chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/unpool_kernel.cc

@@ -0,0 +1,169 @@
+// 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 "paddle/phi/kernels/unpool_kernel.h"
+
+#include <algorithm>
+#include <vector>
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+
+namespace phi {
+
+template <typename T, typename IndT, typename Context>
+void Unpool(const Context& dev_ctx,
+            const DenseTensor& x,
+            const DenseTensor& indices,
+            DenseTensor* out) {
+  T* output_data = dev_ctx.template Alloc<T>(out);
+  if (output_data) {
+    funcs::SetConstant<Context, T> set_zero;
+    set_zero(dev_ctx, out, static_cast<T>(0));
+  }
+  const int batch_size = static_cast<int>(x.dims()[0]);
+  const int input_height = static_cast<int>(x.dims()[2]);
+  const int input_width = static_cast<int>(x.dims()[3]);
+  const int output_channels = static_cast<int>(out->dims()[1]);
+  const int output_height = static_cast<int>(out->dims()[2]);
+  const int output_width = static_cast<int>(out->dims()[3]);
+  int64_t input_feasize = static_cast<int64_t>(input_height) * input_width;
+  int64_t output_feasize = static_cast<int64_t>(output_height) * output_width;
+  const T* input_data = x.data<T>();
+  const IndT* indices_data = indices.data<IndT>();
+  for (int b = 0; b < batch_size; ++b) {
+    for (int c = 0; c < output_channels; ++c) {
+      for (int i = 0; i < input_feasize; ++i) {
+        IndT index = indices_data[i];
+        PADDLE_ENFORCE_LT(
+            index,
+            output_feasize,
+            common::errors::InvalidArgument(
+                "index should less than output tensor height * output tensor "
+                "width. Expected %ld < %ld, but got "
+                "%ld >= %ld. Please check input value.",
+                index,
+                output_feasize,
+                index,
+                output_feasize));
+        output_data[index] = input_data[i];
+      }
+      input_data += input_feasize;
+      indices_data += input_feasize;
+      output_data += output_feasize;
+    }
+  }
+}
+
+template <typename T, typename Context>
+void UnpoolKernel(const Context& dev_ctx,
+                  const DenseTensor& x,
+                  const DenseTensor& indices,
+                  const std::vector<int>& ksize UNUSED,
+                  const std::vector<int>& strides UNUSED,
+                  const std::vector<int>& paddings UNUSED,
+                  const IntArray& output_size UNUSED,
+                  const std::string& data_format UNUSED,
+                  DenseTensor* out) {
+  if (out && out->numel() == 0) {
+    dev_ctx.template Alloc<T>(out);
+    return;
+  }
+  const auto& indices_type = indices.dtype();
+  if (indices_type == DataType::INT32) {
+    Unpool<T, int, Context>(dev_ctx, x, indices, out);
+  } else {
+    Unpool<T, int64_t, Context>(dev_ctx, x, indices, out);
+  }
+}
+
+template <typename T, typename IndT, typename Context>
+void Unpool3d(const Context& dev_ctx,
+              const DenseTensor& x,
+              const DenseTensor& indices,
+              DenseTensor* out) {
+  T* output_data = dev_ctx.template Alloc<T>(out);
+  if (output_data) {
+    funcs::SetConstant<Context, T> set_zero;
+    set_zero(dev_ctx, out, static_cast<T>(0));
+  }
+  const int batch_size = static_cast<int>(x.dims()[0]);
+  const int input_depth = static_cast<int>(x.dims()[2]);
+  const int input_height = static_cast<int>(x.dims()[3]);
+  const int input_width = static_cast<int>(x.dims()[4]);
+  const int output_channels = static_cast<int>(out->dims()[1]);
+  const int output_depth = static_cast<int>(out->dims()[2]);
+  const int output_height = static_cast<int>(out->dims()[3]);
+  const int output_width = static_cast<int>(out->dims()[4]);
+  int64_t input_feasize =
+      static_cast<int64_t>(input_depth) * input_height * input_width;
+  int64_t output_feasize =
+      static_cast<int64_t>(output_depth) * output_height * output_width;
+  const T* input_data = x.data<T>();
+  const IndT* indices_data = indices.data<IndT>();
+  for (int b = 0; b < batch_size; ++b) {
+    for (int c = 0; c < output_channels; ++c) {
+      for (int i = 0; i < input_feasize; ++i) {
+        IndT index = indices_data[i];
+        PADDLE_ENFORCE_LT(
+            index,
+            output_feasize,
+            common::errors::InvalidArgument(
+                "index should less than output tensor depth * output tensor "
+                "height "
+                "* output tensor width. Expected %ld < %ld, but got "
+                "%ld >= %ld. Please check input value.",
+                index,
+                output_feasize,
+                index,
+                output_feasize));
+        output_data[index] = input_data[i];
+      }
+      input_data += input_feasize;
+      indices_data += input_feasize;
+      output_data += output_feasize;
+    }
+  }
+}
+
+template <typename T, typename Context>
+void Unpool3dKernel(const Context& dev_ctx,
+                    const DenseTensor& x,
+                    const DenseTensor& indices,
+                    const std::vector<int>& ksize UNUSED,
+                    const std::vector<int>& strides UNUSED,
+                    const std::vector<int>& paddings UNUSED,
+                    const std::vector<int>& output_size UNUSED,
+                    const std::string& data_format UNUSED,
+                    DenseTensor* out) {
+  if (out && out->numel() == 0) {
+    dev_ctx.template Alloc<T>(out);
+    return;
+  }
+  const auto& indices_type = indices.dtype();
+  if (indices_type == DataType::INT32) {
+    Unpool3d<T, int, Context>(dev_ctx, x, indices, out);
+  } else {
+    Unpool3d<T, int64_t, Context>(dev_ctx, x, indices, out);
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(
+    unpool, CPU, ALL_LAYOUT, phi::UnpoolKernel, float, double, int64_t) {}
+
+PD_REGISTER_KERNEL(
+    unpool3d, CPU, ALL_LAYOUT, phi::Unpool3dKernel, float, double, int64_t) {}