wehub-resource-sync
85 lines
2.8 KiB
C++
85 lines
2.8 KiB
C++
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
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namespace paddle::inference::tensorrt {
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class Squeeze2OpConverter : public OpConverter {
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public:
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void operator()(const framework::proto::OpDesc& op,
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const framework::Scope& scope,
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bool test_mode) override {
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VLOG(4) << "convert a squeeze2 op to tensorrt shuffle layer";
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framework::OpDesc op_desc(op, nullptr);
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// Declare inputs
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auto* input = engine_->GetITensor(op_desc.Input("X")[0]);
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auto input_dims = input->getDimensions();
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auto output_name = op_desc.Output("Out")[0];
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// Get Attrs
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std::vector<int> axes;
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if (op_desc.HasAttr("axes")) {
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axes = PADDLE_GET_CONST(std::vector<int>, op_desc.GetAttr("axes"));
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}
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if (axes.empty()) {
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for (int i = 0; i < input_dims.nbDims; i++) {
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if (input_dims.d[i] == -1) {
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PADDLE_THROW(common::errors::InvalidArgument(
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"The necessary attributes of the squeeze2 operator axes is "
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"missing."));
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} else if (input_dims.d[i] == 1) {
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axes.push_back(i);
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}
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}
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}
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PADDLE_ENFORCE_GT(
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axes.size(),
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0,
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common::errors::InvalidArgument(
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"Attr(axes).size should be > 0 in squeeze2 op in TensorRT,"
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"but received axes.size() = %d.",
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axes.size()));
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std::vector<bool> should_squeeze(input_dims.nbDims, false);
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for (int& axis : axes) {
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axis += (axis < 0) ? input_dims.nbDims : 0;
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should_squeeze[axis] = true;
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}
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nvinfer1::Dims trt_out_dims;
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trt_out_dims.nbDims = 0;
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std::vector<int32_t> gather_indices;
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for (size_t i = 0; i < should_squeeze.size(); i++) {
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if (should_squeeze[i]) continue;
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gather_indices.push_back(i);
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// for static shape
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trt_out_dims.d[trt_out_dims.nbDims] = input_dims.d[i];
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trt_out_dims.nbDims++;
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}
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auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input);
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auto* shape_tensor = Shape(input);
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auto* real_shape_tensor = Gather(shape_tensor, gather_indices);
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layer->setInput(1, *real_shape_tensor);
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ReplenishLayerAndOutput(layer, "squeeze2", {output_name}, test_mode);
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}
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};
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} // namespace paddle::inference::tensorrt
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REGISTER_TRT_OP_CONVERTER(squeeze2, Squeeze2OpConverter);
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