Add ConvTranspose + BatchNorm fuse pass (#20161)
* Add ConvTranspose + BatchNorm fuse pass test=develop * Add tests for conv+bn and conv_transpose+bn passes test=developrevert-20712-fix_depthwise_conv
Adam
6 years ago
committed by
Tao Luo
parent
27d1ef6081
commit
7faa3e9555
@ -0,0 +1,86 @@
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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/framework/ir/conv_bn_fuse_pass.h"
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#include <gtest/gtest.h>
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#include "paddle/fluid/framework/ir/pass_tester_helper.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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void AddVarToScope(Scope* param_scope, const std::string& name,
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const DDim& dims) {
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auto* tensor = param_scope->Var(name)->GetMutable<LoDTensor>();
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tensor->Resize(dims);
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tensor->mutable_data<float>(platform::CPUPlace());
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}
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Scope* CreateParamScope() {
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auto param_scope = new Scope();
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AddVarToScope(param_scope, "bias_1", {3});
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AddVarToScope(param_scope, "scale", {3});
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AddVarToScope(param_scope, "mean", {3});
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AddVarToScope(param_scope, "variance", {3});
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AddVarToScope(param_scope, "filters", {3, 3, 2, 2});
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return param_scope;
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}
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void TestMain(const std::string& conv_type) {
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// inputs operator output
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// ------------------------------------------------------------------
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// (in, filters, bias_0) conv -> conv_out
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// (conv_out, scale,
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// bias_1, mean, varaince) batch_norm -> (...)
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Layers layers;
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auto* in = layers.data("in", {1, 3, 20, 20});
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auto* filters = layers.data("filters", {3, 3, 2, 2}, true);
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auto* bias_0 = layers.data("bias_0", {3}, true);
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VarDesc* conv_out;
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if (conv_type == "conv_transpose") {
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conv_out = layers.conv2d_transpose(in, filters, bias_0);
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} else {
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conv_out = layers.conv2d(in, filters, bias_0);
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}
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conv_out->SetShape({1, 3, 20, 20});
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auto* scale = layers.data("scale", {3}, true);
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auto* bias_1 = layers.data("bias_1", {3}, true);
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auto* mean = layers.data("mean", {3}, true);
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auto* variance = layers.data("variance", {3}, true);
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layers.batch_norm(conv_out, scale, bias_1, mean, variance);
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std::unique_ptr<ir::Graph> graph(new ir::Graph(layers.main_program()));
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graph->Set("__param_scope__", CreateParamScope());
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auto pass = PassRegistry::Instance().Get(conv_type + "_bn_fuse_pass");
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int num_bn_nodes_before = GetNumOpNodes(graph, "batch_norm");
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VLOG(3) << DebugString(graph);
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graph.reset(pass->Apply(graph.release()));
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int num_bn_nodes_after = GetNumOpNodes(graph, "batch_norm");
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VLOG(3) << DebugString(graph);
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PADDLE_ENFORCE_EQ(num_bn_nodes_before, 1);
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PADDLE_ENFORCE_EQ(num_bn_nodes_after, 0);
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}
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TEST(ConvBNFusePass, conv2d) { TestMain("conv"); }
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TEST(ConvBNFusePass, conv2d_tranpose) { TestMain("conv_transpose"); }
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} // namespace ir
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} // namespace framework
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} // namespace paddle
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USE_PASS(conv_bn_fuse_pass);
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