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提交 cc534530 编写于 作者: J jerrywgz
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add comment and refine code, test=develop

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Showing with 170 addition and 121 deletion
paddle/fluid/API.spec
浏览文件 @ cc534530
......@@ -318,7 +318,7 @@ paddle.fluid.layers.iou_similarity ArgSpec(args=['x', 'y', 'name'], varargs=None
paddle.fluid.layers.box_coder ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name'], varargs=None, keywords=None, defaults=('encode_center_size', True, None))
paddle.fluid.layers.polygon_box_transform ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.yolov3_loss ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'class_num', 'ignore_thresh', 'loss_weight_xy', 'loss_weight_wh', 'loss_weight_conf_target', 'loss_weight_conf_notarget', 'loss_weight_class', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None))
paddle.fluid.layers.multiclass_nms ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'nms_threshold', 'keep_top_k', 'normalized', 'nms_eta', 'background_label'], varargs=None, keywords=None, defaults=(True, 1.0, 0))
paddle.fluid.layers.multiclass_nms ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'nms_threshold', 'keep_top_k', 'normalized', 'nms_eta', 'background_label', 'name'], varargs=None, keywords=None, defaults=(True, 1.0, 0, None))
paddle.fluid.layers.accuracy ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None))
paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1))
paddle.fluid.layers.exponential_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
......
paddle/fluid/operators/detection/bbox_util.h
浏览文件 @ cc534530
......@@ -93,25 +93,5 @@ void BboxOverlaps(const framework::Tensor& r_boxes,
}
}
template <class T>
void SliceOneClass(const platform::DeviceContext& ctx,
const framework::Tensor& items, const int class_id,
framework::Tensor* one_class_item) {
T* item_data = one_class_item->mutable_data<T>(ctx.GetPlace());
const T* items_data = items.data<T>();
const int64_t num_item = items.dims()[0];
const int class_num = items.dims()[1];
int item_size = 1;
if (items.dims().size() == 3) {
item_size = items.dims()[2];
}
for (int i = 0; i < num_item; ++i) {
for (int j = 0; j < item_size; ++j) {
item_data[i * item_size + j] =
items_data[i * class_num * item_size + class_id * item_size + j];
}
}
}
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detection/multiclass_nms_op.cc
浏览文件 @ cc534530
/* 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.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
......@@ -10,7 +13,6 @@ limitations under the License. */
#include <glog/logging.h>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detection/bbox_util.h"
#include "paddle/fluid/operators/detection/poly_util.h"
namespace paddle {
......@@ -136,12 +138,9 @@ static inline T JaccardOverlap(const T* box1, const T* box2,
const T inter_ymin = std::max(box1[1], box2[1]);
const T inter_xmax = std::min(box1[2], box2[2]);
const T inter_ymax = std::min(box1[3], box2[3]);
T inter_w = inter_xmax - inter_xmin;
T inter_h = inter_ymax - inter_ymin;
if (!normalized) {
inter_w += 1;
inter_h += 1;
}
T norm = normalized ? static_cast<T>(0.) : static_cast<T>(1.);
T inter_w = inter_xmax - inter_xmin + norm;
T inter_h = inter_ymax - inter_ymin + norm;
const T inter_area = inter_w * inter_h;
const T bbox1_area = BBoxArea<T>(box1, normalized);
const T bbox2_area = BBoxArea<T>(box2, normalized);
......@@ -164,6 +163,25 @@ T PolyIoU(const T* box1, const T* box2, const size_t box_size,
}
}
template <class T>
void SliceOneClass(const platform::DeviceContext& ctx,
const framework::Tensor& items, const int class_id,
framework::Tensor* one_class_item) {
T* item_data = one_class_item->mutable_data<T>(ctx.GetPlace());
const T* items_data = items.data<T>();
const int64_t num_item = items.dims()[0];
const int class_num = items.dims()[1];
int item_size = 1;
if (items.dims().size() == 3) {
item_size = items.dims()[2];
}
for (int i = 0; i < num_item; ++i) {
std::memcpy(item_data + i * item_size,
items_data + i * class_num * item_size + class_id * item_size,
sizeof(T) * item_size);
}
}
template <typename T>
class MultiClassNMSKernel : public framework::OpKernel<T> {
public:
......@@ -237,33 +255,26 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
auto& dev_ctx = ctx.template device_context<platform::CPUDeviceContext>();
int num_det = 0;
int64_t box_num = 0, class_num = 0, predict_dim = 0;
if (scores_size == 3) {
class_num = scores.dims()[0];
predict_dim = scores.dims()[1];
int64_t class_num = scores_size == 3 ? scores.dims()[0] : scores.dims()[1];
Tensor bbox_slice, score_slice;
for (int64_t c = 0; c < class_num; ++c) {
if (c == background_label) continue;
Tensor score = scores.Slice(c, c + 1);
NMSFast(bboxes, score, score_threshold, nms_threshold, nms_eta,
nms_top_k, &((*indices)[c]), normalized);
num_det += (*indices)[c].size();
}
if (scores_size == 3) {
score_slice = scores.Slice(c, c + 1);
bbox_slice = bboxes;
} else {
box_num = scores.dims()[0];
class_num = scores.dims()[1];
Tensor score;
score.Resize({box_num, 1});
Tensor bbox;
bbox.Resize({box_num, 4});
for (int64_t c = 0; c < class_num; ++c) {
if (c == background_label) continue;
SliceOneClass<T>(dev_ctx, scores, c, &score);
SliceOneClass<T>(dev_ctx, bboxes, c, &bbox);
NMSFast(bbox, score, score_threshold, nms_threshold, nms_eta, nms_top_k,
&((*indices)[c]), normalized);
score_slice.Resize({scores.dims()[0], 1});
bbox_slice.Resize({scores.dims()[0], 4});
SliceOneClass<T>(dev_ctx, scores, c, &score_slice);
SliceOneClass<T>(dev_ctx, bboxes, c, &bbox_slice);
}
NMSFast(bbox_slice, score_slice, score_threshold, nms_threshold, nms_eta,
nms_top_k, &((*indices)[c]), normalized);
if (scores_size == 2) {
std::stable_sort((*indices)[c].begin(), (*indices)[c].end());
num_det += (*indices)[c].size();
}
num_det += (*indices)[c].size();
}
*num_nmsed_out = num_det;
......@@ -274,12 +285,11 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
for (const auto& it : *indices) {
int label = it.first;
if (scores_size == 3) {
sdata = scores_data + label * predict_dim;
sdata = scores_data + label * scores.dims()[1];
} else {
Tensor score;
score.Resize({box_num, 1});
SliceOneClass<T>(dev_ctx, scores, label, &score);
sdata = score.data<T>();
score_slice.Resize({scores.dims()[0], 1});
SliceOneClass<T>(dev_ctx, scores, label, &score_slice);
sdata = score_slice.data<T>();
}
const std::vector<int>& label_indices = it.second;
for (size_t j = 0; j < label_indices.size(); ++j) {
......@@ -362,44 +372,34 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
auto* outs = ctx.Output<LoDTensor>("Out");
auto score_dims = scores->dims();
int64_t class_num = score_dims[1];
auto score_size = score_dims.size();
auto& dev_ctx = ctx.template device_context<platform::CPUDeviceContext>();
std::vector<std::map<int, std::vector<int>>> all_indices;
std::vector<size_t> batch_starts = {0};
int64_t batch_size = score_dims[0];
int64_t predict_dim = 0;
int64_t box_dim = boxes->dims()[2];
int64_t out_dim = box_dim + 2;
int num_nmsed_out = 0;
if (score_dims.size() == 3) {
predict_dim = score_dims[2];
for (int64_t i = 0; i < batch_size; ++i) {
Tensor ins_score = scores->Slice(i, i + 1);
ins_score.Resize({class_num, predict_dim});
Tensor ins_boxes = boxes->Slice(i, i + 1);
ins_boxes.Resize({predict_dim, box_dim});
std::map<int, std::vector<int>> indices;
MultiClassNMS(ctx, ins_score, ins_boxes, score_dims.size(), &indices,
&num_nmsed_out);
all_indices.push_back(indices);
batch_starts.push_back(batch_starts.back() + num_nmsed_out);
}
Tensor boxes_slice, scores_slice;
int n = score_size == 3 ? batch_size : boxes->lod().back().size() - 1;
for (int i = 0; i < n; ++i) {
if (score_size == 3) {
scores_slice = scores->Slice(i, i + 1);
scores_slice.Resize({score_dims[1], score_dims[2]});
boxes_slice = boxes->Slice(i, i + 1);
boxes_slice.Resize({score_dims[2], box_dim});
} else {
auto boxes_lod = boxes->lod().back();
int64_t n = static_cast<int64_t>(boxes_lod.size() - 1);
for (int i = 0; i < n; ++i) {
Tensor boxes_slice = boxes->Slice(boxes_lod[i], boxes_lod[i + 1]);
Tensor scores_slice = scores->Slice(boxes_lod[i], boxes_lod[i + 1]);
scores_slice = scores->Slice(boxes_lod[i], boxes_lod[i + 1]);
boxes_slice = boxes->Slice(boxes_lod[i], boxes_lod[i + 1]);
}
std::map<int, std::vector<int>> indices;
MultiClassNMS(ctx, scores_slice, boxes_slice, score_dims.size(),
&indices, &num_nmsed_out);
MultiClassNMS(ctx, scores_slice, boxes_slice, score_size, &indices,
&num_nmsed_out);
all_indices.push_back(indices);
batch_starts.push_back(batch_starts.back() + num_nmsed_out);
}
}
int num_kept = batch_starts.back();
if (num_kept == 0) {
......@@ -408,28 +408,17 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
batch_starts = {0, 1};
} else {
outs->mutable_data<T>({num_kept, out_dim}, ctx.GetPlace());
if (score_dims.size() == 3) {
for (int64_t i = 0; i < batch_size; ++i) {
Tensor ins_score = scores->Slice(i, i + 1);
ins_score.Resize({class_num, predict_dim});
Tensor ins_boxes = boxes->Slice(i, i + 1);
ins_boxes.Resize({predict_dim, box_dim});
int64_t s = batch_starts[i];
int64_t e = batch_starts[i + 1];
if (e > s) {
Tensor out = outs->Slice(s, e);
MultiClassOutput(dev_ctx, ins_score, ins_boxes, all_indices[i],
score_dims.size(), &out);
}
}
for (int i = 0; i < n; ++i) {
if (score_size == 3) {
scores_slice = scores->Slice(i, i + 1);
boxes_slice = boxes->Slice(i, i + 1);
scores_slice.Resize({score_dims[1], score_dims[2]});
boxes_slice.Resize({score_dims[2], box_dim});
} else {
auto boxes_lod = boxes->lod().back();
int64_t n = static_cast<int64_t>(boxes_lod.size() - 1);
for (int i = 0; i < n; ++i) {
Tensor boxes_slice = boxes->Slice(boxes_lod[i], boxes_lod[i + 1]);
Tensor scores_slice = scores->Slice(boxes_lod[i], boxes_lod[i + 1]);
scores_slice = scores->Slice(boxes_lod[i], boxes_lod[i + 1]);
boxes_slice = boxes->Slice(boxes_lod[i], boxes_lod[i + 1]);
}
int64_t s = batch_starts[i];
int64_t e = batch_starts[i + 1];
if (e > s) {
......@@ -439,7 +428,6 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
}
}
}
}
framework::LoD lod;
lod.emplace_back(batch_starts);
......@@ -458,17 +446,18 @@ class MultiClassNMSOpMaker : public framework::OpProtoAndCheckerMaker {
"predicted locations of M bounding bboxes, N is the batch size. "
"Each bounding box has four coordinate values and the layout is "
"[xmin, ymin, xmax, ymax], when box size equals to 4."
"2. (LoDTensor) A 3-D Tensor with shape [N, M, 4]"
"N is the number of boxes, M is the class number");
"2. (LoDTensor) A 3-D Tensor with shape [M, C, 4]"
"M is the number of bounding boxes, C is the class number");
AddInput("Scores",
"Two types of scores are supported:"
"1. (Tensor) A 3-D Tensor with shape [N, C, M] represents the "
"predicted confidence predictions. N is the batch size, C is the "
"class number, M is number of bounding boxes. For each category "
"there are total M scores which corresponding M bounding boxes. "
" Please note, M is equal to the 1st dimension of BBoxes. "
"2. (LoDTensor) A 2-D LoDTensor with shape"
"[N, num_class]. N is the number of bbox");
" Please note, M is equal to the 2nd dimension of BBoxes. "
"2. (LoDTensor) A 2-D LoDTensor with shape [M, C]. "
"M is the number of bbox, C is the class number. In this case, "
"Input BBoxes should be the second case with shape [M, C, 4].");
AddAttr<int>(
"background_label",
"(int, defalut: 0) "
......@@ -528,8 +517,8 @@ independently for each class. The outputs is a 2-D LoDTenosr, for each
image, the offsets in first dimension of LoDTensor are called LoD, the number
of offset is N + 1, where N is the batch size. If LoD[i + 1] - LoD[i] == 0,
means there is no detected bbox for this image. If there is no detected boxes
for all images, all the elements in LoD are 0, and the Out only contains one
value which is -1.
for all images, all the elements in LoD are set to {0,1}, and the Out only
contains one value which is -1.
)DOC");
}
};
......
python/paddle/fluid/layers/detection.py
浏览文件 @ cc534530
......@@ -1821,8 +1821,88 @@ def multiclass_nms(bboxes,
keep_top_k,
normalized=True,
nms_eta=1.,
background_label=0):
background_label=0,
name=None):
"""
**Multiclass NMS**
This operator is to do multi-class non maximum suppression (NMS) on
boxes and scores.
In the NMS step, this operator greedily selects a subset of detection bounding
boxes that have high scores larger than score_threshold, if providing this
threshold, then selects the largest nms_top_k confidences scores if nms_top_k
is larger than -1. Then this operator pruns away boxes that have high IOU
(intersection over union) overlap with already selected boxes by adaptive
threshold NMS based on parameters of nms_threshold and nms_eta.
Aftern NMS step, at most keep_top_k number of total bboxes are to be kept
per image if keep_top_k is larger than -1.
Args:
bboxes (Variable): Two types of bboxes are supported:
1. (Tensor) A 3-D Tensor with shape
[N, M, 4 or 8 16 24 32] represents the
predicted locations of M bounding bboxes,
N is the batch size. Each bounding box has four
coordinate values and the layout is
[xmin, ymin, xmax, ymax], when box size equals to 4.
2. (LoDTensor) A 3-D Tensor with shape [M, C, 4]
M is the number of bounding boxes, C is the
class number
scores (Variable): Two types of scores are supported:
1. (Tensor) A 3-D Tensor with shape [N, C, M]
represents the predicted confidence predictions.
N is the batch size, C is the class number, M is
number of bounding boxes. For each category there
are total M scores which corresponding M bounding
boxes. Please note, M is equal to the 2nd dimension
of BBoxes.
2. (LoDTensor) A 2-D LoDTensor with shape [M, C].
M is the number of bbox, C is the class number.
In this case, input BBoxes should be the second
case with shape [M, C, 4].
background_label (int): The index of background label, the background
label will be ignored. If set to -1, then all
categories will be considered. Default: 0
score_threshold (float): Threshold to filter out bounding boxes with
low confidence score. If not provided,
consider all boxes.
nms_top_k (int): Maximum number of detections to be kept according to
the confidences aftern the filtering detections based
on score_threshold.
nms_threshold (float): The threshold to be used in NMS. Default: 0.3
nms_eta (float): The threshold to be used in NMS. Default: 1.0
keep_top_k (int): Number of total bboxes to be kept per image after NMS
step. -1 means keeping all bboxes after NMS step.
normalized (bool): Whether detections are normalized. Default: True
name(str): Name of the multiclass nms op. Default: None.
Returns:
Out: A 2-D LoDTensor with shape [No, 6] represents the detections.
Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax]
or A 2-D LoDTensor with shape [No, 10] represents the detections.
Each row has 10 values:
[label, confidence, x1, y1, x2, y2, x3, y3, x4, y4]. No is the
total number of detections. If there is no detected boxes for all
images, lod will be set to {0, 1} and Out only contains one value
which is -1.
Examples:
.. code-block:: python
boxes = fluid.layers.data(name='bboxes', shape=[81, 4],
dtype='float32', lod_level=1)
scores = fluid.layers.data(name='scores', shape=[81],
dtype='float32', lod_level=1)
out = fluid.layers.multiclass_nms(bboxes=boxes,
scores=scores,
background_label=0,
score_threshold=0.5,
nms_top_k=400,
nms_threshold=0.3,
keep_top_k=200,
normalized=False)
"""
helper = LayerHelper('multiclass_nms', **locals())
......
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