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[new api] add new api paddle.vision.ops.distribute_fpn_proposals (#43736
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* add distribute_fpn_proposals

* change to new dygraph

* fix doc and example code

* change fluid impl to current version
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@zoooo0820
zoooo0820 committed Jul 19, 2022
1 parent 08cada9 commit 130c108
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Showing 3 changed files with 187 additions and 47 deletions.
55 changes: 9 additions & 46 deletions python/paddle/fluid/layers/detection.py
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Expand Up @@ -17,6 +17,8 @@

from __future__ import print_function

import paddle

from .layer_function_generator import generate_layer_fn
from .layer_function_generator import autodoc, templatedoc
from ..layer_helper import LayerHelper
Expand Down Expand Up @@ -3774,52 +3776,13 @@ def distribute_fpn_proposals(fpn_rois,
refer_level=4,
refer_scale=224)
"""
num_lvl = max_level - min_level + 1

if _non_static_mode():
assert rois_num is not None, "rois_num should not be None in dygraph mode."
attrs = ('min_level', min_level, 'max_level', max_level, 'refer_level',
refer_level, 'refer_scale', refer_scale)
multi_rois, restore_ind, rois_num_per_level = _C_ops.distribute_fpn_proposals(
fpn_rois, rois_num, num_lvl, num_lvl, *attrs)
return multi_rois, restore_ind, rois_num_per_level

check_variable_and_dtype(fpn_rois, 'fpn_rois', ['float32', 'float64'],
'distribute_fpn_proposals')
helper = LayerHelper('distribute_fpn_proposals', **locals())
dtype = helper.input_dtype('fpn_rois')
multi_rois = [
helper.create_variable_for_type_inference(dtype) for i in range(num_lvl)
]

restore_ind = helper.create_variable_for_type_inference(dtype='int32')

inputs = {'FpnRois': fpn_rois}
outputs = {
'MultiFpnRois': multi_rois,
'RestoreIndex': restore_ind,
}

if rois_num is not None:
inputs['RoisNum'] = rois_num
rois_num_per_level = [
helper.create_variable_for_type_inference(dtype='int32')
for i in range(num_lvl)
]
outputs['MultiLevelRoIsNum'] = rois_num_per_level

helper.append_op(type='distribute_fpn_proposals',
inputs=inputs,
outputs=outputs,
attrs={
'min_level': min_level,
'max_level': max_level,
'refer_level': refer_level,
'refer_scale': refer_scale
})
if rois_num is not None:
return multi_rois, restore_ind, rois_num_per_level
return multi_rois, restore_ind
return paddle.vision.ops.distribute_fpn_proposals(fpn_rois=fpn_rois,
min_level=min_level,
max_level=max_level,
refer_level=refer_level,
refer_scale=refer_scale,
rois_num=rois_num,
name=name)


@templatedoc()
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61 changes: 60 additions & 1 deletion python/paddle/fluid/tests/unittests/test_distribute_fpn_proposals_op.py
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@@ -1,4 +1,4 @@
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
Expand All @@ -18,6 +18,8 @@
import numpy as np
import math
import sys
import paddle

from op_test import OpTest


Expand Down Expand Up @@ -164,5 +166,62 @@ def init_test_case(self):
self.pixel_offset = False


class TestDistributeFpnProposalsAPI(unittest.TestCase):

def setUp(self):
np.random.seed(678)
self.rois_np = np.random.rand(10, 4).astype('float32')
self.rois_num_np = np.array([4, 6]).astype('int32')

def test_dygraph_with_static(self):
paddle.enable_static()
rois = paddle.static.data(name='rois', shape=[10, 4], dtype='float32')
rois_num = paddle.static.data(name='rois_num',
shape=[None],
dtype='int32')
multi_rois, restore_ind, rois_num_per_level = paddle.vision.ops.distribute_fpn_proposals(
fpn_rois=rois,
min_level=2,
max_level=5,
refer_level=4,
refer_scale=224,
rois_num=rois_num)
fetch_list = multi_rois + [restore_ind] + rois_num_per_level

exe = paddle.static.Executor()
output_stat = exe.run(paddle.static.default_main_program(),
feed={
'rois': self.rois_np,
'rois_num': self.rois_num_np
},
fetch_list=fetch_list,
return_numpy=False)
output_stat_np = []
for output in output_stat:
output_np = np.array(output)
if len(output_np) > 0:
output_stat_np.append(output_np)

paddle.disable_static()
rois_dy = paddle.to_tensor(self.rois_np)
rois_num_dy = paddle.to_tensor(self.rois_num_np)
multi_rois_dy, restore_ind_dy, rois_num_per_level_dy = paddle.vision.ops.distribute_fpn_proposals(
fpn_rois=rois_dy,
min_level=2,
max_level=5,
refer_level=4,
refer_scale=224,
rois_num=rois_num_dy)
output_dy = multi_rois_dy + [restore_ind_dy] + rois_num_per_level_dy
output_dy_np = []
for output in output_dy:
output_np = output.numpy()
if len(output_np) > 0:
output_dy_np.append(output_np)

for res_stat, res_dy in zip(output_stat_np, output_dy_np):
self.assertTrue(np.allclose(res_stat, res_dy))


if __name__ == '__main__':
unittest.main()
118 changes: 118 additions & 0 deletions python/paddle/vision/ops.py
View file
Expand Up @@ -28,6 +28,7 @@
'yolo_box',
'deform_conv2d',
'DeformConv2D',
'distribute_fpn_proposals',
'read_file',
'decode_jpeg',
'roi_pool',
Expand Down Expand Up @@ -835,6 +836,123 @@ def forward(self, x, offset, mask=None):
return out


def distribute_fpn_proposals(fpn_rois,
min_level,
max_level,
refer_level,
refer_scale,
pixel_offset=False,
rois_num=None,
name=None):
r"""
In Feature Pyramid Networks (FPN) models, it is needed to distribute
all proposals into different FPN level, with respect to scale of the proposals,
the referring scale and the referring level. Besides, to restore the order of
proposals, we return an array which indicates the original index of rois
in current proposals. To compute FPN level for each roi, the formula is given as follows:
.. math::
roi\_scale &= \sqrt{BBoxArea(fpn\_roi)}
level = floor(&\log(\\frac{roi\_scale}{refer\_scale}) + refer\_level)
where BBoxArea is a function to compute the area of each roi.
Args:
fpn_rois (Tensor): The input fpn_rois. 2-D Tensor with shape [N, 4] and data type can be
float32 or float64.
min_level (int): The lowest level of FPN layer where the proposals come
from.
max_level (int): The highest level of FPN layer where the proposals
come from.
refer_level (int): The referring level of FPN layer with specified scale.
refer_scale (int): The referring scale of FPN layer with specified level.
pixel_offset (bool, optional): Whether there is pixel offset. If True, the offset of
image shape will be 1. 'False' by default.
rois_num (Tensor, optional): 1-D Tensor contains the number of RoIs in each image.
The shape is [B] and data type is int32. B is the number of images.
If rois_num not None, it will return a list of 1-D Tensor. Each element
is the output RoIs' number of each image on the corresponding level
and the shape is [B]. None by default.
name (str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
Returns:
multi_rois (List) : The proposals in each FPN level. It is a list of 2-D Tensor with shape [M, 4], where M is
and data type is same as `fpn_rois` . The length is max_level-min_level+1.
restore_ind (Tensor): The index used to restore the order of fpn_rois. It is a 2-D Tensor with shape [N, 1]
, where N is the number of total rois. The data type is int32.
rois_num_per_level (List): A list of 1-D Tensor and each Tensor is
the RoIs' number in each image on the corresponding level. The shape
is [B] and data type of int32, where B is the number of images.
Examples:
.. code-block:: python
import paddle
fpn_rois = paddle.rand((10, 4))
rois_num = paddle.to_tensor([3, 1, 4, 2], dtype=paddle.int32)
multi_rois, restore_ind, rois_num_per_level = paddle.vision.ops.distribute_fpn_proposals(
fpn_rois=fpn_rois,
min_level=2,
max_level=5,
refer_level=4,
refer_scale=224,
rois_num=rois_num)
"""
num_lvl = max_level - min_level + 1

if _non_static_mode():
assert rois_num is not None, "rois_num should not be None in dygraph mode."
attrs = ('min_level', min_level, 'max_level', max_level, 'refer_level',
refer_level, 'refer_scale', refer_scale, 'pixel_offset',
pixel_offset)
multi_rois, restore_ind, rois_num_per_level = _C_ops.distribute_fpn_proposals(
fpn_rois, rois_num, num_lvl, num_lvl, *attrs)
return multi_rois, restore_ind, rois_num_per_level

else:
check_variable_and_dtype(fpn_rois, 'fpn_rois', ['float32', 'float64'],
'distribute_fpn_proposals')
helper = LayerHelper('distribute_fpn_proposals', **locals())
dtype = helper.input_dtype('fpn_rois')
multi_rois = [
helper.create_variable_for_type_inference(dtype)
for i in range(num_lvl)
]

restore_ind = helper.create_variable_for_type_inference(dtype='int32')

inputs = {'FpnRois': fpn_rois}
outputs = {
'MultiFpnRois': multi_rois,
'RestoreIndex': restore_ind,
}

if rois_num is not None:
inputs['RoisNum'] = rois_num
rois_num_per_level = [
helper.create_variable_for_type_inference(dtype='int32')
for i in range(num_lvl)
]
outputs['MultiLevelRoIsNum'] = rois_num_per_level
else:
rois_num_per_level = None

helper.append_op(type='distribute_fpn_proposals',
inputs=inputs,
outputs=outputs,
attrs={
'min_level': min_level,
'max_level': max_level,
'refer_level': refer_level,
'refer_scale': refer_scale,
'pixel_offset': pixel_offset
})
return multi_rois, restore_ind, rois_num_per_level


def read_file(filename, name=None):
"""
Reads and outputs the bytes contents of a file as a uint8 Tensor
Expand Down

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