[Other] Add FDTensor function Pad (#532)

* Add InferShape func for all the vision processors

* fix infer shape of limit short

* Fix infer shape bug of stride_pad

* revert modify of processor

* add function pad

fastdeploy/function/pad.cc

@@ -0,0 +1,126 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/function/pad.h"
+
+#include <cstdlib>
+
+#include "fastdeploy/function/eigen.h"
+#include "fastdeploy/utils/utils.h"
+
+namespace fastdeploy {
+
+template <typename T, int Rank>
+struct PadEigen {
+  using Array = std::array<std::pair<int64_t, int64_t>, Rank>;
+  using Array32Bit = std::array<std::pair<int, int>, Rank>;
+  using InType = Eigen::TensorMap<
+      Eigen::Tensor<const T, Rank, Eigen::RowMajor, Eigen::DenseIndex>>;
+  using InType32BitIndex =
+      Eigen::TensorMap<Eigen::Tensor<const T, Rank, Eigen::RowMajor, int>,
+                       Eigen::Aligned>;
+  using OutType = Eigen::TensorMap<
+      Eigen::Tensor<T, Rank, Eigen::RowMajor, Eigen::DenseIndex>>;
+  using OutType32BitIndex =
+      Eigen::TensorMap<Eigen::Tensor<T, Rank, Eigen::RowMajor, int>,
+                       Eigen::Aligned>;
+
+  static void Eval(const Eigen::DefaultDevice& dev,
+                   OutType out,
+                   const InType& in,
+                   const Array& padding,
+                   const T value) {
+    out.device(dev) = in.pad(padding, value);
+  }
+
+  static void Eval32(const Eigen::DefaultDevice& dev,
+                     OutType32BitIndex out,
+                     const InType32BitIndex& in,
+                     const Array32Bit& padding,
+                     const T value) {
+    out.device(dev) = in.pad(padding, value);
+  }
+};
+
+template <typename T, size_t D>
+void PadFunction(const std::vector<int>& pads,
+                 const FDTensor& src,
+                 T pad_value,
+                 FDTensor* out) {
+  std::array<std::pair<int64_t, int64_t>, D> paddings;
+
+  for (size_t i = 0; i < paddings.size(); ++i) {
+    paddings[i].first = pads[i * 2];
+    paddings[i].second = pads[i * 2 + 1];
+  }
+
+  auto src_tensor = EigenTensor<T, D>::From(src);
+  auto out_tensor = EigenTensor<T, D>::From(*out);
+
+  const auto& dev = *EigenDeviceWrapper::GetInstance()->GetDevice();
+  PadEigen<T, D>::Eval(
+      dev, out_tensor, src_tensor, paddings, pad_value);
+}
+
+
+template <typename T>
+void PaddingFunctor(int rank, const std::vector<int>& pads, T pad_value, const FDTensor& src, FDTensor* out) {
+  switch (rank) {
+    case 1:
+      PadFunction<T, 1>(pads, src, pad_value, out);
+      break;
+    case 2:
+      PadFunction<T, 2>(pads, src, pad_value, out);
+      break;
+    case 3:
+      PadFunction<T, 3>(pads, src, pad_value, out);
+      break;
+    case 4:
+      PadFunction<T, 4>(pads, src, pad_value, out);
+      break;
+    case 5:
+      PadFunction<T, 5>(pads, src, pad_value, out);
+      break;
+    case 6:
+      PadFunction<T, 6>(pads, src, pad_value, out);
+      break;
+    default:
+      FDASSERT(false, "Pad only support tensors with no more than 6 dimensions currently.");
+  }
+}
+
+template <typename T>
+void PadKernel(const FDTensor& x,
+               const std::vector<int>& paddings,
+               const T& pad_value,
+               FDTensor* out) {
+  std::vector<int64_t> new_shape(x.shape.size());
+  for (size_t i = 0; i < x.shape.size(); ++i) {
+    new_shape[i] = x.shape[i] + paddings[2 * i] + paddings[2 * i + 1];
+  }
+  out->Allocate(new_shape, x.dtype);
+  PaddingFunctor<T>(x.shape.size(), paddings, pad_value, x, out);
+}
+
+void Pad(const FDTensor& x, FDTensor* out, const std::vector<int>& pads, float value) {
+  FDASSERT(pads.size() == x.shape.size() * 2, "Size of pads:%zu must be 2 times of rank:%zu.", pads.size(), x.shape.size());
+  FDTensor out_tmp;
+  FD_VISIT_ALL_TYPES(x.dtype, "PadKernel",
+                       ([&] { PadKernel<data_t>(x, pads, value, &out_tmp); }));
+  *out = std::move(out_tmp);
+}
+
+
+
+}  // namespace fastdeploy

fastdeploy/function/pad.h

@@ -0,0 +1,30 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "fastdeploy/core/fd_tensor.h"
+
+namespace fastdeploy {
+
+/** Excute the pad operation for input FDTensor along given dims.
+    @param x The input tensor.
+    @param out The output tensor which stores the result.
+    @param pads The size of padding for each dimension, for 3-D tensor, the pads should be [1d-left, 1d-right, 2d-left, 2d-right, 3d-left, 3d-right]
+    @param pad_value The value which will fill into out tensor
+*/
+FASTDEPLOY_DECL void Pad(const FDTensor& x, FDTensor* out,
+            const std::vector<int>& pads, float pad_value = 0);
+
+}  // namespace fastdeploy