未验证 提交 f6915d42 编写于 作者: Z zqw_1997 提交者: GitHub

remove prior_box (#49006)

* remove prior_box

* modify the sequence of paras of prior_box in multi_box_head api
上级 90ed6f5d
...@@ -39,7 +39,6 @@ from paddle import _C_ops, _legacy_C_ops ...@@ -39,7 +39,6 @@ from paddle import _C_ops, _legacy_C_ops
from ..framework import in_dygraph_mode from ..framework import in_dygraph_mode
__all__ = [ __all__ = [
'prior_box',
'density_prior_box', 'density_prior_box',
'multi_box_head', 'multi_box_head',
'anchor_generator', 'anchor_generator',
...@@ -58,135 +57,6 @@ __all__ = [ ...@@ -58,135 +57,6 @@ __all__ = [
] ]
def prior_box(
input,
image,
min_sizes,
max_sizes=None,
aspect_ratios=[1.0],
variance=[0.1, 0.1, 0.2, 0.2],
flip=False,
clip=False,
steps=[0.0, 0.0],
offset=0.5,
name=None,
min_max_aspect_ratios_order=False,
):
"""
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.
Parameters:
input(Variable): 4-D tensor(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): 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] 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
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)
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.
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
Examples:
.. code-block:: python
#declarative mode
import paddle.fluid as fluid
import numpy as np
import paddle
paddle.enable_static()
input = fluid.data(name="input", shape=[None,3,6,9])
image = fluid.data(name="image", shape=[None,3,9,12])
box, var = fluid.layers.prior_box(
input=input,
image=image,
min_sizes=[100.],
clip=True,
flip=True)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
# prepare a batch of data
input_data = np.random.rand(1,3,6,9).astype("float32")
image_data = np.random.rand(1,3,9,12).astype("float32")
box_out, var_out = exe.run(fluid.default_main_program(),
feed={"input":input_data,"image":image_data},
fetch_list=[box,var],
return_numpy=True)
# print(box_out.shape)
# (6, 9, 1, 4)
# print(var_out.shape)
# (6, 9, 1, 4)
# imperative mode
import paddle.fluid.dygraph as dg
with dg.guard(place) as g:
input = dg.to_variable(input_data)
image = dg.to_variable(image_data)
box, var = fluid.layers.prior_box(
input=input,
image=image,
min_sizes=[100.],
clip=True,
flip=True)
# print(box.shape)
# [6L, 9L, 1L, 4L]
# print(var.shape)
# [6L, 9L, 1L, 4L]
"""
return paddle.vision.ops.prior_box(
input=input,
image=image,
min_sizes=min_sizes,
max_sizes=max_sizes,
aspect_ratios=aspect_ratios,
variance=variance,
flip=flip,
clip=clip,
steps=steps,
offset=offset,
min_max_aspect_ratios_order=min_max_aspect_ratios_order,
name=name,
)
def density_prior_box( def density_prior_box(
input, input,
image, image,
...@@ -623,7 +493,7 @@ def multi_box_head( ...@@ -623,7 +493,7 @@ def multi_box_head(
aspect_ratio = [aspect_ratio] aspect_ratio = [aspect_ratio]
step = [step_w[i] if step_w else 0.0, step_h[i] if step_w else 0.0] step = [step_w[i] if step_w else 0.0, step_h[i] if step_w else 0.0]
box, var = prior_box( box, var = paddle.vision.ops.prior_box(
input, input,
image, image,
min_size, min_size,
...@@ -634,8 +504,8 @@ def multi_box_head( ...@@ -634,8 +504,8 @@ def multi_box_head(
clip, clip,
step, step,
offset, offset,
None,
min_max_aspect_ratios_order, min_max_aspect_ratios_order,
None,
) )
box_results.append(box) box_results.append(box)
......
...@@ -75,48 +75,6 @@ class LayerTest(unittest.TestCase): ...@@ -75,48 +75,6 @@ class LayerTest(unittest.TestCase):
yield yield
class TestPriorBox(unittest.TestCase):
def test_prior_box(self):
program = Program()
with program_guard(program):
data_shape = [3, 224, 224]
images = fluid.layers.data(
name='pixel', shape=data_shape, dtype='float32'
)
conv1 = fluid.layers.conv2d(images, 3, 3, 2)
box, var = layers.prior_box(
input=conv1,
image=images,
min_sizes=[100.0],
aspect_ratios=[1.0],
flip=True,
clip=True,
)
assert len(box.shape) == 4
assert box.shape == var.shape
assert box.shape[3] == 4
class TestPriorBox2(unittest.TestCase):
def test_prior_box(self):
program = Program()
with program_guard(program):
data_shape = [None, 3, None, None]
images = fluid.data(name='pixel', shape=data_shape, dtype='float32')
conv1 = fluid.layers.conv2d(images, 3, 3, 2)
box, var = layers.prior_box(
input=conv1,
image=images,
min_sizes=[100.0],
aspect_ratios=[1.0],
flip=True,
clip=True,
)
assert len(box.shape) == 4
assert box.shape == var.shape
assert box.shape[3] == 4
class TestDensityPriorBox(unittest.TestCase): class TestDensityPriorBox(unittest.TestCase):
def test_density_prior_box(self): def test_density_prior_box(self):
program = Program() program = Program()
......
...@@ -36,7 +36,7 @@ def python_prior_box( ...@@ -36,7 +36,7 @@ def python_prior_box(
min_max_aspect_ratios_order=False, min_max_aspect_ratios_order=False,
name=None, name=None,
): ):
return paddle.fluid.layers.detection.prior_box( return paddle.vision.ops.prior_box(
input, input,
image, image,
min_sizes=min_sizes, min_sizes=min_sizes,
......
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