m53297601
/
Paddle
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

remove useless code and update doc. test=develop

Browse Source
revert-15296-async_double_buffered_py_reader
dengkaipeng 6 years ago
parent 577a92d992
commit db8ff57a61
4 changed files with 45 additions and 69 deletions
Show Stats Download Patch File Download Diff File

32
paddle/fluid/operators/yolov3_loss_op.cc
Unescape View File

@ -138,17 +138,23 @@ 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 L1 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.
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");
}
@ -204,11 +210,7 @@ namespace ops = paddle::operators;
REGISTER_OPERATOR(yolov3_loss, ops::Yolov3LossOp, ops::Yolov3LossOpMaker,
ops::Yolov3LossGradMaker);
REGISTER_OPERATOR(yolov3_loss_grad, ops::Yolov3LossOpGrad);
REGISTER_OP_CPU_KERNEL(
yolov3_loss,
ops::Yolov3LossKernel<paddle::platform::CPUDeviceContext, float>,
ops::Yolov3LossKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
yolov3_loss_grad,
ops::Yolov3LossGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::Yolov3LossGradKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(yolov3_loss, ops::Yolov3LossKernel<float>,
ops::Yolov3LossKernel<double>);
REGISTER_OP_CPU_KERNEL(yolov3_loss_grad, ops::Yolov3LossGradKernel<float>,
ops::Yolov3LossGradKernel<double>);

64
paddle/fluid/operators/yolov3_loss_op.h
Unescape View File

@ -260,26 +260,18 @@ static void CalcYolov3Loss(T* loss_data, const Tensor& input, const Tensor& tx,
const int class_num = tclass.dims()[4];
const int grid_num = h * w;
// T l = 0.0;
CalcSCE<T>(loss_data, input_data, tx_data, tweight_data, obj_mask_data, n,
an_num, grid_num, class_num, 1);
CalcSCE<T>(loss_data, input_data + grid_num, ty_data, tweight_data,
obj_mask_data, n, an_num, grid_num, class_num, 1);
// LOG(ERROR) << "C++ xy: " << loss_data[0] - l;
// l = loss_data[0];
CalcL1Loss<T>(loss_data, input_data + 2 * grid_num, tw_data, tweight_data,
obj_mask_data, n, an_num, grid_num, class_num);
CalcL1Loss<T>(loss_data, input_data + 3 * grid_num, th_data, tweight_data,
obj_mask_data, n, an_num, grid_num, class_num);
// LOG(ERROR) << "C++ wh: " << loss_data[0] - l;
// l = loss_data[0];
CalcSCE<T>(loss_data, input_data + 4 * grid_num, tconf_data, conf_mask_data,
conf_mask_data, n, an_num, grid_num, class_num, 1);
// LOG(ERROR) << "C++ conf: " << loss_data[0] - l;
// l = loss_data[0];
CalcSCE<T>(loss_data, input_data + 5 * grid_num, tclass_data, obj_mask_data,
obj_mask_data, n, an_num, grid_num, class_num, class_num);
// LOG(ERROR) << "C++ class: " << loss_data[0] - l;
}
template <typename T>
@ -329,7 +321,7 @@ static void CalcYolov3LossGrad(T* input_grad_data, const Tensor& loss_grad,
obj_mask_data, n, an_num, grid_num, class_num, class_num);
}
template <typename DeviceContext, typename T>
template <typename T>
class Yolov3LossKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
@ -359,24 +351,24 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
tconf.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tclass.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
math::SetConstant<DeviceContext, T> constant;
constant(ctx.template device_context<DeviceContext>(), &conf_mask,
static_cast<T>(1.0));
constant(ctx.template device_context<DeviceContext>(), &obj_mask,
static_cast<T>(0.0));
constant(ctx.template device_context<DeviceContext>(), &tx,
static_cast<T>(0.0));
constant(ctx.template device_context<DeviceContext>(), &ty,
math::SetConstant<platform::CPUDeviceContext, T> constant;
constant(ctx.template device_context<platform::CPUDeviceContext>(),
&conf_mask, static_cast<T>(1.0));
constant(ctx.template device_context<platform::CPUDeviceContext>(),
&obj_mask, static_cast<T>(0.0));
constant(ctx.template device_context<platform::CPUDeviceContext>(), &tx,
static_cast<T>(0.0));
constant(ctx.template device_context<DeviceContext>(), &tw,
constant(ctx.template device_context<platform::CPUDeviceContext>(), &ty,
static_cast<T>(0.0));
constant(ctx.template device_context<DeviceContext>(), &th,
constant(ctx.template device_context<platform::CPUDeviceContext>(), &tw,
static_cast<T>(0.0));
constant(ctx.template device_context<DeviceContext>(), &tweight,
constant(ctx.template device_context<platform::CPUDeviceContext>(), &th,
static_cast<T>(0.0));
constant(ctx.template device_context<DeviceContext>(), &tconf,
constant(ctx.template device_context<platform::CPUDeviceContext>(),
&tweight, static_cast<T>(0.0));
constant(ctx.template device_context<platform::CPUDeviceContext>(), &tconf,
static_cast<T>(0.0));
constant(ctx.template device_context<DeviceContext>(), &tclass,
constant(ctx.template device_context<platform::CPUDeviceContext>(), &tclass,
static_cast<T>(0.0));
PreProcessGTBox<T>(*gt_box, *gt_label, ignore_thresh, anchors, input_size,
@ -390,7 +382,7 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
}
};
template <typename DeviceContext, typename T>
template <typename T>
class Yolov3LossGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
@ -422,24 +414,24 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
tconf.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tclass.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
math::SetConstant<DeviceContext, T> constant;
constant(ctx.template device_context<DeviceContext>(), &conf_mask,
static_cast<T>(1.0));
constant(ctx.template device_context<DeviceContext>(), &obj_mask,
static_cast<T>(0.0));
constant(ctx.template device_context<DeviceContext>(), &tx,
static_cast<T>(0.0));
constant(ctx.template device_context<DeviceContext>(), &ty,
math::SetConstant<platform::CPUDeviceContext, T> constant;
constant(ctx.template device_context<platform::CPUDeviceContext>(),
&conf_mask, static_cast<T>(1.0));
constant(ctx.template device_context<platform::CPUDeviceContext>(),
&obj_mask, static_cast<T>(0.0));
constant(ctx.template device_context<platform::CPUDeviceContext>(), &tx,
static_cast<T>(0.0));
constant(ctx.template device_context<DeviceContext>(), &tw,
constant(ctx.template device_context<platform::CPUDeviceContext>(), &ty,
static_cast<T>(0.0));
constant(ctx.template device_context<DeviceContext>(), &th,
constant(ctx.template device_context<platform::CPUDeviceContext>(), &tw,
static_cast<T>(0.0));
constant(ctx.template device_context<DeviceContext>(), &tweight,
constant(ctx.template device_context<platform::CPUDeviceContext>(), &th,
static_cast<T>(0.0));
constant(ctx.template device_context<DeviceContext>(), &tconf,
constant(ctx.template device_context<platform::CPUDeviceContext>(),
&tweight, static_cast<T>(0.0));
constant(ctx.template device_context<platform::CPUDeviceContext>(), &tconf,
static_cast<T>(0.0));
constant(ctx.template device_context<DeviceContext>(), &tclass,
constant(ctx.template device_context<platform::CPUDeviceContext>(), &tclass,
static_cast<T>(0.0));
PreProcessGTBox<T>(*gt_box, *gt_label, ignore_thresh, anchors, input_size,

13
python/paddle/fluid/layers/detection.py
Unescape View File

@ -485,19 +485,6 @@ def yolov3_loss(x,
"input_size": input_size,
}
# 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,

5
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
Unescape View File

@ -157,11 +157,6 @@ def YoloV3Loss(x, gtbox, gtlabel, attrs):
loss_obj = sce(pred_conf, tconf, conf_mask)
loss_class = sce(pred_cls, tcls, obj_mask_expand)
# print("python loss_xy: ", loss_x + loss_y)
# print("python loss_wh: ", loss_w + loss_h)
# print("python loss_obj: ", loss_obj)
# print("python loss_class: ", loss_class)
return loss_x + loss_y + loss_w + loss_h + loss_obj + loss_class

Write Preview
Loading…
Cancel
Save