support testing when training and handle dropout and batch_norm operator in testing mode#5734
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QiJune merged 13 commits intoPaddlePaddle:developfrom Nov 24, 2017
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QiJune
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QiJune
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JiayiFeng
reviewed
Nov 21, 2017
paddle/framework/prune.cc
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| op_desc.type() == kBatchNormOpType) { | ||
| for (auto& attr : *op_desc.mutable_attrs()) { | ||
| if (attr.name() == "is_test") { | ||
| attr.set_b(true); |
paddle/framework/prune.cc
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| void Prune(const ProgramDesc& input, ProgramDesc* output) { | ||
| prune_impl(input, output, 0); | ||
| void Prune(const ProgramDesc& input, ProgramDesc* output, bool is_test) { | ||
| prune_impl(input, output, 0, is_test); |
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I think setting attribute is_test true has no relationship with prune. We shall add an independent function or module to do this.
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We now have prune method to transform ProgramDesc and generate another ProgramDesc.
Exactly, we will do some optimization in inference stage and handle some special operators. Then, we will generate an inference ProgramDesc.
You are right. Maybe we need another method like prune, the interface could be:
void InferenceOptimize(const ProgramDesc& input, ProgramDesc* output);
reyoung
reviewed
Nov 24, 2017
| prune_impl(input, output, 0); | ||
| } | ||
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| void inference_optimize_impl(const ProgramDesc& input, ProgramDesc* output, |
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The logic of inference_optimize_impl is quite simple. Maybe we can implement it in Python.
reyoung
approved these changes
Nov 24, 2017
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Fix #5733 and Fix #5814