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[MXNET-794] Remove Wrong InferType for AdaptiveAvgPool and BilinearReisze2D #12098

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Aug 10, 2018
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[MXNET-794] Remove Wrong InferType for AdaptiveAvgPool and BilinearReisze2D #12098

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35 changes: 0 additions & 35 deletions src/operator/contrib/adaptive_avg_pooling-inl.h
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Original file line number Diff line number Diff line change
Expand Up @@ -144,41 +144,6 @@ static bool AdaptiveAvgPoolOpInferShape(const nnvm::NodeAttrs& attrs,
return true;
}

static bool AdaptiveAvgPoolOpInferType(const nnvm::NodeAttrs& attrs,
std::vector<int> *in_type,
std::vector<int> *out_type) {
using namespace mshadow;
CHECK_EQ(in_type->size(), 1U);
int dtype = (*in_type)[0];
CHECK_NE(dtype, -1) << "First input must have specified type";
// For float16 input type beta, gamma, mean, and average are stored in float32.
// For other input types, these parameters have the same type as input
// NOTE: This requirement is from cuDNN (v. 4 and 5)
int dtype_param = 0;
MSHADOW_REAL_TYPE_SWITCH_EX(dtype, DTypeX, AccRealX, {
dtype_param = mshadow::DataType<AccRealX>::kFlag; });
out_type->clear();
out_type->push_back(dtype_param);
return true;
}

static inline bool AdaptiveAvgPoolOpStorageType(const nnvm::NodeAttrs &attrs,
const int dev_mask,
DispatchMode *dispatch_mode,
std::vector<int> *in_attrs,
std::vector<int> *out_attrs) {
CHECK_EQ(in_attrs->size(), 1);
CHECK_EQ(out_attrs->size(), 1);
*dispatch_mode = DispatchMode::kFCompute;
for (int& v : *in_attrs) {
if (v == - 1) v = kDefaultStorage;
}
for (size_t i = 0; i < out_attrs->size(); i++) {
(*out_attrs)[i] = kDefaultStorage;
}
return true;
}

using namespace mshadow;
template<typename xpu, int Dim, typename DType>
MSHADOW_XINLINE int get_stride(Tensor<xpu, Dim, DType> tensor, int idx) {
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3 changes: 0 additions & 3 deletions src/operator/contrib/adaptive_avg_pooling.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -216,8 +216,6 @@ The pooling kernel and stride sizes are automatically chosen for desired output
.set_num_inputs(1)
.set_num_outputs(1)
.set_attr<nnvm::FInferShape>("FInferShape", AdaptiveAvgPoolOpInferShape)
.set_attr<nnvm::FInferType>("FInferType", AdaptiveAvgPoolOpInferType)
.set_attr<FInferStorageType>("FInferStorageType", AdaptiveAvgPoolOpStorageType)
.set_attr<FCompute>("FCompute<cpu>", AdaptiveAvgPoolOpForward<cpu>)
.set_attr<nnvm::FGradient>("FGradient",
ElemwiseGradUseNone{"_backward_contrib_AdaptiveAvgPooling2D"})
Expand All @@ -229,7 +227,6 @@ NNVM_REGISTER_OP(_backward_contrib_AdaptiveAvgPooling2D)
.set_num_inputs(1)
.set_num_outputs(1)
.set_attr<nnvm::TIsBackward>("TIsBackward", true)
.set_attr<FInferStorageType>("FInferStorageType", AdaptiveAvgPoolOpStorageType)
.set_attr<FCompute>("FCompute<cpu>", AdaptiveAvgPoolOpBackward<cpu>);


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36 changes: 0 additions & 36 deletions src/operator/contrib/bilinear_resize-inl.h
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Expand Up @@ -136,42 +136,6 @@ static bool BilinearSampleOpInferShape(const nnvm::NodeAttrs& attrs,
return true;
}

static bool BilinearSampleOpInferType(const nnvm::NodeAttrs& attrs,
std::vector<int> *in_type,
std::vector<int> *out_type) {
using namespace mshadow;
CHECK_EQ(in_type->size(), 1U);
int dtype = (*in_type)[0];
CHECK_NE(dtype, -1) << "First input must have specified type";
// For float16 input type beta, gamma, mean, and average are stored in float32.
// For other input types, these parameters have the same type as input
// NOTE: This requirement is from cuDNN (v. 4 and 5)
int dtype_param = 0;
MSHADOW_REAL_TYPE_SWITCH_EX(dtype, DTypeX, AccRealX, {
dtype_param = mshadow::DataType<AccRealX>::kFlag; });
out_type->clear();
out_type->push_back(dtype_param);
return true;
}

static inline bool BilinearSampleOpStorageType(const nnvm::NodeAttrs &attrs,
const int dev_mask,
DispatchMode *dispatch_mode,
std::vector<int> *in_attrs,
std::vector<int> *out_attrs) {
CHECK_EQ(in_attrs->size(), 1);
CHECK_EQ(out_attrs->size(), 1);
*dispatch_mode = DispatchMode::kFCompute;
for (int& v : *in_attrs) {
if (v == - 1) v = kDefaultStorage;
}
for (size_t i = 0; i < out_attrs->size(); i++) {
(*out_attrs)[i] = kDefaultStorage;
}
return true;
}


} // namespace op
} // namespace mxnet

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3 changes: 0 additions & 3 deletions src/operator/contrib/bilinear_resize.cc
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Expand Up @@ -177,8 +177,6 @@ for more details.
.set_num_inputs(1)
.set_num_outputs(1)
.set_attr<nnvm::FInferShape>("FInferShape", BilinearSampleOpInferShape)
.set_attr<nnvm::FInferType>("FInferType", BilinearSampleOpInferType)
.set_attr<FInferStorageType>("FInferStorageType", BilinearSampleOpStorageType)
.set_attr<FCompute>("FCompute<cpu>", BilinearSampleOpForward<cpu>)
.set_attr<nnvm::FGradient>("FGradient",
ElemwiseGradUseNone{"_backward_contrib_BilinearResize2D"})
Expand All @@ -190,7 +188,6 @@ NNVM_REGISTER_OP(_backward_contrib_BilinearResize2D)
.set_num_inputs(1)
.set_num_outputs(1)
.set_attr<nnvm::TIsBackward>("TIsBackward", true)
.set_attr<FInferStorageType>("FInferStorageType", BilinearSampleOpStorageType)
.set_attr<FCompute>("FCompute<cpu>", BilinearSampleOpBackward<cpu>);


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