance(data, list) or isinstance(data, tuple))
def _is_list_or_tuple_and_equal(data, length, err_info):
if not (_is_list_or_tuple_(data) and len(data) == length):
raise ValueError(err_info)
if not _is_list_or_tuple_(inputs):
raise ValueError('inputs should be a list or tuple.')
num_layer = len(inputs)
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 = []
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.)
max_sizes.append(base_size * (ratio + step) / 100.)
min_sizes = [base_size * .10] + min_sizes
max_sizes = [base_size * .20] + max_sizes
if aspect_ratios:
_is_list_or_tuple_and_equal(
aspect_ratios, num_layer,
'aspect_ratios should be list or tuple, and the length of inputs '
'and aspect_ratios should be the same.')
if step_h:
_is_list_or_tuple_and_equal(
step_h, num_layer,
'step_h should be list or tuple, and the length of inputs and '
'step_h should be the same.')
if step_w:
_is_list_or_tuple_and_equal(
step_w, num_layer,
'step_w should be list or tuple, and the length of inputs and '
'step_w should be the same.')
if steps:
_is_list_or_tuple_and_equal(
steps, num_layer,
'steps should be list or tuple, and the length of inputs and '
'step_w should be the same.')
step_w = steps
step_h = steps
box_results = []
var_results = []
for i, input in enumerate(inputs):
min_size = min_sizes[i]
max_size = max_sizes[i]
aspect_ratio = []
if not _is_list_or_tuple_(min_size):
min_size = [min_size]
if not _is_list_or_tuple_(max_size):
max_size = [max_size]
if aspect_ratios:
aspect_ratio = aspect_ratios[i]
if not _is_list_or_tuple_(aspect_ratio):
aspect_ratio = [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)
box_results.append(box)
var_results.append(var)
if len(box_results) == 1:
box = box_results[0]
var = var_results[0]
else:
reshaped_boxes = []
reshaped_vars = []
for i in range(len(box_results)):
reshaped_boxes.append(_reshape_with_axis_(box_results[i], axis=3))
reshaped_vars.append(_reshape_with_axis_(var_results[i], axis=3))
box = tensor.concat(reshaped_boxes)
var = tensor.concat(reshaped_vars)
return box, var
def bipartite_match(dist_matrix, name=None): def bipartite_match(dist_matrix, name=None):
""" """
**Bipartite matchint operator** **Bipartite matchint operator**
...@@ -672,106 +458,162 @@ def ssd_loss(location, ...@@ -672,106 +458,162 @@ def ssd_loss(location,
def multi_box_head(inputs, def multi_box_head(inputs,
image,
base_size,
num_classes, num_classes,
aspect_ratios,
min_ratio,
max_ratio,
min_sizes=None, min_sizes=None,
max_sizes=None, max_sizes=None,
min_ratio=None, steps=None,
max_ratio=None, step_w=None,
aspect_ratios=None, step_h=None,
offset=0.5,
variance=[0.1, 0.1, 0.1, 0.1],
flip=False, flip=False,
share_location=True, clip=False,
kernel_size=1, kernel_size=1,
pad=1, pad=0,
stride=1, stride=1,
use_batchnorm=False, name=None):
base_size=None):
""" """
**Multi Box Head** **Prior_boxes**
Generate prior boxes' location and confidence for SSD(Single Generate prior boxes for SSD(Single Shot MultiBox Detector)
Shot MultiBox Detector)algorithm. The details of this algorithm, algorithm. The details of this algorithm, please refer the
please refer the section 2.1 of SSD paper (SSD: Single Shot section 2.2 of SSD paper (SSD: Single Shot MultiBox Detector)
MultiBox Detector)<https://arxiv.org/abs/1512.02325>`_ . <https://arxiv.org/abs/1512.02325>`_ .
Args: Args:
inputs(list|tuple): The list of input Variables, the format inputs(list|tuple): The list of input Variables, the format
of all Variables is NCHW. of all Variables is NCHW.
num_classes(int): The number of classes. image(Variable): The input image data of PriorBoxOp,
min_sizes(list|tuple|None): The number of the layout is NCHW.
min_sizes is used to compute the number of predicted box.
If the min_size is None, it will be computed according
to min_ratio and max_ratio.
max_sizes(list|tuple|None): The number of max_sizes
is used to compute the the number of predicted box.
min_ratio(int|None): If the min_sizes is None, min_ratio and max_ratio
will be used to compute the min_sizes and max_sizes.
max_ratio(int|None): If the min_sizes is None, max_ratio and min_ratio
will be used to compute the min_sizes and max_sizes.
aspect_ratios(list|tuple): The number of the aspect ratios is used to
compute the number of prior box.
base_size(int): the base_size is used to get min_size base_size(int): the base_size is used to get min_size
and max_size according to min_ratio and max_ratio. and max_size according to min_ratio and max_ratio.
flip(bool|False): Whether to flip num_classes(int): The number of classes.
aspect ratios. aspect_ratios(list|tuple): the aspect ratios of generated prior
name(str|None): Name of the prior box layer. boxes. The length of input and aspect_ratios must be equal.
min_ratio(int): the min ratio of generated prior boxes.
max_ratio(int): the max ratio of generated prior boxes.
min_sizes(list|tuple|None): If `len(inputs) <=2`,
min_sizes must be set up, and the length of min_sizes
should equal to the length of inputs. Default: None.
max_sizes(list|tuple|None): If `len(inputs) <=2`,
max_sizes must be set up, and the length of min_sizes
should equal to the length of inputs. Default: None.
steps(list|tuple): If step_w and step_h are the same,
step_w and step_h can be replaced by steps.
step_w(list|tuple): Prior boxes step
across width. If step_w[i] == 0.0, the prior boxes step
across width of the inputs[i] will be automatically
calculated. Default: None.
step_h(list|tuple): Prior boxes step across height, If
step_h[i] == 0.0, the prior boxes step across height of
the inputs[i] will be automatically calculated. Default: None.
offset(float): Prior boxes center offset. Default: 0.5
variance(list|tuple): the variances to be encoded in prior boxes.
Default:[0.1, 0.1, 0.1, 0.1].
flip(bool): Whether to flip aspect ratios. Default:False.
clip(bool): Whether to clip out-of-boundary boxes. Default: False.
kernel_size(int): The kernel size of conv2d. Default: 1.
pad(int|list|tuple): The padding of conv2d. Default:0.
stride(int|list|tuple): The stride of conv2d. Default:1,
name(str): Name of the prior box layer. Default: None.
Returns: Returns:
mbox_loc(list): The predicted boxes' location of the inputs. mbox_loc(list): The predicted boxes' location of the inputs.
The layout of each element is [N, H, W, Priors]. Priors The layout of each element is [N, H, W, Priors]. Priors
is the number of predicted boxof each position of each input. is the number of predicted boxof each position of each input.
mbox_conf(list): The predicted boxes' confidence of the inputs. mbox_conf(list): The predicted boxes' confidence of the inputs.
The layout of each element is [N, H, W, Priors]. Priors The layout of each element is [N, H, W, Priors]. Priors
is the number of predicted box of each position of each input. is the number of predicted box of each position of each input.
boxes(Variable): the output prior boxes of PriorBox.
The layout is [num_priors, 4]. num_priors is the total
box count of each position of inputs.
Variances(Variable): the expanded variances of PriorBox.
The layout is [num_priors, 4]. num_priors is the total
box count of each position of inputs
Examples: Examples:
.. code-block:: python .. code-block:: python
mbox_locs, mbox_confs, box, var = layers.multi_box_head(
mbox_locs, mbox_confs = detection.multi_box_head(
inputs=[conv1, conv2, conv3, conv4, conv5, conv5], inputs=[conv1, conv2, conv3, conv4, conv5, conv5],
image=images,
num_classes=21, num_classes=21,
min_ratio=20, min_ratio=20,
max_ratio=90, max_ratio=90,
aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]], aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
base_size=300, base_size=300,
flip=True) offset=0.5,
flip=True,
clip=True)
""" """
def _is_equal_(len1, len2, err_info): def _prior_box_(input,
if not (len1 == len2): image,
raise ValueError(err_info) 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 and bigger than 0.")
new_shape = [
-1, reduce(lambda x, y: x * y, input.shape[axis:len(input.shape)])
]
out = ops.reshape(x=input, shape=new_shape)
return out
def _is_list_or_tuple_(data): def _is_list_or_tuple_(data):
return (isinstance(data, list) or isinstance(data, tuple)) return (isinstance(data, list) or isinstance(data, tuple))
def _is_list_or_tuple_and_equal(data, length, err_info):
if not (_is_list_or_tuple_(data) and len(data) == length):
raise ValueError(err_info)
if not _is_list_or_tuple_(inputs): if not _is_list_or_tuple_(inputs):
raise ValueError('inputs should be a list or tuple.') raise ValueError('inputs should be a list or tuple.')
if min_sizes is not None:
_is_equal_(
len(inputs),
len(min_sizes), 'the length of min_sizes '
'and inputs should be equal.')
if max_sizes is not None:
_is_equal_(
len(inputs),
len(max_sizes), 'the length of max_sizes '
'and inputs should be equal.')
if aspect_ratios is not None:
_is_equal_(
len(inputs),
len(aspect_ratios), 'the length of aspect_ratios '
'and inputs should be equal.')
if min_sizes is None:
# If min_sizes is None, min_sizes and max_sizes
# will be set according to max_ratio and min_ratio.
num_layer = len(inputs) num_layer = len(inputs)
assert max_ratio is not None and min_ratio is not None,\
'max_ratio and min_ratio must be not None.' if num_layer <= 2:
assert num_layer >= 3, 'The length of the input data is at least three.' 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 = [] min_sizes = []
max_sizes = [] max_sizes = []
step = int(math.floor(((max_ratio - min_ratio)) / (num_layer - 2))) step = int(math.floor(((max_ratio - min_ratio)) / (num_layer - 2)))
...@@ -781,21 +623,43 @@ def multi_box_head(inputs, ...@@ -781,21 +623,43 @@ def multi_box_head(inputs,
min_sizes = [base_size * .10] + min_sizes min_sizes = [base_size * .10] + min_sizes
max_sizes = [base_size * .20] + max_sizes max_sizes = [base_size * .20] + max_sizes
if aspect_ratios:
_is_list_or_tuple_and_equal(
aspect_ratios, num_layer,
'aspect_ratios should be list or tuple, and the length of inputs '
'and aspect_ratios should be the same.')
if step_h:
_is_list_or_tuple_and_equal(
step_h, num_layer,
'step_h should be list or tuple, and the length of inputs and '
'step_h should be the same.')
if step_w:
_is_list_or_tuple_and_equal(
step_w, num_layer,
'step_w should be list or tuple, and the length of inputs and '
'step_w should be the same.')
if steps:
_is_list_or_tuple_and_equal(
steps, num_layer,
'steps should be list or tuple, and the length of inputs and '
'step_w should be the same.')
step_w = steps
step_h = steps
mbox_locs = [] mbox_locs = []
mbox_confs = [] mbox_confs = []
box_results = []
var_results = []
for i, input in enumerate(inputs): for i, input in enumerate(inputs):
min_size = min_sizes[i] min_size = min_sizes[i]
max_size = max_sizes[i]
if not _is_list_or_tuple_(min_size): if not _is_list_or_tuple_(min_size):
min_size = [min_size] min_size = [min_size]
max_size = []
if max_sizes is not None:
max_size = max_sizes[i]
if not _is_list_or_tuple_(max_size): if not _is_list_or_tuple_(max_size):
max_size = [max_size] max_size = [max_size]
_is_equal_( if not (len(max_size) == len(min_size)):
len(max_size), raise ValueError(
len(min_size),
'the length of max_size and min_size should be equal.') 'the length of max_size and min_size should be equal.')
aspect_ratio = [] aspect_ratio = []
...@@ -804,23 +668,18 @@ def multi_box_head(inputs, ...@@ -804,23 +668,18 @@ def multi_box_head(inputs,
if not _is_list_or_tuple_(aspect_ratio): if not _is_list_or_tuple_(aspect_ratio):
aspect_ratio = [aspect_ratio] aspect_ratio = [aspect_ratio]
# get the number of prior box on each location box, var = _prior_box_(input, image, min_size, max_size, aspect_ratio,
num_priors_per_location = 0 variance, flip, clip, step_w[i]
if max_sizes is not None: if step_w else 0.0, step_h[i]
num_priors_per_location = len(min_size) + \ if step_w else 0.0, offset)
len(aspect_ratio) * len(min_size) +\
len(max_size)
else:
num_priors_per_location = len(min_size) +\
len(aspect_ratio) * len(min_size)
if flip:
num_priors_per_location += len(aspect_ratio) * len(min_size)
# get mbox_loc box_results.append(box)
num_loc_output = num_priors_per_location * 4 var_results.append(var)
if share_location:
num_loc_output *= num_classes num_boxes = box.shape[2]
# get box_loc
num_loc_output = num_boxes * num_classes * 4
mbox_loc = nn.conv2d( mbox_loc = nn.conv2d(
input=input, input=input,
num_filters=num_loc_output, num_filters=num_loc_output,
...@@ -832,7 +691,7 @@ def multi_box_head(inputs, ...@@ -832,7 +691,7 @@ def multi_box_head(inputs,
mbox_locs.append(mbox_loc) mbox_locs.append(mbox_loc)
# get conf_loc # get conf_loc
num_conf_output = num_priors_per_location * num_classes num_conf_output = num_boxes * num_classes
conf_loc = nn.conv2d( conf_loc = nn.conv2d(
input=input, input=input,
num_filters=num_conf_output, num_filters=num_conf_output,
...@@ -842,4 +701,17 @@ def multi_box_head(inputs, ...@@ -842,4 +701,17 @@ def multi_box_head(inputs,
conf_loc = nn.transpose(conf_loc, perm=[0, 2, 3, 1]) conf_loc = nn.transpose(conf_loc, perm=[0, 2, 3, 1])
mbox_confs.append(conf_loc) mbox_confs.append(conf_loc)
return mbox_locs, mbox_confs if len(box_results) == 1:
box = box_results[0]
var = var_results[0]
else:
reshaped_boxes = []
reshaped_vars = []
for i in range(len(box_results)):
reshaped_boxes.append(_reshape_with_axis_(box_results[i], axis=3))
reshaped_vars.append(_reshape_with_axis_(var_results[i], axis=3))
box = tensor.concat(reshaped_boxes)
var = tensor.concat(reshaped_vars)
return mbox_locs, mbox_confs, box, var
python/paddle/v2/fluid/tests/test_detection.py
浏览文件 @ 6e79d01b
...@@ -109,16 +109,19 @@ class TestDetection(unittest.TestCase): ...@@ -109,16 +109,19 @@ class TestDetection(unittest.TestCase):
print(str(program)) print(str(program))
class TestPriorBox(unittest.TestCase): class TestMultiBoxHead(unittest.TestCase):
def test_prior_box(self): def test_multi_box_head(self):
data_shape = [3, 224, 224] data_shape = [3, 224, 224]
box, var = self.prior_box_output(data_shape) mbox_locs, mbox_confs, box, var = self.multi_box_head_output(data_shape)
assert len(box.shape) == 2 assert len(box.shape) == 2
assert box.shape == var.shape assert box.shape == var.shape
assert box.shape[1] == 4 assert box.shape[1] == 4
def prior_box_output(self, data_shape): for loc, conf in zip(mbox_locs, mbox_confs):
assert loc.shape[1:3] == conf.shape[1:3]
def multi_box_head_output(self, data_shape):
images = fluid.layers.data( images = fluid.layers.data(
name='pixel', shape=data_shape, dtype='float32') name='pixel', shape=data_shape, dtype='float32')
conv1 = fluid.layers.conv2d(images, 3, 3, 2) conv1 = fluid.layers.conv2d(images, 3, 3, 2)
...@@ -127,46 +130,19 @@ class TestPriorBox(unittest.TestCase): ...@@ -127,46 +130,19 @@ class TestPriorBox(unittest.TestCase):
conv4 = fluid.layers.conv2d(conv3, 3, 3, 2) conv4 = fluid.layers.conv2d(conv3, 3, 3, 2)
conv5 = fluid.layers.conv2d(conv4, 3, 3, 2) conv5 = fluid.layers.conv2d(conv4, 3, 3, 2)
box, var = layers.prior_box( mbox_locs, mbox_confs, box, var = layers.multi_box_head(
inputs=[conv1, conv2, conv3, conv4, conv5, conv5], inputs=[conv1, conv2, conv3, conv4, conv5, conv5],
image=images, image=images,
num_classes=21,
min_ratio=20, min_ratio=20,
max_ratio=90, max_ratio=90,
# steps=[8, 16, 32, 64, 100, 300],
aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]], aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
base_size=300, base_size=300,
offset=0.5, offset=0.5,
flip=True, flip=True,
clip=True) clip=True)
return box, var
class TestMultiBoxHead(unittest.TestCase):
def test_prior_box(self):
data_shape = [3, 224, 224]
mbox_locs, mbox_confs = self.multi_box_output(data_shape)
for loc, conf in zip(mbox_locs, mbox_confs):
assert loc.shape[1:3] == conf.shape[1:3]
def multi_box_output(self, data_shape):
images = fluid.layers.data(
name='pixel', shape=data_shape, dtype='float32')
conv1 = fluid.layers.conv2d(images, 3, 3, 2)
conv2 = fluid.layers.conv2d(conv1, 3, 3, 2)
conv3 = fluid.layers.conv2d(conv2, 3, 3, 2)
conv4 = fluid.layers.conv2d(conv3, 3, 3, 2)
conv5 = fluid.layers.conv2d(conv4, 3, 3, 2)
mbox_locs, mbox_confs = detection.multi_box_head( return mbox_locs, mbox_confs, box, var
inputs=[conv1, conv2, conv3, conv4, conv5, conv5],
num_classes=21,
min_ratio=20,
max_ratio=90,
aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
base_size=300,
flip=True)
return mbox_locs, mbox_confs
if __name__ == '__main__': if __name__ == '__main__':
......
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