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onnx2ncnn.cpp
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onnx2ncnn.cpp
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// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2017 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.
#include "onnx.pb.h"
#include <algorithm>
#include <float.h>
#include <fstream>
#include <google/protobuf/io/coded_stream.h>
#include <google/protobuf/io/zero_copy_stream_impl.h>
#include <google/protobuf/message.h>
#include <google/protobuf/text_format.h>
#include <iostream>
#include <limits.h>
#include <limits>
#include <set>
#include <stdio.h>
static bool read_proto_from_binary(const char* filepath, onnx::ModelProto* message)
{
std::ifstream fs(filepath, std::ifstream::in | std::ifstream::binary);
if (!fs.is_open())
{
fprintf(stderr, "open failed %s\n", filepath);
return false;
}
google::protobuf::io::IstreamInputStream input(&fs);
google::protobuf::io::CodedInputStream codedstr(&input);
#if GOOGLE_PROTOBUF_VERSION >= 3011000
codedstr.SetTotalBytesLimit(INT_MAX);
#else
codedstr.SetTotalBytesLimit(INT_MAX, INT_MAX / 2);
#endif
bool success = message->ParseFromCodedStream(&codedstr);
fs.close();
return success;
}
static std::vector<int> get_node_attr_ai(const onnx::NodeProto& node, const char* key)
{
std::vector<int> v;
for (int i = 0; i < node.attribute_size(); i++)
{
const onnx::AttributeProto& attr = node.attribute(i);
if (attr.name() == key)
{
v.resize(attr.ints_size());
for (int j = 0; j < attr.ints_size(); j++)
{
v[j] = std::max(std::min(attr.ints(j), (::google::protobuf::int64)INT_MAX), (::google::protobuf::int64)INT_MIN);
}
break;
}
}
return v;
}
static void set_node_attr_ai(onnx::NodeProto& node, const char* key,
const std::vector<int>& value)
{
onnx::AttributeProto* attr_group = node.add_attribute();
attr_group->set_name(key);
for (auto v : value)
{
attr_group->add_ints(v);
}
return;
}
static std::vector<float> get_node_attr_af(const onnx::NodeProto& node, const char* key)
{
std::vector<float> v;
for (int i = 0; i < node.attribute_size(); i++)
{
const onnx::AttributeProto& attr = node.attribute(i);
if (attr.name() == key)
{
v.resize(attr.floats_size());
for (int j = 0; j < attr.floats_size(); j++)
{
v[j] = attr.floats(j);
}
break;
}
}
return v;
}
static int get_node_attr_i(const onnx::NodeProto& node, const char* key, int def = 0)
{
for (int i = 0; i < node.attribute_size(); i++)
{
const onnx::AttributeProto& attr = node.attribute(i);
if (attr.name() == key)
{
return std::max(std::min(attr.i(), (::google::protobuf::int64)INT_MAX), (::google::protobuf::int64)INT_MIN);
}
}
return def;
}
static float get_node_attr_f(const onnx::NodeProto& node, const char* key, float def = 0.f)
{
for (int i = 0; i < node.attribute_size(); i++)
{
const onnx::AttributeProto& attr = node.attribute(i);
if (attr.name() == key)
{
return attr.f();
}
}
return def;
}
static std::string get_node_attr_s(const onnx::NodeProto& node, const char* key, const std::string& def = std::string())
{
for (int i = 0; i < node.attribute_size(); i++)
{
const onnx::AttributeProto& attr = node.attribute(i);
if (attr.name() == key)
{
return attr.s();
}
}
return def;
}
static onnx::TensorProto get_node_attr_tensor(const onnx::NodeProto& node, const char* key)
{
for (int i = 0; i < node.attribute_size(); i++)
{
const onnx::AttributeProto& attr = node.attribute(i);
if (attr.name() == key)
{
return attr.t();
}
}
return onnx::TensorProto();
}
static float get_node_attr_from_input_f(const onnx::TensorProto& tp)
{
float v = 0.f;
// float
if (tp.data_type() == 1)
{
const float* shape_data = 0;
if (tp.has_raw_data())
{
shape_data = (const float*)tp.raw_data().data();
}
else
{
shape_data = tp.float_data().data();
}
v = shape_data[0];
}
// double
else if (tp.data_type() == 11)
{
const double* shape_data = 0;
if (tp.has_raw_data())
{
shape_data = (const double*)tp.raw_data().data();
}
else
{
shape_data = tp.double_data().data();
}
v = shape_data[0];
}
// int64
else if (tp.data_type() == 7)
{
const int64_t* shape_data = 0;
if (tp.has_raw_data())
{
shape_data = (const int64_t*)tp.raw_data().data();
}
else
{
shape_data = tp.int64_data().data();
}
v = std::max(std::min(shape_data[0], (::google::protobuf::int64)INT_MAX), (::google::protobuf::int64)INT_MIN);
}
// int32
else if (tp.data_type() == 6)
{
const int32_t* shape_data = 0;
if (tp.has_raw_data())
{
shape_data = (const int32_t*)tp.raw_data().data();
}
else
{
shape_data = tp.int32_data().data();
}
v = shape_data[0];
}
else
{
fprintf(stderr, "Unknown data type %d\n", tp.data_type());
abort();
}
return v;
}
static std::vector<int> get_node_attr_from_input_ai(const onnx::TensorProto& tp)
{
int size = 0;
std::vector<int> v;
// int64
if (tp.data_type() == 7)
{
const int64_t* shape_data = 0;
if (tp.has_raw_data())
{
shape_data = (const int64_t*)tp.raw_data().data();
size = (int)(tp.raw_data().size() / 8);
}
else
{
shape_data = tp.int64_data().data();
size = tp.int64_data_size();
}
for (int j = 0; j < size; j++)
{
int vi = std::max(std::min(shape_data[j], (::google::protobuf::int64)INT_MAX), (::google::protobuf::int64)INT_MIN);
v.push_back(vi);
}
}
// int32
else if (tp.data_type() == 6)
{
const int32_t* shape_data = 0;
if (tp.has_raw_data())
{
shape_data = (const int32_t*)tp.raw_data().data();
size = (int)(tp.raw_data().size() / 4);
}
else
{
shape_data = tp.int32_data().data();
size = tp.int32_data_size();
}
for (int j = 0; j < size; j++)
{
v.push_back(shape_data[j]);
}
}
else
{
fprintf(stderr, "Unknown data type %d\n", tp.data_type());
}
return v;
}
static std::vector<float> get_node_attr_from_input_af(const onnx::TensorProto& tp)
{
int size = 0;
std::vector<float> v;
// float
if (tp.data_type() == 1)
{
const float* shape_data = 0;
if (tp.has_raw_data())
{
shape_data = (const float*)tp.raw_data().data();
size = (int)(tp.raw_data().size() / 4);
}
else
{
shape_data = tp.float_data().data();
size = tp.float_data_size();
}
for (int j = 0; j < size; j++)
{
v.push_back(shape_data[j]);
}
}
// double
else if (tp.data_type() == 11)
{
const double* shape_data = 0;
if (tp.has_raw_data())
{
shape_data = (const double*)tp.raw_data().data();
size = (int)(tp.raw_data().size() / 8);
}
else
{
shape_data = tp.double_data().data();
size = tp.double_data_size();
}
for (int j = 0; j < size; j++)
{
v.push_back((float)shape_data[j]);
}
}
else
{
fprintf(stderr, "Unknown data type %d\n", tp.data_type());
}
return v;
}
static int get_tensor_proto_data_size(const onnx::TensorProto& tp)
{
if (tp.has_raw_data())
{
const std::string& raw_data = tp.raw_data();
int size = (int)raw_data.size() / 4;
return size;
}
else if (tp.data_type() == 1)
{
return tp.float_data_size();
}
return 0;
}
static void fwrite_tensor_proto_data(const onnx::TensorProto& tp, FILE* bp)
{
int size = get_tensor_proto_data_size(tp);
if (tp.has_raw_data())
{
const std::string& raw_data = tp.raw_data();
fwrite(raw_data.data(), sizeof(float), size, bp);
}
else if (tp.data_type() == 1)
{
fwrite(tp.float_data().data(), sizeof(float), size, bp);
}
}
static void fuse_rewrite_gather(onnx::GraphProto* mutable_graph,
std::map<std::string, onnx::TensorProto>& weights,
std::map<std::string, int>& node_reference,
std::set<std::string>& blob_names, int& reduced_node_count)
{
const int node_count = mutable_graph->node_size();
for (int i = 0; i < node_count; ++i)
{
onnx::NodeProto* gather = mutable_graph->mutable_node(i);
if (gather->op_type() != "Gather")
{
continue;
}
auto indices = get_node_attr_from_input_ai(weights[gather->input(1)]);
if (indices.size() != 1)
{
continue;
}
{
// reconstruct node connections
node_reference[gather->input(1)] -= 1;
std::string origin_inp = gather->input(0);
gather->clear_input();
gather->add_input(origin_inp);
}
{
// update axis, starts and ends
int axis = get_node_attr_i(*gather, "axis", 1) - 1;
gather->set_op_type("Crop");
gather->clear_attribute();
int indice = indices[0];
set_node_attr_ai(*gather, "starts", std::vector<int> {indice});
set_node_attr_ai(*gather, "ends", std::vector<int> {indice + 1});
set_node_attr_ai(*gather, "axis", std::vector<int> {axis});
}
}
}
static void fuse_weight_reshape(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
{
int node_count = mutable_graph->node_size();
for (int i = 0; i < node_count; i++)
{
onnx::NodeProto* node = mutable_graph->mutable_node(i);
// weight <= Reshape(weight)
if (node->op_type() == "Reshape")
{
// check weight
if (weights.find(node->input(0)) == weights.end())
continue;
weights[node->output(0)] = weights[node->input(0)];
// set weight shape directly
std::vector<int> shape;
if (node->input_size() == 1)
{
shape = get_node_attr_ai(*node, "shape");
}
else if (node->input_size() == 2)
{
// opset 5
shape = get_node_attr_from_input_ai(weights[node->input(1)]);
}
weights[node->output(0)].clear_dims();
for (int j = 0; j < shape.size(); j++)
{
weights[node->output(0)].add_dims(shape[j]);
}
// reduce
node->set_op_type("noop_reducedncnn");
node_reference[node->input(0)] -= 1;
if (node->input_size() == 2)
{
node_reference[node->input(1)] -= 1;
}
reduced_node_count += 1;
i += 1;
}
}
}
static void fuse_weight_transpose(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
{
int node_count = mutable_graph->node_size();
for (int i = 0; i < node_count; i++)
{
onnx::NodeProto* node = mutable_graph->mutable_node(i);
// weight <= Transpose(weight)
if (node->op_type() == "Transpose")
{
// check weight
if (weights.find(node->input(0)) == weights.end())
continue;
if (weights[node->input(0)].dims_size() != 2)
continue;
// perm = (1, 0)
std::vector<int> perm = get_node_attr_ai(*node, "perm");
if (perm.size() != 2)
continue;
if (perm[0] != 1 || perm[1] != 0)
continue;
weights[node->output(0)] = weights[node->input(0)];
// permute weight
{
onnx::TensorProto& B = weights[node->output(0)];
const int h = B.dims(0);
const int w = B.dims(1);
std::vector<float> permuted_data;
permuted_data.reserve((size_t)h * w);
const float* bptr = B.has_raw_data() ? (const float*)B.raw_data().data() : B.float_data().data();
for (int j = 0; j < w; j++)
{
for (int k = 0; k < h; k++)
{
float vb = bptr[k * w + j];
permuted_data.push_back(vb);
}
}
B.set_dims(0, w);
B.set_dims(1, h);
if (B.has_raw_data())
{
B.set_raw_data(permuted_data.data(), permuted_data.size() * sizeof(float));
}
else
{
for (int j = 0; j < (int)permuted_data.size(); j++)
B.set_float_data(j, permuted_data[j]);
}
}
// reduce
node->set_op_type("noop_reducedncnn");
node_reference[node->input(0)] -= 1;
reduced_node_count += 1;
i += 1;
}
}
}
static void fuse_shufflechannel(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
{
int node_count = mutable_graph->node_size();
for (int i = 0; i < node_count; i++)
{
onnx::NodeProto* node = mutable_graph->mutable_node(i);
// ShuffleChannel <= Reshape - Transpose - Reshape
// ShuffleChannel <= Reshape - Transpose - Constant - Reshape
if (node->op_type() == "Reshape")
{
if (node_reference[node->output(0)] != 1)
continue;
std::vector<int> shape;
if (node->input_size() == 1)
{
shape = get_node_attr_ai(*node, "shape");
}
else
{
// skip weight reshape
if (weights.find(node->input(1)) == weights.end())
continue;
shape = get_node_attr_from_input_ai(weights[node->input(1)]);
}
// 1 groups channels_per_group, height, width
// reverse style = channels_per_group, groups, height * width
if (shape.size() != 5 && shape.size() != 3)
continue;
if (shape.size() == 5 && shape[0] != 1)
continue;
if (i + 2 >= node_count)
continue;
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
onnx::NodeProto* node3 = mutable_graph->mutable_node(i + 2);
if (node3->op_type() == "Constant")
{
if (i + 3 >= node_count)
continue;
node3 = mutable_graph->mutable_node(i + 3);
}
if (node2->op_type() != "Transpose" || node3->op_type() != "Reshape")
continue;
if (node_reference[node2->output(0)] != 1)
continue;
// 0 2 1 3 4
// reverse style = 1 0 2
std::vector<int> perm = get_node_attr_ai(*node2, "perm");
if (perm.size() != 5 && perm.size() != 3)
continue;
if (perm.size() == 5 && (perm[0] != 0 || perm[1] != 2 || perm[2] != 1 || perm[3] != 3 || perm[4] != 4))
continue;
if (perm.size() == 3 && (perm[0] != 1 || perm[1] != 0 || perm[2] != 2))
continue;
std::vector<int> shape3;
if (node3->input_size() == 1)
{
shape3 = get_node_attr_ai(*node3, "shape");
}
else
{
// skip weight reshape
if (weights.find(node3->input(1)) == weights.end())
continue;
shape3 = get_node_attr_from_input_ai(weights[node3->input(1)]);
}
// 1, -1, height, width
// reverse style = group, -1, channels_per_group, height, width
if (shape3.size() != 4 && shape3.size() != 5)
continue;
if (shape3.size() == 4 && (shape3[0] != 1 || (shape3[1] != -1 && shape3[1] != shape[1] * shape[2])))
continue;
if (shape3.size() == 5 && (shape3[0] != shape[1] || shape3[2] != shape[0] || shape3[3] * shape3[4] != shape[2]))
continue;
// reduce
node->set_op_type("noop_reducedncnn");
node2->set_op_type("noop_reducedncnn");
if (node->input_size() == 2)
{
node_reference[node->input(1)] -= 1;
}
node_reference[node->output(0)] -= 1;
node_reference[node2->output(0)] -= 1;
if (node3->input_size() == 2)
{
node_reference[node3->input(1)] -= 1;
}
blob_names.erase(node->output(0));
blob_names.erase(node2->output(0));
node3->set_op_type("ShuffleChannel");
node3->set_input(0, node->input(0));
onnx::AttributeProto* attr_group = node3->add_attribute();
attr_group->set_name("group");
attr_group->set_i(shape[1]);
onnx::AttributeProto* attr_reverse = node3->add_attribute();
attr_reverse->set_name("reverse");
attr_reverse->set_i(shape.size() == 3);
reduced_node_count += 2;
i += 2;
}
}
}
static void fuse_shufflechannel_split(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
{
int node_count = mutable_graph->node_size();
for (int i = 0; i < node_count; i++)
{
onnx::NodeProto* node = mutable_graph->mutable_node(i);
// Split <= ShuffleChannel(reverse type) - Gather(0) - Gather(1)
if (node->op_type() == "ShuffleChannel")
{
// reverse = 1
int reverse = get_node_attr_i(*node, "reverse");
if (reverse != 1)
continue;
if (i + 2 >= node_count)
continue;
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
onnx::NodeProto* node3 = mutable_graph->mutable_node(i + 2);
if (node2->op_type() != "Gather" || node3->op_type() != "Gather")
continue;
if (node2->input(0) != node->output(0) || node3->input(0) != node->output(0))
continue;
// axis = 0
int gather2_axis = get_node_attr_i(*node2, "axis");
if (gather2_axis != 0)
continue;
// indices = 0
if (weights.find(node2->input(1)) == weights.end())
continue;
std::vector<int> gather2_indices = get_node_attr_from_input_ai(weights[node2->input(1)]);
if (gather2_indices.size() != 1 || gather2_indices[0] != 0)
continue;
// axis = 0
int gather3_axis = get_node_attr_i(*node3, "axis");
if (gather3_axis != 0)
continue;
// indices = 1
if (weights.find(node3->input(1)) == weights.end())
continue;
std::vector<int> gather3_indices = get_node_attr_from_input_ai(weights[node3->input(1)]);
if (gather3_indices.size() != 1 || gather3_indices[0] != 1)
continue;
// reduce
node2->set_op_type("noop_reducedncnn");
node_reference[node->output(0)] -= 2;
node_reference[node2->input(1)] -= 1;
node_reference[node3->input(1)] -= 1;
node3->set_op_type("Split");
node3->clear_input();
node3->add_input(node->output(0));
node3->add_output(node3->output(0));
node3->set_output(0, node2->output(0));
node3->clear_attribute();
onnx::AttributeProto* attr_axis = node3->add_attribute();
attr_axis->set_name("axis");
attr_axis->set_i(1);
reduced_node_count += 1;
i += 1;
}
}
}
static void fuse_hardswish(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
{
int node_count = mutable_graph->node_size();
for (int i = 0; i < node_count; i++)
{
onnx::NodeProto* node = mutable_graph->mutable_node(i);
// HardSwish <= Add(+3) - Clip(0,6) - Mul(X,) - Div(/6)
// HardSwish <= Add(+3) - Clip(0,6) - Mul(X,) - Mul(*(1/6))
// HardSwish <= Add(+3) - Clip(0,6) - Mul(X,) - Constant - Div(/6)
// HardSwish <= Add(+3) - Clip(0,6) - Mul(X,) - Constant - Mul(*(1/6))
// out = x * F.relu6(x + 3, inplace=True) / 6
if (node->op_type() == "Add")
{
if (node_reference[node->output(0)] != 1)
continue;
if (i + 3 >= node_count)
continue;
if (weights.find(node->input(1)) == weights.end())
continue;
const onnx::TensorProto& add_three = weights[node->input(1)];
if (add_three.dims_size() != 0 || get_tensor_proto_data_size(add_three) != 1)
continue;
float constant_add_three = get_node_attr_from_input_f(add_three);
if (constant_add_three != 3.f)
continue;
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
onnx::NodeProto* node3 = mutable_graph->mutable_node(i + 2);
onnx::NodeProto* node4 = mutable_graph->mutable_node(i + 3);
if (node4->op_type() == "Constant")
{
if (i + 4 >= node_count)
continue;
node4 = mutable_graph->mutable_node(i + 4);
}
if (node2->op_type() != "Clip" || node3->op_type() != "Mul" || (node4->op_type() != "Div" && node4->op_type() != "Mul"))
continue;
if (node_reference[node2->output(0)] != 1)
continue;
float relu6_min;
float relu6_max;
if (node2->input_size() == 1)
{
relu6_min = get_node_attr_f(*node2, "min", -FLT_MAX);
relu6_max = get_node_attr_f(*node2, "max", FLT_MAX);
}
else
{
const onnx::TensorProto& min_tp = weights[node2->input(1)];
const onnx::TensorProto& max_tp = weights[node2->input(2)];
relu6_min = get_node_attr_from_input_f(min_tp);
relu6_max = get_node_attr_from_input_f(max_tp);
}
if (relu6_min != 0.f || relu6_max != 6.f)
continue;
if (node_reference[node3->output(0)] != 1)
continue;
if (node3->input(0) != node->input(0) || node3->input(1) != node2->output(0))
continue;
if (weights.find(node4->input(1)) == weights.end())
continue;
const onnx::TensorProto& div_six = weights[node4->input(1)];
if (div_six.dims_size() != 0 || get_tensor_proto_data_size(div_six) != 1)
continue;
float constant_div_six = get_node_attr_from_input_f(div_six);
if (node4->op_type() == "Div" && constant_div_six != 6.f)
continue;
if (node4->op_type() == "Mul" && constant_div_six != 1 / 6.f)
continue;
// reduce
node->set_op_type("noop_reducedncnn");
node2->set_op_type("noop_reducedncnn");
node3->set_op_type("noop_reducedncnn");
node_reference[node->input(0)] -= 1;
node_reference[node->input(1)] -= 1;
node_reference[node->output(0)] -= 1;
if (node2->input_size() == 3)
{
node_reference[node2->input(1)] -= 1;
node_reference[node2->input(2)] -= 1;
}
node_reference[node2->output(0)] -= 1;
node_reference[node3->output(0)] -= 1;
node_reference[node4->input(1)] -= 1;
blob_names.erase(node->output(0));
blob_names.erase(node2->output(0));
blob_names.erase(node3->output(0));
node4->set_op_type("HardSwish");
node4->clear_input();
node4->add_input(node->input(0));
onnx::AttributeProto* attr_alpha = node4->add_attribute();
attr_alpha->set_name("alpha");
attr_alpha->set_f(1.f / 6.f);
onnx::AttributeProto* attr_beta = node4->add_attribute();
attr_beta->set_name("beta");
attr_beta->set_f(3.f / 6.f);
reduced_node_count += 3;
i += 3;
}
}
for (int i = 0; i < node_count; i++)
{
onnx::NodeProto* node = mutable_graph->mutable_node(i);
// HardSwish <= HardSigmoid - Mul
// out = x * hsigmoid(x)
if (node->op_type() == "HardSigmoid")
{
if (node_reference[node->output(0)] != 1)
continue;
float alpha = get_node_attr_f(*node, "alpha", 0.2f);
float beta = get_node_attr_f(*node, "beta", 0.5f);
if (i + 1 >= node_count)
continue;
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
if (node2->op_type() != "Mul")
continue;
if (node2->input(0) != node->input(0) || node2->input(1) != node->output(0))
continue;
// reduce
node->set_op_type("noop_reducedncnn");
node_reference[node->input(0)] -= 1;
node_reference[node->output(0)] -= 1;
blob_names.erase(node->output(0));
node2->set_op_type("HardSwish");
node2->clear_input();
node2->add_input(node->input(0));
onnx::AttributeProto* attr_alpha = node2->add_attribute();
attr_alpha->set_name("alpha");
attr_alpha->set_f(alpha);
onnx::AttributeProto* attr_beta = node2->add_attribute();
attr_beta->set_name("beta");
attr_beta->set_f(beta);
reduced_node_count += 1;
i += 1;
}
}
}
static void fuse_hardsigmoid(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
{
int node_count = mutable_graph->node_size();
for (int i = 0; i < node_count; i++)
{
onnx::NodeProto* node = mutable_graph->mutable_node(i);
// HardSigmoid <= Add(+3) - Clip(0,6) - Div(/6)
// HardSigmoid <= Add(+3) - Clip(0,6) - Mul(*(1/6))
// HardSigmoid <= Add(+3) - Clip(0,6) - Constant - Div(/6)
// HardSigmoid <= Add(+3) - Clip(0,6) - Constant - Mul(*(1/6))
// out = F.relu6(x + 3, inplace=True) / 6
if (node->op_type() == "Add")
{
if (node_reference[node->output(0)] != 1)
continue;
if (i + 2 >= node_count)
continue;
if (weights.find(node->input(1)) == weights.end())
continue;
const onnx::TensorProto& add_three = weights[node->input(1)];
if (add_three.dims_size() != 0 || get_tensor_proto_data_size(add_three) != 1)
continue;
float constant_add_three = get_node_attr_from_input_f(add_three);
if (constant_add_three != 3.f)
continue;
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
onnx::NodeProto* node3 = mutable_graph->mutable_node(i + 2);
if (node3->op_type() == "Constant")
{
if (i + 3 >= node_count)
continue;
node3 = mutable_graph->mutable_node(i + 3);
}
if (node2->op_type() != "Clip" || (node3->op_type() != "Div" && node3->op_type() != "Mul"))
continue;
if (node_reference[node2->output(0)] != 1)
continue;
float relu6_min;
float relu6_max;
if (node2->input_size() == 1)
{
relu6_min = get_node_attr_f(*node2, "min", -FLT_MAX);
relu6_max = get_node_attr_f(*node2, "max", FLT_MAX);
}
else
{
const onnx::TensorProto& min_tp = weights[node2->input(1)];
const onnx::TensorProto& max_tp = weights[node2->input(2)];
relu6_min = get_node_attr_from_input_f(min_tp);
relu6_max = get_node_attr_from_input_f(max_tp);
}
if (relu6_min != 0.f || relu6_max != 6.f)
continue;
if (weights.find(node3->input(1)) == weights.end())
continue;
const onnx::TensorProto& div_six = weights[node3->input(1)];
if (div_six.dims_size() != 0 || get_tensor_proto_data_size(div_six) != 1)
continue;
float constant_div_six = get_node_attr_from_input_f(div_six);
if (node3->op_type() == "Div" && constant_div_six != 6.f)
continue;
if (node3->op_type() == "Mul" && constant_div_six != 1 / 6.f)
continue;
// reduce
node->set_op_type("noop_reducedncnn");
node2->set_op_type("noop_reducedncnn");
node_reference[node->input(1)] -= 1;
node_reference[node->output(0)] -= 1;
if (node2->input_size() == 3)