jerrywgz
7 years ago
parent
5fc305220c
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/* Copyright (c) 2018 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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#include "paddle/fluid/operators/roi_align_op.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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using LoDTensor = framework::LoDTensor;
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class ROIAlignOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of ROIAlignOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("ROIs"),
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"Input(ROIs) of ROIAlignOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of ROIAlignOp should not be null.");
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auto input_dims = ctx->GetInputDim("X");
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auto rois_dims = ctx->GetInputDim("ROIs");
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PADDLE_ENFORCE(input_dims.size() == 4,
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"The format of input tensor is NCHW.");
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PADDLE_ENFORCE(rois_dims.size() == 2,
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"ROIs should be a 2-D LoDTensor of shape (num_rois, 4)"
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"given as [[x1, y1, x2, y2], …].");
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PADDLE_ENFORCE(rois_dims[1] == 4,
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"ROIs should be a 2-D LoDTensor of shape (num_rois, 4)"
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"given as [[x1, y1, x2, y2], …].");
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int pooled_height = ctx->Attrs().Get<int>("pooled_height");
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int pooled_width = ctx->Attrs().Get<int>("pooled_width");
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float spatial_scale = ctx->Attrs().Get<float>("spatial_scale");
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PADDLE_ENFORCE_GT(pooled_height, 0,
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"The pooled output height must greater than 0");
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PADDLE_ENFORCE_GT(pooled_width, 0,
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"The pooled output width must greater than 0");
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PADDLE_ENFORCE_GT(spatial_scale, 0.0f,
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"The spatial scale must greater than 0");
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auto out_dims = input_dims;
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out_dims[0] = rois_dims[0];
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out_dims[1] = input_dims[1];
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out_dims[2] = pooled_height;
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out_dims[3] = pooled_width;
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ctx->SetOutputDim("Out", out_dims);
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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return framework::OpKernelType(
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framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
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ctx.device_context());
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}
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};
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class ROIAlignGradOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
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"The GRAD@Out of ROIAlignGradOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName("X")),
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"The GRAD@X of ROIAlignGradOp should not be null.");
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ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("X"));
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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return framework::OpKernelType(
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framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
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ctx.device_context());
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}
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};
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class ROIAlignOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X",
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"(Tensor), "
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"the input of ROIAlignOp. "
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"The format of input tensor is NCHW. Where N is batch size, "
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"C is the number of input channels, "
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"H is the height of the feature, and "
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"W is the width of the feature.");
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AddInput("ROIs",
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"(LoDTensor), "
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"ROIs (Regions of Interest) to pool over. "
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"should be a 2-D LoDTensor of shape (num_rois, 4)"
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"given as [[x1, y1, x2, y2], …]. "
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"Where batch_id is the id of the data, "
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"(x1, y1) is the top left coordinates, and "
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"(x2, y2) is the bottom right coordinates.");
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AddOutput("Out",
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"(Tensor), "
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"The output of ROIAlignOp is a 4-D tensor with shape "
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"(num_rois, channels, pooled_h, pooled_w).");
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AddAttr<float>("spatial_scale",
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"(float, default 1.0), "
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"Multiplicative spatial scale factor "
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"to translate ROI coords from their input scale "
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"to the scale used when pooling.")
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.SetDefault(1.0);
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AddAttr<int>("pooled_height",
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"(int, default 1), "
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"The pooled output height.")
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.SetDefault(1);
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AddAttr<int>("pooled_width",
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"(int, default 1), "
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"The pooled output width.")
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.SetDefault(1);
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AddAttr<int>("sampling_ratio",
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"(int,default -1),"
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"number of sampling points in the interpolation grid"
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"If <=0, then grid points are adaptive to roi_width "
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"and pooled_w, likewise for height")
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.SetDefault(-1);
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AddComment(R"DOC(
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)DOC");
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(roi_align, ops::ROIAlignOp, ops::ROIAlignOpMaker,
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paddle::framework::DefaultGradOpDescMaker<true>);
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REGISTER_OPERATOR(roi_align_grad, ops::ROIAlignGradOp);
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REGISTER_OP_CPU_KERNEL(
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roi_align,
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ops::CPUROIAlignOpKernel<paddle::platform::CPUDeviceContext, float>,
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ops::CPUROIAlignOpKernel<paddle::platform::CPUDeviceContext, double>);
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REGISTER_OP_CPU_KERNEL(
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roi_align_grad,
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ops::CPUROIAlignGradOpKernel<paddle::platform::CPUDeviceContext, float>,
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ops::CPUROIAlignGradOpKernel<paddle::platform::CPUDeviceContext, double>);
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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from __future__ import print_function
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import unittest
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import numpy as np
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import math
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import sys
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from op_test import OpTest
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class TestROIAlignOp(OpTest):
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def set_data(self):
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self.init_test_case()
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self.make_rois()
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self.calc_roi_align()
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self.inputs = {'X': self.x, 'ROIs': (self.rois[:, 1:5], self.rois_lod)}
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self.attrs = {
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'spatial_scale': self.spatial_scale,
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'pooled_height': self.pooled_height,
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'pooled_width': self.pooled_width,
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'sampling_ratio': self.sampling_ratio
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}
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self.outputs = {'Out': self.out_data}
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def init_test_case(self):
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self.batch_size = 1
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self.channels = 3
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self.height = 8
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self.width = 6
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# n, c, h, w
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self.x_dim = (self.batch_size, self.channels, self.height, self.width)
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self.spatial_scale = 1.0 / 1.0
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self.pooled_height = 2
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self.pooled_width = 2
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self.sampling_ratio = 2
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self.x = np.random.random(self.x_dim).astype('float32')
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def pre_calc(self, x_i, roi_xmin, roi_ymin, roi_bin_grid_h, roi_bin_grid_w,
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bin_size_h, bin_size_w):
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count = roi_bin_grid_h * roi_bin_grid_w
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bilinear_pos = np.zeros(
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[self.channels, self.pooled_height, self.pooled_width, count, 4],
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np.int32)
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bilinear_w = np.zeros(
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[self.pooled_height, self.pooled_width, count, 4], np.float32)
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for ph in range(self.pooled_width):
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for pw in range(self.pooled_height):
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c = 0
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for iy in range(roi_bin_grid_h):
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y = roi_ymin + ph * bin_size_h + (iy + 0.5) * \
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bin_size_h / roi_bin_grid_h
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for ix in range(roi_bin_grid_w):
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x = roi_xmin + pw * bin_size_w + (ix + 0.5) * \
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bin_size_w / roi_bin_grid_w
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if y < -1.0 or y > self.height or \
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x < -1.0 or x > self.width:
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continue
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if y <= 0:
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y = 0
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if x <= 0:
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x = 0
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y_low = int(y)
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x_low = int(x)
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if y_low >= self.height - 1:
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y = y_high = y_low = self.height - 1
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else:
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y_high = y_low + 1
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if x_low >= self.width - 1:
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x = x_high = x_low = self.width - 1
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else:
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x_high = x_low = self.width - 1
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ly = y - y_low
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lx = x - x_low
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hy = 1 - ly
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hx = 1 - lx
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for ch in range(self.channels):
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bilinear_pos[ch, ph, pw, c, 0] = x_i[ch, y_low,
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x_low]
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bilinear_pos[ch, ph, pw, c, 1] = x_i[ch, y_low,
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x_high]
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bilinear_pos[ch, ph, pw, c, 2] = x_i[ch, y_high,
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x_low]
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bilinear_pos[ch, ph, pw, c, 3] = x_i[ch, y_high,
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x_high]
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bilinear_w[ph, pw, c, 0] = hy * hx
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bilinear_w[ph, pw, c, 1] = hy * lx
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bilinear_w[ph, pw, c, 2] = ly * hx
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bilinear_w[ph, pw, c, 3] = ly * lx
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c = c + 1
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return bilinear_pos, bilinear_w
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def calc_roi_align(self):
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self.out_data = np.zeros((self.rois_num, self.channels,
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self.pooled_height, self.pooled_width))
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for i in range(self.rois_num):
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roi = self.rois[i]
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roi_batch_id = int(roi[0])
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x_i = self.x[roi_batch_id]
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roi_xmin = roi[1] * self.spatial_scale
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roi_ymin = roi[2] * self.spatial_scale
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roi_xmax = roi[3] * self.spatial_scale
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roi_ymax = roi[4] * self.spatial_scale
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roi_width = int(max(roi_xmax - roi_xmin, 1))
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roi_height = int(max(roi_ymax - roi_ymin, 1))
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bin_size_h = float(roi_height) / float(self.pooled_height)
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bin_size_w = float(roi_width) / float(self.pooled_width)
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roi_bin_grid_h = self.sampling_ratio if self.sampling_ratio > 0 else \
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math.ceil(roi_height / pooled_height)
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roi_bin_grid_w = self.sampling_ratio if self.sampling_ratio > 0 else \
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math.ceil(roi_width / pooled_width)
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count = int(roi_bin_grid_h * roi_bin_grid_w)
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pre_size = count * self.pooled_width * self.pooled_height
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bilinear_pos, bilinear_w = self.pre_calc(x_i, roi_xmin, roi_ymin,
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int(roi_bin_grid_h),
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int(roi_bin_grid_w),
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bin_size_h, bin_size_w)
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for ch in range(self.channels):
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align_per_bin = (bilinear_pos[ch] * bilinear_w).sum(axis=-1)
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output_val = align_per_bin.mean(axis=-1)
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self.out_data[i, ch, :, :] = output_val
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def make_rois(self):
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rois = []
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self.rois_lod = [[0]]
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for bno in range(self.batch_size):
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self.rois_lod[0].append(bno + 1)
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for i in range(bno + 1):
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x1 = np.random.random_integers(
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0, self.width // self.spatial_scale - self.pooled_width)
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y1 = np.random.random_integers(
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0, self.height // self.spatial_scale - self.pooled_height)
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x2 = np.random.random_integers(x1 + self.pooled_width,
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self.width // self.spatial_scale)
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y2 = np.random.random_integers(
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y1 + self.pooled_height, self.height // self.spatial_scale)
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roi = [bno, x1, y1, x2, y2]
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rois.append(roi)
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self.rois_num = len(rois)
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self.rois = np.array(rois).astype("float32")
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def setUp(self):
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self.op_type = "roi_align"
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self.set_data()
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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