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83 lines
3.3 KiB
83 lines
3.3 KiB
// 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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#include "paddle/fluid/operators/random_crop_op.h"
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namespace paddle {
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namespace operators {
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class RandomCropOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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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::LoDTensor>("X")->type()),
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ctx.device_context());
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}
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};
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class RandomCropOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X", "A batch of instances to random crop.");
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AddInput("Seed", "The random seed.");
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AddOutput("Out", "The cropped instance batch.");
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AddOutput("SeedOut", "The random seed after random cropping.")
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.AsIntermediate();
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AddAttr<std::vector<int>>("shape", "The shape of a cropped instance.");
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AddAttr<int>("startup_seed",
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"If the input 'Seed' is not initialized, the 'startup_seed' "
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"will be used to replace it. Even so, the seed after random "
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"crop will also be outputed to the 'SeedOut'.")
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.SetDefault(0);
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AddComment(R"DOC(
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This operator takes a batch of instance, and do random cropping on each instance.
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It means that cropping positions differs on each instance, which is determined
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by an uniform random generator. All cropped instances have the same shape, which
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is determined by the operator's attribute 'shape'.
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)DOC");
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}
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};
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class RandomCropOpInferShape : public framework::InferShapeBase {
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public:
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void operator()(framework::InferShapeContext* ctx) const override {
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auto shape = ctx->Attrs().Get<std::vector<int>>("shape");
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auto x_dim = ctx->GetInputDim("X");
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PADDLE_ENFORCE_GT(x_dim.size(), static_cast<int64_t>(shape.size()));
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auto out_dim = framework::vectorize2int(x_dim);
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for (size_t i = 1; i <= shape.size(); ++i) {
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size_t x_i = x_dim.size() - i;
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size_t shape_i = shape.size() - i;
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PADDLE_ENFORCE_GE(x_dim[x_i], shape[shape_i]);
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out_dim[x_i] = shape[shape_i];
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}
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ctx->SetOutputDim("Out", framework::make_ddim(out_dim));
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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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namespace f = paddle::framework;
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REGISTER_OPERATOR(random_crop, ops::RandomCropOp, ops::RandomCropOpMaker,
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ops::RandomCropOpInferShape, f::EmptyGradOpMaker);
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template <typename T>
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using Kernel = ops::RandomCropKernel<paddle::platform::CPUDeviceContext, T>;
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REGISTER_OP_CPU_KERNEL(random_crop, Kernel<float>, Kernel<int>, Kernel<double>,
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Kernel<uint8_t>, Kernel<int16_t>);
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