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117 lines
3.8 KiB
117 lines
3.8 KiB
/* Copyright (c) 2016 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 "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/platform/for_range.h"
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template <typename T>
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inline HOSTDEVICE T IOUSimilarity(T xmin1, T ymin1, T xmax1, T ymax1, T xmin2,
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T ymin2, T xmax2, T ymax2, bool normalized,
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T eps) {
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constexpr T zero = static_cast<T>(0);
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T area1;
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T area2;
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if (!normalized) {
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area1 = (ymax1 - ymin1 + 1) * (xmax1 - xmin1 + 1);
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area2 = (ymax2 - ymin2 + 1) * (xmax2 - xmin2 + 1);
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} else {
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area1 = (ymax1 - ymin1) * (xmax1 - xmin1);
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area2 = (ymax2 - ymin2) * (xmax2 - xmin2);
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}
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T inter_xmax = xmax1 > xmax2 ? xmax2 : xmax1;
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T inter_ymax = ymax1 > ymax2 ? ymax2 : ymax1;
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T inter_xmin = xmin1 > xmin2 ? xmin1 : xmin2;
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T inter_ymin = ymin1 > ymin2 ? ymin1 : ymin2;
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T inter_height = inter_ymax - inter_ymin;
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T inter_width = inter_xmax - inter_xmin;
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if (!normalized) {
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inter_height = inter_height + 1;
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inter_width = inter_width + 1;
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}
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inter_height = inter_height > zero ? inter_height : zero;
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inter_width = inter_width > zero ? inter_width : zero;
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T inter_area = inter_width * inter_height;
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T union_area = area1 + area2 - inter_area + eps;
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T sim_score = inter_area / union_area;
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return sim_score;
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}
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template <typename T>
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struct IOUSimilarityFunctor {
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IOUSimilarityFunctor(const T* x, const T* y, T* z, int cols, bool normalized,
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T eps)
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: x_(x),
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y_(y),
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z_(z),
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cols_(static_cast<size_t>(cols)),
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normalized_(normalized),
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eps_(eps) {}
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inline HOSTDEVICE void operator()(size_t tid) const {
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size_t row_id = tid / cols_;
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size_t col_id = tid % cols_;
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T x_min1 = x_[row_id * 4];
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T y_min1 = x_[row_id * 4 + 1];
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T x_max1 = x_[row_id * 4 + 2];
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T y_max1 = x_[row_id * 4 + 3];
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T x_min2 = y_[col_id * 4];
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T y_min2 = y_[col_id * 4 + 1];
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T x_max2 = y_[col_id * 4 + 2];
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T y_max2 = y_[col_id * 4 + 3];
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T sim = IOUSimilarity(x_min1, y_min1, x_max1, y_max1, x_min2, y_min2,
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x_max2, y_max2, normalized_, eps_);
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z_[row_id * cols_ + col_id] = sim;
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}
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const T* x_;
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const T* y_;
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T* z_;
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const size_t cols_;
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bool normalized_;
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T eps_;
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};
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class IOUSimilarityKernel : 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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const framework::LoDTensor* in_x = ctx.Input<framework::LoDTensor>("X");
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const framework::Tensor* in_y = ctx.Input<framework::Tensor>("Y");
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bool normalized = ctx.Attr<bool>("box_normalized");
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framework::LoDTensor* out = ctx.Output<framework::LoDTensor>("Out");
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int x_n = in_x->dims()[0];
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int y_n = in_y->dims()[0];
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T eps = static_cast<T>(1e-10);
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IOUSimilarityFunctor<T> functor(in_x->data<T>(), in_y->data<T>(),
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out->mutable_data<T>(ctx.GetPlace()), y_n,
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normalized, eps);
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platform::ForRange<DeviceContext> for_range(
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static_cast<const DeviceContext&>(ctx.device_context()), x_n * y_n);
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for_range(functor);
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}
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}; // namespace operators
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} // namespace operators
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} // namespace paddle
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