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未验证 提交 9bdf7f02 编写于 作者: S Sanbu 提交者： GitHub
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Support static graph code-gen for yolo_box (#52714) 

* Support static graph code-gen for yolo_box

* Support static graph code-gen for yolo_box

* Support static graph code-gen for yolo_box

* Update op_compat.yaml

* fix

* fix
上级 5664ea26
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paddle/fluid/operators/detection/CMakeLists.txt
浏览文件 @ 9bdf7f02
......@@ -36,13 +36,11 @@ if(WITH_XPU)
detection_library(iou_similarity_op SRCS iou_similarity_op.cc
iou_similarity_op_xpu.cc)
detection_library(prior_box_op SRCS prior_box_op.cc)
detection_library(yolo_box_op SRCS yolo_box_op.cc)
detection_library(generate_proposals_v2_op SRCS generate_proposals_v2_op.cc)
else()
detection_library(iou_similarity_op SRCS iou_similarity_op.cc
iou_similarity_op.cu)
detection_library(prior_box_op SRCS prior_box_op.cc)
detection_library(yolo_box_op SRCS yolo_box_op.cc)
# detection_library(generate_proposals_v2_op SRCS generate_proposals_v2_op.cc)
endif()
......
paddle/fluid/operators/detection/yolo_box_op.cc 已删除 100644 → 0
浏览文件 @ 5664ea26
/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
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.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/infershape_utils.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/phi/infermeta/binary.h"
namespace paddle {
namespace operators {
class YoloBoxOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "YoloBoxOp");
OP_INOUT_CHECK(ctx->HasInput("ImgSize"), "Input", "ImgSize", "YoloBoxOp");
OP_INOUT_CHECK(ctx->HasOutput("Boxes"), "Output", "Boxes", "YoloBoxOp");
OP_INOUT_CHECK(ctx->HasOutput("Scores"), "Output", "Scores", "YoloBoxOp");
auto dim_x = ctx->GetInputDim("X");
auto dim_imgsize = ctx->GetInputDim("ImgSize");
auto anchors = ctx->Attrs().Get<std::vector<int>>("anchors");
int anchor_num = anchors.size() / 2;
auto class_num = ctx->Attrs().Get<int>("class_num");
auto iou_aware = ctx->Attrs().Get<bool>("iou_aware");
auto iou_aware_factor = ctx->Attrs().Get<float>("iou_aware_factor");
PADDLE_ENFORCE_EQ(
dim_x.size(),
4,
platform::errors::InvalidArgument("Input(X) should be a 4-D tensor."
"But received X dimension(%s)",
dim_x.size()));
if (iou_aware) {
PADDLE_ENFORCE_EQ(
dim_x[1],
anchor_num * (6 + class_num),
platform::errors::InvalidArgument(
"Input(X) dim[1] should be equal to (anchor_mask_number * (6 "
"+ class_num)) while iou_aware is true."
"But received dim[1](%s) != (anchor_mask_number * "
"(6+class_num)(%s).",
dim_x[1],
anchor_num * (6 + class_num)));
PADDLE_ENFORCE_GE(
iou_aware_factor,
0,
platform::errors::InvalidArgument(
"Attr(iou_aware_factor) should greater than or equal to 0."
"But received iou_aware_factor (%s)",
iou_aware_factor));
PADDLE_ENFORCE_LE(
iou_aware_factor,
1,
platform::errors::InvalidArgument(
"Attr(iou_aware_factor) should less than or equal to 1."
"But received iou_aware_factor (%s)",
iou_aware_factor));
} else {
PADDLE_ENFORCE_EQ(
dim_x[1],
anchor_num * (5 + class_num),
platform::errors::InvalidArgument(
"Input(X) dim[1] should be equal to (anchor_mask_number * (5 "
"+ class_num))."
"But received dim[1](%s) != (anchor_mask_number * "
"(5+class_num)(%s).",
dim_x[1],
anchor_num * (5 + class_num)));
}
PADDLE_ENFORCE_EQ(dim_imgsize.size(),
2,
platform::errors::InvalidArgument(
"Input(ImgSize) should be a 2-D tensor."
"But received Imgsize size(%s)",
dim_imgsize.size()));
if ((dim_imgsize[0] > 0 && dim_x[0] > 0) || ctx->IsRuntime()) {
PADDLE_ENFORCE_EQ(
dim_imgsize[0],
dim_x[0],
platform::errors::InvalidArgument(
"Input(ImgSize) dim[0] and Input(X) dim[0] should be same."));
}
PADDLE_ENFORCE_EQ(
dim_imgsize[1],
2,
platform::errors::InvalidArgument("Input(ImgSize) dim[1] should be 2."
"But received imgsize dim[1](%s).",
dim_imgsize[1]));
PADDLE_ENFORCE_GT(anchors.size(),
0,
platform::errors::InvalidArgument(
"Attr(anchors) length should be greater than 0."
"But received anchors length(%s).",
anchors.size()));
PADDLE_ENFORCE_EQ(anchors.size() % 2,
0,
platform::errors::InvalidArgument(
"Attr(anchors) length should be even integer."
"But received anchors length (%s)",
anchors.size()));
PADDLE_ENFORCE_GT(class_num,
0,
platform::errors::InvalidArgument(
"Attr(class_num) should be an integer greater than 0."
"But received class_num (%s)",
class_num));
int box_num;
if ((dim_x[2] > 0 && dim_x[3] > 0) || ctx->IsRuntime()) {
box_num = dim_x[2] * dim_x[3] * anchor_num;
} else {
box_num = -1;
}
std::vector<int64_t> dim_boxes({dim_x[0], box_num, 4});
ctx->SetOutputDim("Boxes", phi::make_ddim(dim_boxes));
std::vector<int64_t> dim_scores({dim_x[0], box_num, class_num});
ctx->SetOutputDim("Scores", phi::make_ddim(dim_scores));
}
protected:
phi::KernelKey GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return phi::KernelKey(OperatorWithKernel::IndicateVarDataType(ctx, "X"),
ctx.GetPlace());
}
};
class YoloBoxOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"The input tensor of YoloBox operator is a 4-D tensor with "
"shape of [N, C, H, W]. The second dimension(C) stores "
"box locations, confidence score and classification one-hot "
"keys of each anchor box. Generally, X should be the output "
"of YOLOv3 network.");
AddInput("ImgSize",
"The image size tensor of YoloBox operator, "
"This is a 2-D tensor with shape of [N, 2]. This tensor holds "
"height and width of each input image used for resizing output "
"box in input image scale.");
AddOutput("Boxes",
"The output tensor of detection boxes of YoloBox operator, "
"This is a 3-D tensor with shape of [N, M, 4], N is the "
"batch num, M is output box number, and the 3rd dimension "
"stores [xmin, ymin, xmax, ymax] coordinates of boxes.");
AddOutput("Scores",
"The output tensor of detection boxes scores of YoloBox "
"operator, This is a 3-D tensor with shape of "
"[N, M, :attr:`class_num`], N is the batch num, M is "
"output box number.");
AddAttr<int>("class_num", "The number of classes to predict.");
AddAttr<std::vector<int>>("anchors",
"The anchor width and height, "
"it will be parsed pair by pair.")
.SetDefault(std::vector<int>{});
AddAttr<int>("downsample_ratio",
"The downsample ratio from network input to YoloBox operator "
"input, so 32, 16, 8 should be set for the first, second, "
"and thrid YoloBox operators.")
.SetDefault(32);
AddAttr<float>("conf_thresh",
"The confidence scores threshold of detection boxes. "
"Boxes with confidence scores under threshold should "
"be ignored.")
.SetDefault(0.01);
AddAttr<bool>("clip_bbox",
"Whether clip output bonding box in Input(ImgSize) "
"boundary. Default true.")
.SetDefault(true);
AddAttr<float>("scale_x_y",
"Scale the center point of decoded bounding "
"box. Default 1.0")
.SetDefault(1.);
AddAttr<bool>("iou_aware", "Whether use iou aware. Default false.")
.SetDefault(false);
AddAttr<float>("iou_aware_factor", "iou aware factor. Default 0.5.")
.SetDefault(0.5);
AddComment(R"DOC(
This operator generates YOLO detection boxes from output of YOLOv3 network.
The output of previous network is in shape [N, C, H, W], while H and W
should be the same, H and W specify the grid size, each grid point predict
given number boxes, this given number, which following will be represented as S,
is specified by the number of anchors. In the second dimension(the channel
dimension), C should be equal to S * (5 + class_num) if :attr:`iou_aware` is false,
otherwise C should be equal to S * (6 + class_num). class_num is the object
category number of source dataset(such as 80 in coco dataset), so the
second(channel) dimension, apart from 4 box location coordinates x, y, w, h,
also includes confidence score of the box and class one-hot key of each anchor
box.
Assume the 4 location coordinates are :math:`t_x, t_y, t_w, t_h`, the box
predictions should be as follows:
$$
b_x = \\sigma(t_x) + c_x
$$
$$
b_y = \\sigma(t_y) + c_y
$$
$$
b_w = p_w e^{t_w}
$$
$$
b_h = p_h e^{t_h}
$$
in the equation above, :math:`c_x, c_y` is the left top corner of current grid
and :math:`p_w, p_h` is specified by anchors.
The logistic regression value of the 5th channel of each anchor prediction boxes
represents the confidence score of each prediction box, and the logistic
regression value of the last :attr:`class_num` channels of each anchor prediction
boxes represents the classifcation scores. Boxes with confidence scores less than
:attr:`conf_thresh` should be ignored, and box final scores is the product of
confidence scores and classification scores.
$$
score_{pred} = score_{conf} * score_{class}
$$
where the confidence scores follow the formula bellow
.. math::
score_{conf} = \begin{case}
obj, \text{if } iou_aware == false \\
obj^{1 - iou_aware_factor} * iou^{iou_aware_factor}, \text{otherwise}
\end{case}
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
DECLARE_INFER_SHAPE_FUNCTOR(yolo_box,
YoloBoxInferShapeFunctor,
PD_INFER_META(phi::YoloBoxInferMeta));
REGISTER_OPERATOR(
yolo_box,
ops::YoloBoxOp,
ops::YoloBoxOpMaker,
paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
YoloBoxInferShapeFunctor);
REGISTER_OP_VERSION(yolo_box).AddCheckpoint(
R"ROC(
Upgrade yolo box to add new attribute [iou_aware, iou_aware_factor].
)ROC",
paddle::framework::compatible::OpVersionDesc()
.NewAttr("iou_aware", "Whether use iou aware", false)
.NewAttr("iou_aware_factor", "iou aware factor", 0.5f));
paddle/phi/api/yaml/legacy_ops.yaml
浏览文件 @ 9bdf7f02
......@@ -1384,15 +1384,6 @@
data_type: x
backward: unpool3d_grad
- op : yolo_box
args : (Tensor x, Tensor img_size, int[] anchors, int class_num, float conf_thresh, int downsample_ratio, bool clip_bbox, float scale_x_y=1.0, bool iou_aware=false, float iou_aware_factor=0.5)
output : Tensor(boxes), Tensor(scores)
infer_meta :
func : YoloBoxInferMeta
kernel :
func : yolo_box
data_type : x
- op : yolo_loss
args : (Tensor x, Tensor gt_box, Tensor gt_label, Tensor gt_score, int[] anchors, int[] anchor_mask, int class_num, float ignore_thresh, int downsample_ratio, bool use_label_smooth=true, float scale_x_y=1.0)
output : Tensor(loss), Tensor(objectness_mask), Tensor(gt_match_mask)
......
paddle/phi/api/yaml/op_compat.yaml
浏览文件 @ 9bdf7f02
......@@ -2328,6 +2328,12 @@
extra :
attrs : ['str[] skip_eager_deletion_vars = {}']
- op : yolo_box
inputs :
{x : X, img_size : ImgSize}
outputs :
{boxes : Boxes, scores : Scores}
- op: sigmoid_cross_entropy_with_logits
backward: sigmoid_cross_entropy_with_logits_grad
inputs :
......
paddle/phi/api/yaml/op_version.yaml
浏览文件 @ 9bdf7f02
......@@ -250,3 +250,14 @@
- add_attr : axis
comment : The axis to apply unique. If None, the input will be flattened.
default : std::vector<int>{}
- op : yolo_box
version :
- checkpoint : Upgrade yolo box to add new attribute [iou_aware, iou_aware_factor].
action :
- add_attr : iou_aware
comment : Whether use iou aware.
default : "false"
- add_attr : iou_aware_factor
comment : iou aware factor.
default : 0.5f
paddle/phi/api/yaml/ops.yaml
浏览文件 @ 9bdf7f02
......@@ -2028,3 +2028,12 @@
kernel :
func : where
backward : where_grad
- op : yolo_box
args : (Tensor x, Tensor img_size, int[] anchors={}, int class_num = 1, float conf_thresh = 0.01, int downsample_ratio = 32, bool clip_bbox = true, float scale_x_y=1.0, bool iou_aware=false, float iou_aware_factor=0.5)
output : Tensor(boxes), Tensor(scores)
infer_meta :
func : YoloBoxInferMeta
kernel :
func : yolo_box
data_type : x
paddle/phi/ops/compat/yolo_box_sig.cc 已删除 100644 → 0
浏览文件 @ 5664ea26
// Copyright (c) 2022 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.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/phi/core/compat/op_utils.h"
namespace phi {
KernelSignature YoloBoxOpArgumentMapping(const ArgumentMappingContext& ctx) {
return KernelSignature("yolo_box",
{"X", "ImgSize"},
{"anchors",
"class_num",
"conf_thresh",
"downsample_ratio",
"clip_bbox",
"scale_x_y",
"iou_aware",
"iou_aware_factor"},
{"Boxes", "Scores"});
}
} // namespace phi
PD_REGISTER_ARG_MAPPING_FN(yolo_box, phi::YoloBoxOpArgumentMapping);
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