You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
233 lines
10 KiB
233 lines
10 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License. */
|
|
|
|
#include "paddle/fluid/operators/positive_negative_pair_op.h"
|
|
#include "paddle/fluid/platform/enforce.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
class PositiveNegativePairOp : public framework::OperatorWithKernel {
|
|
public:
|
|
using framework::OperatorWithKernel::OperatorWithKernel;
|
|
|
|
void InferShape(framework::InferShapeContext *ctx) const override {
|
|
OP_INOUT_CHECK(ctx->HasInput("Score"), "Input", "Score",
|
|
"positive_negative_pair");
|
|
OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label",
|
|
"positive_negative_pair");
|
|
OP_INOUT_CHECK(ctx->HasInput("QueryID"), "Input", "QueryID",
|
|
"positive_negative_pair");
|
|
OP_INOUT_CHECK(ctx->HasOutput("PositivePair"), "Output", "PositivePair",
|
|
"positive_negative_pair");
|
|
OP_INOUT_CHECK(ctx->HasOutput("NegativePair"), "Output", "NegativePair",
|
|
"positive_negative_pair");
|
|
OP_INOUT_CHECK(ctx->HasOutput("NeutralPair"), "Output", "NeutralPair",
|
|
"positive_negative_pair");
|
|
|
|
auto scalar_dim = framework::make_ddim({1});
|
|
if (ctx->HasInput("AccumulatePositivePair") ||
|
|
ctx->HasInput("AccumulateNegativePair") ||
|
|
ctx->HasInput("AccumulateNeutralPair")) {
|
|
PADDLE_ENFORCE(ctx->HasInput("AccumulatePositivePair") &&
|
|
ctx->HasInput("AccumulateNegativePair") &&
|
|
ctx->HasInput("AccumulateNeutralPair"),
|
|
"All optional inputs(AccumulatePositivePair, "
|
|
"AccumulateNegativePair, AccumulateNeutralPair) of "
|
|
"PositiveNegativePairOp are required if one of them is "
|
|
"specified.");
|
|
PADDLE_ENFORCE_EQ(
|
|
ctx->GetInputDim("AccumulatePositivePair"), scalar_dim,
|
|
platform::errors::InvalidArgument(
|
|
"Shape of Input(AccumulatePositivePair) should be [1]. Received "
|
|
"shape of Input(AccumulatePositivePair): [%s].",
|
|
ctx->GetInputDim("AccumulatePositivePair")));
|
|
PADDLE_ENFORCE_EQ(
|
|
ctx->GetInputDim("AccumulateNegativePair"), scalar_dim,
|
|
platform::errors::InvalidArgument(
|
|
"Shape of Input(AccumulateNegativePair) should be [1]. Received "
|
|
"shape of Input(AccumulateNegativePair): [%s].",
|
|
ctx->GetInputDim("AccumulateNegativePair")));
|
|
PADDLE_ENFORCE_EQ(
|
|
ctx->GetInputDim("AccumulateNeutralPair"), scalar_dim,
|
|
platform::errors::InvalidArgument(
|
|
"Shape of Input(AccumulateNeutralPair) should be [1]. Received "
|
|
"shape of Input(AccumulateNeutralPair): [%s].",
|
|
ctx->GetInputDim("AccumulateNeutralPair")));
|
|
}
|
|
|
|
auto score_dim = ctx->GetInputDim("Score");
|
|
auto label_dim = ctx->GetInputDim("Label");
|
|
auto query_dim = ctx->GetInputDim("QueryID");
|
|
PADDLE_ENFORCE_EQ(score_dim.size(), 2,
|
|
platform::errors::InvalidArgument(
|
|
"Score should be a 2-D tensor. Received shape of "
|
|
"Input(Score): [%s].",
|
|
score_dim));
|
|
PADDLE_ENFORCE_EQ(label_dim.size(), 2,
|
|
platform::errors::InvalidArgument(
|
|
"Label should be a 2-D tensor. Received shape of "
|
|
"Input(Label): [%s].",
|
|
label_dim));
|
|
|
|
if (ctx->IsRuntime() ||
|
|
(score_dim[0] > 0 && label_dim[0] > 0 && query_dim[0] > 0)) {
|
|
PADDLE_ENFORCE_EQ(
|
|
label_dim[0], score_dim[0],
|
|
platform::errors::InvalidArgument(
|
|
"Input(Score) and Input(Label) should have the same "
|
|
"height (batch size). Received: the shape of Input(Score) is "
|
|
"[%s], while the shape of Input(Label) is [%s]. The first "
|
|
"dimensions of them are different.",
|
|
label_dim, score_dim));
|
|
|
|
PADDLE_ENFORCE_EQ(
|
|
label_dim[1], 1,
|
|
platform::errors::InvalidArgument(
|
|
"The width of Label should be 1, i.e. each item should "
|
|
"have a scalar label. Received shape of Input(Label) is [%s]. "
|
|
"The second dimension of it is %d, while the expected is %d.",
|
|
label_dim, label_dim[1], 1));
|
|
|
|
PADDLE_ENFORCE_EQ(
|
|
query_dim, label_dim,
|
|
platform::errors::InvalidArgument(
|
|
"Input(QueryID) should have the same shape as Input(Label). "
|
|
"Received: the shape of Input(QueryID) is [%s], "
|
|
"while the shape of Input(Label) is [%s].",
|
|
query_dim, label_dim));
|
|
|
|
if (ctx->HasInput("Weight")) {
|
|
PADDLE_ENFORCE_EQ(
|
|
ctx->GetInputDim("Weight"), label_dim,
|
|
platform::errors::InvalidArgument(
|
|
"Input(Weight) should have the same shape as Input(Label). "
|
|
"Received: the shape of Input(Weight) is [%s] while the shape "
|
|
"of Input(Label) is [%s].",
|
|
ctx->GetInputDim("Weight"), label_dim));
|
|
}
|
|
|
|
int column = ctx->Attrs().Get<int>("column");
|
|
auto depth = score_dim[1];
|
|
PADDLE_ENFORCE_LT(
|
|
column, depth,
|
|
platform::errors::OutOfRange(
|
|
"Attr(column) should be less than depth(the second "
|
|
"dimension of Input(Score)). Recieved Attr(column): %d, while "
|
|
"depth is %d.",
|
|
column, depth));
|
|
PADDLE_ENFORCE_GE(
|
|
column, -depth,
|
|
platform::errors::OutOfRange(
|
|
"Attr(column) should be greater than equal to negative "
|
|
"depth, i.e. the second dimension of Input(Score). "
|
|
"Recieved Attr(column): %d, while negative depth is %d.",
|
|
column, -depth));
|
|
}
|
|
|
|
ctx->SetOutputDim("PositivePair", scalar_dim);
|
|
ctx->SetOutputDim("NegativePair", scalar_dim);
|
|
ctx->SetOutputDim("NeutralPair", scalar_dim);
|
|
}
|
|
|
|
protected:
|
|
framework::OpKernelType GetExpectedKernelType(
|
|
const framework::ExecutionContext &ctx) const override {
|
|
return framework::OpKernelType(
|
|
OperatorWithKernel::IndicateVarDataType(ctx, "Score"),
|
|
ctx.device_context());
|
|
}
|
|
};
|
|
|
|
class PositiveNegativePairOpMaker : public framework::OpProtoAndCheckerMaker {
|
|
public:
|
|
void Make() override {
|
|
AddInput("Score",
|
|
"(Tensor, float) Model Score on an item (with "
|
|
"respect to QueryID). It's a 2-D tensor with shape [batch_size, "
|
|
"depth], where the column specified by the attribute \"column\" "
|
|
"is used as item score.");
|
|
AddInput("Label",
|
|
"(Tensor, float) Label of an item (with repsect to "
|
|
"QueryId). It's a 2-D tensor with shape [batch_size, 1].");
|
|
AddInput("QueryID",
|
|
"(Tensor, int64) Query ID that indicates the context. Its shape "
|
|
"should be the same as Label.");
|
|
AddInput(
|
|
"AccumulatePositivePair",
|
|
"(float) Optional. The accumulated number of positive pairs over a "
|
|
"stream of data. If provided, the output PositivePair will be "
|
|
"initialized with this number rather than 0. it won't be modified "
|
|
"in place.")
|
|
.AsDispensable();
|
|
AddInput(
|
|
"AccumulateNegativePair",
|
|
"(float) Optional. The accumulated number of negative pairs over a "
|
|
"stream of data. If provided, the output NegativePair will be "
|
|
"initialized with this number rather than 0. it won't be modified "
|
|
"in place.")
|
|
.AsDispensable();
|
|
AddInput("AccumulateNeutralPair",
|
|
"(float) Optional. The accumulated number of neutral pairs over a "
|
|
"stream of data. If provided, the output NeutralPair will be "
|
|
"initialized with this number rather than 0. it won't be modified "
|
|
"in place.")
|
|
.AsDispensable();
|
|
AddInput("Weight",
|
|
"(float) Optional. Weight of current item. If specified, its "
|
|
"shape should be the same as Label, and the meaning of the output "
|
|
"changes from numbers of pairs to the total sum of pairs' "
|
|
"weights. Weight of a pair of items is the average of their "
|
|
"weights.")
|
|
.AsDispensable();
|
|
AddOutput("PositivePair",
|
|
"(float) Number of positive pairs, i.e. the pairs of "
|
|
"items that are ranked correctly.");
|
|
AddOutput("NegativePair",
|
|
"(float) Number of negative pairs, i.e. the pairs of "
|
|
"items that are ranked incorrectly.");
|
|
AddOutput("NeutralPair",
|
|
"(float) Number of neutral pairs, i.e. the pairs of items "
|
|
"that have the same score.")
|
|
.AsDispensable();
|
|
AddAttr<int>(
|
|
"column",
|
|
"(int, default -1) The column position of Score used to rank items in "
|
|
"descending order. It must be in the range of [-rank(Score), "
|
|
"rank(Score)). "
|
|
"If `dim < 0`, the dim to reduce is `rank + dim`. "
|
|
"Noting that reducing on the first dim will make the LoD info lost.")
|
|
.SetDefault(0);
|
|
AddComment(R"DOC(
|
|
PositiveNegativePairOp can be used to evaluate Learning To Rank(LTR) model's
|
|
performance.
|
|
|
|
Within some context, e.g. the "query", a LTR model generates scores for a list
|
|
of items, which gives a partial order of the items. PositiveNegativePairOp
|
|
takes a list of reference rank order (Input("Label")) and the model generated
|
|
scores (Input(Score)) as inputs and counts the pairs that ranked correctly
|
|
and incorrectly.
|
|
)DOC");
|
|
}
|
|
};
|
|
|
|
} // namespace operators
|
|
} // namespace paddle
|
|
|
|
namespace ops = paddle::operators;
|
|
REGISTER_OP_WITHOUT_GRADIENT(positive_negative_pair,
|
|
ops::PositiveNegativePairOp,
|
|
ops::PositiveNegativePairOpMaker);
|
|
REGISTER_OP_CPU_KERNEL(
|
|
positive_negative_pair,
|
|
ops::PositiveNegativePairKernel<paddle::platform::CPUPlace, float>,
|
|
ops::PositiveNegativePairKernel<paddle::platform::CPUPlace, double>);
|