718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
1820 lines
58 KiB
C++
1820 lines
58 KiB
C++
// Tencent is pleased to support the open source community by making ncnn available.
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//
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// Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
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//
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// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
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// in compliance with the License. You may obtain a copy of the License at
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//
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// https://opensource.org/licenses/BSD-3-Clause
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//
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// Unless required by applicable law or agreed to in writing, software distributed
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// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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// CONDITIONS OF ANY KIND, either express or implied. See the License for the
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// specific language governing permissions and limitations under the License.
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#include <stdio.h>
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#include <map>
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#include <set>
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#include <mlir/Dialect/StandardOps/IR/Ops.h>
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#include <mlir/IR/PatternMatch.h>
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#include <mlir/Parser.h>
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#include <mlir/Pass/Pass.h>
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#include <mlir/Pass/PassManager.h>
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#include <mlir/Transforms/Passes.h>
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#include "tf_dialect.h"
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#include "ncnn_dialect.h"
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static std::string get_mlir_value_uniq_id(const mlir::Value& value)
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{
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if (value.getLoc().isa<mlir::FileLineColLoc>())
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{
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mlir::FileLineColLoc floc = value.getLoc().cast<mlir::FileLineColLoc>();
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return std::to_string(floc.getLine()) + ":" + std::to_string(floc.getColumn());
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}
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if (value.getLoc().isa<mlir::FusedLoc>())
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{
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mlir::FileLineColLoc floc = value.getLoc().cast<mlir::FusedLoc>().getLocations().front().cast<mlir::FileLineColLoc>();
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return std::to_string(floc.getLine()) + ":" + std::to_string(floc.getColumn());
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}
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fprintf(stderr, "unhandled get_mlir_value_uniq_id\n");
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return std::string();
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}
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static std::string get_attr_s(const mlir::Attribute& attr)
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{
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std::string s;
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if (attr.isa<mlir::StringAttr>())
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{
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mlir::StringAttr a = attr.cast<mlir::StringAttr>();
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s = a.getValue().str();
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}
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return s;
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}
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static int get_attr_b(const mlir::Attribute& attr)
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{
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int i;
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if (attr.isa<mlir::BoolAttr>())
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{
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mlir::BoolAttr a = attr.cast<mlir::BoolAttr>();
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i = a.getValue() ? 1 : 0;
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}
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else
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{
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fprintf(stderr, "not BoolAttr\n");
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}
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return i;
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}
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static int get_attr_i(const mlir::Attribute& attr)
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{
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int i;
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if (attr.isa<mlir::IntegerAttr>())
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{
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mlir::IntegerAttr a = attr.cast<mlir::IntegerAttr>();
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i = (int)a.getInt();
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}
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else
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{
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fprintf(stderr, "not IntegerAttr\n");
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}
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return i;
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}
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static float get_attr_f(const mlir::Attribute& attr)
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{
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float f;
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if (attr.isa<mlir::FloatAttr>())
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{
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mlir::FloatAttr a = attr.cast<mlir::FloatAttr>();
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f = (float)a.getValueAsDouble();
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}
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else
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{
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fprintf(stderr, "not FloatAttr\n");
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}
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return f;
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}
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static std::vector<int> get_attr_ai(const mlir::Attribute& attr)
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{
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std::vector<int> v;
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if (attr.isa<mlir::ArrayAttr>())
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{
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mlir::ArrayAttr a = attr.cast<mlir::ArrayAttr>();
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const int array_size = a.getValue().size();
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v.resize(array_size);
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for (int j = 0; j < array_size; j++)
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{
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if (a[j].isa<mlir::IntegerAttr>())
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{
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int64_t ii = a[j].cast<mlir::IntegerAttr>().getInt();
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v[j] = std::max(std::min(ii, (int64_t)INT_MAX), (int64_t)INT_MIN);
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}
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}
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}
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else if (attr.isa<mlir::DenseIntElementsAttr>())
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{
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mlir::DenseIntElementsAttr ai = attr.cast<mlir::DenseIntElementsAttr>();
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for (auto ii : ai.getIntValues())
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{
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v.push_back(ii.getSExtValue());
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}
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}
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else
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{
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fprintf(stderr, "not ArrayAttr or DenseIntElementsAttr\n");
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}
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return v;
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}
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static std::vector<float> get_attr_af(const mlir::Attribute& attr)
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{
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std::vector<float> v;
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if (attr.isa<mlir::ArrayAttr>())
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{
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mlir::ArrayAttr a = attr.cast<mlir::ArrayAttr>();
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const int array_size = a.getValue().size();
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v.resize(array_size);
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for (int j = 0; j < array_size; j++)
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{
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if (a[j].isa<mlir::FloatAttr>())
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{
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double ff = a[j].cast<mlir::FloatAttr>().getValueAsDouble();
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v[j] = ff;
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}
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}
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}
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else if (attr.isa<mlir::DenseFPElementsAttr>())
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{
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mlir::DenseFPElementsAttr af = attr.cast<mlir::DenseFPElementsAttr>();
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for (auto ff : af.getFloatValues())
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{
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v.push_back(ff.convertToFloat());
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}
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}
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else
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{
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fprintf(stderr, "not ArrayAttr or DenseFPElementsAttr\n");
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}
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return v;
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}
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static std::string get_operation_attr_s(const mlir::Operation& _operation, const char* key)
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{
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mlir::Operation& operation = const_cast<mlir::Operation&>(_operation);
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mlir::Attribute attr = operation.getAttr(key);
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return get_attr_s(attr);
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}
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static int get_operation_attr_b(const mlir::Operation& _operation, const char* key)
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{
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mlir::Operation& operation = const_cast<mlir::Operation&>(_operation);
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mlir::Attribute attr = operation.getAttr(key);
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return get_attr_b(attr);
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}
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static int get_operation_attr_i(const mlir::Operation& _operation, const char* key)
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{
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mlir::Operation& operation = const_cast<mlir::Operation&>(_operation);
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mlir::Attribute attr = operation.getAttr(key);
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return get_attr_i(attr);
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}
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static float get_operation_attr_f(const mlir::Operation& _operation, const char* key)
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{
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mlir::Operation& operation = const_cast<mlir::Operation&>(_operation);
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mlir::Attribute attr = operation.getAttr(key);
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return get_attr_f(attr);
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}
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static std::vector<int> get_operation_attr_ai(const mlir::Operation& _operation, const char* key)
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{
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mlir::Operation& operation = const_cast<mlir::Operation&>(_operation);
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mlir::Attribute attr = operation.getAttr(key);
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return get_attr_ai(attr);
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}
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static std::vector<float> get_operation_attr_af(const mlir::Operation& _operation, const char* key)
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{
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mlir::Operation& operation = const_cast<mlir::Operation&>(_operation);
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mlir::Attribute attr = operation.getAttr(key);
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return get_attr_af(attr);
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}
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int main(int argc, char** argv)
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{
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if (!(argc == 2 || argc == 4))
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{
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fprintf(stderr, "Usage: %s [mlir] [ncnnparam] [ncnnbin]\n", argv[0]);
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return -1;
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}
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const char* mlirpath = argv[1];
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const char* ncnn_prototxt = argc == 4 ? argv[2] : "ncnn.param";
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const char* ncnn_modelbin = argc == 4 ? argv[3] : "ncnn.bin";
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mlir::MLIRContext context;
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context.getOrLoadDialect<mlir::StandardOpsDialect>();
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context.getOrLoadDialect<mlir::TF::TensorFlowDialect>();
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context.getOrLoadDialect<mlir::ncnn::NCNNDialect>();
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mlir::OwningModuleRef m = mlir::parseSourceFile(mlirpath, &context);
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mlir::PassManager pm(&context);
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pm.addNestedPass<mlir::FuncOp>(mlir::ncnn::createNCNNOptimizePass());
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if (pm.run(*m).failed())
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{
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fprintf(stderr, "canonicalizer pass failed\n");
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return -1;
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}
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// m->dump();
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mlir::FuncOp main_fn = m->lookupSymbol<mlir::FuncOp>("main");
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auto& bb = main_fn.getBlocks().front();
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// bb.dump();
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FILE* pp = fopen(ncnn_prototxt, "wb");
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FILE* bp = fopen(ncnn_modelbin, "wb");
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// node reference
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std::map<std::string, int> node_reference;
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// weight node and weight reshape node
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std::map<std::string, mlir::Attribute> weights;
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fprintf(pp, "7767517\n");
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const mlir::Block::OpListType& operations = bb.getOperations();
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int node_count = operations.size();
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// global definition line
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// [layer count] [blob count]
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std::set<std::string> blob_names;
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for (const mlir::Operation& _operation : operations)
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{
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mlir::Operation& operation = const_cast<mlir::Operation&>(_operation);
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std::string op = operation.getName().getStringRef().str();
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int num_input = (int)operation.getNumOperands();
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int num_output = (int)operation.getNumResults();
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if (op == "tf.Const")
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{
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// weight
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std::string output_name = get_mlir_value_uniq_id(operation.getResult(0));
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weights[output_name] = operation.getAttr("value");
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}
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for (int j = 0; j < num_input; j++)
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{
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std::string input_name = get_mlir_value_uniq_id(operation.getOperand(j));
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blob_names.insert(input_name);
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if (node_reference.find(input_name) == node_reference.end())
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{
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node_reference[input_name] = 1;
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}
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else
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{
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node_reference[input_name] = node_reference[input_name] + 1;
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}
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}
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for (int j = 0; j < num_output; j++)
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{
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std::string output_name = get_mlir_value_uniq_id(operation.getResult(j));
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blob_names.insert(output_name);
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node_reference[output_name] = 0;
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}
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}
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// reduce common const weight node_reference
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for (const mlir::Operation& _operation : operations)
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{
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mlir::Operation& operation = const_cast<mlir::Operation&>(_operation);
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std::string op = operation.getName().getStringRef().str();
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if (op == "ncnn.KerasConv2D")
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{
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std::string weight_name = get_mlir_value_uniq_id(operation.getOperand(1));
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std::string bias_name = get_mlir_value_uniq_id(operation.getOperand(2));
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node_reference[weight_name] -= 1;
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node_reference[bias_name] -= 1;
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}
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else if (op == "ncnn.KerasDense")
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{
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std::string weight_name = get_mlir_value_uniq_id(operation.getOperand(1));
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std::string bias_name = get_mlir_value_uniq_id(operation.getOperand(2));
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node_reference[weight_name] -= 1;
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node_reference[bias_name] -= 1;
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}
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else if (op == "ncnn.KerasBatchNorm")
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{
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std::string gamma_name = get_mlir_value_uniq_id(operation.getOperand(1));
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std::string bias_name = get_mlir_value_uniq_id(operation.getOperand(2));
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node_reference[gamma_name] -= 1;
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node_reference[bias_name] -= 1;
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}
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else if (op == "ncnn.InstanceNormAffine")
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{
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std::string gamma_name = get_mlir_value_uniq_id(operation.getOperand(1));
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std::string bias_name = get_mlir_value_uniq_id(operation.getOperand(2));
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node_reference[gamma_name] -= 1;
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node_reference[bias_name] -= 1;
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}
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else if (op == "tf.ConcatV2")
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{
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std::string axis_name = get_mlir_value_uniq_id(operation.getOperand(operation.getNumOperands() - 1));
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node_reference[axis_name] -= 1;
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}
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else if (op == "tf.Conv2D")
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{
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std::string weight_name = get_mlir_value_uniq_id(operation.getOperand(1));
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node_reference[weight_name] -= 1;
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}
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else if (op == "tf.Conv2DBackpropInput")
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{
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std::string output_shape_name = get_mlir_value_uniq_id(operation.getOperand(0));
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std::string weight_name = get_mlir_value_uniq_id(operation.getOperand(1));
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node_reference[output_shape_name] -= 1;
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node_reference[weight_name] -= 1;
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}
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else if (op == "tf.DepthwiseConv2dNative")
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{
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std::string weight_name = get_mlir_value_uniq_id(operation.getOperand(1));
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node_reference[weight_name] -= 1;
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}
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else if (op == "tf.MatMul")
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{
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int transpose_a = get_operation_attr_b(operation, "transpose_a");
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int transpose_b = get_operation_attr_b(operation, "transpose_b");
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if (transpose_a == 0 && transpose_b == 1)
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{
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// InnerProduct-like A * B + C
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std::string weight_name = get_mlir_value_uniq_id(operation.getOperand(1));
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node_reference[weight_name] -= 1;
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}
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}
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else if (op == "tf.Mean")
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{
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std::string reduction_indices_name = get_mlir_value_uniq_id(operation.getOperand(1));
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node_reference[reduction_indices_name] -= 1;
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}
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else if (op == "tf.Pad")
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{
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std::string weight_name = get_mlir_value_uniq_id(operation.getOperand(1));
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node_reference[weight_name] -= 1;
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}
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else if (op == "tf.Reshape")
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{
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std::string weight_name = get_mlir_value_uniq_id(operation.getOperand(1));
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node_reference[weight_name] -= 1;
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}
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else if (op == "tf.ResizeBilinear")
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{
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std::string weight_name = get_mlir_value_uniq_id(operation.getOperand(1));
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node_reference[weight_name] -= 1;
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}
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else if (op == "tf.ResizeNearestNeighbor")
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{
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std::string weight_name = get_mlir_value_uniq_id(operation.getOperand(1));
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node_reference[weight_name] -= 1;
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}
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else if (op == "tf.StridedSlice")
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{
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std::string begin_name = get_mlir_value_uniq_id(operation.getOperand(1));
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std::string end_name = get_mlir_value_uniq_id(operation.getOperand(2));
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std::string strides_name = get_mlir_value_uniq_id(operation.getOperand(3));
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node_reference[begin_name] -= 1;
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node_reference[end_name] -= 1;
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node_reference[strides_name] -= 1;
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}
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}
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// count all weight node with zero reference
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int zero_reference_weight_node_count = 0;
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for (std::map<std::string, mlir::Attribute>::iterator it = weights.begin(); it != weights.end(); it++)
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{
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const std::string& input_name = it->first;
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int refcount = node_reference[input_name];
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if (refcount == 0)
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zero_reference_weight_node_count++;
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}
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// remove node_reference entry with reference equals to one
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int split_layer_count = 0;
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int splitncnn_blob_count = 0;
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// split node reference
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std::map<std::string, int> split_node_reference;
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for (std::map<std::string, int>::iterator it = node_reference.begin(); it != node_reference.end(); it++)
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{
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if (it->second > 1)
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{
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split_layer_count++;
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splitncnn_blob_count += it->second;
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split_node_reference[it->first] = it->second;
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}
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}
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fprintf(pp, "%lu %lu\n", node_count - zero_reference_weight_node_count + split_layer_count, blob_names.size() - zero_reference_weight_node_count + splitncnn_blob_count);
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int internal_split = 0;
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// place MemoryData next
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for (std::map<std::string, mlir::Attribute>::iterator weight_it = weights.begin(); weight_it != weights.end(); weight_it++)
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{
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const std::string& input_name = weight_it->first;
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int refcount = node_reference[input_name];
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if (refcount == 0)
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{
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continue;
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}
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fprintf(pp, "%-16s %-24s 0 1 %s", "MemoryData", input_name.c_str(), input_name.c_str());
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const mlir::Attribute& M = weights[input_name];
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llvm::ArrayRef<int64_t> shape = M.getType().cast<mlir::RankedTensorType>().getShape();
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// c wc hwc
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if (shape.size() == 0)
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{
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// scalar
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|
fprintf(pp, " 0=1");
|
|
}
|
|
else if (shape.size() == 1)
|
|
{
|
|
fprintf(pp, " 0=%d", (int)shape[0]);
|
|
}
|
|
else if (shape.size() == 2)
|
|
{
|
|
fprintf(pp, " 0=%d", (int)shape[1]);
|
|
fprintf(pp, " 1=%d", (int)shape[0]);
|
|
}
|
|
else if (shape.size() == 3)
|
|
{
|
|
fprintf(pp, " 0=%d", (int)shape[1]);
|
|
fprintf(pp, " 1=%d", (int)shape[0]);
|
|
fprintf(pp, " 2=%d", (int)shape[2]);
|
|
}
|
|
|
|
fprintf(pp, "\n");
|
|
|
|
std::vector<float> v = get_attr_af(M);
|
|
|
|
if (shape.size() != 3)
|
|
{
|
|
fwrite(v.data(), sizeof(float), v.size(), bp);
|
|
}
|
|
else
|
|
{
|
|
int w = (int)shape[1];
|
|
int h = (int)shape[0];
|
|
int c = (int)shape[2];
|
|
|
|
float tmp;
|
|
// h-w-c to c-h-w
|
|
for (int p = 0; p < c; p++)
|
|
{
|
|
for (int i = 0; i < h; i++)
|
|
{
|
|
for (int j = 0; j < w; j++)
|
|
{
|
|
tmp = v[i * w * c + j * c + p];
|
|
fwrite(&tmp, sizeof(float), 1, bp);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if (refcount <= 1)
|
|
{
|
|
continue;
|
|
}
|
|
|
|
char splitname[256];
|
|
sprintf(splitname, "splitncnn_%d", internal_split);
|
|
fprintf(pp, "%-16s %-24s %d %d", "Split", splitname, 1, refcount);
|
|
|
|
fprintf(pp, " %s", input_name.c_str());
|
|
|
|
for (int k = 0; k < refcount; k++)
|
|
{
|
|
fprintf(pp, " %s_splitncnn_%d", input_name.c_str(), k);
|
|
}
|
|
fprintf(pp, "\n");
|
|
|
|
internal_split++;
|
|
}
|
|
|
|
// model op
|
|
int g_opid = 0;
|
|
|
|
for (const mlir::Operation& _operation : operations)
|
|
{
|
|
mlir::Operation& operation = const_cast<mlir::Operation&>(_operation);
|
|
|
|
std::string op = operation.getName().getStringRef().str();
|
|
|
|
int opid = g_opid++;
|
|
|
|
int num_input = (int)operation.getNumOperands();
|
|
int num_output = (int)operation.getNumResults();
|
|
|
|
for (int i = 0; i < (int)operation.getNumOperands(); i++)
|
|
{
|
|
std::string input_name = get_mlir_value_uniq_id(operation.getOperand(i));
|
|
|
|
// check weight
|
|
if (weights.find(input_name) != weights.end() && node_reference[input_name] == 0)
|
|
{
|
|
num_input--;
|
|
}
|
|
}
|
|
|
|
if (op == "std.return")
|
|
{
|
|
fprintf(pp, "%-16s", "Noop");
|
|
}
|
|
else if (op == "ncnn.BinaryOp")
|
|
{
|
|
fprintf(pp, "%-16s", "BinaryOp");
|
|
}
|
|
else if (op == "ncnn.KerasConv2D")
|
|
{
|
|
fprintf(pp, "%-16s", "Convolution");
|
|
}
|
|
else if (op == "ncnn.KerasDense")
|
|
{
|
|
fprintf(pp, "%-16s", "InnerProduct");
|
|
}
|
|
else if (op == "ncnn.KerasBatchNorm")
|
|
{
|
|
fprintf(pp, "%-16s", "BatchNorm");
|
|
}
|
|
else if (op == "ncnn.InstanceNorm")
|
|
{
|
|
fprintf(pp, "%-16s", "InstanceNorm");
|
|
}
|
|
else if (op == "ncnn.InstanceNormAffine")
|
|
{
|
|
fprintf(pp, "%-16s", "InstanceNorm");
|
|
}
|
|
else if (op == "ncnn.Swish")
|
|
{
|
|
fprintf(pp, "%-16s", "Swish");
|
|
}
|
|
else if (op == "tf.AddN")
|
|
{
|
|
fprintf(pp, "%-16s", "Eltwise");
|
|
}
|
|
else if (op == "tf.AddV2")
|
|
{
|
|
fprintf(pp, "%-16s", "BinaryOp");
|
|
}
|
|
else if (op == "tf.AvgPool")
|
|
{
|
|
fprintf(pp, "%-16s", "Pooling");
|
|
}
|
|
else if (op == "tf.BiasAdd")
|
|
{
|
|
fprintf(pp, "%-16s", "BinaryOp");
|
|
}
|
|
else if (op == "tf.ConcatV2")
|
|
{
|
|
fprintf(pp, "%-16s", "Concat");
|
|
}
|
|
else if (op == "tf.Const")
|
|
{
|
|
continue;
|
|
}
|
|
else if (op == "tf.Conv2D")
|
|
{
|
|
fprintf(pp, "%-16s", "Convolution");
|
|
}
|
|
else if (op == "tf.Conv2DBackpropInput")
|
|
{
|
|
fprintf(pp, "%-16s", "Deconvolution");
|
|
}
|
|
else if (op == "tf.DepthToSpace")
|
|
{
|
|
fprintf(pp, "%-16s", "PixelShuffle");
|
|
}
|
|
else if (op == "tf.DepthwiseConv2dNative")
|
|
{
|
|
fprintf(pp, "%-16s", "ConvolutionDepthWise");
|
|
}
|
|
else if (op == "tf.Identity")
|
|
{
|
|
fprintf(pp, "%-16s", "Noop");
|
|
}
|
|
else if (op == "tf.LeakyRelu")
|
|
{
|
|
fprintf(pp, "%-16s", "ReLU");
|
|
}
|
|
else if (op == "tf.MatMul")
|
|
{
|
|
int transpose_a = get_operation_attr_b(operation, "transpose_a");
|
|
int transpose_b = get_operation_attr_b(operation, "transpose_b");
|
|
|
|
if (transpose_a == 0 && transpose_b == 1)
|
|
{
|
|
// InnerProduct-like A * B + C
|
|
fprintf(pp, "%-16s", "InnerProduct");
|
|
}
|
|
else
|
|
{
|
|
fprintf(pp, "%-16s", "Gemm");
|
|
}
|
|
}
|
|
else if (op == "tf.Maximum")
|
|
{
|
|
fprintf(pp, "%-16s", "BinaryOp");
|
|
}
|
|
else if (op == "tf.MaxPool")
|
|
{
|
|
fprintf(pp, "%-16s", "Pooling");
|
|
}
|
|
else if (op == "tf.Mean")
|
|
{
|
|
std::string reduction_indices_name = get_mlir_value_uniq_id(operation.getOperand(1));
|
|
const mlir::Attribute& R = weights[reduction_indices_name];
|
|
|
|
std::vector<int> v = get_attr_ai(R);
|
|
|
|
int keep_dims = get_operation_attr_b(operation, "keep_dims");
|
|
|
|
if (keep_dims == 0 && v.size() == 2 && v[0] == 1 && v[1] == 2)
|
|
{
|
|
// global avg pooling style nhwc -> nc
|
|
fprintf(pp, "%-16s", "Pooling");
|
|
}
|
|
else
|
|
{
|
|
fprintf(pp, "%-16s", "Reduction");
|
|
}
|
|
}
|
|
else if (op == "tf.Minimum")
|
|
{
|
|
fprintf(pp, "%-16s", "BinaryOp");
|
|
}
|
|
else if (op == "tf.Mul")
|
|
{
|
|
fprintf(pp, "%-16s", "BinaryOp");
|
|
}
|
|
else if (op == "tf.Pad")
|
|
{
|
|
fprintf(pp, "%-16s", "Padding");
|
|
}
|
|
else if (op == "tf.Placeholder")
|
|
{
|
|
fprintf(pp, "%-16s", "Input");
|
|
}
|
|
else if (op == "tf.Relu")
|
|
{
|
|
fprintf(pp, "%-16s", "ReLU");
|
|
}
|
|
else if (op == "tf.Relu6")
|
|
{
|
|
fprintf(pp, "%-16s", "Clip");
|
|
}
|
|
else if (op == "tf.Reshape")
|
|
{
|
|
fprintf(pp, "%-16s", "Reshape");
|
|
}
|
|
else if (op == "tf.ResizeBilinear")
|
|
{
|
|
fprintf(pp, "%-16s", "Interp");
|
|
}
|
|
else if (op == "tf.ResizeNearestNeighbor")
|
|
{
|
|
fprintf(pp, "%-16s", "Interp");
|
|
}
|
|
else if (op == "tf.Sigmoid")
|
|
{
|
|
fprintf(pp, "%-16s", "Sigmoid");
|
|
}
|
|
else if (op == "tf.Softmax")
|
|
{
|
|
fprintf(pp, "%-16s", "Softmax");
|
|
}
|
|
else if (op == "tf.SpaceToDepth")
|
|
{
|
|
fprintf(pp, "%-16s", "Reorg");
|
|
}
|
|
else if (op == "tf.StridedSlice")
|
|
{
|
|
fprintf(pp, "%-16s", "Crop");
|
|
}
|
|
else if (op == "tf.Sub")
|
|
{
|
|
fprintf(pp, "%-16s", "BinaryOp");
|
|
}
|
|
else if (op == "tf.Tanh")
|
|
{
|
|
fprintf(pp, "%-16s", "TanH");
|
|
}
|
|
else
|
|
{
|
|
// TODO
|
|
fprintf(stderr, "%s not supported yet!\n", op.c_str());
|
|
fprintf(pp, "%-16s", op.c_str());
|
|
}
|
|
|
|
char opid_name[64];
|
|
sprintf(opid_name, "op_%d", opid);
|
|
|
|
fprintf(pp, " %-24s %d %d", opid_name, num_input, num_output);
|
|
|
|
for (int i = 0; i < (int)operation.getNumOperands(); i++)
|
|
{
|
|
std::string input_name = get_mlir_value_uniq_id(operation.getOperand(i));
|
|
|
|
// check weight
|
|
if (weights.find(input_name) != weights.end() && node_reference[input_name] == 0)
|
|
{
|
|
continue;
|
|
}
|
|
|
|
if (split_node_reference.find(input_name) != split_node_reference.end())
|
|
{
|
|
int refidx = split_node_reference[input_name] - 1;
|
|
split_node_reference[input_name] = refidx;
|
|
|
|
char splitsuffix[256];
|
|
sprintf(splitsuffix, "_splitncnn_%d", refidx);
|
|
input_name = input_name + splitsuffix;
|
|
}
|
|
|
|
fprintf(pp, " %s", input_name.c_str());
|
|
}
|
|
|
|
for (int i = 0; i < num_output; i++)
|
|
{
|
|
std::string output_name = get_mlir_value_uniq_id(operation.getResult(i));
|
|
fprintf(pp, " %s", output_name.c_str());
|
|
}
|
|
|
|
if (op == "std.return")
|
|
{
|
|
}
|
|
else if (op == "ncnn.BinaryOp")
|
|
{
|
|
int op_type = get_operation_attr_i(operation, "op_type");
|
|
int with_scalar = get_operation_attr_i(operation, "with_scalar");
|
|
float b = get_operation_attr_f(operation, "b");
|
|
|
|
fprintf(pp, " 0=%d", op_type);
|
|
fprintf(pp, " 1=%d", with_scalar);
|
|
fprintf(pp, " 2=%e", b);
|
|
}
|
|
else if (op == "ncnn.KerasConv2D")
|
|
{
|
|
std::string weight_name = get_mlir_value_uniq_id(operation.getOperand(1));
|
|
std::string bias_name = get_mlir_value_uniq_id(operation.getOperand(2));
|
|
const mlir::Attribute& W = weights[weight_name];
|
|
const mlir::Attribute& B = weights[bias_name];
|
|
|
|
llvm::ArrayRef<int64_t> shape = W.getType().cast<mlir::RankedTensorType>().getShape();
|
|
|
|
// assert(shape.size() == 4)
|
|
|
|
// kh-kw-inch-outch
|
|
int kernel_size_h = shape[0];
|
|
int kernel_size_w = shape[1];
|
|
int num_input = shape[2];
|
|
int num_output = shape[3];
|
|
int weight_data_size = kernel_size_h * kernel_size_w * num_input * num_output;
|
|
|
|
fprintf(pp, " 0=%d", num_output);
|
|
fprintf(pp, " 1=%d", kernel_size_w);
|
|
fprintf(pp, " 11=%d", kernel_size_h);
|
|
fprintf(pp, " 6=%d", weight_data_size);
|
|
|
|
std::vector<int> dilations = get_operation_attr_ai(operation, "dilations");
|
|
std::vector<int> strides = get_operation_attr_ai(operation, "strides");
|
|
std::string padding = get_operation_attr_s(operation, "padding");
|
|
|
|
if (dilations.size() == 4)
|
|
{
|
|
fprintf(pp, " 2=%d", dilations[2]);
|
|
fprintf(pp, " 12=%d", dilations[1]);
|
|
}
|
|
|
|
if (strides.size() == 4)
|
|
{
|
|
fprintf(pp, " 3=%d", strides[2]);
|
|
fprintf(pp, " 13=%d", strides[1]);
|
|
}
|
|
|
|
if (padding == "EXPLICIT")
|
|
{
|
|
// nhwc = [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]
|
|
std::vector<int> explicit_paddings = get_operation_attr_ai(operation, "explicit_paddings");
|
|
|
|
fprintf(pp, " 4=%d", explicit_paddings[4]);
|
|
fprintf(pp, " 15=%d", explicit_paddings[5]);
|
|
fprintf(pp, " 14=%d", explicit_paddings[2]);
|
|
fprintf(pp, " 16=%d", explicit_paddings[3]);
|
|
}
|
|
else if (padding == "VALID")
|
|
{
|
|
fprintf(pp, " 4=%d", 0);
|
|
}
|
|
else if (padding == "SAME")
|
|
{
|
|
fprintf(pp, " 4=%d", -233);
|
|
}
|
|
|
|
fprintf(pp, " 5=1"); // bias_term
|
|
|
|
std::vector<float> v = get_attr_af(W);
|
|
std::vector<float> bv = get_attr_af(B);
|
|
|
|
// reorder h-w-i-o to o-i-h-w
|
|
{
|
|
int quantize_tag = 0;
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
float tmp;
|
|
for (int p = 0; p < num_output; p++)
|
|
{
|
|
for (int q = 0; q < num_input; q++)
|
|
{
|
|
for (int i = 0; i < kernel_size_h; i++)
|
|
{
|
|
for (int j = 0; j < kernel_size_w; j++)
|
|
{
|
|
tmp = v[i * kernel_size_w * num_input * num_output + j * num_input * num_output + q * num_output + p];
|
|
fwrite(&tmp, sizeof(float), 1, bp);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
fwrite(bv.data(), sizeof(float), bv.size(), bp);
|
|
}
|
|
else if (op == "ncnn.KerasDense")
|
|
{
|
|
std::string weight_name = get_mlir_value_uniq_id(operation.getOperand(1));
|
|
std::string bias_name = get_mlir_value_uniq_id(operation.getOperand(2));
|
|
const mlir::Attribute& W = weights[weight_name];
|
|
const mlir::Attribute& B = weights[bias_name];
|
|
|
|
llvm::ArrayRef<int64_t> shape = W.getType().cast<mlir::RankedTensorType>().getShape();
|
|
|
|
// assert(shape.size() == 2)
|
|
|
|
// inch-outch
|
|
int num_input = shape[0];
|
|
int num_output = shape[1];
|
|
int weight_data_size = shape[0] * shape[1];
|
|
|
|
fprintf(pp, " 0=%d", num_output);
|
|
fprintf(pp, " 1=1"); // bias_term
|
|
fprintf(pp, " 2=%d", weight_data_size);
|
|
|
|
std::vector<float> v = get_attr_af(W);
|
|
std::vector<float> bv = get_attr_af(B);
|
|
|
|
// reorder i-o to o-i
|
|
{
|
|
int quantize_tag = 0;
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
float tmp;
|
|
for (int p = 0; p < num_output; p++)
|
|
{
|
|
for (int q = 0; q < num_input; q++)
|
|
{
|
|
tmp = v[q * num_output + p];
|
|
fwrite(&tmp, sizeof(float), 1, bp);
|
|
}
|
|
}
|
|
}
|
|
|
|
fwrite(bv.data(), sizeof(float), bv.size(), bp);
|
|
}
|
|
else if (op == "ncnn.KerasBatchNorm")
|
|
{
|
|
std::string gamma_name = get_mlir_value_uniq_id(operation.getOperand(1));
|
|
std::string bias_name = get_mlir_value_uniq_id(operation.getOperand(2));
|
|
const mlir::Attribute& W = weights[gamma_name];
|
|
const mlir::Attribute& B = weights[bias_name];
|
|
|
|
std::vector<float> v = get_attr_af(W);
|
|
std::vector<float> bv = get_attr_af(B);
|
|
|
|
int channels = v.size();
|
|
|
|
fprintf(pp, " 0=%d", channels);
|
|
|
|
std::vector<float> mean(channels, 0.f);
|
|
std::vector<float> var(channels, 1.f);
|
|
|
|
fwrite(v.data(), sizeof(float), channels, bp);
|
|
fwrite(mean.data(), sizeof(float), channels, bp);
|
|
fwrite(var.data(), sizeof(float), channels, bp);
|
|
fwrite(bv.data(), sizeof(float), channels, bp);
|
|
}
|
|
else if (op == "ncnn.InstanceNorm")
|
|
{
|
|
float eps = get_operation_attr_f(operation, "epsilon");
|
|
|
|
fprintf(pp, " 0=0"); // channels
|
|
fprintf(pp, " 1=%e", eps);
|
|
fprintf(pp, " 2=0"); // affine
|
|
}
|
|
else if (op == "ncnn.InstanceNormAffine")
|
|
{
|
|
float eps = get_operation_attr_f(operation, "epsilon");
|
|
|
|
std::string gamma_name = get_mlir_value_uniq_id(operation.getOperand(1));
|
|
std::string beta_name = get_mlir_value_uniq_id(operation.getOperand(2));
|
|
const mlir::Attribute& G = weights[gamma_name];
|
|
const mlir::Attribute& B = weights[beta_name];
|
|
|
|
std::vector<float> gv = get_attr_af(G);
|
|
std::vector<float> bv = get_attr_af(B);
|
|
|
|
int channels = gv.size();
|
|
|
|
fprintf(pp, " 0=%d", channels);
|
|
fprintf(pp, " 1=%e", eps);
|
|
fprintf(pp, " 2=1"); // affine
|
|
|
|
fwrite(gv.data(), sizeof(float), gv.size(), bp);
|
|
fwrite(bv.data(), sizeof(float), bv.size(), bp);
|
|
}
|
|
else if (op == "ncnn.Swish")
|
|
{
|
|
// no param
|
|
}
|
|
else if (op == "tf.AddN")
|
|
{
|
|
int op_type = 1;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "tf.AddV2")
|
|
{
|
|
int op_type = 0;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "tf.AvgPool")
|
|
{
|
|
std::vector<int> ksize = get_operation_attr_ai(operation, "ksize");
|
|
std::vector<int> strides = get_operation_attr_ai(operation, "strides");
|
|
std::string padding = get_operation_attr_s(operation, "padding");
|
|
|
|
fprintf(pp, " 0=1"); // avg pool
|
|
|
|
if (ksize.size() == 4)
|
|
{
|
|
fprintf(pp, " 1=%d", ksize[2]);
|
|
fprintf(pp, " 11=%d", ksize[1]);
|
|
}
|
|
|
|
if (strides.size() == 4)
|
|
{
|
|
fprintf(pp, " 2=%d", strides[2]);
|
|
fprintf(pp, " 12=%d", strides[1]);
|
|
}
|
|
|
|
int pad_mode = 1;
|
|
if (padding == "VALID")
|
|
{
|
|
pad_mode = 1;
|
|
}
|
|
else if (padding == "SAME")
|
|
{
|
|
pad_mode = 2;
|
|
}
|
|
|
|
fprintf(pp, " 5=%d", pad_mode);
|
|
}
|
|
else if (op == "tf.ConcatV2")
|
|
{
|
|
std::string axis_name = get_mlir_value_uniq_id(operation.getOperand(operation.getNumOperands() - 1));
|
|
const mlir::Attribute& A = weights[axis_name];
|
|
|
|
int axis = get_attr_ai(A)[0];
|
|
|
|
// axis nhc to nhw
|
|
// axis nhwc to nchw
|
|
int dims = operation.getOperand(0).getType().cast<mlir::RankedTensorType>().getShape().size();
|
|
|
|
if (dims == 2 && axis == 1)
|
|
{
|
|
axis = 0;
|
|
}
|
|
if (dims == 3 && axis == 1)
|
|
{
|
|
axis = 1;
|
|
}
|
|
if (dims == 3 && axis == 2)
|
|
{
|
|
axis = 0;
|
|
}
|
|
if (dims == 4 && axis == 1)
|
|
{
|
|
axis = 1;
|
|
}
|
|
if (dims == 4 && axis == 2)
|
|
{
|
|
axis = 2;
|
|
}
|
|
if (dims == 4 && axis == 3)
|
|
{
|
|
axis = 0;
|
|
}
|
|
|
|
fprintf(pp, " 0=%d", axis);
|
|
}
|
|
else if (op == "tf.Const")
|
|
{
|
|
// never reach here
|
|
}
|
|
else if (op == "tf.Conv2D")
|
|
{
|
|
std::string weight_name = get_mlir_value_uniq_id(operation.getOperand(1));
|
|
const mlir::Attribute& W = weights[weight_name];
|
|
|
|
llvm::ArrayRef<int64_t> shape = W.getType().cast<mlir::RankedTensorType>().getShape();
|
|
|
|
// assert(shape.size() == 4)
|
|
|
|
// kh-kw-inch-outch
|
|
int kernel_size_h = shape[0];
|
|
int kernel_size_w = shape[1];
|
|
int num_input = shape[2];
|
|
int num_output = shape[3];
|
|
int weight_data_size = kernel_size_h * kernel_size_w * num_input * num_output;
|
|
|
|
fprintf(pp, " 0=%d", num_output);
|
|
fprintf(pp, " 1=%d", kernel_size_w);
|
|
fprintf(pp, " 11=%d", kernel_size_h);
|
|
fprintf(pp, " 6=%d", weight_data_size);
|
|
|
|
std::vector<int> dilations = get_operation_attr_ai(operation, "dilations");
|
|
std::vector<int> strides = get_operation_attr_ai(operation, "strides");
|
|
std::string padding = get_operation_attr_s(operation, "padding");
|
|
|
|
if (dilations.size() == 4)
|
|
{
|
|
fprintf(pp, " 2=%d", dilations[2]);
|
|
fprintf(pp, " 12=%d", dilations[1]);
|
|
}
|
|
|
|
if (strides.size() == 4)
|
|
{
|
|
fprintf(pp, " 3=%d", strides[2]);
|
|
fprintf(pp, " 13=%d", strides[1]);
|
|
}
|
|
|
|
if (padding == "EXPLICIT")
|
|
{
|
|
// nhwc = [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]
|
|
std::vector<int> explicit_paddings = get_operation_attr_ai(operation, "explicit_paddings");
|
|
|
|
fprintf(pp, " 4=%d", explicit_paddings[4]);
|
|
fprintf(pp, " 15=%d", explicit_paddings[5]);
|
|
fprintf(pp, " 14=%d", explicit_paddings[2]);
|
|
fprintf(pp, " 16=%d", explicit_paddings[3]);
|
|
}
|
|
else if (padding == "VALID")
|
|
{
|
|
fprintf(pp, " 4=%d", 0);
|
|
}
|
|
else if (padding == "SAME")
|
|
{
|
|
fprintf(pp, " 4=%d", -233);
|
|
}
|
|
|
|
std::vector<float> v = get_attr_af(W);
|
|
|
|
// reorder h-w-i-o to o-i-h-w
|
|
{
|
|
int quantize_tag = 0;
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
float tmp;
|
|
for (int p = 0; p < num_output; p++)
|
|
{
|
|
for (int q = 0; q < num_input; q++)
|
|
{
|
|
for (int i = 0; i < kernel_size_h; i++)
|
|
{
|
|
for (int j = 0; j < kernel_size_w; j++)
|
|
{
|
|
tmp = v[i * kernel_size_w * num_input * num_output + j * num_input * num_output + q * num_output + p];
|
|
fwrite(&tmp, sizeof(float), 1, bp);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else if (op == "tf.Conv2DBackpropInput")
|
|
{
|
|
std::string output_shape_name = get_mlir_value_uniq_id(operation.getOperand(0));
|
|
const std::vector<int> output_shape = get_attr_ai(weights[output_shape_name]);
|
|
|
|
// assert(output_shape.size() == 4)
|
|
|
|
std::string weight_name = get_mlir_value_uniq_id(operation.getOperand(1));
|
|
const mlir::Attribute& W = weights[weight_name];
|
|
|
|
llvm::ArrayRef<int64_t> shape = W.getType().cast<mlir::RankedTensorType>().getShape();
|
|
|
|
// assert(shape.size() == 4)
|
|
|
|
// kh-kw-outch-inch
|
|
int kernel_size_h = shape[0];
|
|
int kernel_size_w = shape[1];
|
|
int num_output = shape[2];
|
|
int num_input = shape[3];
|
|
int weight_data_size = kernel_size_h * kernel_size_w * num_input * num_output;
|
|
|
|
fprintf(pp, " 0=%d", num_output);
|
|
fprintf(pp, " 1=%d", kernel_size_w);
|
|
fprintf(pp, " 11=%d", kernel_size_h);
|
|
fprintf(pp, " 6=%d", weight_data_size);
|
|
|
|
std::vector<int> dilations = get_operation_attr_ai(operation, "dilations");
|
|
std::vector<int> strides = get_operation_attr_ai(operation, "strides");
|
|
std::string padding = get_operation_attr_s(operation, "padding");
|
|
|
|
if (dilations.size() == 4)
|
|
{
|
|
fprintf(pp, " 2=%d", dilations[2]);
|
|
fprintf(pp, " 12=%d", dilations[1]);
|
|
}
|
|
|
|
if (strides.size() == 4)
|
|
{
|
|
fprintf(pp, " 3=%d", strides[2]);
|
|
fprintf(pp, " 13=%d", strides[1]);
|
|
}
|
|
|
|
if (padding == "EXPLICIT")
|
|
{
|
|
// nhwc = [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]
|
|
std::vector<int> explicit_paddings = get_operation_attr_ai(operation, "explicit_paddings");
|
|
|
|
fprintf(pp, " 4=%d", explicit_paddings[4]);
|
|
fprintf(pp, " 15=%d", explicit_paddings[5]);
|
|
fprintf(pp, " 14=%d", explicit_paddings[2]);
|
|
fprintf(pp, " 16=%d", explicit_paddings[3]);
|
|
}
|
|
else if (padding == "VALID")
|
|
{
|
|
fprintf(pp, " 4=%d", 0);
|
|
}
|
|
else if (padding == "SAME")
|
|
{
|
|
fprintf(pp, " 4=%d", -233);
|
|
|
|
fprintf(pp, " 20=%d", output_shape[2]);
|
|
fprintf(pp, " 21=%d", output_shape[1]);
|
|
}
|
|
|
|
std::vector<float> v = get_attr_af(W);
|
|
|
|
// reorder h-w-o-i to o-i-h-w
|
|
{
|
|
int quantize_tag = 0;
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
float tmp;
|
|
for (int p = 0; p < num_output; p++)
|
|
{
|
|
for (int q = 0; q < num_input; q++)
|
|
{
|
|
for (int i = 0; i < kernel_size_h; i++)
|
|
{
|
|
for (int j = 0; j < kernel_size_w; j++)
|
|
{
|
|
tmp = v[i * kernel_size_w * num_output * num_input + j * num_output * num_input + p * num_input + q];
|
|
fwrite(&tmp, sizeof(float), 1, bp);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else if (op == "tf.DepthToSpace")
|
|
{
|
|
int block_size = get_operation_attr_i(operation, "block_size");
|
|
fprintf(pp, " 0=%d", block_size);
|
|
fprintf(pp, " 1=1"); // mode
|
|
}
|
|
else if (op == "tf.DepthwiseConv2dNative")
|
|
{
|
|
std::string weight_name = get_mlir_value_uniq_id(operation.getOperand(1));
|
|
const mlir::Attribute& W = weights[weight_name];
|
|
|
|
llvm::ArrayRef<int64_t> shape = W.getType().cast<mlir::RankedTensorType>().getShape();
|
|
|
|
// assert(shape.size() == 4)
|
|
|
|
// kh-kw-inch-cm
|
|
int kernel_size_h = shape[0];
|
|
int kernel_size_w = shape[1];
|
|
int num_input = shape[2];
|
|
int channel_multiplier = shape[3];
|
|
|
|
int num_output = num_input * channel_multiplier;
|
|
int group = num_input;
|
|
|
|
int weight_data_size = kernel_size_h * kernel_size_w * num_input * channel_multiplier;
|
|
|
|
fprintf(pp, " 0=%d", num_output);
|
|
fprintf(pp, " 1=%d", kernel_size_w);
|
|
fprintf(pp, " 11=%d", kernel_size_h);
|
|
fprintf(pp, " 6=%d", weight_data_size);
|
|
fprintf(pp, " 7=%d", group);
|
|
|
|
std::vector<int> dilations = get_operation_attr_ai(operation, "dilations");
|
|
std::vector<int> strides = get_operation_attr_ai(operation, "strides");
|
|
std::string padding = get_operation_attr_s(operation, "padding");
|
|
|
|
if (dilations.size() == 4)
|
|
{
|
|
fprintf(pp, " 2=%d", dilations[2]);
|
|
fprintf(pp, " 12=%d", dilations[1]);
|
|
}
|
|
|
|
if (strides.size() == 4)
|
|
{
|
|
fprintf(pp, " 3=%d", strides[2]);
|
|
fprintf(pp, " 13=%d", strides[1]);
|
|
}
|
|
|
|
if (padding == "EXPLICIT")
|
|
{
|
|
// nhwc = [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]
|
|
std::vector<int> explicit_paddings = get_operation_attr_ai(operation, "explicit_paddings");
|
|
|
|
fprintf(pp, " 4=%d", explicit_paddings[4]);
|
|
fprintf(pp, " 15=%d", explicit_paddings[5]);
|
|
fprintf(pp, " 14=%d", explicit_paddings[2]);
|
|
fprintf(pp, " 16=%d", explicit_paddings[3]);
|
|
}
|
|
else if (padding == "VALID")
|
|
{
|
|
fprintf(pp, " 4=%d", 0);
|
|
}
|
|
else if (padding == "SAME")
|
|
{
|
|
fprintf(pp, " 4=%d", -233);
|
|
}
|
|
|
|
std::vector<float> v = get_attr_af(W);
|
|
|
|
// reorder h-w-i-cm to i-cm-h-w
|
|
{
|
|
int quantize_tag = 0;
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
float tmp;
|
|
for (int p = 0; p < num_input; p++)
|
|
{
|
|
for (int q = 0; q < channel_multiplier; q++)
|
|
{
|
|
for (int i = 0; i < kernel_size_h; i++)
|
|
{
|
|
for (int j = 0; j < kernel_size_w; j++)
|
|
{
|
|
tmp = v[i * kernel_size_w * channel_multiplier * num_input + j * channel_multiplier * num_input + p * channel_multiplier + q];
|
|
fwrite(&tmp, sizeof(float), 1, bp);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else if (op == "tf.Identity")
|
|
{
|
|
}
|
|
else if (op == "tf.LeakyRelu")
|
|
{
|
|
float alpha = get_operation_attr_f(operation, "alpha");
|
|
|
|
fprintf(pp, " 0=%e", alpha);
|
|
}
|
|
else if (op == "tf.MatMul")
|
|
{
|
|
int transpose_a = get_operation_attr_b(operation, "transpose_a");
|
|
int transpose_b = get_operation_attr_b(operation, "transpose_b");
|
|
|
|
if (transpose_a == 0 && transpose_b == 1)
|
|
{
|
|
// InnerProduct-like A * B + C
|
|
std::string weight_name = get_mlir_value_uniq_id(operation.getOperand(1));
|
|
const mlir::Attribute& W = weights[weight_name];
|
|
|
|
llvm::ArrayRef<int64_t> shape = W.getType().cast<mlir::RankedTensorType>().getShape();
|
|
|
|
// assert(shape.size() == 2)
|
|
|
|
// inch-outch
|
|
int num_input = shape[0];
|
|
int num_output = shape[1];
|
|
int weight_data_size = shape[0] * shape[1];
|
|
|
|
fprintf(pp, " 0=%d", num_output);
|
|
fprintf(pp, " 2=%d", weight_data_size);
|
|
|
|
std::vector<float> v = get_attr_af(W);
|
|
|
|
// reorder i-o to o-i
|
|
{
|
|
int quantize_tag = 0;
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
float tmp;
|
|
for (int p = 0; p < num_output; p++)
|
|
{
|
|
for (int q = 0; q < num_input; q++)
|
|
{
|
|
tmp = v[q * num_output + p];
|
|
fwrite(&tmp, sizeof(float), 1, bp);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
// gemm
|
|
fprintf(pp, " 0=1.0"); // alpha
|
|
fprintf(pp, " 1=1.0"); // beta
|
|
fprintf(pp, " 2=%d", transpose_a);
|
|
fprintf(pp, " 3=%d", transpose_b);
|
|
}
|
|
}
|
|
else if (op == "tf.Maximum")
|
|
{
|
|
int op_type = 4;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "tf.MaxPool")
|
|
{
|
|
std::vector<int> ksize = get_operation_attr_ai(operation, "ksize");
|
|
std::vector<int> strides = get_operation_attr_ai(operation, "strides");
|
|
std::string padding = get_operation_attr_s(operation, "padding");
|
|
|
|
fprintf(pp, " 0=0"); // max pool
|
|
|
|
if (ksize.size() == 4)
|
|
{
|
|
fprintf(pp, " 1=%d", ksize[2]);
|
|
fprintf(pp, " 11=%d", ksize[1]);
|
|
}
|
|
|
|
if (strides.size() == 4)
|
|
{
|
|
fprintf(pp, " 2=%d", strides[2]);
|
|
fprintf(pp, " 12=%d", strides[1]);
|
|
}
|
|
|
|
int pad_mode = 1;
|
|
if (padding == "VALID")
|
|
{
|
|
pad_mode = 1;
|
|
}
|
|
else if (padding == "SAME")
|
|
{
|
|
pad_mode = 2;
|
|
}
|
|
|
|
fprintf(pp, " 5=%d", pad_mode);
|
|
}
|
|
else if (op == "tf.Mean")
|
|
{
|
|
std::string reduction_indices_name = get_mlir_value_uniq_id(operation.getOperand(1));
|
|
const mlir::Attribute& R = weights[reduction_indices_name];
|
|
|
|
std::vector<int> v = get_attr_ai(R);
|
|
|
|
int keep_dims = get_operation_attr_b(operation, "keep_dims");
|
|
|
|
if (keep_dims == 0 && v.size() == 2 && v[0] == 1 && v[1] == 2)
|
|
{
|
|
// global avg pooling style nhwc -> nc
|
|
int pool = 1;
|
|
int global_pool = 1;
|
|
|
|
fprintf(pp, " 0=%d", pool);
|
|
fprintf(pp, " 4=%d", global_pool);
|
|
}
|
|
else
|
|
{
|
|
// Reduction mean
|
|
fprintf(pp, " 0=3");
|
|
fprintf(pp, " 1=0"); // reduce_all
|
|
fprintf(pp, " -23303=%d", (int)v.size());
|
|
for (int i = 0; i < (int)v.size(); i++)
|
|
{
|
|
if (v[i] == 1)
|
|
fprintf(pp, ",1");
|
|
if (v[i] == 2)
|
|
fprintf(pp, ",2");
|
|
if (v[i] == 3)
|
|
fprintf(pp, ",0");
|
|
}
|
|
fprintf(pp, " 4=%d", keep_dims);
|
|
fprintf(pp, " 5=1");
|
|
}
|
|
}
|
|
else if (op == "tf.Minimum")
|
|
{
|
|
int op_type = 5;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "tf.Mul")
|
|
{
|
|
int op_type = 2;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "tf.Pad")
|
|
{
|
|
std::string weight_name = get_mlir_value_uniq_id(operation.getOperand(1));
|
|
const mlir::Attribute& P = weights[weight_name];
|
|
|
|
std::vector<int> v = get_attr_ai(P);
|
|
|
|
// nhwc = [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]
|
|
fprintf(pp, " 0=%d", v[2]);
|
|
fprintf(pp, " 1=%d", v[3]);
|
|
fprintf(pp, " 2=%d", v[4]);
|
|
fprintf(pp, " 3=%d", v[5]);
|
|
}
|
|
else if (op == "tf.Placeholder")
|
|
{
|
|
}
|
|
else if (op == "tf.Relu")
|
|
{
|
|
}
|
|
else if (op == "tf.Relu6")
|
|
{
|
|
float min = 0.f;
|
|
float max = 6.f;
|
|
fprintf(pp, " 0=%e", min);
|
|
fprintf(pp, " 1=%e", max);
|
|
}
|
|
else if (op == "tf.Reshape")
|
|
{
|
|
std::string weight_name = get_mlir_value_uniq_id(operation.getOperand(1));
|
|
const mlir::Attribute& S = weights[weight_name];
|
|
|
|
std::vector<int> v = get_attr_ai(S);
|
|
|
|
int size = v.size();
|
|
|
|
// n h w c
|
|
// n h c
|
|
// n c
|
|
if (size == 4)
|
|
{
|
|
fprintf(pp, " 0=%d 1=%d 2=%d", v[2], v[1], v[3]);
|
|
}
|
|
if (size == 3)
|
|
{
|
|
fprintf(pp, " 0=%d 1=%d 2=-233", v[1], v[2]);
|
|
}
|
|
if (size == 2)
|
|
{
|
|
fprintf(pp, " 0=%d 1=-233 2=-233", v[1]);
|
|
}
|
|
|
|
// FIXME may not always be the case
|
|
fprintf(pp, " 3=1");
|
|
}
|
|
else if (op == "tf.ResizeBilinear")
|
|
{
|
|
std::string weight_name = get_mlir_value_uniq_id(operation.getOperand(1));
|
|
const mlir::Attribute& P = weights[weight_name];
|
|
|
|
std::vector<int> size = get_attr_ai(P);
|
|
|
|
int align_corners = get_operation_attr_b(operation, "align_corners");
|
|
int half_pixel_centers = get_operation_attr_b(operation, "half_pixel_centers");
|
|
if (!(align_corners == 0 && half_pixel_centers == 1))
|
|
{
|
|
fprintf(stderr, "Unsupported ResizeBilinear align_corners %d half_pixel_centers %d !\n", align_corners, half_pixel_centers);
|
|
}
|
|
|
|
fprintf(pp, " 0=2"); // bilinear
|
|
fprintf(pp, " 3=%d 4=%d", size[1], size[0]);
|
|
}
|
|
else if (op == "tf.ResizeNearestNeighbor")
|
|
{
|
|
std::string weight_name = get_mlir_value_uniq_id(operation.getOperand(1));
|
|
const mlir::Attribute& P = weights[weight_name];
|
|
|
|
std::vector<int> size = get_attr_ai(P);
|
|
|
|
int align_corners = get_operation_attr_b(operation, "align_corners");
|
|
int half_pixel_centers = get_operation_attr_b(operation, "half_pixel_centers");
|
|
if (!(align_corners == 0 && half_pixel_centers == 1))
|
|
{
|
|
fprintf(stderr, "Unsupported ResizeNearestNeighbor align_corners %d half_pixel_centers %d !\n", align_corners, half_pixel_centers);
|
|
}
|
|
|
|
fprintf(pp, " 0=1"); // nearest
|
|
fprintf(pp, " 3=%d 4=%d", size[1], size[0]);
|
|
}
|
|
else if (op == "tf.Sigmoid")
|
|
{
|
|
}
|
|
else if (op == "tf.Softmax")
|
|
{
|
|
}
|
|
else if (op == "tf.SpaceToDepth")
|
|
{
|
|
int block_size = get_operation_attr_i(operation, "block_size");
|
|
fprintf(pp, " 0=%d", block_size);
|
|
fprintf(pp, " 1=1"); // mode
|
|
}
|
|
else if (op == "tf.StridedSlice")
|
|
{
|
|
std::string begin_name = get_mlir_value_uniq_id(operation.getOperand(1));
|
|
std::string end_name = get_mlir_value_uniq_id(operation.getOperand(2));
|
|
std::string strides_name = get_mlir_value_uniq_id(operation.getOperand(3));
|
|
const mlir::Attribute& B = weights[begin_name];
|
|
const mlir::Attribute& E = weights[end_name];
|
|
const mlir::Attribute& S = weights[strides_name];
|
|
|
|
std::vector<int> begin = get_attr_ai(B);
|
|
std::vector<int> end = get_attr_ai(E);
|
|
std::vector<int> strides = get_attr_ai(S);
|
|
|
|
int begin_mask = get_operation_attr_i(operation, "begin_mask");
|
|
int end_mask = get_operation_attr_i(operation, "end_mask");
|
|
int ellipsis_mask = get_operation_attr_i(operation, "ellipsis_mask");
|
|
int new_axis_mask = get_operation_attr_i(operation, "new_axis_mask");
|
|
int shrink_axis_mask = get_operation_attr_i(operation, "shrink_axis_mask");
|
|
|
|
int dims = strides.size();
|
|
|
|
// assert strides == 1
|
|
for (int i = 0; i < dims; i++)
|
|
{
|
|
if (strides[i] != 1)
|
|
fprintf(stderr, "Unsupported StridedSlice strides !\n");
|
|
}
|
|
|
|
for (int i = 0; i < dims; i++)
|
|
{
|
|
// TODO strides[i] < 0
|
|
if (begin_mask & (1 << i))
|
|
{
|
|
begin[i] = 0;
|
|
}
|
|
if (end_mask & (1 << i))
|
|
{
|
|
end[i] = -233;
|
|
}
|
|
if (ellipsis_mask & (1 << i))
|
|
{
|
|
begin[i] = 0;
|
|
end[i] = -233;
|
|
}
|
|
}
|
|
|
|
if (new_axis_mask)
|
|
{
|
|
fprintf(stderr, "Unsupported StridedSlice new_axis_mask !\n");
|
|
}
|
|
|
|
if (shrink_axis_mask)
|
|
{
|
|
fprintf(stderr, "Unsupported StridedSlice shrink_axis_mask !\n");
|
|
}
|
|
|
|
// n h w c
|
|
// n h c
|
|
// n c
|
|
if (dims == 4)
|
|
{
|
|
fprintf(pp, " -23309=3,%d,%d,%d", begin[3], begin[1], begin[2]);
|
|
fprintf(pp, " -23310=3,%d,%d,%d", end[3], end[1], end[2]);
|
|
}
|
|
if (dims == 3)
|
|
{
|
|
fprintf(pp, " -23309=2,%d,%d", begin[2], begin[1]);
|
|
fprintf(pp, " -23310=2,%d,%d", end[2], end[1]);
|
|
}
|
|
if (dims == 2)
|
|
{
|
|
fprintf(pp, " -23309=1,%d", begin[1]);
|
|
fprintf(pp, " -23310=1,%d", end[1]);
|
|
}
|
|
}
|
|
else if (op == "tf.Sub")
|
|
{
|
|
int op_type = 1;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "tf.Tanh")
|
|
{
|
|
}
|
|
|
|
#if 0
|
|
for (const mlir::NamedAttribute& attr : operation.getAttrs())
|
|
{
|
|
const mlir::Identifier& identifier = attr.first;
|
|
const mlir::Attribute& attr = attr.second;
|
|
|
|
fprintf(pp, " %s=", identifier.c_str());
|
|
|
|
if (attr.isa<mlir::AffineMapAttr>())
|
|
{
|
|
fprintf(pp, "AffineMap");
|
|
}
|
|
if (attr.isa<mlir::ArrayAttr>())
|
|
{
|
|
// fprintf(pp, "Array");
|
|
mlir::ArrayAttr a = attr.cast<mlir::ArrayAttr>();
|
|
int array_size = a.getValue().size();
|
|
for (int t=0; t<array_size; t++)
|
|
{
|
|
if (a[t].isa<mlir::IntegerAttr>())
|
|
{
|
|
int64_t ii = a[t].cast<mlir::IntegerAttr>().getInt();
|
|
fprintf(pp, "%lld,", ii);
|
|
}
|
|
}
|
|
}
|
|
if (attr.isa<mlir::BoolAttr>())
|
|
{
|
|
// fprintf(pp, "Bool");
|
|
mlir::BoolAttr a = attr.cast<mlir::BoolAttr>();
|
|
fprintf(pp, "%d", a.getValue() ? 1 : 0);
|
|
}
|
|
if (attr.isa<mlir::DictionaryAttr>())
|
|
{
|
|
fprintf(pp, "Dictionary");
|
|
}
|
|
if (attr.isa<mlir::FloatAttr>())
|
|
{
|
|
fprintf(pp, "Float");
|
|
}
|
|
if (attr.isa<mlir::IntegerAttr>())
|
|
{
|
|
fprintf(pp, "Integer");
|
|
}
|
|
if (attr.isa<mlir::IntegerSetAttr>())
|
|
{
|
|
fprintf(pp, "IntegerSet");
|
|
}
|
|
if (attr.isa<mlir::OpaqueAttr>())
|
|
{
|
|
fprintf(pp, "Opaque");
|
|
}
|
|
if (attr.isa<mlir::StringAttr>())
|
|
{
|
|
// fprintf(pp, "String");
|
|
mlir::StringAttr s = attr.cast<mlir::StringAttr>();
|
|
fprintf(pp, "%s", s.getValue().empty() ? "" : s.getValue().data());
|
|
}
|
|
if (attr.isa<mlir::SymbolRefAttr>())
|
|
{
|
|
fprintf(pp, "SymbolRef");
|
|
}
|
|
if (attr.isa<mlir::FlatSymbolRefAttr>())
|
|
{
|
|
fprintf(pp, "FlatSymbolRef");
|
|
}
|
|
if (attr.isa<mlir::TypeAttr>())
|
|
{
|
|
fprintf(pp, "Type");
|
|
}
|
|
if (attr.isa<mlir::UnitAttr>())
|
|
{
|
|
fprintf(pp, "Unit");
|
|
}
|
|
if (attr.isa<mlir::ElementsAttr>())
|
|
{
|
|
fprintf(pp, "Elements");
|
|
}
|
|
if (attr.isa<mlir::DenseElementsAttr>())
|
|
{
|
|
fprintf(pp, "DenseElements");
|
|
}
|
|
if (attr.isa<mlir::DenseFPElementsAttr>())
|
|
{
|
|
fprintf(pp, "DenseFPElements");
|
|
}
|
|
if (attr.isa<mlir::DenseIntElementsAttr>())
|
|
{
|
|
fprintf(pp, "DenseIntElements");
|
|
}
|
|
if (attr.isa<mlir::OpaqueElementsAttr>())
|
|
{
|
|
fprintf(pp, "OpaqueElements");
|
|
}
|
|
if (attr.isa<mlir::SparseElementsAttr>())
|
|
{
|
|
fprintf(pp, "SparseElements");
|
|
}
|
|
if (attr.isa<mlir::SplatElementsAttr>())
|
|
{
|
|
fprintf(pp, "SplatElements");
|
|
}
|
|
|
|
}
|
|
#endif
|
|
|
|
fprintf(pp, "\n");
|
|
|
|
for (int j = 0; j < num_output; j++)
|
|
{
|
|
std::string output_name = get_mlir_value_uniq_id(operation.getResult(j));
|
|
if (node_reference.find(output_name) != node_reference.end())
|
|
{
|
|
int refcount = node_reference[output_name];
|
|
if (refcount > 1)
|
|
{
|
|
char splitname[256];
|
|
sprintf(splitname, "splitncnn_%d", internal_split);
|
|
fprintf(pp, "%-16s %-24s %d %d", "Split", splitname, 1, refcount);
|
|
|
|
fprintf(pp, " %s", output_name.c_str());
|
|
|
|
for (int k = 0; k < refcount; k++)
|
|
{
|
|
fprintf(pp, " %s_splitncnn_%d", output_name.c_str(), k);
|
|
}
|
|
fprintf(pp, "\n");
|
|
|
|
internal_split++;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
fclose(pp);
|
|
fclose(bp);
|
|
|
|
return 0;
|
|
}
|