haojin2 commented on a change in pull request #15987: Numpy add numpy op rot90
URL: https://github.com/apache/incubator-mxnet/pull/15987#discussion_r318874589
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File path: src/operator/numpy/np_matrix_op-inl.h
##########
@@ -60,6 +60,246 @@ void NumpyTranspose(const nnvm::NodeAttrs& attrs,
}
}
+struct NumpyRot90Param : public dmlc::Parameter<NumpyRot90Param> {
+ int k;
+ dmlc::optional<mxnet::TShape> axes;
+ DMLC_DECLARE_PARAMETER(NumpyRot90Param) {
+ DMLC_DECLARE_FIELD(k)
+ .set_default(1)
+ .describe("Number of times the array is rotated by 90 degrees.");
+ DMLC_DECLARE_FIELD(axes)
+ .set_default(dmlc::optional<mxnet::TShape>())
+ .describe(" The array is rotated in the plane defined by the axes. Axes
must be different.");
+ }
+};
+
+struct rot90reverse {
+ MSHADOW_XINLINE static index_t ReverseIndex(index_t idx,
+ index_t nreversedim,
+ const index_t * stride_,
+ const index_t * trailing_) {
+ index_t outputIndex = idx;
+ for (index_t i = 0; i < nreversedim; ++i) {
+ const index_t low = outputIndex % trailing_[i];
+ index_t high = outputIndex / trailing_[i];
+ const index_t x = high % stride_[i];
+ high /= stride_[i];
+ outputIndex = (high * stride_[i] + stride_[i] - 1 - x) * trailing_[i] +
low;
+ }
+ return outputIndex;
+ }
+ template<typename DType>
+ MSHADOW_XINLINE static void Map(index_t index, index_t nreversedim, const
DType *src, DType *dst,
+ const index_t * stride_,
+ const index_t * trailing_) {
+ index_t new_idx = ReverseIndex(index, nreversedim, stride_, trailing_);
+ dst[new_idx] = src[index];
+ }
+};
+
+template<typename xpu>
+void NumpyRot90ComputeFlipIml(const OpContext& ctx,
+ const std::vector<TBlob>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<TBlob>& outputs,
+ const index_t axis0, const index_t axis1) {
+ using namespace mshadow;
+ using namespace mxnet_op;
+
+ const mxnet::TShape& ishape = inputs[0].shape_;
+ Stream<xpu> *s = ctx.get_stream<xpu>();
+
+ std::vector<index_t> stride_(2);
+ std::vector<index_t> trailing_(2);
+ index_t reverse_index = 0;
+ std::vector<index_t> temp{axis0, axis1};
+ for (int axis : temp) {
+ stride_[reverse_index] = ishape[axis];
+ trailing_[reverse_index] = 1;
+ for (int i2 = axis + 1; i2 < ishape.ndim(); ++i2) {
+ trailing_[reverse_index] *= ishape[i2];
+ }
+ reverse_index++;
+ }
+
+ index_t workspace_size = 2 * sizeof(index_t);
+ Tensor<xpu, 1, char> workspace =
+ ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(2 *
workspace_size), s);
+ Tensor<cpu, 1, index_t> stride_cpu_tensor(stride_.data(),
Shape1(stride_.size()));
+ Tensor<xpu, 1, index_t> stride_xpu_tensor(
+ reinterpret_cast<index_t*>(workspace.dptr_), Shape1(stride_.size()));
+ Tensor<cpu, 1, index_t> trailing_cpu_tensor(trailing_.data(),
Shape1(trailing_.size()));
+ Tensor<xpu, 1, index_t> trailing_xpu_tensor(
+ reinterpret_cast<index_t*>(workspace.dptr_ + workspace_size),
Shape1(trailing_.size()));
+
+ mshadow::Copy(stride_xpu_tensor, stride_cpu_tensor, s);
+ mshadow::Copy(trailing_xpu_tensor, trailing_cpu_tensor, s);
+ MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+ Kernel<rot90reverse, xpu>::Launch(s, inputs[0].Size(),
reverse_index,
+ inputs[0].dptr<DType>(), outputs[0].dptr<DType>(),
+ stride_xpu_tensor.dptr_, trailing_xpu_tensor.dptr_);
+ });
+}
+
+struct rot90Transreverse {
+ MSHADOW_XINLINE static index_t ReverseIndex(index_t idx,
+ const index_t stride_,
+ const index_t trailing_) {
+ index_t outputIndex = idx;
+ const index_t low = outputIndex % trailing_;
+ index_t high = outputIndex / trailing_;
+ const index_t x = high % stride_;
+ high /= stride_;
+ outputIndex = (high * stride_ + stride_ - 1 - x) * trailing_ + low;
+
+ return outputIndex;
+ }
+ template<typename DType>
+ MSHADOW_XINLINE static void Map(index_t index, const DType *src, DType *dst,
+ const index_t stride_,
+ const index_t trailing_) {
+ index_t new_idx = ReverseIndex(index, stride_, trailing_);
+ dst[new_idx] = src[index];
+ }
+};
+
+template<typename xpu>
+void NumpyRot90ComputeFlipTransposeIml(const OpContext& ctx,
+ const std::vector<TBlob>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<TBlob>& outputs,
+ const mxnet::TShape axes_list,
+ const index_t axis) {
+ using namespace mshadow;
+ using namespace mxnet_op;
+
+ const mxnet::TShape& ishape = inputs[0].shape_;
+ Stream<xpu> *s = ctx.get_stream<xpu>();
+
+ index_t stride_;
+ index_t trailing_;
+
+ stride_ = ishape[axis];
+ trailing_ = 1;
+ for (int i2 = axis + 1; i2 < ishape.ndim(); ++i2) {
+ trailing_ *= ishape[i2];
+ }
+
+ MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+ index_t workspace_size = inputs[0].Size() * sizeof(DType);
+ Tensor<xpu, 1, char> workspace =
+ ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(workspace_size),
s);
+ DType* data_ptr = reinterpret_cast<DType*>(workspace.dptr_);
+ TBlob mid_data = TBlob(data_ptr, inputs[0].shape_, xpu::kDevMask);
+
+ Kernel<rot90Transreverse, xpu>::Launch(s, inputs[0].Size(),
inputs[0].dptr<DType>(),
+ mid_data.dptr<DType>(),
+ stride_, trailing_);
+ mxnet::op::TransposeImpl<xpu>(ctx.run_ctx, mid_data, outputs[0],
axes_list);
+ });
+}
+
+
+template<typename xpu>
+void NumpyRot90ComputeTransposeFlipIml(const OpContext& ctx,
+ const std::vector<TBlob>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<TBlob>& outputs,
+ const mxnet::TShape axes_list,
+ const index_t axis) {
+ using namespace mshadow;
+ using namespace mxnet_op;
+
+ Stream<xpu> *s = ctx.get_stream<xpu>();
+
+ MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+ index_t workspace_size = inputs[0].Size() * sizeof(DType);
+ Tensor<xpu, 1, char> workspace =
+ ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(workspace_size),
s);
+ DType* data_ptr = reinterpret_cast<DType*>(workspace.dptr_);
+ mxnet::TShape mid_shape(outputs[0].shape_);
+ TBlob mid_data = TBlob(data_ptr, mid_shape, xpu::kDevMask);
+ mxnet::op::TransposeImpl<xpu>(ctx.run_ctx, inputs[0], mid_data, axes_list);
+
+ index_t stride_;
+ index_t trailing_;
+ stride_ = mid_shape[axis];
+ trailing_ = 1;
+ for (int i2 = axis + 1; i2 < mid_shape.ndim(); ++i2) {
+ trailing_ *= mid_shape[i2];
+ }
+ Kernel<rot90Transreverse, xpu>::Launch(s, mid_data.Size(),
mid_data.dptr<DType>(),
+ outputs[0].dptr<DType>(),
+ stride_, trailing_);
+ });
+}
+
+template<int req>
+struct rot90 {
+ template<typename DType>
+ MSHADOW_XINLINE static void Map(index_t i, const DType *in_data, DType
*out_data) {
+ KERNEL_ASSIGN(out_data[i], req, in_data[i]);
+ }
+};
+
+template<typename xpu>
+void NumpyRot90Compute(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<TBlob>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<TBlob>& outputs) {
+ using namespace mshadow;
+ using namespace mxnet_op;
+ const NumpyRot90Param& param = nnvm::get<NumpyRot90Param>(attrs.parsed);
+ CHECK_EQ(inputs.size(), 1U);
+ CHECK_EQ(outputs.size(), 1U);
+ CHECK_EQ(inputs[0].type_flag_, outputs[0].type_flag_);
+ if (outputs[0].Size() == 0) return;
+ Stream<xpu> *s = ctx.get_stream<xpu>();
+
+ int real_k(param.k);
+ real_k = real_k % 4;
+ if (real_k < 0) {
+ real_k += 4;
+ }
+
+ // axis has value
+ mxnet::TShape real_axes(param.axes.value());
+ for (index_t i = 0; i < real_axes.ndim(); i++) {
+ if (real_axes[i] < 0) {
+ real_axes[i] += inputs[0].shape_.ndim();
+ }
+ }
+ if (real_k == 0) {
+ MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+ MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
Review comment:
Indentation.
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