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Paddle
提交 8218e301 编写于 作者: D dengkaipeng
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add gtscore. test=develop

上级 3c08f620
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Showing with 57 addition and 23 deletion
paddle/fluid/API.spec
浏览文件 @ 8218e301
......@@ -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'], varargs=None, keywords=None, defaults=('encode_center_size', True, None))
paddle.fluid.layers.polygon_box_transform ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.yolov3_loss ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.yolov3_loss ArgSpec(args=['x', 'gtbox', 'gtlabel', 'gtscore', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample', '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/yolov3_loss_op.cc
浏览文件 @ 8218e301
......@@ -27,6 +27,8 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
"Input(GTBox) of Yolov3LossOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("GTLabel"),
"Input(GTLabel) of Yolov3LossOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("GTScore"),
"Input(GTScore) of Yolov3LossOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Loss"),
"Output(Loss) of Yolov3LossOp should not be null.");
PADDLE_ENFORCE(
......@@ -38,6 +40,7 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
auto dim_x = ctx->GetInputDim("X");
auto dim_gtbox = ctx->GetInputDim("GTBox");
auto dim_gtlabel = ctx->GetInputDim("GTLabel");
auto dim_gtscore = ctx->GetInputDim("GTScore");
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");
......@@ -54,11 +57,17 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
"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],
"Input(GTBox) and Input(GTLabel) dim[1] should be same");
PADDLE_ENFORCE_EQ(dim_gtscore.size(), 2,
"Input(GTScore) should be a 2-D tensor");
PADDLE_ENFORCE_EQ(dim_gtscore[0], dim_gtbox[0],
"Input(GTBox) and Input(GTScore) dim[0] should be same");
PADDLE_ENFORCE_EQ(dim_gtscore[1], dim_gtbox[1],
"Input(GTBox) and Input(GTScore) dim[1] should be same");
PADDLE_ENFORCE_GT(anchors.size(), 0,
"Attr(anchors) length should be greater then 0.");
PADDLE_ENFORCE_EQ(anchors.size() % 2, 0,
......@@ -109,8 +118,13 @@ 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.");
AddInput("GTScore",
"The score of GTLabel, This is a 2-D tensor in same shape "
"GTLabel, and score values should in range (0, 1). This "
"input is for GTLabel score can be not 1.0 in image mixup "
"augmentation.");
AddOutput("Loss",
"The output yolov3 loss tensor, "
"This is a 1-D tensor with shape of [N]");
......@@ -228,6 +242,7 @@ class Yolov3LossGradMaker : public framework::SingleGradOpDescMaker {
op->SetInput("X", Input("X"));
op->SetInput("GTBox", Input("GTBox"));
op->SetInput("GTLabel", Input("GTLabel"));
op->SetInput("GTScore", Input("GTScore"));
op->SetInput(framework::GradVarName("Loss"), OutputGrad("Loss"));
op->SetInput("ObjectnessMask", Output("ObjectnessMask"));
op->SetInput("GTMatchMask", Output("GTMatchMask"));
......@@ -237,6 +252,7 @@ class Yolov3LossGradMaker : public framework::SingleGradOpDescMaker {
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetOutput(framework::GradVarName("GTBox"), {});
op->SetOutput(framework::GradVarName("GTLabel"), {});
op->SetOutput(framework::GradVarName("GTScore"), {});
return std::unique_ptr<framework::OpDesc>(op);
}
};
......
paddle/fluid/operators/yolov3_loss_op.h
浏览文件 @ 8218e301
......@@ -156,25 +156,25 @@ static void CalcBoxLocationLossGrad(T* input_grad, const T loss, const T* input,
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) {
const int label, const T score,
const int class_num, const int stride) {
for (int i = 0; i < class_num; i++) {
T pred = input[index + i * stride] < -0.5 ? input[index + i * stride]
: 1.0 / class_num;
loss[0] += SCE<T>(pred, (i == label) ? 1.0 : 0.0);
loss[0] += SCE<T>(pred, (i == label) ? score : 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) {
const int label, const T score,
const int class_num, const int stride) {
for (int i = 0; i < class_num; i++) {
T pred = input[index + i * stride] < -0.5 ? input[index + i * stride]
: 1.0 / class_num;
input_grad[index + i * stride] =
SCEGrad<T>(pred, (i == label) ? 1.0 : 0.0) * loss;
SCEGrad<T>(pred, (i == label) ? score : 0.0) * loss;
}
}
......@@ -246,6 +246,7 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
auto* input = ctx.Input<Tensor>("X");
auto* gt_box = ctx.Input<Tensor>("GTBox");
auto* gt_label = ctx.Input<Tensor>("GTLabel");
auto* gt_score = ctx.Input<Tensor>("GTScore");
auto* loss = ctx.Output<Tensor>("Loss");
auto* objness_mask = ctx.Output<Tensor>("ObjectnessMask");
auto* gt_match_mask = ctx.Output<Tensor>("GTMatchMask");
......@@ -269,6 +270,7 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
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* gt_score_data = gt_score->data<T>();
T* loss_data = loss->mutable_data<T>({n}, ctx.GetPlace());
memset(loss_data, 0, loss->numel() * sizeof(T));
int* obj_mask_data =
......@@ -358,9 +360,10 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
obj_mask_data[obj_idx] = 1;
int label = gt_label_data[i * b + t];
T score = gt_score_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,
CalcLabelLoss<T>(loss_data + i, input_data, label_idx, label, score,
class_num, stride);
}
}
......@@ -378,6 +381,7 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
auto* input = ctx.Input<Tensor>("X");
auto* gt_box = ctx.Input<Tensor>("GTBox");
auto* gt_label = ctx.Input<Tensor>("GTLabel");
auto* gt_score = ctx.Input<Tensor>("GTScore");
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");
......@@ -401,6 +405,7 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
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* gt_score_data = gt_score->data<T>();
const T* loss_grad_data = loss_grad->data<T>();
const int* obj_mask_data = objness_mask->data<int>();
const int* gt_match_mask_data = gt_match_mask->data<int>();
......@@ -423,10 +428,11 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
anchor_mask[mask_idx], box_idx, gi, gj, h, input_size, stride);
int label = gt_label_data[i * b + t];
T score = gt_score_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);
label_idx, label, score, class_num, stride);
}
}
}
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 8218e301
......@@ -412,6 +412,7 @@ def polygon_box_transform(input, name=None):
def yolov3_loss(x,
gtbox,
gtlabel,
gtscore,
anchors,
anchor_mask,
class_num,
......@@ -428,8 +429,10 @@ 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].
gtscore (Variable): score of gtlabel, should be in same shape with gtlabel
and score value in range (0, 1).
anchors (list|tuple): ${anchors_comment}
anchor_mask (list|tuple): ${anchor_mask_comment}
class_num (int): ${class_num_comment}
......@@ -444,6 +447,7 @@ def yolov3_loss(x,
TypeError: Input x of yolov3_loss must be Variable
TypeError: Input gtbox of yolov3_loss must be Variable"
TypeError: Input gtlabel of yolov3_loss must be Variable"
TypeError: Input gtscore 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
......@@ -467,6 +471,8 @@ def yolov3_loss(x,
raise TypeError("Input gtbox of yolov3_loss must be Variable")
if not isinstance(gtlabel, Variable):
raise TypeError("Input gtlabel of yolov3_loss must be Variable")
if not isinstance(gtscore, Variable):
raise TypeError("Input gtscore 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):
......@@ -496,9 +502,12 @@ def yolov3_loss(x,
helper.append_op(
type='yolov3_loss',
inputs={"X": x,
inputs={
"X": x,
"GTBox": gtbox,
"GTLabel": gtlabel},
"GTLabel": gtlabel,
"GTScore": gtscore
},
outputs={
'Loss': loss,
'ObjectnessMask': objectness_mask,
......
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
浏览文件 @ 8218e301
......@@ -66,7 +66,7 @@ def batch_xywh_box_iou(box1, box2):
return inter_area / union
def YOLOv3Loss(x, gtbox, gtlabel, attrs):
def YOLOv3Loss(x, gtbox, gtlabel, gtscore, attrs):
n, c, h, w = x.shape
b = gtbox.shape[1]
anchors = attrs['anchors']
......@@ -148,7 +148,7 @@ def YOLOv3Loss(x, gtbox, gtlabel, attrs):
for label_idx in range(class_num):
loss[i] += sce(x[i, an_idx, gj, gi, 5 + label_idx],
int(label_idx == gtlabel[i, j]))
int(label_idx == gtlabel[i, j]) * gtscore[i, j])
for j in range(mask_num * h * w):
if objness[i, j] >= 0:
......@@ -165,6 +165,7 @@ class TestYolov3LossOp(OpTest):
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])
gtscore = np.random.random(self.gtbox_shape[:2]).astype('float32')
gtmask = np.random.randint(0, 2, self.gtbox_shape[:2])
gtbox = gtbox * gtmask[:, :, np.newaxis]
gtlabel = gtlabel * gtmask
......@@ -180,9 +181,11 @@ class TestYolov3LossOp(OpTest):
self.inputs = {
'X': x,
'GTBox': gtbox.astype('float32'),
'GTLabel': gtlabel.astype('int32')
'GTLabel': gtlabel.astype('int32'),
'GTScore': gtscore.astype('float32')
}
loss, objness, gt_matches = YOLOv3Loss(x, gtbox, gtlabel, self.attrs)
loss, objness, gt_matches = YOLOv3Loss(x, gtbox, gtlabel, gtscore,
self.attrs)
self.outputs = {
'Loss': loss,
'ObjectnessMask': objness,
......@@ -198,8 +201,8 @@ class TestYolov3LossOp(OpTest):
self.check_grad_with_place(
place, ['X'],
'Loss',
no_grad_set=set(["GTBox", "GTLabel"]),
max_relative_error=0.15)
no_grad_set=set(["GTBox", "GTLabel", "GTScore"]),
max_relative_error=0.2)
def initTestCase(self):
self.anchors = [
......@@ -207,11 +210,11 @@ class TestYolov3LossOp(OpTest):
373, 326
]
self.anchor_mask = [0, 1, 2]
self.class_num = 5
self.class_num = 10
self.ignore_thresh = 0.7
self.downsample = 32
self.x_shape = (3, len(self.anchor_mask) * (5 + self.class_num), 5, 5)
self.gtbox_shape = (3, 5, 4)
self.gtbox_shape = (3, 10, 4)
if __name__ == "__main__":
......
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