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109 lines
4.0 KiB
109 lines
4.0 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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/operators/maxout_op.h"
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namespace paddle {
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namespace operators {
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using framework::Tensor;
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class MaxOutOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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MaxOutOpMaker(OpProto* proto, OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput(
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"X",
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"(Tensor) The input tensor of maxout operator. "
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"The format of input tensor is NCHW. Where N is batch size, C is the "
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"number of channels, H and W is the height and width of feature.");
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AddOutput("Out",
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"(Tensor) The output tensor of maxout operator."
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"The format of output tensor is also NCHW."
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"Where N is batch size, C is "
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"the number of channels, H and W is the height and "
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"width of feature.");
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AddAttr<int>(
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"groups",
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R"DOC("Specifies how many groups the input tensor will be split"
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"in the channel dimension. And the number of output channel is "
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"the number of channels divided by groups.."
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)DOC");
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AddComment(R"DOC(
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MaxOut Operator.
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Assumed the input shape is (N, Ci, H, W).
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The output shape is (N, Co, H, W).
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Then $Co = Ci / groups$ and the operator formula is as follows:
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$$
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y_{si+j} = \max_k x_{gsi + sk + j} \\
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g = groups \\
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s = \frac{input.size}{num\_channels} \\
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0 \le i < \frac{num\_channels}{groups} \\
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0 \le j < s \\
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0 \le k < groups
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$$
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Please refer to Paper:
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- Maxout Networks: http://www.jmlr.org/proceedings/papers/v28/goodfellow13.pdf
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- Multi-digit Number Recognition from Street View \
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Imagery using Deep Convolutional Neural Networks: \
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https://arxiv.org/pdf/1312.6082v4.pdf
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)DOC");
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}
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};
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class MaxOutOp : 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 MaxoutOp"
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"should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of MaxoutOp should not be null.");
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auto in_x_dims = ctx->GetInputDim("X");
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int groups = ctx->Attrs().Get<int>("groups");
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// check groups > 1
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PADDLE_ENFORCE_GT(groups, 1, "groups should be larger than 1 in maxoutop");
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std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1] / groups});
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output_shape.push_back(in_x_dims[2]);
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output_shape.push_back(in_x_dims[3]);
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ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
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}
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};
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class MaxOutOpGrad : 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"), "Input(X) must not be null.");
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PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
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"Input(X@GRAD) should not be null.");
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ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
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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(maxout, ops::MaxOutOp, ops::MaxOutOpMaker, maxout_grad,
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ops::MaxOutOpGrad);
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REGISTER_OP_CPU_KERNEL(
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maxout, ops::MaxOutKernel<paddle::platform::CPUDeviceContext, float>);
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REGISTER_OP_CPU_KERNEL(
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maxout_grad,
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ops::MaxOutGradKernel<paddle::platform::CPUDeviceContext, float>);
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