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86 lines
3.0 KiB
86 lines
3.0 KiB
/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include <algorithm> // for max
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/math.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename DeviceContext, typename T>
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class BCELossOpKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* x = ctx.Input<Tensor>("X");
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auto* labels = ctx.Input<Tensor>("Label");
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auto* out = ctx.Output<Tensor>("Out");
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auto x_data = x->data<T>();
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auto label_data = labels->data<T>();
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auto out_data = out->mutable_data<T>(ctx.GetPlace());
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int x_numel = x->numel();
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// out = -(label * ln(x) + (1 - label) * ln(1 - x)) = (label - 1) * ln(1 -
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// x) - label * ln(x)
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for (int i = 0; i < x_numel; ++i) {
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PADDLE_ENFORCE_GE(
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x_data[i], static_cast<T>(0),
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platform::errors::InvalidArgument(
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"Illegal input, input must be greater than or equal to 0"));
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PADDLE_ENFORCE_LE(
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x_data[i], static_cast<T>(1),
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platform::errors::InvalidArgument(
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"Illegal input, input must be less than or equal to 1"));
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out_data[i] =
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(label_data[i] - static_cast<T>(1)) *
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std::max(real_log(static_cast<T>(1) - x_data[i]), (T)(-100)) -
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label_data[i] * std::max(real_log(x_data[i]), (T)(-100));
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}
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}
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};
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template <typename DeviceContext, typename T>
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class BCELossGradOpKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* x = ctx.Input<Tensor>("X");
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auto* labels = ctx.Input<Tensor>("Label");
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auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
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auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
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auto dx_data = dx->mutable_data<T>(ctx.GetPlace());
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auto dout_data = dout->data<T>();
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auto x_data = x->data<T>();
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auto label_data = labels->data<T>();
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int x_numel = x->numel();
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// dx = dout * ((x - label)/(x - x^2))
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for (int i = 0; i < x_numel; ++i) {
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dx_data[i] =
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dout_data[i] * ((x_data[i] - label_data[i]) /
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std::max((static_cast<T>(1) - x_data[i]) * x_data[i],
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static_cast<T>(1e-12)));
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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