tonyyang-svail-feed-op-desgin
Yu Yang
7 years ago
commit
874bcb3030
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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 "paddle/framework/op_info.h"
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#include "paddle/framework/op_proto_maker.h"
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#include "paddle/framework/operator.h"
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namespace paddle {
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namespace framework {
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namespace details {
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enum OpInfoFillType {
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kOperator = 0,
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kOpProtoAndCheckerMaker = 1,
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kGradOpDescMaker = 2
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};
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template <typename T>
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struct OpInfoFillTypeID {
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static constexpr OpInfoFillType ID() {
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return std::is_base_of<OperatorBase, T>::value
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? kOperator
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: (std::is_base_of<OpProtoAndCheckerMaker, T>::value
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? kOpProtoAndCheckerMaker
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: (std::is_base_of<GradOpDescMakerBase, T>::value
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? kGradOpDescMaker
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: static_cast<OpInfoFillType>(-1)));
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}
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};
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template <typename T, OpInfoFillType = OpInfoFillTypeID<T>::ID()>
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struct OpInfoFiller;
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template <size_t I, bool at_end, typename... ARGS>
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class OperatorRegistrarRecursive;
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template <size_t I, typename... ARGS>
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class OperatorRegistrarRecursive<I, false, ARGS...> {
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public:
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using T = typename std::tuple_element<I, std::tuple<ARGS...>>::type;
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OperatorRegistrarRecursive(const char* op_type, OpInfo* info) {
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OpInfoFiller<T> fill;
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fill(op_type, info);
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constexpr auto size = sizeof...(ARGS);
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OperatorRegistrarRecursive<I + 1, I + 1 == size, ARGS...> reg(op_type,
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info);
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(void)(reg);
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}
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};
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template <size_t I, typename... ARGS>
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class OperatorRegistrarRecursive<I, true, ARGS...> {
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public:
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OperatorRegistrarRecursive(const char* op_type, OpInfo* info) {}
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};
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template <typename T>
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struct OpInfoFiller<T, kOperator> {
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void operator()(const char* op_type, OpInfo* info) const {
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info->creator_ = [](const std::string& type, const VariableNameMap& inputs,
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const VariableNameMap& outputs,
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const AttributeMap& attrs) {
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return new T(type, inputs, outputs, attrs);
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};
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}
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};
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template <typename T>
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struct OpInfoFiller<T, kOpProtoAndCheckerMaker> {
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void operator()(const char* op_type, OpInfo* info) const {
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info->proto_ = new OpProto;
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info->checker_ = new OpAttrChecker();
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auto maker = T(info->proto_, info->checker_);
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maker.Validate();
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info->proto_->set_type(op_type);
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PADDLE_ENFORCE(
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info->proto_->IsInitialized(),
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"Fail to initialize %s's OpProto, because %s is not initialized",
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op_type, info->proto_->InitializationErrorString());
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}
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};
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template <typename T>
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struct OpInfoFiller<T, kGradOpDescMaker> {
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void operator()(const char* op_type, OpInfo* info) const {
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info->grad_op_maker_ = new T();
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}
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};
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} // namespace details
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} // namespace framework
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} // namespace paddle
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@ -0,0 +1,27 @@
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type: "nn"
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layers {
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name: "input"
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type: "data"
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size: 300
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active_type: ""
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}
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layers {
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name: "__resize_0__"
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type: "resize"
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size: 150
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active_type: ""
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inputs {
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input_layer_name: "input"
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}
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}
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input_layer_names: "input"
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output_layer_names: "__resize_0__"
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sub_models {
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name: "root"
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layer_names: "input"
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layer_names: "__resize_0__"
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input_layer_names: "input"
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output_layer_names: "__resize_0__"
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is_recurrent_layer_group: false
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}
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@ -0,0 +1,6 @@
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from paddle.trainer_config_helpers import *
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data = data_layer(name='input', size=300)
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resized = resize_layer(input=data, size=150)
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outputs(resized)
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