未验证 提交 7757a8ad 编写于 作者: C chengduo 提交者: GitHub

Merge pull request #8265 from chengduoZH/feature/add_prior_box_py

Add Python interface of prior_boxes
......@@ -38,8 +38,8 @@ class PriorBoxOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_LT(input_dims[3], image_dims[3],
"The width of input must smaller than image.");
auto min_sizes = ctx->Attrs().Get<std::vector<int>>("min_sizes");
auto max_sizes = ctx->Attrs().Get<std::vector<int>>("max_sizes");
auto min_sizes = ctx->Attrs().Get<std::vector<float>>("min_sizes");
auto max_sizes = ctx->Attrs().Get<std::vector<float>>("max_sizes");
auto variances = ctx->Attrs().Get<std::vector<float>>("variances");
auto aspect_ratios = ctx->Attrs().Get<std::vector<float>>("aspect_ratios");
bool flip = ctx->Attrs().Get<bool>("flip");
......@@ -47,15 +47,15 @@ class PriorBoxOp : public framework::OperatorWithKernel {
std::vector<float> aspect_ratios_vec;
ExpandAspectRatios(aspect_ratios, flip, aspect_ratios_vec);
int num_priors = aspect_ratios_vec.size() * min_sizes.size();
size_t num_priors = aspect_ratios_vec.size() * min_sizes.size();
if (max_sizes.size() > 0) {
PADDLE_ENFORCE_EQ(max_sizes.size(), min_sizes.size(),
"The number of min_size and max_size must be equal.");
for (size_t i = 0; i < min_sizes.size(); ++i) {
num_priors += max_sizes.size();
for (size_t i = 0; i < max_sizes.size(); ++i) {
PADDLE_ENFORCE_GT(max_sizes[i], min_sizes[i],
"max_size[%d] must be greater than min_size[%d].", i,
i);
num_priors += 1;
}
}
......@@ -90,20 +90,20 @@ class PriorBoxOpMaker : public framework::OpProtoAndCheckerMaker {
"H is the height of input, W is the width of input, num_priors "
"is the box count of each position.");
AddAttr<std::vector<int>>("min_sizes",
"(vector<int>) List of min sizes "
"of generated prior boxes.")
.AddCustomChecker([](const std::vector<int>& min_sizes) {
AddAttr<std::vector<float>>("min_sizes",
"(vector<float>) List of min sizes "
"of generated prior boxes.")
.AddCustomChecker([](const std::vector<float>& min_sizes) {
PADDLE_ENFORCE_GT(min_sizes.size(), 0,
"Size of min_sizes must be at least 1.");
for (size_t i = 0; i < min_sizes.size(); ++i) {
PADDLE_ENFORCE_GT(min_sizes[i], 0,
PADDLE_ENFORCE_GT(min_sizes[i], 0.0,
"min_sizes[%d] must be positive.", i);
}
});
AddAttr<std::vector<int>>(
AddAttr<std::vector<float>>(
"max_sizes",
"(vector<int>) List of max sizes of generated prior boxes.");
"(vector<float>) List of max sizes of generated prior boxes.");
AddAttr<std::vector<float>>(
"aspect_ratios",
"(vector<float>) List of aspect ratios of generated prior boxes.");
......@@ -125,16 +125,16 @@ class PriorBoxOpMaker : public framework::OpProtoAndCheckerMaker {
.SetDefault(true);
AddAttr<float>("step_w",
"Prior boxes step across width, 0 for auto calculation.")
"Prior boxes step across width, 0.0 for auto calculation.")
.SetDefault(0.0)
.AddCustomChecker([](const float& step_w) {
PADDLE_ENFORCE_GT(step_w, 0.0, "step_w should be larger than 0.");
PADDLE_ENFORCE_GE(step_w, 0.0, "step_w should be larger than 0.");
});
AddAttr<float>("step_h",
"Prior boxes step across height, 0 for auto calculation.")
"Prior boxes step across height, 0.0 for auto calculation.")
.SetDefault(0.0)
.AddCustomChecker([](const float& step_h) {
PADDLE_ENFORCE_GT(step_h, 0.0, "step_h should be larger than 0.");
PADDLE_ENFORCE_GE(step_h, 0.0, "step_h should be larger than 0.");
});
AddAttr<float>("offset",
......
......@@ -60,8 +60,8 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
auto* boxes = ctx.Output<paddle::framework::Tensor>("Boxes");
auto* vars = ctx.Output<paddle::framework::Tensor>("Variances");
auto min_sizes = ctx.Attr<std::vector<int>>("min_sizes");
auto max_sizes = ctx.Attr<std::vector<int>>("max_sizes");
auto min_sizes = ctx.Attr<std::vector<float>>("min_sizes");
auto max_sizes = ctx.Attr<std::vector<float>>("max_sizes");
auto input_aspect_ratio = ctx.Attr<std::vector<float>>("aspect_ratios");
auto variances = ctx.Attr<std::vector<float>>("variances");
auto flip = ctx.Attr<bool>("flip");
......@@ -108,7 +108,7 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
T box_width, box_height;
int idx = 0;
for (size_t s = 0; s < min_sizes.size(); ++s) {
int min_size = min_sizes[s];
auto min_size = min_sizes[s];
// first prior: aspect_ratio = 1, size = min_size
box_width = box_height = min_size;
// xmin
......@@ -124,7 +124,7 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
idx++;
if (max_sizes.size() > 0) {
int max_size = max_sizes[s];
auto max_size = max_sizes[s];
// second prior: aspect_ratio = 1,
// size = sqrt(min_size * max_size)
box_width = box_height = sqrt(min_size * max_size);
......
......@@ -28,8 +28,11 @@ import device
from device import *
import math_op_patch
from math_op_patch import *
import detection
from detection import *
__all__ = []
__all__ += math_op_patch.__all__
__all__ += detection.__all__
__all__ += nn.__all__
__all__ += io.__all__
......@@ -37,4 +40,4 @@ __all__ += tensor.__all__
__all__ += control_flow.__all__
__all__ += ops.__all__
__all__ += device.__all__
__all__ += math_op_patch.__all__
__all__ += detection.__all__
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
# Copyright (c) 2018 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.
......@@ -16,8 +16,15 @@ All layers just related to the detection neural network.
"""
from ..layer_helper import LayerHelper
from ..framework import Variable
from tensor import concat
from ops import reshape
import math
__all__ = ['detection_output', ]
__all__ = [
'detection_output',
'prior_box',
]
def detection_output(scores,
......@@ -114,3 +121,208 @@ def detection_output(scores,
'nms_eta': 1.0
})
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): 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. 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, optional, default=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, optional, default=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, optional, default=[0.1, 0.1, 0.1, 0.1]): 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.
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 [num_priors, 4]. num_priors is the total
box count of each position of inputs.
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
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 = reshape(x=input, shape=new_shape)
return out
assert isinstance(inputs, list), 'inputs should be a list.'
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:
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:
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:
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:
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
box_results = []
var_results = []
for i, input in enumerate(inputs):
min_size = min_sizes[i]
max_size = max_sizes[i]
aspect_ratio = []
if not isinstance(min_size, list):
min_size = [min_size]
if not isinstance(max_size, list):
max_size = [max_size]
if aspect_ratios:
aspect_ratio = aspect_ratios[i]
if not isinstance(aspect_ratio, list):
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 = concat(reshaped_boxes)
var = concat(reshaped_vars)
return box, var
......@@ -13,10 +13,13 @@
# limitations under the License.
from __future__ import print_function
import unittest
import paddle.v2.fluid as fluid
import paddle.v2.fluid.core as core
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.layers.detection as detection
from paddle.v2.fluid.framework import Program, program_guard
import unittest
import numpy as np
class TestBook(unittest.TestCase):
......@@ -49,5 +52,62 @@ class TestBook(unittest.TestCase):
print(str(program))
class TestPriorBox(unittest.TestCase):
def test_prior_box(self):
data_shape = [3, 224, 224]
box, var = self.prior_box_output(data_shape)
assert len(box.shape) == 2
assert box.shape == var.shape
assert box.shape[1] == 4
def prior_box_output(self, data_shape):
images = fluid.layers.data(
name='pixel', shape=data_shape, dtype='float32')
conv1 = fluid.layers.conv2d(
input=images,
num_filters=3,
filter_size=3,
stride=2,
use_cudnn=False)
conv2 = fluid.layers.conv2d(
input=conv1,
num_filters=3,
filter_size=3,
stride=2,
use_cudnn=False)
conv3 = fluid.layers.conv2d(
input=conv2,
num_filters=3,
filter_size=3,
stride=2,
use_cudnn=False)
conv4 = fluid.layers.conv2d(
input=conv3,
num_filters=3,
filter_size=3,
stride=2,
use_cudnn=False)
conv5 = fluid.layers.conv2d(
input=conv4,
num_filters=3,
filter_size=3,
stride=2,
use_cudnn=False)
box, var = detection.prior_box(
inputs=[conv1, conv2, conv3, conv4, conv5, conv5],
image=images,
min_ratio=20,
max_ratio=90,
# steps=[8, 16, 32, 64, 100, 300],
aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
base_size=300,
offset=0.5,
flip=True,
clip=True)
return box, var
if __name__ == '__main__':
unittest.main()
......@@ -65,9 +65,9 @@ class TestPriorBoxOp(OpTest):
self.batch_size = 10
self.min_sizes = [2, 4]
self.min_sizes = np.array(self.min_sizes).astype('int64')
self.min_sizes = np.array(self.min_sizes).astype('float32').tolist()
self.max_sizes = [5, 10]
self.max_sizes = np.array(self.max_sizes).astype('int64')
self.max_sizes = np.array(self.max_sizes).astype('float32').tolist()
self.aspect_ratios = [2.0, 3.0]
self.flip = True
self.real_aspect_ratios = [1, 2.0, 1.0 / 2.0, 3.0, 1.0 / 3.0]
......
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