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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/08/10 20:56:31 UTC
[GitHub] eric-haibin-lin closed pull request #12098: [MXNET-794] Remove
Wrong InferType for AdaptiveAvgPool and BilinearReisze2D
eric-haibin-lin closed pull request #12098: [MXNET-794] Remove Wrong InferType for AdaptiveAvgPool and BilinearReisze2D
URL: https://github.com/apache/incubator-mxnet/pull/12098
This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:
As this is a foreign pull request (from a fork), the diff is supplied
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diff --git a/src/operator/contrib/adaptive_avg_pooling-inl.h b/src/operator/contrib/adaptive_avg_pooling-inl.h
index 7331c7bd47a..12284d9d85d 100644
--- a/src/operator/contrib/adaptive_avg_pooling-inl.h
+++ b/src/operator/contrib/adaptive_avg_pooling-inl.h
@@ -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) {
diff --git a/src/operator/contrib/adaptive_avg_pooling.cc b/src/operator/contrib/adaptive_avg_pooling.cc
index 079571177cb..00ab36605bf 100644
--- a/src/operator/contrib/adaptive_avg_pooling.cc
+++ b/src/operator/contrib/adaptive_avg_pooling.cc
@@ -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"})
@@ -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>);
diff --git a/src/operator/contrib/bilinear_resize-inl.h b/src/operator/contrib/bilinear_resize-inl.h
index c096f014975..ff3f794d167 100644
--- a/src/operator/contrib/bilinear_resize-inl.h
+++ b/src/operator/contrib/bilinear_resize-inl.h
@@ -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
diff --git a/src/operator/contrib/bilinear_resize.cc b/src/operator/contrib/bilinear_resize.cc
index e1248ce97bb..074f74aefcc 100644
--- a/src/operator/contrib/bilinear_resize.cc
+++ b/src/operator/contrib/bilinear_resize.cc
@@ -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"})
@@ -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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