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Silenced the last batch of compilation warnings triggered by gcc 4.8

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This commit is contained in:
Benoit Steiner 2015-02-10 12:43:55 -08:00
parent c21e45fbc5
commit 780b2422e2
5 changed files with 19 additions and 19 deletions
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10
unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h
View File

@ -167,7 +167,7 @@ struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
m_stride = 1;
m_inputStride = 1;
if (Layout == ColMajor) {
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = 0; i < m_dim.actualDim(); ++i) {
m_stride *= input_dims[i];
m_inputStride *= input_dims[i];
@ -208,8 +208,8 @@ struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index+packetSize-1 < dimensions().TotalSize());
if ((Layout == ColMajor && m_dim.actualDim() == 0) ||
(Layout == RowMajor && m_dim.actualDim() == NumInputDims-1)) {
if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) && m_dim.actualDim() == 0) ||
(static_cast<int>(Layout) == static_cast<int>(RowMajor) && m_dim.actualDim() == NumInputDims-1)) {
// m_stride is equal to 1, so let's avoid the integer division.
eigen_assert(m_stride == 1);
Index inputIndex = index * m_inputStride + m_inputOffset;
@ -220,8 +220,8 @@ struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
} else if ((Layout == ColMajor && m_dim.actualDim() == NumInputDims - 1) ||
(Layout == RowMajor && m_dim.actualDim() == 0)) {
} else if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) && m_dim.actualDim() == NumInputDims - 1) ||
(static_cast<int>(Layout) == static_cast<int>(RowMajor) && m_dim.actualDim() == 0)) {
// m_stride is aways greater than index, so let's avoid the integer division.
eigen_assert(m_stride > index);
return m_impl.template packet<LoadMode>(index + m_inputOffset);

10
unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h
View File

@ -236,9 +236,9 @@ struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelAr
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_inputImpl(op.inputExpression(), device), m_kernelImpl(op.kernelExpression(), device), m_kernelArg(op.kernelExpression()), m_kernel(NULL), m_local_kernel(false), m_device(device)
{
EIGEN_STATIC_ASSERT((TensorEvaluator<InputArgType, Device>::Layout == TensorEvaluator<KernelArgType, Device>::Layout), YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<InputArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<KernelArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
// Only column major tensors are supported for now.
EIGEN_STATIC_ASSERT((Layout == ColMajor), YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT((static_cast<int>(Layout) == static_cast<int>(ColMajor)), YOU_MADE_A_PROGRAMMING_MISTAKE);
const typename TensorEvaluator<InputArgType, Device>::Dimensions& input_dims = m_inputImpl.dimensions();
const typename TensorEvaluator<KernelArgType, Device>::Dimensions& kernel_dims = m_kernelImpl.dimensions();
@ -339,7 +339,7 @@ struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelAr
}
}
Scalar* data() const { return NULL; }
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
private:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index firstInput(Index index) const {
@ -621,9 +621,9 @@ struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelAr
EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const GpuDevice& device)
: m_inputImpl(op.inputExpression(), device), m_kernelArg(op.kernelExpression()), m_kernelImpl(op.kernelExpression(), device), m_indices(op.indices()), m_buf(NULL), m_kernel(NULL), m_local_kernel(false), m_device(device)
{
EIGEN_STATIC_ASSERT((TensorEvaluator<InputArgType, GpuDevice>::Layout == TensorEvaluator<KernelArgType, GpuDevice>::Layout), YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<InputArgType, GpuDevice>::Layout) == static_cast<int>(TensorEvaluator<KernelArgType, GpuDevice>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
// Only column major tensors are supported for now.
EIGEN_STATIC_ASSERT((Layout == ColMajor), YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT((static_cast<int>(Layout) == static_cast<int>(ColMajor)), YOU_MADE_A_PROGRAMMING_MISTAKE);
const typename TensorEvaluator<InputArgType, GpuDevice>::Dimensions& input_dims = m_inputImpl.dimensions();
const typename TensorEvaluator<KernelArgType, GpuDevice>::Dimensions& kernel_dims = m_kernelImpl.dimensions();

4
unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h
View File

@ -121,7 +121,7 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
: m_impl(op.expression(), device)
{
// Only column major tensors are supported for now.
EIGEN_STATIC_ASSERT((Layout == ColMajor), YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT((static_cast<int>(Layout) == static_cast<int>(ColMajor)), YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT(NumDims >= 4, YOU_MADE_A_PROGRAMMING_MISTAKE);
@ -295,7 +295,7 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
return packetWithPossibleZero(index);
}
Scalar* data() const { return NULL; }
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }

10
unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h
View File

@ -104,7 +104,7 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
m_dimensions[i] += m_padding[i].first + m_padding[i].second;
}
const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
if (Layout == ColMajor) {
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
m_inputStrides[0] = 1;
m_outputStrides[0] = 1;
for (int i = 1; i < NumDims; ++i) {
@ -141,7 +141,7 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
{
eigen_assert(index < dimensions().TotalSize());
Index inputIndex = 0;
if (Layout == ColMajor) {
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = NumDims - 1; i > 0; --i) {
const Index idx = index / m_outputStrides[i];
if (idx < m_padding[i].first || idx >= m_dimensions[i] - m_padding[i].second) {
@ -175,7 +175,7 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
if (Layout == ColMajor) {
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
return packetColMajor(index);
}
return packetRowMajor(index);
@ -184,7 +184,7 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<Index, NumDims>& coords) const
{
Index inputIndex;
if (Layout == ColMajor) {
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
const Index idx = coords[0];
if (idx < m_padding[0].first || idx >= m_dimensions[0] - m_padding[0].second) {
return Scalar(0);
@ -214,7 +214,7 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
return m_impl.coeff(inputIndex);
}
Scalar* data() const { return NULL; }
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
protected:

4
unsupported/Eigen/CXX11/src/Tensor/TensorPatch.h
View File

@ -100,7 +100,7 @@ struct TensorEvaluator<const TensorPatchOp<PatchDim, ArgType>, Device>
: m_impl(op.expression(), device)
{
// Only column major tensors are supported for now.
EIGEN_STATIC_ASSERT((Layout == ColMajor), YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT((static_cast<int>(Layout) == static_cast<int>(ColMajor)), YOU_MADE_A_PROGRAMMING_MISTAKE);
Index num_patches = 1;
const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
@ -232,7 +232,7 @@ struct TensorEvaluator<const TensorPatchOp<PatchDim, ArgType>, Device>
}
}
Scalar* data() const { return NULL; }
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
protected:
Dimensions m_dimensions;