From f799c58fe04a1f5080209ab4237190ce51b43b94 Mon Sep 17 00:00:00 2001
From: Franck Dary <franck.dary@lis-lab.fr>
Date: Thu, 4 Jun 2020 16:02:52 +0200
Subject: [PATCH] Fixed modules using values instead of embeddings
---
torch_modules/src/NumericColumnModule.cpp | 3 +--
torch_modules/src/UppercaseRateModule.cpp | 3 +--
2 files changed, 2 insertions(+), 4 deletions(-)
diff --git a/torch_modules/src/NumericColumnModule.cpp b/torch_modules/src/NumericColumnModule.cpp
index 45ebb1a..5f8c8d4 100644
--- a/torch_modules/src/NumericColumnModule.cpp
+++ b/torch_modules/src/NumericColumnModule.cpp
@@ -42,8 +42,7 @@ NumericColumnModuleImpl::NumericColumnModuleImpl(std::string name, const std::st
torch::Tensor NumericColumnModuleImpl::forward(torch::Tensor input)
{
auto context = input.narrow(1, firstInputIndex, getInputSize());
- void * dataPtr = context.flatten().data_ptr();
- auto values = torch::from_blob(dataPtr, {(long)(context.size(0)*getInputSize())}, torch::TensorOptions(torch::kDouble).requires_grad(false).device(NeuralNetworkImpl::device)).clone().to(torch::kFloat).view({(long)context.size(0), (long)context.size(1), 1});
+ auto values = torch::from_blob(context.data_ptr(), context.sizes(), context.strides(), torch::TensorOptions(torch::kDouble).requires_grad(false).device(NeuralNetworkImpl::device)).to(torch::kFloat).unsqueeze(-1).clone();
return myModule->forward(values);
}
diff --git a/torch_modules/src/UppercaseRateModule.cpp b/torch_modules/src/UppercaseRateModule.cpp
index 2118745..7f92e05 100644
--- a/torch_modules/src/UppercaseRateModule.cpp
+++ b/torch_modules/src/UppercaseRateModule.cpp
@@ -40,8 +40,7 @@ UppercaseRateModuleImpl::UppercaseRateModuleImpl(std::string name, const std::st
torch::Tensor UppercaseRateModuleImpl::forward(torch::Tensor input)
{
auto context = input.narrow(1, firstInputIndex, getInputSize());
- void * dataPtr = context.flatten().data_ptr();
- auto values = torch::from_blob(dataPtr, {(long)(context.size(0)*getInputSize())}, torch::TensorOptions(torch::kDouble).requires_grad(false).device(NeuralNetworkImpl::device)).clone().to(torch::kFloat).view({(long)context.size(0), (long)context.size(1), 1});
+ auto values = torch::from_blob(context.data_ptr(), context.sizes(), context.strides(), torch::TensorOptions(torch::kDouble).requires_grad(false).device(NeuralNetworkImpl::device)).to(torch::kFloat).unsqueeze(-1).clone();
return myModule->forward(values);
}
--
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