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remove cu file. test=develop

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revert-14398-imperative
dengkaipeng 6 years ago
parent a0284f6fbc
commit 2faa2b4048
7 changed files with 182 additions and 112 deletions
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2
paddle/fluid/API.spec
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@ -183,7 +183,6 @@ paddle.fluid.layers.similarity_focus ArgSpec(args=['input', 'axis', 'indexes', '
paddle.fluid.layers.hash ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None))
paddle.fluid.layers.grid_sampler ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.log_loss ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None))
paddle.fluid.layers.yolov3_loss ArgSpec(args=['x', 'gtbox', 'anchors', 'class_num', 'ignore_thresh', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.add_position_encoding ArgSpec(args=['input', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.bilinear_tensor_product ArgSpec(args=['x', 'y', 'size', 'act', 'name', 'param_attr', 'bias_attr'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
@ -289,6 +288,7 @@ paddle.fluid.layers.generate_proposals ArgSpec(args=['scores', 'bbox_deltas', 'i
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'], 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', 'anchors', 'class_num', 'ignore_thresh', 'lambda_xy', 'lambda_wh', 'lambda_conf_obj', 'lambda_conf_noobj', 'lambda_class', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 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,))

36
paddle/fluid/operators/yolov3_loss_op.cc
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@ -55,7 +55,8 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace());
framework::ToDataType(ctx.Input<Tensor>("X")->type()),
platform::CPUPlace());
}
};
@ -63,8 +64,11 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"The input tensor of bilinear interpolation, "
"This is a 4-D tensor with shape of [N, C, H, W]");
"The input tensor of YOLO v3 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");
AddInput("GTBox",
"The input tensor of ground truth boxes, "
"This is a 3-D tensor with shape of [N, max_box_num, 5], "
@ -84,6 +88,20 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
"it will be parsed pair by pair.");
AddAttr<float>("ignore_thresh",
"The ignore threshold to ignore confidence loss.");
AddAttr<float>("lambda_xy", "The weight of x, y location loss.")
.SetDefault(1.0);
AddAttr<float>("lambda_wh", "The weight of w, h location loss.")
.SetDefault(1.0);
AddAttr<float>(
"lambda_conf_obj",
"The weight of confidence score loss in locations with target object.")
.SetDefault(1.0);
AddAttr<float>("lambda_conf_noobj",
"The weight of confidence score loss in locations without "
"target object.")
.SetDefault(1.0);
AddAttr<float>("lambda_class", "The weight of classification loss.")
.SetDefault(1.0);
AddComment(R"DOC(
This operator generate yolov3 loss by given predict result and ground
truth boxes.
@ -119,6 +137,15 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
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.
Final loss will be represented as follow.
$$
loss = \lambda_{xy} * loss_{xy} + \lambda_{wh} * loss_{wh}
+ \lambda_{conf_obj} * loss_{conf_obj}
+ \lambda_{conf_noobj} * loss_{conf_noobj}
+ \lambda_{class} * loss_{class}
$$
)DOC");
}
};
@ -140,7 +167,8 @@ class Yolov3LossOpGrad : public framework::OperatorWithKernel {
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace());
framework::ToDataType(ctx.Input<Tensor>("X")->type()),
platform::CPUPlace());
}
};

23
paddle/fluid/operators/yolov3_loss_op.cu
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@ -1,23 +0,0 @@
/* Copyright (c) 2016 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. */
#define EIGEN_USE_GPU
#include "paddle/fluid/operators/yolov3_loss_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
yolov3_loss,
ops::Yolov3LossKernel<paddle::platform::CUDADeviceContext, float>);
REGISTER_OP_CUDA_KERNEL(
yolov3_loss_grad,
ops::Yolov3LossGradKernel<paddle::platform::CUDADeviceContext, float>);

43
paddle/fluid/operators/yolov3_loss_op.h
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@ -267,7 +267,9 @@ static void AddAllGradToInputGrad(
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_obj, const Tensor& grad_conf_noobj,
const Tensor& grad_class, const int class_num) {
const Tensor& grad_class, const int class_num, const float lambda_xy,
const float lambda_wh, const float lambda_conf_obj,
const float lambda_conf_noobj, const float lambda_class) {
const int n = pred_x.dims()[0];
const int an_num = pred_x.dims()[1];
const int h = pred_x.dims()[2];
@ -290,25 +292,27 @@ static void AddAllGradToInputGrad(
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;
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 * lambda_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;
grad_t(i, j * attr_num + 2, k, l) = grad_w_t(i, j, k, l) * loss;
grad_t(i, j * attr_num + 3, k, l) = grad_h_t(i, j, k, l) * loss;
(1.0 - pred_y_t(i, j, k, l)) * loss * lambda_xy;
grad_t(i, j * attr_num + 2, k, l) =
grad_w_t(i, j, k, l) * loss * lambda_wh;
grad_t(i, j * attr_num + 3, k, l) =
grad_h_t(i, j, k, l) * loss * lambda_wh;
grad_t(i, j * attr_num + 4, k, l) =
grad_conf_obj_t(i, j, k, l) * pred_conf_t(i, j, k, l) *
(1.0 - pred_conf_t(i, j, k, l)) * loss;
(1.0 - pred_conf_t(i, j, k, l)) * loss * lambda_conf_obj;
grad_t(i, j * attr_num + 4, k, l) +=
grad_conf_noobj_t(i, j, k, l) * pred_conf_t(i, j, k, l) *
(1.0 - pred_conf_t(i, j, k, l)) * loss;
(1.0 - pred_conf_t(i, j, k, l)) * loss * lambda_conf_noobj;
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;
(1.0 - pred_class_t(i, j, k, l, c)) * loss * lambda_class;
}
}
}
@ -326,6 +330,11 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
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 lambda_xy = ctx.Attr<float>("lambda_xy");
float lambda_wh = ctx.Attr<float>("lambda_wh");
float lambda_conf_obj = ctx.Attr<float>("lambda_conf_obj");
float lambda_conf_noobj = ctx.Attr<float>("lambda_conf_noobj");
float lambda_class = ctx.Attr<float>("lambda_class");
const int n = input->dims()[0];
const int h = input->dims()[2];
@ -370,8 +379,10 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
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_x + loss_y + loss_w + loss_h + loss_conf_obj +
loss_conf_noobj + loss_class;
loss_data[0] =
lambda_xy * (loss_x + loss_y) + lambda_wh * (loss_w + loss_h) +
lambda_conf_obj * loss_conf_obj + lambda_conf_noobj * loss_conf_noobj +
lambda_class * loss_class;
}
};
@ -387,6 +398,11 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
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 lambda_xy = ctx.Attr<float>("lambda_xy");
float lambda_wh = ctx.Attr<float>("lambda_wh");
float lambda_conf_obj = ctx.Attr<float>("lambda_conf_obj");
float lambda_conf_noobj = ctx.Attr<float>("lambda_conf_noobj");
float lambda_class = ctx.Attr<float>("lambda_class");
const int n = input->dims()[0];
const int c = input->dims()[1];
@ -448,7 +464,8 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
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_obj, grad_conf_noobj, grad_class, class_num);
grad_w, grad_h, grad_conf_obj, grad_conf_noobj, grad_class, class_num,
lambda_xy, lambda_wh, lambda_conf_obj, lambda_conf_noobj, lambda_class);
}
};

98
python/paddle/fluid/layers/detection.py
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@ -20,6 +20,7 @@ from __future__ import print_function
from .layer_function_generator import generate_layer_fn
from .layer_function_generator import autodoc, templatedoc
from ..layer_helper import LayerHelper
from ..framework import Variable
from . import tensor
from . import nn
from . import ops
@ -45,6 +46,7 @@ __all__ = [
'iou_similarity',
'box_coder',
'polygon_box_transform',
'yolov3_loss',
]
@ -404,6 +406,102 @@ def polygon_box_transform(input, name=None):
return output
@templatedoc(op_type="yolov3_loss")
def yolov3_loss(x,
gtbox,
anchors,
class_num,
ignore_thresh,
lambda_xy=None,
lambda_wh=None,
lambda_conf_obj=None,
lambda_conf_noobj=None,
lambda_class=None,
name=None):
"""
${comment}
Args:
x (Variable): ${x_comment}
gtbox (Variable): groud truth boxes, shoulb be in shape of [N, B, 5],
in the third dimenstion, class_id, x, y, w, h should
be stored and x, y, w, h should be relative valud of
input image.
anchors (list|tuple): ${anchors_comment}
class_num (int): ${class_num_comment}
ignore_thresh (float): ${ignore_thresh_comment}
lambda_xy (float|None): ${lambda_xy_comment}
lambda_wh (float|None): ${lambda_wh_comment}
lambda_conf_obj (float|None): ${lambda_conf_obj_comment}
lambda_conf_noobj (float|None): ${lambda_conf_noobj_comment}
lambda_class (float|None): ${lambda_class_comment}
name (string): the name of yolov3 loss
Returns:
Variable: A 1-D tensor with shape [1], the value of yolov3 loss
Raises:
TypeError: Input x of yolov3_loss must be Variable
TypeError: Input gtbox of yolov3_loss must be Variable"
TypeError: Attr anchors of yolov3_loss must be list or tuple
TypeError: Attr class_num of yolov3_loss must be an integer
TypeError: Attr ignore_thresh of yolov3_loss must be a float number
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[10, 255, 13, 13], dtype='float32')
gtbox = fluid.layers.data(name='gtbox', shape=[10, 6, 5], dtype='float32')
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)
"""
helper = LayerHelper('yolov3_loss', **locals())
if not isinstance(x, Variable):
raise TypeError("Input x of yolov3_loss must be Variable")
if not isinstance(gtbox, Variable):
raise TypeError("Input gtbox 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(class_num, int):
raise TypeError("Attr class_num of yolov3_loss must be an integer")
if not isinstance(ignore_thresh, float):
raise TypeError(
"Attr ignore_thresh of yolov3_loss must be a float number")
if name is None:
loss = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
loss = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
attrs = {
"anchors": anchors,
"class_num": class_num,
"ignore_thresh": ignore_thresh,
}
if lambda_xy is not None and isinstance(lambda_xy, float):
self.attrs['lambda_xy'] = lambda_xy
if lambda_wh is not None and isinstance(lambda_wh, float):
self.attrs['lambda_wh'] = lambda_wh
if lambda_conf_obj is not None and isinstance(lambda_conf_obj, float):
self.attrs['lambda_conf_obj'] = lambda_conf_obj
if lambda_conf_noobj is not None and isinstance(lambda_conf_noobj, float):
self.attrs['lambda_conf_noobj'] = lambda_conf_noobj
if lambda_class is not None and isinstance(lambda_class, float):
self.attrs['lambda_class'] = lambda_class
helper.append_op(
type='yolov3_loss',
inputs={'X': x,
"GTBox": gtbox},
outputs={'Loss': loss},
attrs=attrs)
return loss
@templatedoc()
def detection_map(detect_res,
label,

69
python/paddle/fluid/layers/nn.py
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@ -164,7 +164,6 @@ __all__ = [
'hash',
'grid_sampler',
'log_loss',
'yolov3_loss',
'add_position_encoding',
'bilinear_tensor_product',
]
@ -8244,74 +8243,6 @@ def log_loss(input, label, epsilon=1e-4, name=None):
return loss
@templatedoc(op_type="yolov3_loss")
def yolov3_loss(x, gtbox, anchors, class_num, ignore_thresh, name=None):
"""
${comment}
Args:
x (Variable): ${x_comment}
gtbox (Variable): groud truth boxes, shoulb be in shape of [N, B, 5],
in the third dimenstion, class_id, x, y, w, h should
be stored and x, y, w, h should be relative valud of
input image.
anchors (list|tuple): ${anchors_comment}
class_num (int): ${class_num_comment}
ignore_thresh (float): ${ignore_thresh_comment}
name (string): the name of yolov3 loss
Returns:
Variable: A 1-D tensor with shape [1], the value of yolov3 loss
Raises:
TypeError: Input x of yolov3_loss must be Variable
TypeError: Input gtbox of yolov3_loss must be Variable"
TypeError: Attr anchors of yolov3_loss must be list or tuple
TypeError: Attr class_num of yolov3_loss must be an integer
TypeError: Attr ignore_thresh of yolov3_loss must be a float number
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[10, 255, 13, 13], dtype='float32')
gtbox = fluid.layers.data(name='gtbox', shape=[10, 6, 5], dtype='float32')
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)
"""
helper = LayerHelper('yolov3_loss', **locals())
if not isinstance(x, Variable):
raise TypeError("Input x of yolov3_loss must be Variable")
if not isinstance(gtbox, Variable):
raise TypeError("Input gtbox 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(class_num, int):
raise TypeError("Attr class_num of yolov3_loss must be an integer")
if not isinstance(ignore_thresh, float):
raise TypeError(
"Attr ignore_thresh of yolov3_loss must be a float number")
if name is None:
loss = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
loss = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
helper.append_op(
type='yolov3_loss',
inputs={'X': x,
"GTBox": gtbox},
outputs={'Loss': loss},
attrs={
"anchors": anchors,
"class_num": class_num,
"ignore_thresh": ignore_thresh,
})
return loss
def add_position_encoding(input, alpha, beta, name=None):
"""
**Add Position Encoding Layer**

23
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
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@ -148,11 +148,20 @@ def YoloV3Loss(x, gtbox, attrs):
loss_class = bce(pred_cls * obj_mask_expand, tcls * obj_mask_expand,
obj_mask_expand)
return loss_x + loss_y + loss_w + loss_h + loss_conf_obj + loss_conf_noobj + loss_class
return attrs['lambda_xy'] * (loss_x + loss_y) \
+ attrs['lambda_wh'] * (loss_w + loss_h) \
+ attrs['lambda_conf_obj'] * loss_conf_obj \
+ attrs['lambda_conf_noobj'] * loss_conf_noobj \
+ attrs['lambda_class'] * loss_class
class TestYolov3LossOp(OpTest):
def setUp(self):
self.lambda_xy = 1.0
self.lambda_wh = 1.0
self.lambda_conf_obj = 1.0
self.lambda_conf_noobj = 1.0
self.lambda_class = 1.0
self.initTestCase()
self.op_type = 'yolov3_loss'
x = np.random.random(size=self.x_shape).astype('float32')
@ -164,6 +173,11 @@ class TestYolov3LossOp(OpTest):
"anchors": self.anchors,
"class_num": self.class_num,
"ignore_thresh": self.ignore_thresh,
"lambda_xy": self.lambda_xy,
"lambda_wh": self.lambda_wh,
"lambda_conf_obj": self.lambda_conf_obj,
"lambda_conf_noobj": self.lambda_conf_noobj,
"lambda_class": self.lambda_class,
}
self.inputs = {'X': x, 'GTBox': gtbox}
@ -182,7 +196,7 @@ class TestYolov3LossOp(OpTest):
place, ['X'],
'Loss',
no_grad_set=set("GTBox"),
max_relative_error=0.1)
max_relative_error=0.06)
def initTestCase(self):
self.anchors = [10, 13, 12, 12]
@ -190,6 +204,11 @@ class TestYolov3LossOp(OpTest):
self.ignore_thresh = 0.5
self.x_shape = (5, len(self.anchors) // 2 * (5 + self.class_num), 7, 7)
self.gtbox_shape = (5, 5, 5)
self.lambda_xy = 2.5
self.lambda_wh = 0.8
self.lambda_conf_obj = 1.5
self.lambda_conf_noobj = 0.5
self.lambda_class = 1.2
if __name__ == "__main__":

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