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* add bilinear_interp_v2 converter * update op_teller.cc * add unittest for bilinear_interp_v2 converter * code format * bug fix * code format and add unittest * remove merged modify in op_teller.cc * code format * code format * fix scale init error
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paddle/fluid/inference/tensorrt/convert/bilinear_interp_v2_op.cc
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| /* 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. */ | ||
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| #include "paddle/fluid/framework/data_layout.h" | ||
| #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" | ||
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| namespace paddle { | ||
| namespace framework { | ||
| class Scope; | ||
| namespace proto { | ||
| class OpDesc; | ||
| } // namespace proto | ||
| } // namespace framework | ||
| } // namespace paddle | ||
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| namespace paddle { | ||
| namespace inference { | ||
| namespace tensorrt { | ||
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| class BilinearInterpolateV2OpConverter : public OpConverter { | ||
| public: | ||
| void operator()(const framework::proto::OpDesc& op, | ||
| const framework::Scope& scope, bool test_mode) override { | ||
| VLOG(3) << "convert a fluid bilinear_interp_v2 op"; | ||
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| framework::OpDesc op_desc(op, nullptr); | ||
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| std::string input_name = op_desc.Input("X").front(); | ||
| std::string output_name = op_desc.Output("Out").front(); | ||
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| auto input = engine_->GetITensor(input_name); | ||
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| auto data_layout = framework::StringToDataLayout( | ||
| BOOST_GET_CONST(std::string, op_desc.GetAttr("data_layout"))); | ||
| auto interp_method = | ||
| BOOST_GET_CONST(std::string, op_desc.GetAttr("interp_method")); | ||
| bool align_corners = | ||
| BOOST_GET_CONST(bool, op_desc.GetAttr("align_corners")); | ||
| auto align_mode = BOOST_GET_CONST(int, op_desc.GetAttr("align_mode")); | ||
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| auto resize_inputs = op_desc.Inputs(); | ||
| auto input_names = op_desc.Input("X"); | ||
| auto out_h = BOOST_GET_CONST(int, op_desc.GetAttr("out_h")); | ||
| auto out_w = BOOST_GET_CONST(int, op_desc.GetAttr("out_w")); | ||
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| auto layer = TRT_ENGINE_ADD_LAYER(engine_, Resize, *input); | ||
| if (align_mode == 0 && !align_corners) { | ||
| layer->setResizeMode(nvinfer1::ResizeMode::kLINEAR); | ||
| } | ||
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| auto in_dim = input->getDimensions(); | ||
| float scale_h = -1.f; | ||
| float scale_w = -1.f; | ||
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| // Scales Priority: Scale(tensor) > scale(attr) > out_d/out_h/out_w(attr) | ||
| bool has_scale_input_attr = | ||
| (resize_inputs.find("Scale") != resize_inputs.end()); | ||
| bool has_scale_input = | ||
| has_scale_input_attr && (op_desc.Input("Scale").size() > 0); | ||
| if (has_scale_input) { | ||
| auto* scale_var = scope.FindVar(op_desc.Input("Scale")[0]); | ||
| auto* scale_tensor = scale_var->GetMutable<framework::LoDTensor>(); | ||
| auto* scale_d = scale_tensor->data<float>(); | ||
| scale_h = scale_d[0]; | ||
| scale_w = scale_d[1]; | ||
| } else { | ||
| const std::vector<float> scale_attr = | ||
| BOOST_GET_CONST(std::vector<float>, op_desc.GetAttr("scale")); | ||
| if (scale_attr.size() > 1) { | ||
| scale_h = scale_attr[0]; | ||
| scale_w = scale_attr[1]; | ||
| } | ||
| } | ||
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| // axis are different in static/dynamic mode | ||
| bool with_dynamic = engine_->with_dynamic_shape(); | ||
| int h_axis = (data_layout == framework::DataLayout::kNCHW) + with_dynamic; | ||
| int w_axis = | ||
| (data_layout == framework::DataLayout::kNCHW) + 1 + with_dynamic; | ||
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| if (scale_w > 0. && scale_h > 0.) { | ||
| out_h = static_cast<int>(in_dim.d[h_axis] * scale_h); | ||
| out_w = static_cast<int>(in_dim.d[w_axis] * scale_w); | ||
| } | ||
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| if (out_h > 0 && out_w > 0) { | ||
| scale_h = | ||
| static_cast<float>(out_h) / static_cast<float>(in_dim.d[h_axis]); | ||
| scale_w = | ||
| static_cast<float>(out_w) / static_cast<float>(in_dim.d[w_axis]); | ||
| } | ||
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| std::vector<float> scales; | ||
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| if (engine_->with_dynamic_shape()) { | ||
| scales.push_back(1.f); | ||
| } | ||
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| if (data_layout == framework::DataLayout::kNCHW) { | ||
| scales.push_back(1.f); | ||
| scales.push_back(scale_h); | ||
| scales.push_back(scale_w); | ||
| } else if (data_layout == framework::DataLayout::kNHWC) { | ||
| scales.push_back(scale_h); | ||
| scales.push_back(scale_w); | ||
| scales.push_back(1.f); | ||
| } else { | ||
| PADDLE_THROW(platform::errors::InvalidArgument( | ||
| "Data layout must be NCHW or NHWC.")); | ||
| } | ||
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| layer->setScales(scales.data(), scales.size()); | ||
| RreplenishLayerAndOutput(layer, "bilinear_interp_v2", {output_name}, | ||
| test_mode); | ||
| } | ||
| }; | ||
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| } // namespace tensorrt | ||
| } // namespace inference | ||
| } // namespace paddle | ||
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| REGISTER_TRT_OP_CONVERTER(bilinear_interp_v2, BilinearInterpolateV2OpConverter); |
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