jerrywgz
7 years ago
parent
3f815e079f
commit
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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/detection/box_clip_op.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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class BoxClipOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContext *ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("InputBox"),
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"Input(InputBox) of BoxClipOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("ImInfo"),
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"Input(ImInfo) of BoxClipOp should not be null.");
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auto input_box_dims = ctx->GetInputDim("InputBox");
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auto im_info_dims = ctx->GetInputDim("ImInfo");
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if (ctx->IsRuntime()) {
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auto input_box_size = input_box_dims.size();
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PADDLE_ENFORCE_EQ(input_box_dims[input_box_size - 1], 4,
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"The last dimension of InputBox must be 4");
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PADDLE_ENFORCE_EQ(im_info_dims.size(), 2,
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"The rank of Input(InputBox) in BoxClipOp must be 2");
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PADDLE_ENFORCE_EQ(im_info_dims[1], 2,
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"The last dimension of ImInfo must be 2");
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}
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ctx->ShareDim("InputBox", /*->*/ "OutputBox");
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ctx->ShareLoD("InputBox", /*->*/ "OutputBox");
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}
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};
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class BoxClipOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("InputBox",
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"(LoDTensor) "
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"InputBox is a LoDTensor with shape [..., 4] holds 4 points"
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"in last dimension in format [xmin, ymin, xmax, ymax]");
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AddInput("ImInfo",
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"(Tensor) Information for image reshape is in shape (N, 2), "
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"in format (height, width)");
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AddOutput("OutputBox",
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"(LoDTensor) "
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"OutputBox is a LoDTensor with the same shape as InputBox"
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"and it is the result after clip");
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AddComment(R"DOC(
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This operator clips input boxes to original input images.
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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(box_clip, ops::BoxClipOp, ops::BoxClipOpMaker,
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paddle::framework::EmptyGradOpMaker);
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REGISTER_OP_CPU_KERNEL(
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box_clip, ops::BoxClipKernel<paddle::platform::CPUDeviceContext, float>,
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ops::BoxClipKernel<paddle::platform::CPUDeviceContext, double>);
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@ -0,0 +1,50 @@
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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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#pragma once
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#include <string>
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/detection/bbox_util.h"
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#include "paddle/fluid/operators/math/math_function.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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template <typename DeviceContext, typename T>
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class BoxClipKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* input_box = context.Input<LoDTensor>("InputBox");
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auto* im_info = context.Input<LoDTensor>("ImInfo");
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auto* output_box = context.Output<LoDTensor>("OutputBox");
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auto& dev_ctx =
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context.template device_context<platform::CPUDeviceContext>();
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output_box->mutable_data<T>(context.GetPlace());
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if (input_box->lod().size()) {
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PADDLE_ENFORCE_EQ(input_box->lod().size(), 1UL,
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"Only support 1 level of LoD.");
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}
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auto box_lod = input_box->lod().back();
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int64_t n = static_cast<int64_t>(box_lod.size() - 1);
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for (int i = 0; i < n; ++i) {
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Tensor im_info_slice = im_info->Slice(i, i + 1);
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Tensor box_slice = input_box->Slice(box_lod[i], box_lod[i + 1]);
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Tensor output_slice = output_box->Slice(box_lod[i], box_lod[i + 1]);
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ClipTiledBoxes<T>(dev_ctx, im_info_slice, box_slice, &output_slice);
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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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@ -0,0 +1,70 @@
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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 sys
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import math
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from op_test import OpTest
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import copy
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def box_clip(input_box, im_info, output_box):
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im_w = round(im_info[1] / im_info[2])
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im_h = round(im_info[0] / im_info[2])
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output_box[:, :, 0] = np.maximum(
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np.minimum(input_box[:, :, 0], im_w - 1), 0)
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output_box[:, :, 1] = np.maximum(
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np.minimum(input_box[:, :, 1], im_h - 1), 0)
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output_box[:, :, 2] = np.maximum(
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np.minimum(input_box[:, :, 2], im_w - 1), 0)
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output_box[:, :, 3] = np.maximum(
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np.minimum(input_box[:, :, 3], im_h - 1), 0)
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def batch_box_clip(input_boxes, im_info, lod):
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n = input_boxes.shape[0]
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m = input_boxes.shape[1]
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output_boxes = np.zeros((n, m, 4), dtype=np.float32)
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cur_offset = 0
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for i in range(len(lod)):
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box_clip(input_boxes[cur_offset:(cur_offset + lod[i]), :, :],
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im_info[i, :],
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output_boxes[cur_offset:(cur_offset + lod[i]), :, :])
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cur_offset += lod[i]
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return output_boxes
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class TestBoxClipOp(OpTest):
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def test_check_output(self):
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self.check_output()
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def setUp(self):
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self.op_type = "box_clip"
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lod = [[1, 2, 3]]
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input_boxes = np.random.random((6, 10, 4)) * 5
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im_info = np.array([[5, 8, 1.], [6, 6, 1.], [7, 5, 1.]])
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output_boxes = batch_box_clip(input_boxes, im_info, lod[0])
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self.inputs = {
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'InputBox': (input_boxes.astype('float32'), lod),
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'ImInfo': im_info.astype('float32'),
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}
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self.outputs = {'OutputBox': output_boxes}
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if __name__ == '__main__':
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unittest.main()
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