[host][kernel]feat: add deformable_conv v2 host kernel

lite/kernels/host/CMakeLists.txt

@@ -10,6 +10,7 @@ add_kernel(expand_compute_host Host basic SRCS expand_compute.cc DEPS ${lite_ker
 add_kernel(expand_as_compute_host Host basic SRCS expand_as_compute.cc DEPS ${lite_kernel_deps})
 add_kernel(fill_constant_compute_host Host basic SRCS fill_constant_compute.cc DEPS ${lite_kernel_deps})
 add_kernel(fill_constant_batch_size_like_compute_host Host basic SRCS fill_constant_batch_size_like_compute.cc DEPS ${lite_kernel_deps})
+add_kernel(deformable_conv_compute_host Host basic SRCS deformable_conv_compute.cc DEPS ${lite_kernel_deps})
 add_kernel(shape_compute_host Host extra SRCS shape_compute.cc DEPS ${lite_kernel_deps})
 add_kernel(is_empty_compute_host Host extra SRCS is_empty_compute.cc DEPS ${lite_kernel_deps})
 add_kernel(crf_decoding_compute_host Host extra SRCS crf_decoding_compute.cc DEPS ${lite_kernel_deps})

lite/kernels/host/deformable_conv_compute.cc

+// Copyright (c) 2020 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 "lite/kernels/host/deformable_conv_compute.h"
+
+#include <vector>
+
+#include "lite/core/op_registry.h"
+#include "lite/core/type_system.h"
+#include "lite/kernels/host/deformable_conv_op.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace host {
+
+// todo: use blas if necessary
+/**
+ * naive row majored mat mul
+ */
+template <class T>
+void MatMul(const Tensor& mat_a,
+            const Tensor& mat_b,
+            T alpha,
+            Tensor* mat_out,
+            T beta) {
+  auto dim_a = mat_a.dims();
+  auto dim_b = mat_b.dims();
+  auto dim_out = mat_out->dims();
+
+  int M = dim_out[0];
+  int N = dim_out[1];
+  int K = dim_a[1];
+  auto* pA = mat_a.data<T>();
+  auto* pB = mat_b.data<T>();
+  auto* pC = mat_out->mutable_data<T>();
+  for (int i = 0; i < M; ++i) {
+    for (int j = 0; j < N; ++j) {
+      T sum = 0;
+      for (int k = 0; k < K; ++k) {
+        sum += pA[i * K + k] * pB[k * N + j];
+      }
+      pC[i * N + j] = sum * alpha + beta;
+    }
+  }
+}
+
+/**
+ * @note this function is modified from paddle fluid
+ * paddle commit id: f4c750d721a1226738bea382f6c0cf725cca8481
+ *
+ * check "paddle/fluid/operators/deformable_conv_op.h"
+ * if necessary
+ */
+template <>
+void DeformableConvComputeHost<PRECISION(kFloat), PRECISION(kFloat)>::Run() {
+  const auto& param = this->Param<operators::DeformableConvParam>();
+
+  // this implementation only support v2
+  // to support v1, you could follow
+  // "paddle/fluid/operators/deformable_conv_v1_op.h"
+
+  const auto* input = param.x;
+  const auto* offset = param.offset;
+  const auto* mask = param.mask;
+  const auto& filter = *param.conv_param.filter;
+  auto* output = param.output;
+
+  const int groups = param.conv_param.groups;
+  const int deformable_groups = param.deformable_groups;
+  const int im2col_step = param.im2col_step;
+  const std::vector<int>& strides = param.conv_param.strides;
+  const std::vector<int>& paddings = *param.conv_param.paddings;
+  const std::vector<int>& dilations = *param.conv_param.dilations;
+
+  const int batch_size = static_cast<int>(input->dims()[0]);
+
+  std::vector<int64_t> filter_shape_vec(filter.dims().Vectorize());
+  std::vector<int64_t> output_shape_vec(output->dims().Vectorize());
+
+  // col_shape_vec: {c_i * k_h * k_w, im2col_step, o_h, o_w}
+  std::vector<int64_t> col_buffer_shape_vec(filter_shape_vec.size());
+  col_buffer_shape_vec[0] =
+      input->dims()[1] * filter.dims()[2] * filter.dims()[3];
+  col_buffer_shape_vec[1] = im2col_step;
+  for (size_t j = 0; j < filter_shape_vec.size() - 2; ++j) {
+    col_buffer_shape_vec[j + 2] = output_shape_vec[j + 2];
+  }
+  DDim col_shape(col_buffer_shape_vec);
+  std::vector<int64_t> output_buffer_shape_vec(1);
+  output_buffer_shape_vec[0] = batch_size * output_shape_vec[1] *
+                               output_shape_vec[2] * output_shape_vec[3];
+  DDim output_shape(output_buffer_shape_vec);
+  Tensor col_buffer;
+  Tensor output_buffer;
+  col_buffer.Resize(col_shape);
+  col_buffer.mutable_data<float>();
+  output_buffer.Resize(output_shape);
+  output_buffer.mutable_data<float>();
+  int64_t M = output_shape_vec[1] / groups;
+  int64_t N = im2col_step * output_shape_vec[2] * output_shape_vec[3];
+  int64_t K =
+      input->dims()[1] * filter_shape_vec[2] * filter_shape_vec[3] / groups;
+
+  Tensor weight_3d;
+  weight_3d.ShareDataWith(filter);
+  weight_3d.Resize(DDim({groups, M, K}));
+  Tensor col_buffer_3d;
+  col_buffer_3d.ShareDataWith(col_buffer);
+  col_buffer_3d.Resize(DDim({groups, K, N}));
+  Tensor output_4d;
+  output_4d.ShareDataWith(output_buffer);
+  output_4d.Resize(DDim({batch_size / im2col_step, groups, M, N}));
+  output_4d.mutable_data<float>();
+  DDim input_shape = input->dims().Slice(1, input->dims().size());
+  std::vector<int64_t> input_shape_vec = input_shape.Vectorize();
+  int input_dim = input->numel() / input->dims()[0];
+  int input_offset_dim = offset->numel() / offset->dims()[0];
+  int input_mask_dim = mask->numel() / mask->dims()[0];
+  const float* input_ptr = input->data<float>();
+  const float* offset_ptr = offset->data<float>();
+  const float* mask_ptr = mask->data<float>();
+  col_buffer.mutable_data<float>();
+  float* col_buffer_ptr = col_buffer.mutable_data<float>();
+  for (int i = 0; i < batch_size / im2col_step; ++i) {
+    ModulatedDeformableIm2colCPU<float>(
+        input_ptr + i * im2col_step * input_dim,
+        offset_ptr + i * im2col_step * input_offset_dim,
+        mask_ptr + i * im2col_step * input_mask_dim,
+        input_shape_vec,
+        col_buffer_shape_vec,
+        filter_shape_vec,
+        paddings,
+        strides,
+        dilations,
+        deformable_groups,
+        col_buffer_ptr);
+    Tensor output_3d = output_4d.Slice<float>(i, i + 1);
+    output_3d.Resize(DDim(output_4d.dims()).Slice(1, output_4d.dims().size()));
+    // get the product of pixel and weight
+    for (int g = 0; g < groups; ++g) {
+      Tensor weight_3d_slice = weight_3d.Slice<float>(g, g + 1);
+      weight_3d_slice.Resize(
+          DDim(weight_3d.dims()).Slice(1, weight_3d.dims().size()));
+      Tensor col_buffer_3d_slice = col_buffer_3d.Slice<float>(g, g + 1);
+      col_buffer_3d_slice.Resize(
+          DDim(col_buffer_3d.dims()).Slice(1, col_buffer_3d.dims().size()));
+      Tensor output_3d_slice = output_3d.Slice<float>(g, g + 1);
+      output_3d_slice.Resize(
+          DDim(output_3d.dims()).Slice(1, output_3d.dims().size()));
+      MatMul<float>(
+          weight_3d_slice, col_buffer_3d_slice, 1.0f, &output_3d_slice, 0.0f);
+    }
+  }
+  output->ShareDataWith(output_buffer);
+  output->Resize(DDim(output_shape_vec));
+}
+
+}  // namespace host
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+using DeformableConvFp32Host =
+    paddle::lite::kernels::host::DeformableConvComputeHost<PRECISION(kFloat),
+                                                           PRECISION(kFloat)>;
+
+REGISTER_LITE_KERNEL(
+    deformable_conv, kHost, kFloat, kNCHW, DeformableConvFp32Host, def)
+    .BindInput("Input", {LiteType::GetTensorTy(TARGET(kHost))})
+    .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kHost))})
+    .BindInput("Filter", {LiteType::GetTensorTy(TARGET(kHost))})
+    .BindInput("Mask", {LiteType::GetTensorTy(TARGET(kHost))})
+    .BindInput("Offset", {LiteType::GetTensorTy(TARGET(kHost))})
+    .BindOutput("Output", {LiteType::GetTensorTy(TARGET(kHost))})
+    .Finalize();

lite/kernels/host/deformable_conv_compute.h

+// Copyright (c) 2020 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 "lite/core/kernel.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace host {
+
+template <PrecisionType Ptype, PrecisionType OutType>
+class DeformableConvComputeHost : public KernelLite<TARGET(kHost), Ptype> {
+ public:
+  void Run() override;
+  ~DeformableConvComputeHost() = default;
+};
+
+}  // namespace host
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle