Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix-beam-search-size
test=developrevert-15296-async_double_buffered_py_reader
guoshengCS
6 years ago
commit
b6c3b69af8
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// Copyright (c) 2019 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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#include "paddle/fluid/framework/ir/identity_scale_op_clean_pass.h"
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#include <string>
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#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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std::unique_ptr<ir::Graph> IdentityScaleOpCleanPass::ApplyImpl(
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std::unique_ptr<ir::Graph> graph) const {
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FusePassBase::Init("identity_scale_op_clean", graph.get());
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// pre_op -> scale_in -> scale_op -> scale_out
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// ->
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// pre_op -> scale_out
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GraphPatternDetector detector;
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auto pre_op = detector.mutable_pattern()->NewNode("pre_op")->assert_is_op();
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auto scale_in = detector.mutable_pattern()
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->NewNode("scale_in")
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->assert_is_op_input("scale")
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->AsIntermediate();
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auto scale_op = detector.mutable_pattern()
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->NewNode("scale_fuse")
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->assert_is_op("scale")
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->assert_op_attr<float>("scale", 1.)
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->assert_op_attr<float>("bias", 0.);
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auto scale_out = detector.mutable_pattern()
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->NewNode("scale_out")
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->assert_is_op_output("scale");
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pre_op->LinksTo({scale_in});
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scale_op->LinksFrom({scale_in}).LinksTo({scale_out});
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GraphPatternDetector::handle_t handler = [&](
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const GraphPatternDetector::subgraph_t& subgraph, Graph* graph) {
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Node* scale_op_var = subgraph.at(scale_op);
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Node* scale_in_var = subgraph.at(scale_in);
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Node* scale_out_var = subgraph.at(scale_out);
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Node* pre_op_var = subgraph.at(pre_op);
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// Link pre_op directly to scale_out
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const std::string scale_in_name = scale_in_var->Name();
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const std::string scale_out_name = scale_out_var->Name();
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// Remove links in graph
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GraphSafeRemoveNodes(graph, {scale_in_var, scale_op_var});
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// Modify proto message
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auto* pre_op_desc = pre_op_var->Op();
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for (auto& parameter : *pre_op_desc->Proto()->mutable_outputs()) {
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auto* arguments = parameter.mutable_arguments();
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auto it = std::find(arguments->begin(), arguments->end(), scale_in_name);
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PADDLE_ENFORCE(it != arguments->end());
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*it = scale_out_name;
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}
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IR_NODE_LINK_TO(pre_op_var, scale_out_var);
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};
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detector(graph.get(), handler);
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return graph;
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}
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} // namespace ir
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} // namespace framework
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} // namespace paddle
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REGISTER_PASS(identity_scale_op_clean_pass,
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paddle::framework::ir::IdentityScaleOpCleanPass);
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@ -0,0 +1,33 @@
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// Copyright (c) 2019 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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#pragma once
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#include "paddle/fluid/framework/ir/fuse_pass_base.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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class IdentityScaleOpCleanPass : public FusePassBase {
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protected:
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std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
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private:
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virtual ~IdentityScaleOpCleanPass() = default;
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};
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} // namespace ir
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} // namespace framework
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} // namespace paddle
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@ -0,0 +1,86 @@
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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("Input"),
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"Input(Input) 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("Input");
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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 Input must be 4");
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PADDLE_ENFORCE_EQ(im_info_dims.size(), 2,
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"The rank of Input(Input) in BoxClipOp must be 2");
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PADDLE_ENFORCE_EQ(im_info_dims[1], 3,
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"The last dimension of ImInfo must be 3");
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}
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ctx->ShareDim("Input", /*->*/ "Output");
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ctx->ShareLoD("Input", /*->*/ "Output");
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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("Input",
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"(LoDTensor) "
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"Input 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, 3), "
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"in format (height, width, im_scale)");
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AddOutput("Output",
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"(LoDTensor) "
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"Output is a LoDTensor with the same shape as Input"
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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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For each input box, The formula is given as follows:
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$$xmin = \max(\min(xmin, im_w - 1), 0)$$
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$$ymin = \max(\min(ymin, im_h - 1), 0)$$
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$$xmax = \max(\min(xmax, im_w - 1), 0)$$
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$$ymax = \max(\min(ymax, im_h - 1), 0)$$
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where im_w and im_h are computed from ImInfo, the formula is given as follows:
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$$im_w = \round(width / im_scale)$$
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$$im_h = \round(height / im_scale)$$
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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 +1,74 @@
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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 <algorithm>
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/detection/box_clip_op.h"
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#include "paddle/fluid/operators/math/math_function.h"
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#include "paddle/fluid/platform/cuda_primitives.h"
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#include "paddle/fluid/platform/hostdevice.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 LoDTenso = framework::LoDTensor;
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static constexpr int ImInfoSize = 3;
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template <typename T, int BlockSize>
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static __global__ void GPUBoxClip(const T *input, const size_t *lod,
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const size_t width, const T *im_info,
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T *output) {
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T im_w = round(im_info[blockIdx.x * ImInfoSize + 1] /
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im_info[blockIdx.x * ImInfoSize + 2]);
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T im_h = round(im_info[blockIdx.x * ImInfoSize] /
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im_info[blockIdx.x * ImInfoSize + 2]);
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for (int i = threadIdx.x; i < (lod[blockIdx.x + 1] - lod[blockIdx.x]) * width;
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i += BlockSize) {
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int idx = lod[blockIdx.x] * width + i;
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T im_size = (idx % 2 == 0) ? im_w : im_h;
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output[idx] = max(min(input[idx], im_size - 1), T(0.));
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}
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}
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template <typename DeviceContext, typename T>
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class GPUBoxClipKernel : 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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PADDLE_ENFORCE(platform::is_gpu_place(context.GetPlace()),
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"This kernel only runs on GPU device.");
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auto *input = context.Input<LoDTensor>("Input");
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auto *im_info = context.Input<Tensor>("ImInfo");
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auto *output = context.Output<LoDTensor>("Output");
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const int64_t num = input->dims()[0];
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const int64_t bbox_width = input->numel() / num;
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auto lod = input->lod();
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framework::LoD abs_offset_lod = framework::ToAbsOffset(lod);
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auto &dev_ctx = context.template device_context<DeviceContext>();
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auto stream = dev_ctx.stream();
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const size_t batch_size = lod.back().size() - 1;
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T *output_data = output->mutable_data<T>(dev_ctx.GetPlace());
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GPUBoxClip<T, 512><<<batch_size, 512, 0, stream>>>(
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input->data<T>(), abs_offset_lod[0].CUDAMutableData(dev_ctx.GetPlace()),
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bbox_width, im_info->data<T>(), output_data);
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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_OP_CUDA_KERNEL(
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box_clip, ops::GPUBoxClipKernel<paddle::platform::CUDADeviceContext, float>,
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ops::GPUBoxClipKernel<paddle::platform::CUDADeviceContext, 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>("Input");
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auto* im_info = context.Input<LoDTensor>("ImInfo");
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auto* output_box = context.Output<LoDTensor>("Output");
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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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