remove net op and cond_op (#9663)
* remove net op and cond_op * fix cpplint * fix dependency * delete backward_test; fix compile * disable batch_norm backward * rm test_net.py * make batchnorm test independent of backward.cc * make test_layer_norm_op independent of backward.cc * make test_layer_norm_op independent of backward.cc * delete unused code * clean upwangkuiyi-patch-2
Yang Yang(Tony)
7 years ago
committed by
GitHub
parent
8d3ce01f36
commit
b26f505002
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@ -1,235 +0,0 @@
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/* Copyright (c) 2016 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/cond_op.h"
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#include "paddle/fluid/operators/gather.h"
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#include "paddle/fluid/operators/scatter.h"
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#include "paddle/fluid/platform/device_context.h"
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namespace paddle {
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namespace operators {
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using Scope = framework::Scope;
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using Variable = framework::Variable;
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using Tensor = framework::Tensor;
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using LoDTensor = framework::LoDTensor;
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using DDim = framework::DDim;
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framework::Scope& CondOp::AddSubScope(const Scope& scope) const {
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auto sub_scopes_var = scope.FindVar("SubScopes");
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PADDLE_ENFORCE_NOT_NULL(sub_scopes_var,
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"Output(SubScopes) of CondOp should not be null.");
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auto sub_scopes = sub_scopes_var->GetMutable<std::vector<Scope*>>();
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auto& sub_scope = scope.NewScope();
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sub_scopes->push_back(&sub_scope);
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return sub_scope;
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}
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std::vector<framework::Scope*>& CondOp::GetSubScopes(
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const framework::Scope& scope) const {
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auto sub_scopes_var = scope.FindVar("SubScopes");
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PADDLE_ENFORCE_NOT_NULL(sub_scopes_var,
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"Output(SubScopes) of CondOp should not be null.");
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return *sub_scopes_var->GetMutable<std::vector<framework::Scope*>>();
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}
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LoDTensor& CondOp::AddIndexTensor(const Scope& scope) const {
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auto index_tensors_var = scope.FindVar("IndexTensors");
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PADDLE_ENFORCE_NOT_NULL(index_tensors_var,
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"Output(IndexTensors) of CondOp should not be null.");
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auto& index_tensors =
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*index_tensors_var->GetMutable<std::vector<LoDTensor>>();
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index_tensors.push_back(LoDTensor());
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return index_tensors.back();
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}
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std::vector<framework::LoDTensor>& CondOp::GetIndexTensors(
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const framework::Scope& scope) const {
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auto* index_tensors_var = scope.FindVar("IndexTensors");
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PADDLE_ENFORCE_NOT_NULL(index_tensors_var,
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"Output(IndexTensors) of CondOp should not be null.");
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return *index_tensors_var->GetMutable<std::vector<framework::LoDTensor>>();
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}
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void CondOp::PrepareDataForSubnet(
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const framework::Scope& scope,
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const platform::DeviceContext& dev_ctx) const {
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PADDLE_ENFORCE(!Inputs("Xs").empty(), "Inputs(Xs) of CondOp can't be empty.");
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for (int i = 0; i < BRANCH_NUM; ++i) {
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// Create two sub scopes for true and false branches
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// sub_scopes[0] for the true branch
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// sub_scopes[1] for the false branch
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AddSubScope(scope);
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// Create two tensors for true and false indices:
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// index_tensors[0] for the true branch
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// index_tensors[1] for the false branch
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AddIndexTensor(scope);
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}
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Variable* cond_var = scope.FindVar(Input("Cond"));
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PADDLE_ENFORCE_NOT_NULL(cond_var,
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"Input(Cond) of CondOp should not be null.");
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const LoDTensor* cond = cond_var->GetMutable<LoDTensor>();
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// get the true/false index at runtime according to cond tensor
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// index_vectors[0]: vector<int>, contains all index for cond[i] == true
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// index_vectors[1]: vector<int>, contains all index for cond[i] == false
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std::vector<std::vector<int>> index_vectors;
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index_vectors.resize(BRANCH_NUM);
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const int* cond_data = cond->data<int>();
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for (int i = 0; i < cond->dims()[0]; ++i) {
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if (cond_data[i])
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index_vectors[TRUE_BRANCH].push_back(i);
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else
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index_vectors[FALSE_BRANCH].push_back(i);
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}
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// put index_vectors[0] and index_vectors[1] into two tensors:
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// index_tensors[0] and index_tensors[1]
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std::vector<framework::LoDTensor>& index_tensors = GetIndexTensors(scope);
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std::vector<framework::Scope*>& sub_scopes = GetSubScopes(scope);
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for (int i = 0; i < BRANCH_NUM; ++i) {
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DDim dim = {static_cast<int64_t>(index_vectors[i].size())};
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int* index_tensor_data_ptr =
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index_tensors[i].mutable_data<int>(dim, platform::CPUPlace());
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memcpy(index_tensor_data_ptr, index_vectors[i].data(),
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dim[0] * sizeof(int));
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}
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// create input in subscopes according to index_vectors
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for (auto& input : Inputs("Xs")) {
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Variable* var_parent = scope.FindVar(input);
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PADDLE_ENFORCE_NOT_NULL(var_parent);
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const auto* tensor_parent = &var_parent->Get<LoDTensor>();
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for (int i = 0; i < BRANCH_NUM; ++i) {
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Variable* var_child = sub_scopes[i]->FindVar(input);
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PADDLE_ENFORCE_NOT_NULL(var_child);
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auto* tensor_child = var_child->GetMutable<LoDTensor>();
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// Resize child
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DDim dim = tensor_parent->dims();
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dim[0] = index_tensors[i].dims()[0];
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tensor_child->mutable_data<float>(dim, platform::CPUPlace());
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CPUGather<float>(dev_ctx, *tensor_parent, index_tensors[i], tensor_child);
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}
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}
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// create output_tensors in subscope for sub_net
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for (int i = 0; i < BRANCH_NUM; ++i) {
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for (auto& output : (*sub_net_op_[i]).Outputs()) {
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for (auto& var_name : output.second) {
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sub_scopes[i]->Var(var_name);
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}
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}
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}
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}
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void CondOp::MergeDataFromSubnet(const framework::Scope& scope,
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const platform::DeviceContext& dev_ctx) const {
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std::vector<framework::Scope*>& sub_scopes = GetSubScopes(scope);
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const std::vector<framework::LoDTensor>& index_tensors =
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GetIndexTensors(scope);
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// Infer the output dim, out_dim[0] = true_dim[0] + false_dim[0]
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PADDLE_ENFORCE(!Outputs("Outs").empty(),
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"Outputs(Outs) of CondOp can't be empty.");
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for (auto& output : Outputs("Outs")) {
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const LoDTensor* tensor_t_out =
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&sub_scopes[TRUE_BRANCH]->FindVar(output)->Get<LoDTensor>();
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PADDLE_ENFORCE_NOT_NULL(tensor_t_out, "True output should not be NULL");
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const LoDTensor* tensor_f_out =
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&sub_scopes[FALSE_BRANCH]->FindVar(output)->Get<LoDTensor>();
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PADDLE_ENFORCE_NOT_NULL(tensor_f_out, "False output should not be NULL");
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auto* var_out = scope.FindVar(output);
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PADDLE_ENFORCE_NOT_NULL(var_out, "Output not found");
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LoDTensor* tensor_out = var_out->GetMutable<LoDTensor>();
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PADDLE_ENFORCE_NOT_NULL(tensor_t_out,
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"True output tensor should not be NULL");
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DDim true_dim = tensor_t_out->dims();
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DDim false_dim = tensor_f_out->dims();
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true_dim[0] = 0;
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false_dim[0] = 0;
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PADDLE_ENFORCE_EQ(true_dim, false_dim,
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"Outputs not of the same shape except the first dim");
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DDim out_dim = tensor_t_out->dims();
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out_dim[0] = tensor_t_out->dims()[0] + tensor_f_out->dims()[0];
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tensor_out->Resize(out_dim);
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tensor_out->mutable_data<float>(platform::CPUPlace());
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}
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// merge output results:
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// output_tensor = true_output_tensor + false_output_tensor
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for (auto& output : Outputs("Outs")) {
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Variable* var_parent = scope.FindVar(output);
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PADDLE_ENFORCE_NOT_NULL(var_parent);
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auto* tensor_parent = var_parent->GetMutable<LoDTensor>();
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for (int i = 0; i < BRANCH_NUM; ++i) {
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Variable* var_child = sub_scopes[i]->FindVar(output);
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PADDLE_ENFORCE_NOT_NULL(var_child);
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auto* tensor_child = &var_child->Get<LoDTensor>();
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ScatterAssign<float>(dev_ctx, *tensor_child, index_tensors[i],
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tensor_parent);
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}
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}
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}
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void CondOp::RunImpl(const Scope& scope, const platform::Place& place) const {
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// get device context from pool
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platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
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auto& dev_ctx = *pool.Get(place);
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PrepareDataForSubnet(scope, dev_ctx);
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std::vector<framework::Scope*>& sub_scopes = GetSubScopes(scope);
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for (int i = 0; i < BRANCH_NUM; ++i) {
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sub_net_op_[i]->Run(*sub_scopes[i], place);
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}
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MergeDataFromSubnet(scope, dev_ctx);
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}
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class CondOpProtoAndCheckerMaker : public framework::OpProtoAndCheckerMaker {
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public:
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CondOpProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("Cond", "The condition, which is a bool vector");
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AddInput("Xs", "Inputs of Subnets").AsDuplicable();
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AddOutput("Outs", "Outputs of Cond_Op after merge").AsDuplicable();
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AddOutput("SubScopes", "sub scopes for true and false branches");
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AddOutput("IndexTensors", "Index Tensors contains indices for true/false");
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AddComment(R"DOC(
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Sample Dependent Conditional Operator.
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Given Cond[i] as a 1/0 vector to indicate true/false:
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Out[i] = subnet_true[i], if Cond[i] == true
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Out[i] = subnet_false[i], if Cond[i] == false
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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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REGISTER_OP_WITHOUT_GRADIENT(cond, paddle::operators::CondOp,
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paddle::operators::CondOpProtoAndCheckerMaker);
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@ -1,96 +0,0 @@
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/* Copyright (c) 2016 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 <vector>
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#include "glog/logging.h"
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#include "paddle/fluid/framework/ddim.h"
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/operator.h"
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#include "paddle/fluid/framework/tensor.h"
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#include "paddle/fluid/operators/net_op.h"
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namespace paddle {
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namespace operators {
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/*
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* @brief CondOp is a dynamic if-else Operator
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*
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* It has a input tensor named cond indicating which netop each instance will
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* run.
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*
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* if cond == 1, it will run true_net, which is a NetOp.
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*
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* if cond == 0, it will run false_net, which is another NetOp.
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*/
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class CondOp : public framework::OperatorBase {
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public:
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CondOp(const std::string& type, const framework::VariableNameMap& inputs,
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const framework::VariableNameMap& outputs,
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const framework::AttributeMap& attrs)
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: OperatorBase(type, inputs, outputs, attrs) {
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sub_net_op_.resize(BRANCH_NUM);
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}
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CondOp(const CondOp& o)
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: framework::OperatorBase(
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static_cast<const framework::OperatorBase&>(o)) {
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// TODO(yuyang18): Implement copy ctor well.
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PADDLE_THROW("Not implemented");
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}
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framework::Scope& AddSubScope(const framework::Scope& scope) const;
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std::vector<framework::Scope*>& GetSubScopes(
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const framework::Scope& scope) const;
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framework::LoDTensor& AddIndexTensor(const framework::Scope& scope) const;
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std::vector<framework::LoDTensor>& GetIndexTensors(
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const framework::Scope& scope) const;
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void PrepareDataForSubnet(const framework::Scope& scope,
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const platform::DeviceContext& dev_ctx) const;
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void MergeDataFromSubnet(const framework::Scope& scope,
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const platform::DeviceContext& dev_ctx) const;
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/*
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* Set True Block
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*/
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void set_truenet(std::unique_ptr<OperatorBase>&& net) {
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sub_net_op_[TRUE_BRANCH] = std::move(net);
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}
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/*
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* Set False Block
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*/
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void set_falsenet(std::unique_ptr<OperatorBase>&& net) {
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sub_net_op_[FALSE_BRANCH] = std::move(net);
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}
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private:
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void RunImpl(const framework::Scope& scope,
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const platform::Place& place) const override;
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private:
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const int TRUE_BRANCH = 0;
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const int FALSE_BRANCH = 1;
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const int BRANCH_NUM = 2;
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// sub_net_op_[0]: subnet_t
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// sub_net_op_[1]: subnet_f
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std::vector<std::unique_ptr<framework::OperatorBase>> sub_net_op_;
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};
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} // namespace operators
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} // namespace paddle
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@ -1,103 +0,0 @@
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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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#include "paddle/fluid/operators/net_op.h"
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#include <set>
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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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const char NetOp::kAll[] = "all";
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void NetOp::CompleteAddOp(bool calc) {
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add_op_done_ = true;
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if (!calc) return;
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std::set<std::string> input_set;
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std::set<std::string> output_set;
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for (auto& op : ops_) {
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for (auto& ipt : op->Inputs()) {
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for (auto& var_name : ipt.second) {
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// If input variable has been in output set, then it will be
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// added into intermediate_outputs_. Otherwise, it will be
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// added into input set.
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if (Contains(output_set, var_name)) {
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intermediate_outputs_.insert(var_name);
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} else {
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input_set.insert(var_name);
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}
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}
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}
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for (auto& opt : op->Outputs()) {
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for (auto& var_name : opt.second) {
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output_set.insert(var_name);
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}
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}
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}
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auto& inputs = inputs_[kAll];
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inputs.reserve(input_set.size());
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std::copy(input_set.begin(), input_set.end(), std::back_inserter(inputs));
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auto& outputs = outputs_[kAll];
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outputs.reserve(output_set.size());
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std::copy(output_set.begin(), output_set.end(), std::back_inserter(outputs));
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}
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std::string NetOp::DebugStringEx(const framework::Scope* scope) const {
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std::ostringstream os;
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os << OperatorBase::DebugStringEx(scope) << std::endl;
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for (auto& op : ops_) {
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std::istringstream is(op->DebugStringEx(scope));
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for (std::string line; std::getline(is, line);) {
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os << " " << line << std::endl;
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}
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}
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return os.str();
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}
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bool NetOp::IsNetOp() const { return true; }
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std::vector<std::string> NetOp::OutputVars(bool has_intermediate) const {
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std::vector<std::string> all;
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for (auto& pair : this->outputs_) {
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for (auto& var_name : pair.second) {
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all.push_back(var_name);
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}
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}
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if (has_intermediate) {
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return all;
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}
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std::vector<std::string> ret_val;
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for (auto& each : all) {
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if (!Contains(intermediate_outputs_, each)) {
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ret_val.push_back(each);
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}
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}
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return ret_val;
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}
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NetOp::NetOp(const std::string& type, const framework::VariableNameMap& inputs,
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const framework::VariableNameMap& outputs,
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const framework::AttributeMap& attrs)
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: framework::OperatorBase(type, inputs, outputs, attrs) {}
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std::unique_ptr<framework::OperatorBase> NetOp::Clone() const {
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PADDLE_ENFORCE(
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add_op_done_,
|
||||
"Must clone a sealed NetOp, invoke Net::CompleteAddOp before clone");
|
||||
return std::unique_ptr<OperatorBase>(new NetOp(*this));
|
||||
}
|
||||
|
||||
} // namespace operators
|
||||
} // namespace paddle
|
||||
@ -1,130 +0,0 @@
|
||||
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <set>
|
||||
#include "paddle/fluid/framework/framework.pb.h"
|
||||
#include "paddle/fluid/framework/op_registry.h"
|
||||
|
||||
namespace paddle {
|
||||
namespace operators {
|
||||
|
||||
/**
|
||||
* @brief Network is also a type of Operator
|
||||
*
|
||||
* It will manage the operators it has.
|
||||
*
|
||||
* Network is the container and controller of a set of operators.
|
||||
|
||||
* A network object knows all Operators belonging to this network. Variables,
|
||||
* which are inputs and outputs of these operators, are created and managed by a
|
||||
* hierarchy of Scope objects.
|
||||
*
|
||||
* This is the base class of network, all the networks should implement the APIs
|
||||
* it defines.
|
||||
*/
|
||||
class NetOp : public framework::OperatorBase {
|
||||
public:
|
||||
static const char kAll[];
|
||||
NetOp()
|
||||
: framework::OperatorBase("plain_net", framework::VariableNameMap{},
|
||||
framework::VariableNameMap{},
|
||||
framework::AttributeMap{}) {}
|
||||
|
||||
NetOp(const std::string& type, const framework::VariableNameMap& inputs,
|
||||
const framework::VariableNameMap& outputs,
|
||||
const framework::AttributeMap& attrs);
|
||||
|
||||
NetOp(const NetOp& o) : framework::OperatorBase(o.type_, {}, {}, o.attrs_) {
|
||||
this->ops_.reserve(o.ops_.size());
|
||||
std::transform(
|
||||
o.ops_.begin(), o.ops_.end(), std::back_inserter(this->ops_),
|
||||
[](const std::unique_ptr<framework::OperatorBase>& op) {
|
||||
return std::unique_ptr<framework::OperatorBase>(op->Clone());
|
||||
});
|
||||
this->CompleteAddOp();
|
||||
}
|
||||
|
||||
bool SupportGPU() const override {
|
||||
for (auto& op : ops_) {
|
||||
if (!op->SupportGPU()) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
void AppendOp(const framework::OperatorBase& op) { AppendOp(op.Clone()); }
|
||||
|
||||
/**
|
||||
* @brief Add an operator by ptr
|
||||
*/
|
||||
void AppendOp(std::unique_ptr<framework::OperatorBase> op) {
|
||||
PADDLE_ENFORCE(!add_op_done_,
|
||||
"Cannot AppendOp when this network is sealed");
|
||||
PADDLE_ENFORCE_NOT_NULL(op, "Cannot Insert Null op");
|
||||
ops_.push_back(std::move(op));
|
||||
}
|
||||
|
||||
void InsertOp(size_t pos, std::unique_ptr<framework::OperatorBase> op) {
|
||||
PADDLE_ENFORCE(!add_op_done_,
|
||||
"Cannot InsertOp when this network is sealed");
|
||||
PADDLE_ENFORCE_NOT_NULL(op, "Cannot Insert Null op");
|
||||
PADDLE_ENFORCE_LE(pos, ops_.size(), "Out of range");
|
||||
ops_.insert(ops_.begin() + pos, std::move(op));
|
||||
}
|
||||
|
||||
void InsertOp(size_t pos, const framework::OperatorBase& op) {
|
||||
InsertOp(pos, op.Clone());
|
||||
}
|
||||
|
||||
void CompleteAddOp(bool calculate = true);
|
||||
|
||||
std::string DebugStringEx(
|
||||
const framework::Scope* scope = nullptr) const override;
|
||||
|
||||
bool IsNetOp() const override;
|
||||
std::vector<std::string> OutputVars(bool has_intermediate) const override;
|
||||
|
||||
std::unique_ptr<framework::OperatorBase> Clone() const override;
|
||||
|
||||
std::vector<std::unique_ptr<framework::OperatorBase>> ops_;
|
||||
|
||||
private:
|
||||
/**
|
||||
* @brief Run the network.
|
||||
*
|
||||
* Run all the operators with the `scope`, if no scope is provided, default
|
||||
* scope will be used instead. If no OpContext is provicded, default context
|
||||
* will be used.
|
||||
*/
|
||||
void RunImpl(const framework::Scope& scope,
|
||||
const platform::Place& place) const override {
|
||||
for (auto& op : ops_) {
|
||||
op->Run(scope, place);
|
||||
}
|
||||
}
|
||||
|
||||
bool add_op_done_{false};
|
||||
std::set<std::string> intermediate_outputs_;
|
||||
|
||||
template <typename T, typename KeyType>
|
||||
static bool Contains(T container, KeyType key) {
|
||||
return container.find(key) != container.end();
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace operators
|
||||
} // namespace paddle
|
||||
@ -1,103 +0,0 @@
|
||||
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
#include "paddle/fluid/operators/net_op.h"
|
||||
|
||||
#include <gtest/gtest.h>
|
||||
|
||||
namespace paddle {
|
||||
namespace operators {
|
||||
using Scope = framework::Scope;
|
||||
using DeviceContext = platform::DeviceContext;
|
||||
|
||||
static int run_cnt = 0;
|
||||
|
||||
class TestOp : public framework::OperatorBase {
|
||||
public:
|
||||
using framework::OperatorBase::OperatorBase;
|
||||
DEFINE_OP_CLONE_METHOD(TestOp);
|
||||
|
||||
private:
|
||||
void RunImpl(const Scope& scope,
|
||||
const platform::Place& place) const override {
|
||||
++run_cnt;
|
||||
}
|
||||
};
|
||||
|
||||
template <typename T>
|
||||
void AssertSameVectorWithoutOrder(const std::vector<T>& expected,
|
||||
const std::vector<T>& actual) {
|
||||
ASSERT_EQ(expected.size(), actual.size());
|
||||
std::unordered_set<T> expected_set;
|
||||
for (auto& tmp : expected) {
|
||||
expected_set.insert(tmp);
|
||||
}
|
||||
for (auto& act : actual) {
|
||||
ASSERT_NE(expected_set.end(), expected_set.find(act));
|
||||
}
|
||||
}
|
||||
|
||||
TEST(OpKernel, all) {
|
||||
auto net = std::make_shared<NetOp>();
|
||||
ASSERT_NE(net, nullptr);
|
||||
|
||||
net->AppendOp(std::unique_ptr<TestOp>(
|
||||
new TestOp("test", {{"X", {"x"}}, {"W", {"w1"}}, {"b", {"b1"}}},
|
||||
{{"Out", {"y"}}}, framework::AttributeMap{})));
|
||||
net->AppendOp(std::unique_ptr<TestOp>(
|
||||
new TestOp("test", {{"X", {"y"}}, {"W", {"w2"}}, {"b", {"b2"}}},
|
||||
{{"Out", {"z"}}}, framework::AttributeMap{})));
|
||||
|
||||
net->CompleteAddOp();
|
||||
AssertSameVectorWithoutOrder({"x", "w1", "b1", "w2", "b2"},
|
||||
net->Inputs(NetOp::kAll));
|
||||
AssertSameVectorWithoutOrder({"y", "z"}, net->Outputs(NetOp::kAll));
|
||||
|
||||
auto final_outs = net->OutputVars(false);
|
||||
|
||||
ASSERT_EQ(final_outs.size(), 1UL);
|
||||
ASSERT_EQ(final_outs[0], "z");
|
||||
}
|
||||
|
||||
TEST(NetOp, insert_op) {
|
||||
NetOp net;
|
||||
auto op1 = std::unique_ptr<framework::NOP>(
|
||||
new framework::NOP("empty", {{"X", {"x"}}, {"W", {"w1"}}, {"b", {"b1"}}},
|
||||
{{"Out", {"y"}}}, framework::AttributeMap{}));
|
||||
net.AppendOp(*op1);
|
||||
net.InsertOp(0, *op1);
|
||||
ASSERT_EQ(2UL, net.ops_.size());
|
||||
net.InsertOp(2, std::move(op1));
|
||||
ASSERT_EQ(3UL, net.ops_.size());
|
||||
}
|
||||
|
||||
TEST(NetOp, Clone) {
|
||||
NetOp net;
|
||||
net.AppendOp(std::unique_ptr<framework::NOP>(new framework::NOP{
|
||||
"empty", framework::VariableNameMap{}, framework::VariableNameMap{},
|
||||
framework::AttributeMap{}}));
|
||||
net.AppendOp(std::unique_ptr<framework::NOP>(new framework::NOP{
|
||||
"empty2", framework::VariableNameMap{}, framework::VariableNameMap{},
|
||||
framework::AttributeMap{}}));
|
||||
net.CompleteAddOp(true);
|
||||
auto new_net_op = net.Clone();
|
||||
ASSERT_NE(new_net_op, nullptr);
|
||||
ASSERT_TRUE(new_net_op->IsNetOp());
|
||||
auto* new_net = static_cast<NetOp*>(new_net_op.get());
|
||||
ASSERT_EQ(2UL, new_net->ops_.size());
|
||||
ASSERT_EQ(new_net->ops_[0]->Type(), "empty");
|
||||
ASSERT_EQ(new_net->ops_[1]->Type(), "empty2");
|
||||
}
|
||||
|
||||
} // namespace operators
|
||||
} // namespace paddle
|
||||
File diff suppressed because it is too large
Load Diff
@ -1,128 +0,0 @@
|
||||
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import logging
|
||||
import paddle.fluid.core as core
|
||||
import unittest
|
||||
import numpy as np
|
||||
from paddle.fluid.op import Operator, CondOp
|
||||
|
||||
|
||||
class PySimpleCond(object):
|
||||
'''
|
||||
A simple implementation of dynamic if-else based on numpy
|
||||
'''
|
||||
|
||||
def __init__(self):
|
||||
array = [1] * 10
|
||||
for i in range(1, 10, 2):
|
||||
array[i] = 0
|
||||
self.cond = np.array(array)
|
||||
self.x = np.ones(shape=(10, 1)).astype("float32")
|
||||
|
||||
def forward(self):
|
||||
self.index_t = np.where(self.cond == 1)
|
||||
self.index_f = np.where(self.cond == 0)
|
||||
y_t = self.x[self.index_t]
|
||||
y_f = self.x[self.index_f]
|
||||
y_t = y_t * 2.
|
||||
y_f = y_f * (-2.)
|
||||
output = np.zeros(shape=(10, 1))
|
||||
output[self.index_t] = y_t
|
||||
output[self.index_f] = y_f
|
||||
return output
|
||||
|
||||
|
||||
class PySimpleCondTest(unittest.TestCase):
|
||||
def setUp(self):
|
||||
self.condnn = PySimpleCond()
|
||||
|
||||
def test_forward(self):
|
||||
output = self.condnn.forward()
|
||||
|
||||
|
||||
def create_tensor(scope, name, shape, np_data):
|
||||
tensor = scope.var(name).get_tensor()
|
||||
tensor.set_dims(shape)
|
||||
tensor.set(np_data, core.CPUPlace())
|
||||
return tensor
|
||||
|
||||
|
||||
class TestCondOp(unittest.TestCase):
|
||||
'''
|
||||
Test CondOp
|
||||
|
||||
equation:
|
||||
cond = [True, False, True, False, ...]
|
||||
y[index_t] = x[index_t] * 2.
|
||||
y[index_f] = x[index_f] * -2.
|
||||
outputs:
|
||||
y
|
||||
'''
|
||||
|
||||
def setUp(self):
|
||||
self.py_cond = PySimpleCond()
|
||||
|
||||
def forward(self):
|
||||
self.scope = core.Scope()
|
||||
self.create_global_variables()
|
||||
self.create_cond_op()
|
||||
self.create_sub_net()
|
||||
self.condop.run(self.scope, core.CPUPlace())
|
||||
return np.array(self.scope.find_var("Out").get_tensor())
|
||||
|
||||
def create_global_variables(self):
|
||||
x_np_data = self.py_cond.x
|
||||
create_tensor(self.scope, "X", [10, 1], x_np_data)
|
||||
cond_np_data = self.py_cond.cond.astype("int32")
|
||||
create_tensor(self.scope, "cond", [10, 1], cond_np_data)
|
||||
self.scope.var("SubScopes")
|
||||
self.scope.var("IndexTensors")
|
||||
self.scope.var("Out")
|
||||
|
||||
def create_cond_op(self):
|
||||
self.condop = CondOp(
|
||||
Cond="cond",
|
||||
Xs=["X"],
|
||||
Outs=["Out"],
|
||||
SubScopes="SubScopes",
|
||||
IndexTensors="IndexTensors")
|
||||
|
||||
def create_sub_net(self):
|
||||
truenet = core.Net.create()
|
||||
scale_op_t = Operator("scale", X='X', Out='Out', scale=2.)
|
||||
truenet.append_op(scale_op_t)
|
||||
truenet.complete_add_op(True)
|
||||
self.condop.set_truenet(truenet)
|
||||
|
||||
falsenet = core.Net.create()
|
||||
scale_op_t = Operator("scale", X='X', Out='Out', scale=-2.)
|
||||
falsenet.append_op(scale_op_t)
|
||||
falsenet.complete_add_op(True)
|
||||
self.condop.set_falsenet(falsenet)
|
||||
|
||||
def test_forward(self):
|
||||
print 'test cond op forward'
|
||||
pd_output = self.forward()
|
||||
py_output = self.py_cond.forward()
|
||||
print 'pd_output', pd_output
|
||||
print
|
||||
print 'py_output', py_output
|
||||
self.assertEqual(pd_output.shape, py_output.shape)
|
||||
print 'test passed'
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
File diff suppressed because it is too large
Load Diff
@ -1,53 +0,0 @@
|
||||
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import paddle.fluid.core as core
|
||||
from paddle.fluid.op import Operator
|
||||
import unittest
|
||||
|
||||
|
||||
def fc(X, W, Y):
|
||||
ret_v = core.Net.create()
|
||||
|
||||
ret_v.append_op(Operator("mul", X="X", Y="W", Out="pre_activation"))
|
||||
ret_v.append_op(Operator("sigmoid", X="pre_activation", Out=Y))
|
||||
ret_v.complete_add_op(True)
|
||||
return ret_v
|
||||
|
||||
|
||||
class TestNet(unittest.TestCase):
|
||||
def test_net_all(self):
|
||||
net = core.Net.create()
|
||||
op1 = Operator("sum", X=["X", "Y"], Out="Out")
|
||||
net.append_op(op1)
|
||||
|
||||
net2 = core.Net.create()
|
||||
net2.append_op(fc(X="X", W="w", Y="fc.out"))
|
||||
net2.complete_add_op(True)
|
||||
net.append_op(net2)
|
||||
net.complete_add_op(True)
|
||||
|
||||
expected = '''
|
||||
Op(plain_net), inputs:{all[W, X, Y]}, outputs:{all[Out, fc.out, pre_activation]}.
|
||||
Op(sum), inputs:{X[X, Y]}, outputs:{Out[Out]}.
|
||||
Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
|
||||
Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
|
||||
Op(mul), inputs:{X[X], Y[W]}, outputs:{Out[pre_activation]}.
|
||||
Op(sigmoid), inputs:{X[pre_activation]}, outputs:{Out[fc.out]}.
|
||||
'''
|
||||
self.assertEqual(expected, "\n" + str(net))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Loading…
Reference in new issue