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未验证 提交 30cc8b7a 编写于 作者: X Xin Pan 提交者： GitHub
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Merge pull request #15554 from heavengate/yolo_loss_darknet 

Yolo loss darknet
上级 1a252f4b 23d34d1f
隐藏空白更改
内联 并排
Showing with 694 addition and 691 deletion
paddle/fluid/API.spec
浏览文件 @ 30cc8b7a
......@@ -324,7 +324,7 @@ paddle.fluid.layers.generate_mask_labels ArgSpec(args=['im_info', 'gt_classes',
paddle.fluid.layers.iou_similarity ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.box_coder ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name', 'axis'], varargs=None, keywords=None, defaults=('encode_center_size', True, None, 0))
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.yolov3_loss ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.multiclass_nms ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'normalized', 'nms_eta', 'background_label', 'name'], varargs=None, keywords=None, defaults=(0.3, 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/operators/detection/CMakeLists.txt
浏览文件 @ 30cc8b7a
......@@ -31,6 +31,7 @@ detection_library(polygon_box_transform_op SRCS polygon_box_transform_op.cc
polygon_box_transform_op.cu)
detection_library(rpn_target_assign_op SRCS rpn_target_assign_op.cc)
detection_library(generate_proposal_labels_op SRCS generate_proposal_labels_op.cc)
detection_library(yolov3_loss_op SRCS yolov3_loss_op.cc)
if(WITH_GPU)
detection_library(generate_proposals_op SRCS generate_proposals_op.cc generate_proposals_op.cu DEPS memory cub)
......
paddle/fluid/operators/yolov3_loss_op.cc → paddle/fluid/operators/detection/yolov3_loss_op.cc
浏览文件 @ 30cc8b7a
......@@ -9,7 +9,7 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/yolov3_loss_op.h"
#include "paddle/fluid/operators/detection/yolov3_loss_op.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
......@@ -29,23 +29,33 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
"Input(GTLabel) of Yolov3LossOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Loss"),
"Output(Loss) of Yolov3LossOp should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("ObjectnessMask"),
"Output(ObjectnessMask) of Yolov3LossOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("GTMatchMask"),
"Output(GTMatchMask) of Yolov3LossOp should not be null.");
auto dim_x = ctx->GetInputDim("X");
auto dim_gtbox = ctx->GetInputDim("GTBox");
auto dim_gtlabel = ctx->GetInputDim("GTLabel");
auto anchors = ctx->Attrs().Get<std::vector<int>>("anchors");
int anchor_num = anchors.size() / 2;
auto anchor_mask = ctx->Attrs().Get<std::vector<int>>("anchor_mask");
int mask_num = anchor_mask.size();
auto class_num = ctx->Attrs().Get<int>("class_num");
PADDLE_ENFORCE_EQ(dim_x.size(), 4, "Input(X) should be a 4-D tensor.");
PADDLE_ENFORCE_EQ(dim_x[2], dim_x[3],
"Input(X) dim[3] and dim[4] should be euqal.");
PADDLE_ENFORCE_EQ(dim_x[1], anchors.size() / 2 * (5 + class_num),
"Input(X) dim[1] should be equal to (anchor_number * (5 "
"+ class_num)).");
PADDLE_ENFORCE_EQ(
dim_x[1], mask_num * (5 + class_num),
"Input(X) dim[1] should be equal to (anchor_mask_number * (5 "
"+ class_num)).");
PADDLE_ENFORCE_EQ(dim_gtbox.size(), 3,
"Input(GTBox) should be a 3-D tensor");
PADDLE_ENFORCE_EQ(dim_gtbox[2], 4, "Input(GTBox) dim[2] should be 5");
PADDLE_ENFORCE_EQ(dim_gtlabel.size(), 2,
"Input(GTBox) should be a 2-D tensor");
"Input(GTLabel) should be a 2-D tensor");
PADDLE_ENFORCE_EQ(dim_gtlabel[0], dim_gtbox[0],
"Input(GTBox) and Input(GTLabel) dim[0] should be same");
PADDLE_ENFORCE_EQ(dim_gtlabel[1], dim_gtbox[1],
......@@ -54,11 +64,22 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
"Attr(anchors) length should be greater then 0.");
PADDLE_ENFORCE_EQ(anchors.size() % 2, 0,
"Attr(anchors) length should be even integer.");
for (size_t i = 0; i < anchor_mask.size(); i++) {
PADDLE_ENFORCE_LT(
anchor_mask[i], anchor_num,
"Attr(anchor_mask) should not crossover Attr(anchors).");
}
PADDLE_ENFORCE_GT(class_num, 0,
"Attr(class_num) should be an integer greater then 0.");
std::vector<int64_t> dim_out({1});
std::vector<int64_t> dim_out({dim_x[0]});
ctx->SetOutputDim("Loss", framework::make_ddim(dim_out));
std::vector<int64_t> dim_obj_mask({dim_x[0], mask_num, dim_x[2], dim_x[3]});
ctx->SetOutputDim("ObjectnessMask", framework::make_ddim(dim_obj_mask));
std::vector<int64_t> dim_gt_match_mask({dim_gtbox[0], dim_gtbox[1]});
ctx->SetOutputDim("GTMatchMask", framework::make_ddim(dim_gt_match_mask));
}
protected:
......@@ -73,11 +94,11 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"The input tensor of YOLO v3 loss operator, "
"The input tensor of YOLOv3 loss operator, "
"This is a 4-D tensor with shape of [N, C, H, W]."
"H and W should be same, and the second dimention(C) stores"
"box locations, confidence score and classification one-hot"
"key of each anchor box");
"keys of each anchor box");
AddInput("GTBox",
"The input tensor of ground truth boxes, "
"This is a 3-D tensor with shape of [N, max_box_num, 5], "
......@@ -89,32 +110,39 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("GTLabel",
"The input tensor of ground truth label, "
"This is a 2-D tensor with shape of [N, max_box_num], "
"and each element shoudl be an integer to indicate the "
"and each element should be an integer to indicate the "
"box class id.");
AddOutput("Loss",
"The output yolov3 loss tensor, "
"This is a 1-D tensor with shape of [1]");
"This is a 1-D tensor with shape of [N]");
AddOutput("ObjectnessMask",
"This is an intermediate tensor with shape of [N, M, H, W], "
"M is the number of anchor masks. This parameter caches the "
"mask for calculate objectness loss in gradient kernel.")
.AsIntermediate();
AddOutput("GTMatchMask",
"This is an intermediate tensor with shape of [N, B], "
"B is the max box number of GT boxes. This parameter caches "
"matched mask index of each GT boxes for gradient calculate.")
.AsIntermediate();
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.");
"it will be parsed pair by pair.")
.SetDefault(std::vector<int>{});
AddAttr<std::vector<int>>("anchor_mask",
"The mask index of anchors used in "
"current YOLOv3 loss calculation.")
.SetDefault(std::vector<int>{});
AddAttr<int>("downsample_ratio",
"The downsample ratio from network input to YOLOv3 loss "
"input, so 32, 16, 8 should be set for the first, second, "
"and thrid YOLOv3 loss operators.")
.SetDefault(32);
AddAttr<float>("ignore_thresh",
"The ignore threshold to ignore confidence loss.");
AddAttr<float>("loss_weight_xy", "The weight of x, y location loss.")
.SetDefault(1.0);
AddAttr<float>("loss_weight_wh", "The weight of w, h location loss.")
.SetDefault(1.0);
AddAttr<float>(
"loss_weight_conf_target",
"The weight of confidence score loss in locations with target object.")
.SetDefault(1.0);
AddAttr<float>("loss_weight_conf_notarget",
"The weight of confidence score loss in locations without "
"target object.")
.SetDefault(1.0);
AddAttr<float>("loss_weight_class", "The weight of classification loss.")
.SetDefault(1.0);
"The ignore threshold to ignore confidence loss.")
.SetDefault(0.7);
AddComment(R"DOC(
This operator generate yolov3 loss by given predict result and ground
truth boxes.
......@@ -147,17 +175,28 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
thresh, the confidence score loss of this anchor box will be ignored.
Therefore, the yolov3 loss consist of three major parts, box location loss,
confidence score loss, and classification loss. The MSE loss is used for
box location, and binary cross entropy loss is used for confidence score
loss and classification loss.
confidence score loss, and classification loss. The L2 loss is used for
box coordinates (w, h), and sigmoid cross entropy loss is used for box
coordinates (x, y), confidence score loss and classification loss.
Each groud truth box find a best matching anchor box in all anchors,
prediction of this anchor box will incur all three parts of losses, and
prediction of anchor boxes with no GT box matched will only incur objectness
loss.
In order to trade off box coordinate losses between big boxes and small
boxes, box coordinate losses will be mutiplied by scale weight, which is
calculated as follow.
$$
weight_{box} = 2.0 - t_w * t_h
$$
Final loss will be represented as follow.
$$
loss = \loss_weight_{xy} * loss_{xy} + \loss_weight_{wh} * loss_{wh}
+ \loss_weight_{conf_target} * loss_{conf_target}
+ \loss_weight_{conf_notarget} * loss_{conf_notarget}
+ \loss_weight_{class} * loss_{class}
loss = (loss_{xy} + loss_{wh}) * weight_{box}
+ loss_{conf} + loss_{class}
$$
)DOC");
}
......@@ -196,6 +235,8 @@ class Yolov3LossGradMaker : public framework::SingleGradOpDescMaker {
op->SetInput("GTBox", Input("GTBox"));
op->SetInput("GTLabel", Input("GTLabel"));
op->SetInput(framework::GradVarName("Loss"), OutputGrad("Loss"));
op->SetInput("ObjectnessMask", Output("ObjectnessMask"));
op->SetInput("GTMatchMask", Output("GTMatchMask"));
op->SetAttrMap(Attrs());
......
paddle/fluid/operators/detection/yolov3_loss_op.h 0 → 100644
浏览文件 @ 30cc8b7a
/* Copyright (c) 2018 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. */
#pragma once
#include <algorithm>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename T>
static inline bool LessEqualZero(T x) {
return x < 1e-6;
}
template <typename T>
static T SigmoidCrossEntropy(T x, T label) {
return (x > 0 ? x : 0.0) - x * label + std::log(1.0 + std::exp(-std::abs(x)));
}
template <typename T>
static T L2Loss(T x, T y) {
return 0.5 * (y - x) * (y - x);
}
template <typename T>
static T SigmoidCrossEntropyGrad(T x, T label) {
return 1.0 / (1.0 + std::exp(-x)) - label;
}
template <typename T>
static T L2LossGrad(T x, T y) {
return x - y;
}
static int GetMaskIndex(std::vector<int> mask, int val) {
for (size_t i = 0; i < mask.size(); i++) {
if (mask[i] == val) {
return i;
}
}
return -1;
}
template <typename T>
struct Box {
T x, y, w, h;
};
template <typename T>
static inline T sigmoid(T x) {
return 1.0 / (1.0 + std::exp(-x));
}
template <typename T>
static inline Box<T> GetYoloBox(const T* x, std::vector<int> anchors, int i,
int j, int an_idx, int grid_size,
int input_size, int index, int stride) {
Box<T> b;
b.x = (i + sigmoid<T>(x[index])) / grid_size;
b.y = (j + sigmoid<T>(x[index + stride])) / grid_size;
b.w = std::exp(x[index + 2 * stride]) * anchors[2 * an_idx] / input_size;
b.h = std::exp(x[index + 3 * stride]) * anchors[2 * an_idx + 1] / input_size;
return b;
}
template <typename T>
static inline Box<T> GetGtBox(const T* gt, int batch, int max_boxes, int idx) {
Box<T> b;
b.x = gt[(batch * max_boxes + idx) * 4];
b.y = gt[(batch * max_boxes + idx) * 4 + 1];
b.w = gt[(batch * max_boxes + idx) * 4 + 2];
b.h = gt[(batch * max_boxes + idx) * 4 + 3];
return b;
}
template <typename T>
static inline T BoxOverlap(T c1, T w1, T c2, T w2) {
T l1 = c1 - w1 / 2.0;
T l2 = c2 - w2 / 2.0;
T left = l1 > l2 ? l1 : l2;
T r1 = c1 + w1 / 2.0;
T r2 = c2 + w2 / 2.0;
T right = r1 < r2 ? r1 : r2;
return right - left;
}
template <typename T>
static inline T CalcBoxIoU(Box<T> b1, Box<T> b2) {
T w = BoxOverlap(b1.x, b1.w, b2.x, b2.w);
T h = BoxOverlap(b1.y, b1.h, b2.y, b2.h);
T inter_area = (w < 0 || h < 0) ? 0.0 : w * h;
T union_area = b1.w * b1.h + b2.w * b2.h - inter_area;
return inter_area / union_area;
}
static inline int GetEntryIndex(int batch, int an_idx, int hw_idx, int an_num,
int an_stride, int stride, int entry) {
return (batch * an_num + an_idx) * an_stride + entry * stride + hw_idx;
}
template <typename T>
static void CalcBoxLocationLoss(T* loss, const T* input, Box<T> gt,
std::vector<int> anchors, int an_idx,
int box_idx, int gi, int gj, int grid_size,
int input_size, int stride) {
T tx = gt.x * grid_size - gi;
T ty = gt.y * grid_size - gj;
T tw = std::log(gt.w * input_size / anchors[2 * an_idx]);
T th = std::log(gt.h * input_size / anchors[2 * an_idx + 1]);
T scale = (2.0 - gt.w * gt.h);
loss[0] += SigmoidCrossEntropy<T>(input[box_idx], tx) * scale;
loss[0] += SigmoidCrossEntropy<T>(input[box_idx + stride], ty) * scale;
loss[0] += L2Loss<T>(input[box_idx + 2 * stride], tw) * scale;
loss[0] += L2Loss<T>(input[box_idx + 3 * stride], th) * scale;
}
template <typename T>
static void CalcBoxLocationLossGrad(T* input_grad, const T loss, const T* input,
Box<T> gt, std::vector<int> anchors,
int an_idx, int box_idx, int gi, int gj,
int grid_size, int input_size, int stride) {
T tx = gt.x * grid_size - gi;
T ty = gt.y * grid_size - gj;
T tw = std::log(gt.w * input_size / anchors[2 * an_idx]);
T th = std::log(gt.h * input_size / anchors[2 * an_idx + 1]);
T scale = (2.0 - gt.w * gt.h);
input_grad[box_idx] =
SigmoidCrossEntropyGrad<T>(input[box_idx], tx) * scale * loss;
input_grad[box_idx + stride] =
SigmoidCrossEntropyGrad<T>(input[box_idx + stride], ty) * scale * loss;
input_grad[box_idx + 2 * stride] =
L2LossGrad<T>(input[box_idx + 2 * stride], tw) * scale * loss;
input_grad[box_idx + 3 * stride] =
L2LossGrad<T>(input[box_idx + 3 * stride], th) * scale * loss;
}
template <typename T>
static inline void CalcLabelLoss(T* loss, const T* input, const int index,
const int label, const int class_num,
const int stride) {
for (int i = 0; i < class_num; i++) {
T pred = input[index + i * stride];
loss[0] += SigmoidCrossEntropy<T>(pred, (i == label) ? 1.0 : 0.0);
}
}
template <typename T>
static inline void CalcLabelLossGrad(T* input_grad, const T loss,
const T* input, const int index,
const int label, const int class_num,
const int stride) {
for (int i = 0; i < class_num; i++) {
T pred = input[index + i * stride];
input_grad[index + i * stride] =
SigmoidCrossEntropyGrad<T>(pred, (i == label) ? 1.0 : 0.0) * loss;
}
}
template <typename T>
static inline void CalcObjnessLoss(T* loss, const T* input, const T* objness,
const int n, const int an_num, const int h,
const int w, const int stride,
const int an_stride) {
for (int i = 0; i < n; i++) {
for (int j = 0; j < an_num; j++) {
for (int k = 0; k < h; k++) {
for (int l = 0; l < w; l++) {
T obj = objness[k * w + l];
if (obj > 1e-5) {
// positive sample: obj = 1
loss[i] += SigmoidCrossEntropy<T>(input[k * w + l], 1.0);
} else if (obj > -0.5) {
// negetive sample: obj = 0
loss[i] += SigmoidCrossEntropy<T>(input[k * w + l], 0.0);
}
}
}
objness += stride;
input += an_stride;
}
}
}
template <typename T>
static inline void CalcObjnessLossGrad(T* input_grad, const T* loss,
const T* input, const T* objness,
const int n, const int an_num,
const int h, const int w,
const int stride, const int an_stride) {
for (int i = 0; i < n; i++) {
for (int j = 0; j < an_num; j++) {
for (int k = 0; k < h; k++) {
for (int l = 0; l < w; l++) {
T obj = objness[k * w + l];
if (obj > 1e-5) {
input_grad[k * w + l] =
SigmoidCrossEntropyGrad<T>(input[k * w + l], 1.0) * loss[i];
} else if (obj > -0.5) {
input_grad[k * w + l] =
SigmoidCrossEntropyGrad<T>(input[k * w + l], 0.0) * loss[i];
}
}
}
objness += stride;
input += an_stride;
input_grad += an_stride;
}
}
}
template <typename T>
static void inline GtValid(bool* valid, const T* gtbox, const int n,
const int b) {
for (int i = 0; i < n; i++) {
for (int j = 0; j < b; j++) {
if (LessEqualZero(gtbox[j * 4 + 2]) || LessEqualZero(gtbox[j * 4 + 3])) {
valid[j] = false;
} else {
valid[j] = true;
}
}
valid += b;
gtbox += b * 4;
}
}
template <typename T>
class Yolov3LossKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<Tensor>("X");
auto* gt_box = ctx.Input<Tensor>("GTBox");
auto* gt_label = ctx.Input<Tensor>("GTLabel");
auto* loss = ctx.Output<Tensor>("Loss");
auto* objness_mask = ctx.Output<Tensor>("ObjectnessMask");
auto* gt_match_mask = ctx.Output<Tensor>("GTMatchMask");
auto anchors = ctx.Attr<std::vector<int>>("anchors");
auto anchor_mask = ctx.Attr<std::vector<int>>("anchor_mask");
int class_num = ctx.Attr<int>("class_num");
float ignore_thresh = ctx.Attr<float>("ignore_thresh");
int downsample_ratio = ctx.Attr<int>("downsample_ratio");
const int n = input->dims()[0];
const int h = input->dims()[2];
const int w = input->dims()[3];
const int an_num = anchors.size() / 2;
const int mask_num = anchor_mask.size();
const int b = gt_box->dims()[1];
int input_size = downsample_ratio * h;
const int stride = h * w;
const int an_stride = (class_num + 5) * stride;
const T* input_data = input->data<T>();
const T* gt_box_data = gt_box->data<T>();
const int* gt_label_data = gt_label->data<int>();
T* loss_data = loss->mutable_data<T>({n}, ctx.GetPlace());
memset(loss_data, 0, loss->numel() * sizeof(T));
T* obj_mask_data =
objness_mask->mutable_data<T>({n, mask_num, h, w}, ctx.GetPlace());
memset(obj_mask_data, 0, objness_mask->numel() * sizeof(T));
int* gt_match_mask_data =
gt_match_mask->mutable_data<int>({n, b}, ctx.GetPlace());
// calc valid gt box mask, avoid calc duplicately in following code
Tensor gt_valid_mask;
bool* gt_valid_mask_data =
gt_valid_mask.mutable_data<bool>({n, b}, ctx.GetPlace());
GtValid<T>(gt_valid_mask_data, gt_box_data, n, b);
for (int i = 0; i < n; i++) {
for (int j = 0; j < mask_num; j++) {
for (int k = 0; k < h; k++) {
for (int l = 0; l < w; l++) {
// each predict box find a best match gt box, if overlap is bigger
// then ignore_thresh, ignore the objectness loss.
int box_idx =
GetEntryIndex(i, j, k * w + l, mask_num, an_stride, stride, 0);
Box<T> pred = GetYoloBox(input_data, anchors, l, k, anchor_mask[j],
h, input_size, box_idx, stride);
T best_iou = 0;
for (int t = 0; t < b; t++) {
if (!gt_valid_mask_data[i * b + t]) {
continue;
}
Box<T> gt = GetGtBox(gt_box_data, i, b, t);
T iou = CalcBoxIoU(pred, gt);
if (iou > best_iou) {
best_iou = iou;
}
}
// If best IoU is bigger then ignore_thresh,
// ignore the objectness loss.
if (best_iou > ignore_thresh) {
int obj_idx = (i * mask_num + j) * stride + k * w + l;
obj_mask_data[obj_idx] = static_cast<T>(-1);
}
// all losses should be calculated if best IoU
// is bigger then truth thresh, but currently,
// truth thresh is an unreachable value as 1.0.
}
}
}
for (int t = 0; t < b; t++) {
if (!gt_valid_mask_data[i * b + t]) {
gt_match_mask_data[i * b + t] = -1;
continue;
}
Box<T> gt = GetGtBox(gt_box_data, i, b, t);
int gi = static_cast<int>(gt.x * w);
int gj = static_cast<int>(gt.y * h);
Box<T> gt_shift = gt;
gt_shift.x = 0.0;
gt_shift.y = 0.0;
T best_iou = 0.0;
int best_n = 0;
// each gt box find a best match anchor box as positive sample,
// for positive sample, all losses should be calculated, and for
// other samples, only objectness loss is required.
for (int an_idx = 0; an_idx < an_num; an_idx++) {
Box<T> an_box;
an_box.x = 0.0;
an_box.y = 0.0;
an_box.w = anchors[2 * an_idx] / static_cast<T>(input_size);
an_box.h = anchors[2 * an_idx + 1] / static_cast<T>(input_size);
float iou = CalcBoxIoU<T>(an_box, gt_shift);
if (iou > best_iou) {
best_iou = iou;
best_n = an_idx;
}
}
int mask_idx = GetMaskIndex(anchor_mask, best_n);
gt_match_mask_data[i * b + t] = mask_idx;
if (mask_idx >= 0) {
int box_idx = GetEntryIndex(i, mask_idx, gj * w + gi, mask_num,
an_stride, stride, 0);
CalcBoxLocationLoss<T>(loss_data + i, input_data, gt, anchors, best_n,
box_idx, gi, gj, h, input_size, stride);
int obj_idx = (i * mask_num + mask_idx) * stride + gj * w + gi;
obj_mask_data[obj_idx] = 1.0;
int label = gt_label_data[i * b + t];
int label_idx = GetEntryIndex(i, mask_idx, gj * w + gi, mask_num,
an_stride, stride, 5);
CalcLabelLoss<T>(loss_data + i, input_data, label_idx, label,
class_num, stride);
}
}
}
CalcObjnessLoss<T>(loss_data, input_data + 4 * stride, obj_mask_data, n,
mask_num, h, w, stride, an_stride);
}
};
template <typename T>
class Yolov3LossGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<Tensor>("X");
auto* gt_box = ctx.Input<Tensor>("GTBox");
auto* gt_label = ctx.Input<Tensor>("GTLabel");
auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* loss_grad = ctx.Input<Tensor>(framework::GradVarName("Loss"));
auto* objness_mask = ctx.Input<Tensor>("ObjectnessMask");
auto* gt_match_mask = ctx.Input<Tensor>("GTMatchMask");
auto anchors = ctx.Attr<std::vector<int>>("anchors");
auto anchor_mask = ctx.Attr<std::vector<int>>("anchor_mask");
int class_num = ctx.Attr<int>("class_num");
int downsample_ratio = ctx.Attr<int>("downsample_ratio");
const int n = input_grad->dims()[0];
const int c = input_grad->dims()[1];
const int h = input_grad->dims()[2];
const int w = input_grad->dims()[3];
const int mask_num = anchor_mask.size();
const int b = gt_match_mask->dims()[1];
int input_size = downsample_ratio * h;
const int stride = h * w;
const int an_stride = (class_num + 5) * stride;
const T* input_data = input->data<T>();
const T* gt_box_data = gt_box->data<T>();
const int* gt_label_data = gt_label->data<int>();
const T* loss_grad_data = loss_grad->data<T>();
const T* obj_mask_data = objness_mask->data<T>();
const int* gt_match_mask_data = gt_match_mask->data<int>();
T* input_grad_data =
input_grad->mutable_data<T>({n, c, h, w}, ctx.GetPlace());
memset(input_grad_data, 0, input_grad->numel() * sizeof(T));
for (int i = 0; i < n; i++) {
for (int t = 0; t < b; t++) {
int mask_idx = gt_match_mask_data[i * b + t];
if (mask_idx >= 0) {
Box<T> gt = GetGtBox(gt_box_data, i, b, t);
int gi = static_cast<int>(gt.x * w);
int gj = static_cast<int>(gt.y * h);
int box_idx = GetEntryIndex(i, mask_idx, gj * w + gi, mask_num,
an_stride, stride, 0);
CalcBoxLocationLossGrad<T>(
input_grad_data, loss_grad_data[i], input_data, gt, anchors,
anchor_mask[mask_idx], box_idx, gi, gj, h, input_size, stride);
int label = gt_label_data[i * b + t];
int label_idx = GetEntryIndex(i, mask_idx, gj * w + gi, mask_num,
an_stride, stride, 5);
CalcLabelLossGrad<T>(input_grad_data, loss_grad_data[i], input_data,
label_idx, label, class_num, stride);
}
}
}
CalcObjnessLossGrad<T>(input_grad_data + 4 * stride, loss_grad_data,
input_data + 4 * stride, obj_mask_data, n, mask_num,
h, w, stride, an_stride);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/yolov3_loss_op.h 已删除 100644 → 0
浏览文件 @ 1a252f4b
/* Copyright (c) 2018 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. */
#pragma once
#include <algorithm>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
using Array5 = Eigen::DSizes<int64_t, 5>;
template <typename T>
static inline bool isZero(T x) {
return fabs(x) < 1e-6;
}
template <typename T>
static inline T sigmoid(T x) {
return 1.0 / (exp(-1.0 * x) + 1.0);
}
template <typename T>
static inline T CalcMaskPointNum(const Tensor& mask) {
auto mask_t = EigenVector<int>::Flatten(mask);
T count = 0.0;
for (int i = 0; i < mask_t.dimensions()[0]; i++) {
if (mask_t(i)) {
count += 1.0;
}
}
return count;
}
template <typename T>
static inline T CalcMSEWithMask(const Tensor& x, const Tensor& y,
const Tensor& mask) {
auto x_t = EigenVector<T>::Flatten(x);
auto y_t = EigenVector<T>::Flatten(y);
auto mask_t = EigenVector<int>::Flatten(mask);
T error_sum = 0.0;
T points = 0.0;
for (int i = 0; i < x_t.dimensions()[0]; i++) {
if (mask_t(i)) {
error_sum += pow(x_t(i) - y_t(i), 2);
points += 1;
}
}
return (error_sum / points);
}
template <typename T>
static void CalcMSEGradWithMask(Tensor* grad, const Tensor& x, const Tensor& y,
const Tensor& mask, T mf) {
auto grad_t = EigenVector<T>::Flatten(*grad).setConstant(0.0);
auto x_t = EigenVector<T>::Flatten(x);
auto y_t = EigenVector<T>::Flatten(y);
auto mask_t = EigenVector<int>::Flatten(mask);
for (int i = 0; i < x_t.dimensions()[0]; i++) {
if (mask_t(i)) {
grad_t(i) = 2.0 * (x_t(i) - y_t(i)) / mf;
}
}
}
template <typename T>
static inline T CalcBCEWithMask(const Tensor& x, const Tensor& y,
const Tensor& mask) {
auto x_t = EigenVector<T>::Flatten(x);
auto y_t = EigenVector<T>::Flatten(y);
auto mask_t = EigenVector<int>::Flatten(mask);
T error_sum = 0.0;
T points = 0.0;
for (int i = 0; i < x_t.dimensions()[0]; i++) {
if (mask_t(i)) {
error_sum +=
-1.0 * (y_t(i) * log(x_t(i)) + (1.0 - y_t(i)) * log(1.0 - x_t(i)));
points += 1;
}
}
return (error_sum / points);
}
template <typename T>
static inline void CalcBCEGradWithMask(Tensor* grad, const Tensor& x,
const Tensor& y, const Tensor& mask,
T mf) {
auto grad_t = EigenVector<T>::Flatten(*grad).setConstant(0.0);
auto x_t = EigenVector<T>::Flatten(x);
auto y_t = EigenVector<T>::Flatten(y);
auto mask_t = EigenVector<int>::Flatten(mask);
for (int i = 0; i < x_t.dimensions()[0]; i++) {
if (mask_t(i)) {
grad_t(i) = ((1.0 - y_t(i)) / (1.0 - x_t(i)) - y_t(i) / x_t(i)) / mf;
}
}
}
template <typename T>
static void CalcPredResult(const Tensor& input, Tensor* pred_conf,
Tensor* pred_class, Tensor* pred_x, Tensor* pred_y,
Tensor* pred_w, Tensor* pred_h, const int anchor_num,
const int class_num) {
const int n = input.dims()[0];
const int h = input.dims()[2];
const int w = input.dims()[3];
const int box_attr_num = 5 + class_num;
auto input_t = EigenTensor<T, 4>::From(input);
auto pred_conf_t = EigenTensor<T, 4>::From(*pred_conf);
auto pred_class_t = EigenTensor<T, 5>::From(*pred_class);
auto pred_x_t = EigenTensor<T, 4>::From(*pred_x);
auto pred_y_t = EigenTensor<T, 4>::From(*pred_y);
auto pred_w_t = EigenTensor<T, 4>::From(*pred_w);
auto pred_h_t = EigenTensor<T, 4>::From(*pred_h);
for (int i = 0; i < n; i++) {
for (int an_idx = 0; an_idx < anchor_num; an_idx++) {
for (int j = 0; j < h; j++) {
for (int k = 0; k < w; k++) {
pred_x_t(i, an_idx, j, k) =
sigmoid(input_t(i, box_attr_num * an_idx, j, k));
pred_y_t(i, an_idx, j, k) =
sigmoid(input_t(i, box_attr_num * an_idx + 1, j, k));
pred_w_t(i, an_idx, j, k) =
input_t(i, box_attr_num * an_idx + 2, j, k);
pred_h_t(i, an_idx, j, k) =
input_t(i, box_attr_num * an_idx + 3, j, k);
pred_conf_t(i, an_idx, j, k) =
sigmoid(input_t(i, box_attr_num * an_idx + 4, j, k));
for (int c = 0; c < class_num; c++) {
pred_class_t(i, an_idx, j, k, c) =
sigmoid(input_t(i, box_attr_num * an_idx + 5 + c, j, k));
}
}
}
}
}
}
template <typename T>
static T CalcBoxIoU(std::vector<T> box1, std::vector<T> box2) {
T b1_x1 = box1[0] - box1[2] / 2;
T b1_x2 = box1[0] + box1[2] / 2;
T b1_y1 = box1[1] - box1[3] / 2;
T b1_y2 = box1[1] + box1[3] / 2;
T b2_x1 = box2[0] - box2[2] / 2;
T b2_x2 = box2[0] + box2[2] / 2;
T b2_y1 = box2[1] - box2[3] / 2;
T b2_y2 = box2[1] + box2[3] / 2;
T b1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1);
T b2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1);
T inter_rect_x1 = std::max(b1_x1, b2_x1);
T inter_rect_y1 = std::max(b1_y1, b2_y1);
T inter_rect_x2 = std::min(b1_x2, b2_x2);
T inter_rect_y2 = std::min(b1_y2, b2_y2);
T inter_area = std::max(inter_rect_x2 - inter_rect_x1, static_cast<T>(0.0)) *
std::max(inter_rect_y2 - inter_rect_y1, static_cast<T>(0.0));
return inter_area / (b1_area + b2_area - inter_area);
}
template <typename T>
static void PreProcessGTBox(const Tensor& gt_box, const Tensor& gt_label,
const float ignore_thresh, std::vector<int> anchors,
const int grid_size, Tensor* obj_mask,
Tensor* noobj_mask, Tensor* tx, Tensor* ty,
Tensor* tw, Tensor* th, Tensor* tconf,
Tensor* tclass) {
const int n = gt_box.dims()[0];
const int b = gt_box.dims()[1];
const int anchor_num = anchors.size() / 2;
auto gt_box_t = EigenTensor<T, 3>::From(gt_box);
auto gt_label_t = EigenTensor<int, 2>::From(gt_label);
auto obj_mask_t = EigenTensor<int, 4>::From(*obj_mask).setConstant(0);
auto noobj_mask_t = EigenTensor<int, 4>::From(*noobj_mask).setConstant(1);
auto tx_t = EigenTensor<T, 4>::From(*tx).setConstant(0.0);
auto ty_t = EigenTensor<T, 4>::From(*ty).setConstant(0.0);
auto tw_t = EigenTensor<T, 4>::From(*tw).setConstant(0.0);
auto th_t = EigenTensor<T, 4>::From(*th).setConstant(0.0);
auto tconf_t = EigenTensor<T, 4>::From(*tconf).setConstant(0.0);
auto tclass_t = EigenTensor<T, 5>::From(*tclass).setConstant(0.0);
for (int i = 0; i < n; i++) {
for (int j = 0; j < b; j++) {
if (isZero<T>(gt_box_t(i, j, 0)) && isZero<T>(gt_box_t(i, j, 1)) &&
isZero<T>(gt_box_t(i, j, 2)) && isZero<T>(gt_box_t(i, j, 3))) {
continue;
}
int cur_label = gt_label_t(i, j);
T gx = gt_box_t(i, j, 0) * grid_size;
T gy = gt_box_t(i, j, 1) * grid_size;
T gw = gt_box_t(i, j, 2) * grid_size;
T gh = gt_box_t(i, j, 3) * grid_size;
int gi = static_cast<int>(gx);
int gj = static_cast<int>(gy);
T max_iou = static_cast<T>(0);
T iou;
int best_an_index = -1;
std::vector<T> gt_box_shape({0, 0, gw, gh});
for (int an_idx = 0; an_idx < anchor_num; an_idx++) {
std::vector<T> anchor_shape({0, 0, static_cast<T>(anchors[2 * an_idx]),
static_cast<T>(anchors[2 * an_idx + 1])});
iou = CalcBoxIoU<T>(gt_box_shape, anchor_shape);
if (iou > max_iou) {
max_iou = iou;
best_an_index = an_idx;
}
if (iou > ignore_thresh) {
noobj_mask_t(i, an_idx, gj, gi) = 0;
}
}
obj_mask_t(i, best_an_index, gj, gi) = 1;
noobj_mask_t(i, best_an_index, gj, gi) = 0;
tx_t(i, best_an_index, gj, gi) = gx - gi;
ty_t(i, best_an_index, gj, gi) = gy - gj;
tw_t(i, best_an_index, gj, gi) = log(gw / anchors[2 * best_an_index]);
th_t(i, best_an_index, gj, gi) = log(gh / anchors[2 * best_an_index + 1]);
tclass_t(i, best_an_index, gj, gi, cur_label) = 1;
tconf_t(i, best_an_index, gj, gi) = 1;
}
}
}
static void ExpandObjMaskByClassNum(Tensor* obj_mask_expand,
const Tensor& obj_mask) {
const int n = obj_mask_expand->dims()[0];
const int an_num = obj_mask_expand->dims()[1];
const int h = obj_mask_expand->dims()[2];
const int w = obj_mask_expand->dims()[3];
const int class_num = obj_mask_expand->dims()[4];
auto obj_mask_expand_t = EigenTensor<int, 5>::From(*obj_mask_expand);
auto obj_mask_t = EigenTensor<int, 4>::From(obj_mask);
obj_mask_expand_t = obj_mask_t.reshape(Array5(n, an_num, h, w, 1))
.broadcast(Array5(1, 1, 1, 1, class_num));
}
template <typename T>
static void AddAllGradToInputGrad(
Tensor* grad, T loss, const Tensor& pred_x, const Tensor& pred_y,
const Tensor& pred_conf, const Tensor& pred_class, const Tensor& grad_x,
const Tensor& grad_y, const Tensor& grad_w, const Tensor& grad_h,
const Tensor& grad_conf_target, const Tensor& grad_conf_notarget,
const Tensor& grad_class, const int class_num, const float loss_weight_xy,
const float loss_weight_wh, const float loss_weight_conf_target,
const float loss_weight_conf_notarget, const float loss_weight_class) {
const int n = pred_x.dims()[0];
const int an_num = pred_x.dims()[1];
const int h = pred_x.dims()[2];
const int w = pred_x.dims()[3];
const int attr_num = class_num + 5;
auto grad_t = EigenTensor<T, 4>::From(*grad).setConstant(0.0);
auto pred_x_t = EigenTensor<T, 4>::From(pred_x);
auto pred_y_t = EigenTensor<T, 4>::From(pred_y);
auto pred_conf_t = EigenTensor<T, 4>::From(pred_conf);
auto pred_class_t = EigenTensor<T, 5>::From(pred_class);
auto grad_x_t = EigenTensor<T, 4>::From(grad_x);
auto grad_y_t = EigenTensor<T, 4>::From(grad_y);
auto grad_w_t = EigenTensor<T, 4>::From(grad_w);
auto grad_h_t = EigenTensor<T, 4>::From(grad_h);
auto grad_conf_target_t = EigenTensor<T, 4>::From(grad_conf_target);
auto grad_conf_notarget_t = EigenTensor<T, 4>::From(grad_conf_notarget);
auto grad_class_t = EigenTensor<T, 5>::From(grad_class);
for (int i = 0; i < n; i++) {
for (int j = 0; j < an_num; j++) {
for (int k = 0; k < h; k++) {
for (int l = 0; l < w; l++) {
grad_t(i, j * attr_num, k, l) =
grad_x_t(i, j, k, l) * pred_x_t(i, j, k, l) *
(1.0 - pred_x_t(i, j, k, l)) * loss * loss_weight_xy;
grad_t(i, j * attr_num + 1, k, l) =
grad_y_t(i, j, k, l) * pred_y_t(i, j, k, l) *
(1.0 - pred_y_t(i, j, k, l)) * loss * loss_weight_xy;
grad_t(i, j * attr_num + 2, k, l) =
grad_w_t(i, j, k, l) * loss * loss_weight_wh;
grad_t(i, j * attr_num + 3, k, l) =
grad_h_t(i, j, k, l) * loss * loss_weight_wh;
grad_t(i, j * attr_num + 4, k, l) =
grad_conf_target_t(i, j, k, l) * pred_conf_t(i, j, k, l) *
(1.0 - pred_conf_t(i, j, k, l)) * loss * loss_weight_conf_target;
grad_t(i, j * attr_num + 4, k, l) +=
grad_conf_notarget_t(i, j, k, l) * pred_conf_t(i, j, k, l) *
(1.0 - pred_conf_t(i, j, k, l)) * loss *
loss_weight_conf_notarget;
for (int c = 0; c < class_num; c++) {
grad_t(i, j * attr_num + 5 + c, k, l) =
grad_class_t(i, j, k, l, c) * pred_class_t(i, j, k, l, c) *
(1.0 - pred_class_t(i, j, k, l, c)) * loss * loss_weight_class;
}
}
}
}
}
}
template <typename T>
class Yolov3LossKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<Tensor>("X");
auto* gt_box = ctx.Input<Tensor>("GTBox");
auto* gt_label = ctx.Input<Tensor>("GTLabel");
auto* loss = ctx.Output<Tensor>("Loss");
auto anchors = ctx.Attr<std::vector<int>>("anchors");
int class_num = ctx.Attr<int>("class_num");
float ignore_thresh = ctx.Attr<float>("ignore_thresh");
float loss_weight_xy = ctx.Attr<float>("loss_weight_xy");
float loss_weight_wh = ctx.Attr<float>("loss_weight_wh");
float loss_weight_conf_target = ctx.Attr<float>("loss_weight_conf_target");
float loss_weight_conf_notarget =
ctx.Attr<float>("loss_weight_conf_notarget");
float loss_weight_class = ctx.Attr<float>("loss_weight_class");
const int n = input->dims()[0];
const int h = input->dims()[2];
const int w = input->dims()[3];
const int an_num = anchors.size() / 2;
Tensor pred_x, pred_y, pred_w, pred_h;
Tensor pred_conf, pred_class;
pred_x.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
pred_y.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
pred_w.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
pred_h.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
pred_conf.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
pred_class.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
CalcPredResult<T>(*input, &pred_conf, &pred_class, &pred_x, &pred_y,
&pred_w, &pred_h, an_num, class_num);
Tensor obj_mask, noobj_mask;
Tensor tx, ty, tw, th, tconf, tclass;
obj_mask.mutable_data<int>({n, an_num, h, w}, ctx.GetPlace());
noobj_mask.mutable_data<int>({n, an_num, h, w}, ctx.GetPlace());
tx.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
ty.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tw.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
th.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tconf.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tclass.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
PreProcessGTBox<T>(*gt_box, *gt_label, ignore_thresh, anchors, h, &obj_mask,
&noobj_mask, &tx, &ty, &tw, &th, &tconf, &tclass);
Tensor obj_mask_expand;
obj_mask_expand.mutable_data<int>({n, an_num, h, w, class_num},
ctx.GetPlace());
ExpandObjMaskByClassNum(&obj_mask_expand, obj_mask);
T loss_x = CalcMSEWithMask<T>(pred_x, tx, obj_mask);
T loss_y = CalcMSEWithMask<T>(pred_y, ty, obj_mask);
T loss_w = CalcMSEWithMask<T>(pred_w, tw, obj_mask);
T loss_h = CalcMSEWithMask<T>(pred_h, th, obj_mask);
T loss_conf_target = CalcBCEWithMask<T>(pred_conf, tconf, obj_mask);
T loss_conf_notarget = CalcBCEWithMask<T>(pred_conf, tconf, noobj_mask);
T loss_class = CalcBCEWithMask<T>(pred_class, tclass, obj_mask_expand);
auto* loss_data = loss->mutable_data<T>({1}, ctx.GetPlace());
loss_data[0] = loss_weight_xy * (loss_x + loss_y) +
loss_weight_wh * (loss_w + loss_h) +
loss_weight_conf_target * loss_conf_target +
loss_weight_conf_notarget * loss_conf_notarget +
loss_weight_class * loss_class;
}
};
template <typename T>
class Yolov3LossGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<Tensor>("X");
auto* gt_box = ctx.Input<Tensor>("GTBox");
auto* gt_label = ctx.Input<Tensor>("GTLabel");
auto anchors = ctx.Attr<std::vector<int>>("anchors");
int class_num = ctx.Attr<int>("class_num");
float ignore_thresh = ctx.Attr<float>("ignore_thresh");
auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* output_grad = ctx.Input<Tensor>(framework::GradVarName("Loss"));
const T loss = output_grad->data<T>()[0];
float loss_weight_xy = ctx.Attr<float>("loss_weight_xy");
float loss_weight_wh = ctx.Attr<float>("loss_weight_wh");
float loss_weight_conf_target = ctx.Attr<float>("loss_weight_conf_target");
float loss_weight_conf_notarget =
ctx.Attr<float>("loss_weight_conf_notarget");
float loss_weight_class = ctx.Attr<float>("loss_weight_class");
const int n = input->dims()[0];
const int c = input->dims()[1];
const int h = input->dims()[2];
const int w = input->dims()[3];
const int an_num = anchors.size() / 2;
Tensor pred_x, pred_y, pred_w, pred_h;
Tensor pred_conf, pred_class;
pred_x.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
pred_y.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
pred_w.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
pred_h.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
pred_conf.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
pred_class.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
CalcPredResult<T>(*input, &pred_conf, &pred_class, &pred_x, &pred_y,
&pred_w, &pred_h, an_num, class_num);
Tensor obj_mask, noobj_mask;
Tensor tx, ty, tw, th, tconf, tclass;
obj_mask.mutable_data<int>({n, an_num, h, w}, ctx.GetPlace());
noobj_mask.mutable_data<int>({n, an_num, h, w}, ctx.GetPlace());
tx.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
ty.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tw.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
th.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tconf.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tclass.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
PreProcessGTBox<T>(*gt_box, *gt_label, ignore_thresh, anchors, h, &obj_mask,
&noobj_mask, &tx, &ty, &tw, &th, &tconf, &tclass);
Tensor obj_mask_expand;
obj_mask_expand.mutable_data<int>({n, an_num, h, w, class_num},
ctx.GetPlace());
ExpandObjMaskByClassNum(&obj_mask_expand, obj_mask);
Tensor grad_x, grad_y, grad_w, grad_h;
Tensor grad_conf_target, grad_conf_notarget, grad_class;
grad_x.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
grad_y.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
grad_w.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
grad_h.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
grad_conf_target.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
grad_conf_notarget.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
grad_class.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
T obj_mf = CalcMaskPointNum<int>(obj_mask);
T noobj_mf = CalcMaskPointNum<int>(noobj_mask);
T obj_expand_mf = CalcMaskPointNum<int>(obj_mask_expand);
CalcMSEGradWithMask<T>(&grad_x, pred_x, tx, obj_mask, obj_mf);
CalcMSEGradWithMask<T>(&grad_y, pred_y, ty, obj_mask, obj_mf);
CalcMSEGradWithMask<T>(&grad_w, pred_w, tw, obj_mask, obj_mf);
CalcMSEGradWithMask<T>(&grad_h, pred_h, th, obj_mask, obj_mf);
CalcBCEGradWithMask<T>(&grad_conf_target, pred_conf, tconf, obj_mask,
obj_mf);
CalcBCEGradWithMask<T>(&grad_conf_notarget, pred_conf, tconf, noobj_mask,
noobj_mf);
CalcBCEGradWithMask<T>(&grad_class, pred_class, tclass, obj_mask_expand,
obj_expand_mf);
input_grad->mutable_data<T>({n, c, h, w}, ctx.GetPlace());
AddAllGradToInputGrad<T>(
input_grad, loss, pred_x, pred_y, pred_conf, pred_class, grad_x, grad_y,
grad_w, grad_h, grad_conf_target, grad_conf_notarget, grad_class,
class_num, loss_weight_xy, loss_weight_wh, loss_weight_conf_target,
loss_weight_conf_notarget, loss_weight_class);
}
};
} // namespace operators
} // namespace paddle
python/paddle/fluid/layers/detection.py
浏览文件 @ 30cc8b7a
......@@ -508,13 +508,10 @@ def yolov3_loss(x,
gtbox,
gtlabel,
anchors,
anchor_mask,
class_num,
ignore_thresh,
loss_weight_xy=None,
loss_weight_wh=None,
loss_weight_conf_target=None,
loss_weight_conf_notarget=None,
loss_weight_class=None,
downsample_ratio,
name=None):
"""
${comment}
......@@ -526,16 +523,13 @@ def yolov3_loss(x,
and x, y, w, h should be relative value of input image.
N is the batch number and B is the max box number in
an image.
gtlabel (Variable): class id of ground truth boxes, shoud be ins shape
gtlabel (Variable): class id of ground truth boxes, shoud be in shape
of [N, B].
anchors (list|tuple): ${anchors_comment}
anchor_mask (list|tuple): ${anchor_mask_comment}
class_num (int): ${class_num_comment}
ignore_thresh (float): ${ignore_thresh_comment}
loss_weight_xy (float|None): ${loss_weight_xy_comment}
loss_weight_wh (float|None): ${loss_weight_wh_comment}
loss_weight_conf_target (float|None): ${loss_weight_conf_target_comment}
loss_weight_conf_notarget (float|None): ${loss_weight_conf_notarget_comment}
loss_weight_class (float|None): ${loss_weight_class_comment}
downsample_ratio (int): ${downsample_ratio_comment}
name (string): the name of yolov3 loss
Returns:
......@@ -555,9 +549,10 @@ def yolov3_loss(x,
x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
gtbox = fluid.layers.data(name='gtbox', shape=[6, 5], dtype='float32')
gtlabel = fluid.layers.data(name='gtlabel', shape=[6, 1], dtype='int32')
anchors = [10, 13, 16, 30, 33, 23]
loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, class_num=80
anchors=anchors, ignore_thresh=0.5)
anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
anchors = [0, 1, 2]
loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, class_num=80, anchors=anchors,
ignore_thresh=0.5, downsample_ratio=32)
"""
helper = LayerHelper('yolov3_loss', **locals())
......@@ -569,6 +564,8 @@ def yolov3_loss(x,
raise TypeError("Input gtlabel of yolov3_loss must be Variable")
if not isinstance(anchors, list) and not isinstance(anchors, tuple):
raise TypeError("Attr anchors of yolov3_loss must be list or tuple")
if not isinstance(anchor_mask, list) and not isinstance(anchor_mask, tuple):
raise TypeError("Attr anchor_mask of yolov3_loss must be list or tuple")
if not isinstance(class_num, int):
raise TypeError("Attr class_num of yolov3_loss must be an integer")
if not isinstance(ignore_thresh, float):
......@@ -581,31 +578,29 @@ def yolov3_loss(x,
loss = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
objectness_mask = helper.create_variable_for_type_inference(dtype='int32')
gt_match_mask = helper.create_variable_for_type_inference(dtype='int32')
attrs = {
"anchors": anchors,
"anchor_mask": anchor_mask,
"class_num": class_num,
"ignore_thresh": ignore_thresh,
"downsample_ratio": downsample_ratio,
}
if loss_weight_xy is not None and isinstance(loss_weight_xy, float):
self.attrs['loss_weight_xy'] = loss_weight_xy
if loss_weight_wh is not None and isinstance(loss_weight_wh, float):
self.attrs['loss_weight_wh'] = loss_weight_wh
if loss_weight_conf_target is not None and isinstance(
loss_weight_conf_target, float):
self.attrs['loss_weight_conf_target'] = loss_weight_conf_target
if loss_weight_conf_notarget is not None and isinstance(
loss_weight_conf_notarget, float):
self.attrs['loss_weight_conf_notarget'] = loss_weight_conf_notarget
if loss_weight_class is not None and isinstance(loss_weight_class, float):
self.attrs['loss_weight_class'] = loss_weight_class
helper.append_op(
type='yolov3_loss',
inputs={"X": x,
"GTBox": gtbox,
"GTLabel": gtlabel},
outputs={'Loss': loss},
inputs={
"X": x,
"GTBox": gtbox,
"GTLabel": gtlabel,
},
outputs={
'Loss': loss,
'ObjectnessMask': objectness_mask,
'GTMatchMask': gt_match_mask
},
attrs=attrs)
return loss
......
python/paddle/fluid/tests/test_detection.py
浏览文件 @ 30cc8b7a
......@@ -476,8 +476,8 @@ class TestYoloDetection(unittest.TestCase):
x = layers.data(name='x', shape=[30, 7, 7], dtype='float32')
gtbox = layers.data(name='gtbox', shape=[10, 4], dtype='float32')
gtlabel = layers.data(name='gtlabel', shape=[10], dtype='int32')
loss = layers.yolov3_loss(x, gtbox, gtlabel, [10, 13, 30, 13], 10,
0.5)
loss = layers.yolov3_loss(x, gtbox, gtlabel, [10, 13, 30, 13],
[0, 1], 10, 0.7, 32)
self.assertIsNotNone(loss)
......
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
浏览文件 @ 30cc8b7a
......@@ -16,174 +16,179 @@ from __future__ import division
import unittest
import numpy as np
from scipy.special import logit
from scipy.special import expit
from op_test import OpTest
from paddle.fluid import core
def sigmoid(x):
return 1.0 / (1.0 + np.exp(-1.0 * x))
def l2loss(x, y):
return 0.5 * (y - x) * (y - x)
def mse(x, y, num):
return ((y - x)**2).sum() / num
def sce(x, label):
sigmoid_x = expit(x)
term1 = label * np.log(sigmoid_x)
term2 = (1.0 - label) * np.log(1.0 - sigmoid_x)
return -term1 - term2
def bce(x, y, mask):
x = x.reshape((-1))
y = y.reshape((-1))
mask = mask.reshape((-1))
def sigmoid(x):
return 1.0 / (1.0 + np.exp(-1.0 * x))
error_sum = 0.0
count = 0
for i in range(x.shape[0]):
if mask[i] > 0:
error_sum += y[i] * np.log(x[i]) + (1 - y[i]) * np.log(1 - x[i])
count += 1
return error_sum / (-1.0 * count)
def batch_xywh_box_iou(box1, box2):
b1_left = box1[:, :, 0] - box1[:, :, 2] / 2
b1_right = box1[:, :, 0] + box1[:, :, 2] / 2
b1_top = box1[:, :, 1] - box1[:, :, 3] / 2
b1_bottom = box1[:, :, 1] + box1[:, :, 3] / 2
def box_iou(box1, box2):
b1_x1 = box1[0] - box1[2] / 2
b1_x2 = box1[0] + box1[2] / 2
b1_y1 = box1[1] - box1[3] / 2
b1_y2 = box1[1] + box1[3] / 2
b2_x1 = box2[0] - box2[2] / 2
b2_x2 = box2[0] + box2[2] / 2
b2_y1 = box2[1] - box2[3] / 2
b2_y2 = box2[1] + box2[3] / 2
b2_left = box2[:, :, 0] - box2[:, :, 2] / 2
b2_right = box2[:, :, 0] + box2[:, :, 2] / 2
b2_top = box2[:, :, 1] - box2[:, :, 3] / 2
b2_bottom = box2[:, :, 1] + box2[:, :, 3] / 2
b1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
b2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
left = np.maximum(b1_left[:, :, np.newaxis], b2_left[:, np.newaxis, :])
right = np.minimum(b1_right[:, :, np.newaxis], b2_right[:, np.newaxis, :])
top = np.maximum(b1_top[:, :, np.newaxis], b2_top[:, np.newaxis, :])
bottom = np.minimum(b1_bottom[:, :, np.newaxis],
b2_bottom[:, np.newaxis, :])
inter_rect_x1 = max(b1_x1, b2_x1)
inter_rect_y1 = max(b1_y1, b2_y1)
inter_rect_x2 = min(b1_x2, b2_x2)
inter_rect_y2 = min(b1_y2, b2_y2)
inter_area = max(inter_rect_x2 - inter_rect_x1, 0) * max(
inter_rect_y2 - inter_rect_y1, 0)
inter_w = np.clip(right - left, 0., 1.)
inter_h = np.clip(bottom - top, 0., 1.)
inter_area = inter_w * inter_h
return inter_area / (b1_area + b2_area + inter_area)
b1_area = (b1_right - b1_left) * (b1_bottom - b1_top)
b2_area = (b2_right - b2_left) * (b2_bottom - b2_top)
union = b1_area[:, :, np.newaxis] + b2_area[:, np.newaxis, :] - inter_area
return inter_area / union
def build_target(gtboxs, gtlabel, attrs, grid_size):
n, b, _ = gtboxs.shape
ignore_thresh = attrs["ignore_thresh"]
anchors = attrs["anchors"]
class_num = attrs["class_num"]
an_num = len(anchors) // 2
obj_mask = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
noobj_mask = np.ones((n, an_num, grid_size, grid_size)).astype('float32')
tx = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
ty = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
tw = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
th = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
tconf = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
tcls = np.zeros(
(n, an_num, grid_size, grid_size, class_num)).astype('float32')
def YOLOv3Loss(x, gtbox, gtlabel, attrs):
n, c, h, w = x.shape
b = gtbox.shape[1]
anchors = attrs['anchors']
an_num = len(anchors) // 2
anchor_mask = attrs['anchor_mask']
mask_num = len(anchor_mask)
class_num = attrs["class_num"]
ignore_thresh = attrs['ignore_thresh']
downsample = attrs['downsample']
input_size = downsample * h
x = x.reshape((n, mask_num, 5 + class_num, h, w)).transpose((0, 1, 3, 4, 2))
loss = np.zeros((n)).astype('float32')
pred_box = x[:, :, :, :, :4].copy()
grid_x = np.tile(np.arange(w).reshape((1, w)), (h, 1))
grid_y = np.tile(np.arange(h).reshape((h, 1)), (1, w))
pred_box[:, :, :, :, 0] = (grid_x + sigmoid(pred_box[:, :, :, :, 0])) / w
pred_box[:, :, :, :, 1] = (grid_y + sigmoid(pred_box[:, :, :, :, 1])) / h
x[:, :, :, :, 5:] = np.where(x[:, :, :, :, 5:] < -0.5, x[:, :, :, :, 5:],
np.ones_like(x[:, :, :, :, 5:]) * 1.0 /
class_num)
mask_anchors = []
for m in anchor_mask:
mask_anchors.append((anchors[2 * m], anchors[2 * m + 1]))
anchors_s = np.array(
[(an_w / input_size, an_h / input_size) for an_w, an_h in mask_anchors])
anchor_w = anchors_s[:, 0:1].reshape((1, mask_num, 1, 1))
anchor_h = anchors_s[:, 1:2].reshape((1, mask_num, 1, 1))
pred_box[:, :, :, :, 2] = np.exp(pred_box[:, :, :, :, 2]) * anchor_w
pred_box[:, :, :, :, 3] = np.exp(pred_box[:, :, :, :, 3]) * anchor_h
pred_box = pred_box.reshape((n, -1, 4))
pred_obj = x[:, :, :, :, 4].reshape((n, -1))
objness = np.zeros(pred_box.shape[:2]).astype('float32')
ious = batch_xywh_box_iou(pred_box, gtbox)
ious_max = np.max(ious, axis=-1)
objness = np.where(ious_max > ignore_thresh, -np.ones_like(objness),
objness)
gtbox_shift = gtbox.copy()
gtbox_shift[:, :, 0] = 0
gtbox_shift[:, :, 1] = 0
anchors = [(anchors[2 * i], anchors[2 * i + 1]) for i in range(0, an_num)]
anchors_s = np.array(
[(an_w / input_size, an_h / input_size) for an_w, an_h in anchors])
anchor_boxes = np.concatenate(
[np.zeros_like(anchors_s), anchors_s], axis=-1)
anchor_boxes = np.tile(anchor_boxes[np.newaxis, :, :], (n, 1, 1))
ious = batch_xywh_box_iou(gtbox_shift, anchor_boxes)
iou_matches = np.argmax(ious, axis=-1)
gt_matches = iou_matches.copy()
for i in range(n):
for j in range(b):
if gtboxs[i, j, :].sum() == 0:
if gtbox[i, j, 2:].sum() == 0:
gt_matches[i, j] = -1
continue
if iou_matches[i, j] not in anchor_mask:
gt_matches[i, j] = -1
continue
an_idx = anchor_mask.index(iou_matches[i, j])
gt_matches[i, j] = an_idx
gi = int(gtbox[i, j, 0] * w)
gj = int(gtbox[i, j, 1] * h)
gt_label = gtlabel[i, j]
gx = gtboxs[i, j, 0] * grid_size
gy = gtboxs[i, j, 1] * grid_size
gw = gtboxs[i, j, 2] * grid_size
gh = gtboxs[i, j, 3] * grid_size
gi = int(gx)
gj = int(gy)
gtbox = [0, 0, gw, gh]
max_iou = 0
for k in range(an_num):
anchor_box = [0, 0, anchors[2 * k], anchors[2 * k + 1]]
iou = box_iou(gtbox, anchor_box)
if iou > max_iou:
max_iou = iou
best_an_index = k
if iou > ignore_thresh:
noobj_mask[i, best_an_index, gj, gi] = 0
obj_mask[i, best_an_index, gj, gi] = 1
noobj_mask[i, best_an_index, gj, gi] = 0
tx[i, best_an_index, gj, gi] = gx - gi
ty[i, best_an_index, gj, gi] = gy - gj
tw[i, best_an_index, gj, gi] = np.log(gw / anchors[2 *
best_an_index])
th[i, best_an_index, gj, gi] = np.log(
gh / anchors[2 * best_an_index + 1])
tconf[i, best_an_index, gj, gi] = 1
tcls[i, best_an_index, gj, gi, gt_label] = 1
return (tx, ty, tw, th, tconf, tcls, obj_mask, noobj_mask)
def YoloV3Loss(x, gtbox, gtlabel, attrs):
n, c, h, w = x.shape
an_num = len(attrs['anchors']) // 2
class_num = attrs["class_num"]
x = x.reshape((n, an_num, 5 + class_num, h, w)).transpose((0, 1, 3, 4, 2))
pred_x = sigmoid(x[:, :, :, :, 0])
pred_y = sigmoid(x[:, :, :, :, 1])
pred_w = x[:, :, :, :, 2]
pred_h = x[:, :, :, :, 3]
pred_conf = sigmoid(x[:, :, :, :, 4])
pred_cls = sigmoid(x[:, :, :, :, 5:])
tx, ty, tw, th, tconf, tcls, obj_mask, noobj_mask = build_target(
gtbox, gtlabel, attrs, x.shape[2])
obj_mask_expand = np.tile(
np.expand_dims(obj_mask, 4), (1, 1, 1, 1, int(attrs['class_num'])))
loss_x = mse(pred_x * obj_mask, tx * obj_mask, obj_mask.sum())
loss_y = mse(pred_y * obj_mask, ty * obj_mask, obj_mask.sum())
loss_w = mse(pred_w * obj_mask, tw * obj_mask, obj_mask.sum())
loss_h = mse(pred_h * obj_mask, th * obj_mask, obj_mask.sum())
loss_conf_target = bce(pred_conf * obj_mask, tconf * obj_mask, obj_mask)
loss_conf_notarget = bce(pred_conf * noobj_mask, tconf * noobj_mask,
noobj_mask)
loss_class = bce(pred_cls * obj_mask_expand, tcls * obj_mask_expand,
obj_mask_expand)
return attrs['loss_weight_xy'] * (loss_x + loss_y) \
+ attrs['loss_weight_wh'] * (loss_w + loss_h) \
+ attrs['loss_weight_conf_target'] * loss_conf_target \
+ attrs['loss_weight_conf_notarget'] * loss_conf_notarget \
+ attrs['loss_weight_class'] * loss_class
tx = gtbox[i, j, 0] * w - gi
ty = gtbox[i, j, 1] * w - gj
tw = np.log(gtbox[i, j, 2] * input_size / mask_anchors[an_idx][0])
th = np.log(gtbox[i, j, 3] * input_size / mask_anchors[an_idx][1])
scale = (2.0 - gtbox[i, j, 2] * gtbox[i, j, 3])
loss[i] += sce(x[i, an_idx, gj, gi, 0], tx) * scale
loss[i] += sce(x[i, an_idx, gj, gi, 1], ty) * scale
loss[i] += l2loss(x[i, an_idx, gj, gi, 2], tw) * scale
loss[i] += l2loss(x[i, an_idx, gj, gi, 3], th) * scale
objness[i, an_idx * h * w + gj * w + gi] = 1.0
for label_idx in range(class_num):
loss[i] += sce(x[i, an_idx, gj, gi, 5 + label_idx],
float(label_idx == gtlabel[i, j]))
for j in range(mask_num * h * w):
if objness[i, j] > 0:
loss[i] += sce(pred_obj[i, j], 1.0)
elif objness[i, j] == 0:
loss[i] += sce(pred_obj[i, j], 0.0)
return (loss, objness.reshape((n, mask_num, h, w)).astype('float32'), \
gt_matches.astype('int32'))
class TestYolov3LossOp(OpTest):
def setUp(self):
self.loss_weight_xy = 1.0
self.loss_weight_wh = 1.0
self.loss_weight_conf_target = 1.0
self.loss_weight_conf_notarget = 1.0
self.loss_weight_class = 1.0
self.initTestCase()
self.op_type = 'yolov3_loss'
x = np.random.random(size=self.x_shape).astype('float32')
x = logit(np.random.uniform(0, 1, self.x_shape).astype('float32'))
gtbox = np.random.random(size=self.gtbox_shape).astype('float32')
gtlabel = np.random.randint(0, self.class_num,
self.gtbox_shape[:2]).astype('int32')
gtlabel = np.random.randint(0, self.class_num, self.gtbox_shape[:2])
gtmask = np.random.randint(0, 2, self.gtbox_shape[:2])
gtbox = gtbox * gtmask[:, :, np.newaxis]
gtlabel = gtlabel * gtmask
self.attrs = {
"anchors": self.anchors,
"anchor_mask": self.anchor_mask,
"class_num": self.class_num,
"ignore_thresh": self.ignore_thresh,
"loss_weight_xy": self.loss_weight_xy,
"loss_weight_wh": self.loss_weight_wh,
"loss_weight_conf_target": self.loss_weight_conf_target,
"loss_weight_conf_notarget": self.loss_weight_conf_notarget,
"loss_weight_class": self.loss_weight_class,
"downsample": self.downsample,
}
self.inputs = {'X': x, 'GTBox': gtbox, 'GTLabel': gtlabel}
self.inputs = {
'X': x,
'GTBox': gtbox.astype('float32'),
'GTLabel': gtlabel.astype('int32'),
}
loss, objness, gt_matches = YOLOv3Loss(x, gtbox, gtlabel, self.attrs)
self.outputs = {
'Loss': np.array(
[YoloV3Loss(x, gtbox, gtlabel, self.attrs)]).astype('float32')
'Loss': loss,
'ObjectnessMask': objness,
"GTMatchMask": gt_matches
}
def test_check_output(self):
......@@ -196,19 +201,16 @@ class TestYolov3LossOp(OpTest):
place, ['X'],
'Loss',
no_grad_set=set(["GTBox", "GTLabel"]),
max_relative_error=0.06)
max_relative_error=0.3)
def initTestCase(self):
self.anchors = [10, 13, 12, 12]
self.class_num = 10
self.anchors = [10, 13, 16, 30, 33, 23]
self.anchor_mask = [1, 2]
self.class_num = 5
self.ignore_thresh = 0.5
self.x_shape = (5, len(self.anchors) // 2 * (5 + self.class_num), 7, 7)
self.gtbox_shape = (5, 10, 4)
self.loss_weight_xy = 2.5
self.loss_weight_wh = 0.8
self.loss_weight_conf_target = 1.5
self.loss_weight_conf_notarget = 0.5
self.loss_weight_class = 1.2
self.downsample = 32
self.x_shape = (3, len(self.anchor_mask) * (5 + self.class_num), 5, 5)
self.gtbox_shape = (3, 5, 4)
if __name__ == "__main__":
......
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