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1. Add trans_TensorTypeAs2TensorTo pass in pass level 6
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tools/pnnx/src/pass_level6/trans_TensorTypeAs2TensorTo.cpp
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,121 @@ | ||
| // Tencent is pleased to support the open source community by making ncnn available. | ||
| // | ||
| // Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved. | ||
| // | ||
| // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except | ||
| // in compliance with the License. You may obtain a copy of the License at | ||
| // | ||
| // https://opensource.org/licenses/BSD-3-Clause | ||
| // | ||
| // 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 "trans_TensorTypeAs2TensorTo.h" | ||
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| #include <algorithm> | ||
| #include "pass_level2.h" | ||
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| namespace pnnx { | ||
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| void trans_TensorTypeAs2TensorTo(Graph& graph) | ||
| { | ||
| while (1) | ||
| { | ||
| bool matched = false; | ||
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| for (size_t i = 0; i < graph.ops.size(); i++) | ||
| { | ||
| Operator* op = graph.ops[i]; | ||
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| if (op->type != "Tensor.type_as") | ||
| continue; | ||
| matched = true; | ||
| // get the input size of input1 | ||
| Operand* input1 = op->inputs.at(1); | ||
| int tensor_type_index = input1->type; | ||
| if (tensor_type_index == 0) op->params["dtype"] = "torch.float"; | ||
| if (tensor_type_index == 1) op->params["dtype"] = "torch.float"; | ||
| if (tensor_type_index == 2) op->params["dtype"] = "torch.double"; | ||
| if (tensor_type_index == 3) op->params["dtype"] = "torch.half"; | ||
| if (tensor_type_index == 4) op->params["dtype"] = "torch.int"; | ||
| if (tensor_type_index == 5) op->params["dtype"] = "torch.long"; | ||
| if (tensor_type_index == 6) op->params["dtype"] = "torch.short"; | ||
| if (tensor_type_index == 7) op->params["dtype"] = "torch.int8"; | ||
| if (tensor_type_index == 8) op->params["dtype"] = "torch.uint8"; | ||
| if (tensor_type_index == 9) op->params["dtype"] = "torch.bool"; | ||
| if (tensor_type_index == 10) op->params["dtype"] = "torch.complex64"; | ||
| if (tensor_type_index == 11) op->params["dtype"] = "torch.complex128"; | ||
| if (tensor_type_index == 12) op->params["dtype"] = "torch.complex32"; | ||
| // type_as 2 to | ||
| op->type = "Tensor.to"; | ||
| op->params["copy"] = false; | ||
| op->inputs.pop_back(); | ||
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| std::vector<Operand*> delete_operands = {}; | ||
| std::vector<Operator*> delete_ops = {}; | ||
| std::vector<Operand*> operand_squence = {input1}; | ||
| while(operand_squence.size() > 0) | ||
| { | ||
| Operand* cur_operand = operand_squence.front(); | ||
| operand_squence.erase(operand_squence.begin()); | ||
| if (cur_operand->consumers.size() == 1) | ||
| { | ||
| delete_operands.push_back(cur_operand); | ||
| Operator* pre_producer = cur_operand->producer; | ||
| if(pre_producer->outputs.size() == 1) | ||
| { | ||
| delete_ops.push_back(pre_producer); | ||
| for(auto cur_input: pre_producer->inputs) | ||
| { | ||
| operand_squence.push_back(cur_input); | ||
| } | ||
| } | ||
| else | ||
| { | ||
| for(auto out : pre_producer->outputs) | ||
| { | ||
| if (out->name == input1->name) | ||
| { | ||
| std::swap(out, pre_producer->outputs.back()); | ||
| pre_producer->outputs.pop_back(); | ||
| break; | ||
| } | ||
| } | ||
| } | ||
| } | ||
| else | ||
| { | ||
| for(int index = 0; index < cur_operand->consumers.size(); index++) | ||
| { | ||
| if(cur_operand->consumers[index]->name == op->name) | ||
| { | ||
| cur_operand->consumers.erase(cur_operand->consumers.begin() + index); | ||
| } | ||
| } | ||
| } | ||
| } | ||
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| for(auto delete_op: delete_ops) | ||
| { | ||
| delete_op->inputs.clear(); | ||
| delete_op->outputs.clear(); | ||
| graph.ops.erase(std::find(graph.ops.begin(), graph.ops.end(), delete_op)); | ||
| delete delete_op; | ||
| } | ||
| for(auto delete_operand: delete_operands) | ||
| { | ||
| graph.operands.erase(std::find(graph.operands.begin(), graph.operands.end(), delete_operand)); | ||
| delete delete_operand; | ||
| } | ||
| break; | ||
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| } | ||
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| if (!matched) | ||
| break; | ||
| } | ||
| } | ||
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| } // namespace pnnx |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,22 @@ | ||
|
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| // Tencent is pleased to support the open source community by making ncnn available. | ||
| // | ||
| // Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved. | ||
| // | ||
| // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except | ||
| // in compliance with the License. You may obtain a copy of the License at | ||
| // | ||
| // https://opensource.org/licenses/BSD-3-Clause | ||
| // | ||
| // 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 "ir.h" | ||
|
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| namespace pnnx { | ||
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| void trans_TensorTypeAs2TensorTo(Graph& graph); | ||
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| } // namespace pnnx |
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