// Copyright (c) 2019 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/operators/pool_op.h" #include "lite/backends/npu/builder.h" #include "lite/kernels/npu/bridges/registry.h" namespace paddle { namespace lite { namespace kernels { namespace npu { namespace bridges { node_map_type PoolConverter(const std::shared_ptr pool_op, const node_map_type& inputs_map) { auto scope = pool_op->scope(); auto op_info = pool_op->op_info(); auto op_type = op_info->Type(); auto unique_op_type = lite::npu::UniqueName(op_type); LOG(INFO) << "[NPU] Converting " + op_type + "..."; std::shared_ptr pool_node = std::make_shared(unique_op_type); auto x_var_name = op_info->Input("X").front(); auto x = scope->FindTensor(x_var_name); pool_node->set_input_x(*inputs_map.at(x_var_name)); lite::npu::OpList::Global().add(inputs_map.at(x_var_name)); lite::npu::OpList::Global().add(pool_node); int mode = 0; auto pooling_type = op_info->GetAttr("pooling_type"); if (pooling_type == "max") { mode = 0; } else if (pooling_type == "avg") { mode = 1; CHECK(op_info->GetAttr("exclusive")) << "[NPU] exclusive must be true in HiAI DDK"; } else { LOG(FATAL) << "[NPU] Unsupported pooling type: " << pooling_type; } pool_node->set_attr_mode(mode); int pad_mode = 0; std::string padding_algorithm(""); if (op_info->HasAttr("padding_algorithm")) { padding_algorithm = op_info->GetAttr("padding_algorithm"); } if (padding_algorithm == "SAME") { pad_mode = 6; } else if (padding_algorithm == "VALID") { pad_mode = 5; } pool_node->set_attr_pad_mode(pad_mode); bool global_pooling = op_info->GetAttr("global_pooling"); pool_node->set_attr_global_pooling(global_pooling); auto ksize = op_info->GetAttr>("ksize"); auto window = ge::AttrValue::LIST_INT(ksize.begin(), ksize.end()); pool_node->set_attr_window(window); auto paddings = op_info->GetAttr>("paddings"); if (paddings.size() == 2L) { for (size_t i = 0; i < 2L; ++i) { int copy_pad = *(paddings.begin() + 2 * i); paddings.insert(paddings.begin() + 2 * i + 1, copy_pad); } } CHECK_EQ(paddings.size(), 4L) << "Paddings size should be the same or twice as the inputs size."; bool adaptive = false; if (op_info->HasAttr("adaptive")) { adaptive = op_info->GetAttr("adaptive"); } auto strides = op_info->GetAttr>("strides"); operators::UpdatePadding(&paddings, global_pooling, adaptive, padding_algorithm, x->dims(), strides, ksize); auto npu_pad = ge::AttrValue::LIST_INT{ paddings[0], paddings[1], paddings[2], paddings[3]}; pool_node->set_attr_pad(npu_pad); auto npu_stride = ge::AttrValue::LIST_INT(strides.begin(), strides.end()); pool_node->set_attr_stride(npu_stride); int ceil_mode = 0; if (op_info->HasAttr("ceil_mode")) { ceil_mode = op_info->GetAttr("ceil_mode") ? 1 : 0; } pool_node->set_attr_ceil_mode(ceil_mode); // output_node->set_attr_data_mode(npu_data_mode); node_map_type outputs_map; outputs_map[op_info->Output("Out").front()] = pool_node; return outputs_map; } } // namespace bridges } // namespace npu } // namespace kernels } // namespace lite } // namespace paddle REGISTER_NPU_BRIDGE(pool2d, paddle::lite::kernels::npu::bridges::PoolConverter);