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7984ffcb4d3169f6ac9efdcc80683bb15aedc2a2
ncnn/tools/ncnnoptimize.cpp
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nihuini 7984ffcb4d ncnnoptimize tool
2019-04-24 19:08:34 +08:00

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// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2019 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 <set>
#include <vector>
// ncnn public header
#include "net.h"
#include "layer.h"
// ncnn private header
#include "layer/batchnorm.h"
#include "layer/bias.h"
#include "layer/binaryop.h"
#include "layer/clip.h"
#include "layer/concat.h"
#include "layer/convolution.h"
#include "layer/convolutiondepthwise.h"
#include "layer/crop.h"
#include "layer/deconvolution.h"
#include "layer/deconvolutiondepthwise.h"
#include "layer/detectionoutput.h"
#include "layer/dropout.h"
#include "layer/eltwise.h"
#include "layer/elu.h"
#include "layer/exp.h"
#include "layer/innerproduct.h"
#include "layer/input.h"
#include "layer/instancenorm.h"
#include "layer/interp.h"
#include "layer/log.h"
#include "layer/lrn.h"
#include "layer/mvn.h"
#include "layer/normalize.h"
#include "layer/padding.h"
#include "layer/permute.h"
#include "layer/pooling.h"
#include "layer/power.h"
#include "layer/prelu.h"
#include "layer/priorbox.h"
#include "layer/proposal.h"
#include "layer/psroipooling.h"
#include "layer/quantize.h"
#include "layer/reduction.h"
#include "layer/relu.h"
#include "layer/reorg.h"
#include "layer/requantize.h"
#include "layer/reshape.h"
#include "layer/roialign.h"
#include "layer/roipooling.h"
#include "layer/scale.h"
#include "layer/slice.h"
#include "layer/shufflechannel.h"
#include "layer/softmax.h"
#include "layer/threshold.h"
#include "layer/unaryop.h"
#include "layer/yolodetectionoutput.h"
#include "layer/yolov3detectionoutput.h"
class NetOptimize : public ncnn::Net
{
public:
int fuse_batchnorm_scale();
int fuse_convolution_batchnorm();
int fuse_convolutiondepthwise_batchnorm();
int fuse_innerproduct_batchnorm();
int fuse_convolution_relu();
int fuse_convolutiondepthwise_relu();
int fuse_innerproduct_relu();
int eliminate_dropout();
public:
int fprintf_param_int_array(int id, const ncnn::Mat& m, FILE* pp);
int fprintf_param_float_array(int id, const ncnn::Mat& m, FILE* pp);
int fwrite_weight_tag(int tag, FILE* bp);
int fwrite_weight_data(const ncnn::Mat& data, FILE* bp);
int save(const char* parampath, const char* binpath);
};
int NetOptimize::fuse_batchnorm_scale()
{
const int layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "BatchNorm")
continue;
// BatchNorm - Scale
int top_blob_index = layers[i]->tops[0];
int j = i + 1;
for (; j<layer_count; j++)
{
if (layers[j]->type != "Scale")
continue;
if (layers[j]->bottoms.size() != 1)
continue;
if (layers[j]->bottoms[0] == top_blob_index)
break;
}
if (j == layer_count)
continue;
// fuse BatchNorm - Scale to BatchNorm
ncnn::BatchNorm* batchnorm = (ncnn::BatchNorm*)layers[i];
ncnn::Scale* scale = (ncnn::Scale*)layers[j];
fprintf(stderr, "fuse_batchnorm_scale %s %s\n", batchnorm->name.c_str(), scale->name.c_str());
{
// v = ((v - mean) / sqrt(var + eps) * slope + bias) * s + b
// = (v - mean) / sqrt(var + eps) * (slope * s) + (bias * s + b)
int channels = batchnorm->channels;
float* slope = batchnorm->slope_data;
float* bias = batchnorm->bias_data;
for (int q=0; q<channels; q++)
{
slope[q] = slope[q] * scale->scale_data[q];
if (scale->bias_term)
bias[q] = bias[q] * scale->scale_data[q] + scale->bias_data[q];
else
bias[q] = bias[q] * scale->scale_data[q];
}
}
int top_blob_index_final = scale->tops[0];
batchnorm->tops[0] = top_blob_index_final;
blobs[top_blob_index_final].producer = i;
scale->type = "ncnnfused";
}
return 0;
}
int NetOptimize::fuse_convolution_batchnorm()
{
const int layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "Convolution")
continue;
// Convolution - BatchNorm
int top_blob_index = layers[i]->tops[0];
int j = i + 1;
for (; j<layer_count; j++)
{
if (layers[j]->type != "BatchNorm")
continue;
if (layers[j]->bottoms.size() != 1)
continue;
if (layers[j]->bottoms[0] == top_blob_index)
break;
}
if (j == layer_count)
continue;
// fuse Convolution - BatchNorm to Convolution
ncnn::Convolution* convolution = (ncnn::Convolution*)layers[i];
ncnn::BatchNorm* batchnorm = (ncnn::BatchNorm*)layers[j];
fprintf(stderr, "fuse_convolution_batchnorm %s %s\n", convolution->name.c_str(), batchnorm->name.c_str());
{
int channels = batchnorm->channels;
float eps = batchnorm->eps;
// a = bias - slope * mean / sqrt(var + eps)
// b = slope / sqrt(var + eps)
// value = value * b + a
std::vector<float> a(channels);
std::vector<float> b(channels);
for (int i=0; i<channels; i++)
{
float sqrt_var = sqrt(batchnorm->var_data[i] + eps);
a[i] = batchnorm->bias_data[i] - batchnorm->slope_data[i] * batchnorm->mean_data[i] / sqrt_var;
b[i] = batchnorm->slope_data[i] / sqrt_var;
}
if (convolution->bias_term == 0)
{
// init bias as zero
convolution->bias_term = 1;
convolution->bias_data = ncnn::Mat(channels);
convolution->bias_data.fill(0.f);
}
const int weight_per_outch = convolution->weight_data_size / channels;
float* weight = convolution->weight_data;
float* bias = convolution->bias_data;
for (int i=0; i<channels; i++)
{
float* conv_weight_outch = weight + weight_per_outch * i;
for (int j=0; j<weight_per_outch; j++)
{
conv_weight_outch[j] *= b[i];
}
bias[i] += a[i];
}
}
int top_blob_index_final = batchnorm->tops[0];
convolution->tops[0] = top_blob_index_final;
blobs[top_blob_index_final].producer = i;
batchnorm->type = "ncnnfused";
}
return 0;
}
int NetOptimize::fuse_convolutiondepthwise_batchnorm()
{
const int layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "ConvolutionDepthWise")
continue;
// ConvolutionDepthWise - BatchNorm
int top_blob_index = layers[i]->tops[0];
int j = i + 1;
for (; j<layer_count; j++)
{
if (layers[j]->type != "BatchNorm")
continue;
if (layers[j]->bottoms.size() != 1)
continue;
if (layers[j]->bottoms[0] == top_blob_index)
break;
}
if (j == layer_count)
continue;
// fuse ConvolutionDepthWise - BatchNorm to ConvolutionDepthWise
ncnn::ConvolutionDepthWise* convolutiondepthwise = (ncnn::ConvolutionDepthWise*)layers[i];
ncnn::BatchNorm* batchnorm = (ncnn::BatchNorm*)layers[j];
fprintf(stderr, "fuse_convolutiondepthwise_batchnorm %s %s\n", convolutiondepthwise->name.c_str(), batchnorm->name.c_str());
{
int channels = batchnorm->channels;
float eps = batchnorm->eps;
// a = bias - slope * mean / sqrt(var + eps)
// b = slope / sqrt(var + eps)
// value = value * b + a
std::vector<float> a(channels);
std::vector<float> b(channels);
for (int i=0; i<channels; i++)
{
float sqrt_var = sqrt(batchnorm->var_data[i] + eps);
a[i] = batchnorm->bias_data[i] - batchnorm->slope_data[i] * batchnorm->mean_data[i] / sqrt_var;
b[i] = batchnorm->slope_data[i] / sqrt_var;
}
if (convolutiondepthwise->bias_term == 0)
{
// init bias as zero
convolutiondepthwise->bias_term = 1;
convolutiondepthwise->bias_data = ncnn::Mat(channels);
convolutiondepthwise->bias_data.fill(0.f);
}
const int weight_per_outch = convolutiondepthwise->weight_data_size / channels;
float* weight = convolutiondepthwise->weight_data;
float* bias = convolutiondepthwise->bias_data;
for (int i=0; i<channels; i++)
{
float* conv_weight_outch = weight + weight_per_outch * i;
for (int j=0; j<weight_per_outch; j++)
{
conv_weight_outch[j] *= b[i];
}
bias[i] += a[i];
}
}
int top_blob_index_final = batchnorm->tops[0];
convolutiondepthwise->tops[0] = top_blob_index_final;
blobs[top_blob_index_final].producer = i;
batchnorm->type = "ncnnfused";
}
return 0;
}
int NetOptimize::fuse_innerproduct_batchnorm()
{
const int layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "InnerProduct")
continue;
// InnerProduct - BatchNorm
int top_blob_index = layers[i]->tops[0];
int j = i + 1;
for (; j<layer_count; j++)
{
if (layers[j]->type != "BatchNorm")
continue;
if (layers[j]->bottoms.size() != 1)
continue;
if (layers[j]->bottoms[0] == top_blob_index)
break;
}
if (j == layer_count)
continue;
// fuse InnerProduct - BatchNorm to InnerProduct
ncnn::InnerProduct* innerproduct = (ncnn::InnerProduct*)layers[i];
ncnn::BatchNorm* batchnorm = (ncnn::BatchNorm*)layers[j];
fprintf(stderr, "fuse_innerproduct_batchnorm %s %s\n", innerproduct->name.c_str(), batchnorm->name.c_str());
{
int channels = batchnorm->channels;
float eps = batchnorm->eps;
// a = bias - slope * mean / sqrt(var + eps)
// b = slope / sqrt(var + eps)
// value = value * b + a
std::vector<float> a(channels);
std::vector<float> b(channels);
for (int i=0; i<channels; i++)
{
float sqrt_var = sqrt(batchnorm->var_data[i] + eps);
a[i] = batchnorm->bias_data[i] - batchnorm->slope_data[i] * batchnorm->mean_data[i] / sqrt_var;
b[i] = batchnorm->slope_data[i] / sqrt_var;
}
if (innerproduct->bias_term == 0)
{
// init bias as zero
innerproduct->bias_term = 1;
innerproduct->bias_data = ncnn::Mat(channels);
innerproduct->bias_data.fill(0.f);
}
const int weight_per_outch = innerproduct->weight_data_size / channels;
float* weight = innerproduct->weight_data;
float* bias = innerproduct->bias_data;
for (int i=0; i<channels; i++)
{
float* conv_weight_outch = weight + weight_per_outch * i;
for (int j=0; j<weight_per_outch; j++)
{
conv_weight_outch[j] *= b[i];
}
bias[i] += a[i];
}
}
int top_blob_index_final = batchnorm->tops[0];
innerproduct->tops[0] = top_blob_index_final;
blobs[top_blob_index_final].producer = i;
batchnorm->type = "ncnnfused";
}
return 0;
}
int NetOptimize::fuse_convolution_relu()
{
const int layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "Convolution")
continue;
// Convolution - ReLU
int top_blob_index = layers[i]->tops[0];
int j = i + 1;
for (; j<layer_count; j++)
{
if (layers[j]->type != "ReLU")
continue;
if (layers[j]->bottoms.size() != 1)
continue;
if (layers[j]->bottoms[0] == top_blob_index)
break;
}
if (j == layer_count)
continue;
// fuse Convolution - ReLU to Convolution
ncnn::Convolution* convolution = (ncnn::Convolution*)layers[i];
ncnn::ReLU* relu = (ncnn::ReLU*)layers[j];
fprintf(stderr, "fuse_convolution_relu %s %s\n", convolution->name.c_str(), relu->name.c_str());
{
//TODO
}
int top_blob_index_final = relu->tops[0];
convolution->tops[0] = top_blob_index_final;
blobs[top_blob_index_final].producer = i;
relu->type = "ncnnfused";
}
return 0;
}
int NetOptimize::fuse_convolutiondepthwise_relu()
{
const int layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "ConvolutionDepthWise")
continue;
// ConvolutionDepthWise - ReLU
int top_blob_index = layers[i]->tops[0];
int j = i + 1;
for (; j<layer_count; j++)
{
if (layers[j]->type != "ReLU")
continue;
if (layers[j]->bottoms.size() != 1)
continue;
if (layers[j]->bottoms[0] == top_blob_index)
break;
}
if (j == layer_count)
continue;
// fuse ConvolutionDepthWise - ReLU to ConvolutionDepthWise
ncnn::ConvolutionDepthWise* convolutiondepthwise = (ncnn::ConvolutionDepthWise*)layers[i];
ncnn::ReLU* relu = (ncnn::ReLU*)layers[j];
fprintf(stderr, "fuse_convolutiondepthwise_relu %s %s\n", convolutiondepthwise->name.c_str(), relu->name.c_str());
{
//TODO
}
int top_blob_index_final = relu->tops[0];
convolutiondepthwise->tops[0] = top_blob_index_final;
blobs[top_blob_index_final].producer = i;
relu->type = "ncnnfused";
}
return 0;
}
int NetOptimize::fuse_innerproduct_relu()
{
const int layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "InnerProduct")
continue;
// InnerProduct - ReLU
int top_blob_index = layers[i]->tops[0];
int j = i + 1;
for (; j<layer_count; j++)
{
if (layers[j]->type != "ReLU")
continue;
if (layers[j]->bottoms.size() != 1)
continue;
if (layers[j]->bottoms[0] == top_blob_index)
break;
}
if (j == layer_count)
continue;
// fuse InnerProduct - ReLU to InnerProduct
ncnn::InnerProduct* innerproduct = (ncnn::InnerProduct*)layers[i];
ncnn::ReLU* relu = (ncnn::ReLU*)layers[j];
fprintf(stderr, "fuse_innerproduct_relu %s %s\n", innerproduct->name.c_str(), relu->name.c_str());
{
//TODO
}
int top_blob_index_final = relu->tops[0];
innerproduct->tops[0] = top_blob_index_final;
blobs[top_blob_index_final].producer = i;
relu->type = "ncnnfused";
}
return 0;
}
int NetOptimize::eliminate_dropout()
{
const int layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "Dropout")
continue;
// TODO
}
return 0;
}
int NetOptimize::fprintf_param_int_array(int id, const ncnn::Mat& m, FILE* pp)
{
const int count = m.w;
const int* ptr = m;
fprintf(pp, " -%d=%d", 23300 + id, count);
for (int i=0; i<count; i++)
{
fprintf(pp, ",%d", ptr[i]);
}
return 0;
}
int NetOptimize::fprintf_param_float_array(int id, const ncnn::Mat& m, FILE* pp)
{
const int count = m.w;
const float* ptr = m;
fprintf(pp, " -%d=%d", 23300 + id, count);
for (int i=0; i<count; i++)
{
fprintf(pp, ",%f", ptr[i]);
}
return 0;
}
int NetOptimize::fwrite_weight_tag(int tag, FILE* bp)
{
fwrite(&tag, sizeof(int), 1, bp);
return 0;
}
int NetOptimize::fwrite_weight_data(const ncnn::Mat& data, FILE* bp)
{
ncnn::Mat data_flattened = data.reshape(data.w * data.h * data.c);
fwrite(data_flattened.data, data_flattened.elemsize, data_flattened.w, bp);
return 0;
}
int NetOptimize::save(const char* parampath, const char* binpath)
{
FILE* pp = fopen(parampath, "wb");
FILE* bp = fopen(binpath, "wb");
fprintf(pp, "7767517\n");
const int layer_count = layers.size();
int layer_count_fused = 0;
std::set<std::string> blob_names;
for (int i=0; i<layer_count; i++)
{
const ncnn::Layer* layer = layers[i];
if (layer->type == "ncnnfused")
continue;
layer_count_fused++;
int bottom_count = layer->bottoms.size();
for (int j=0; j<bottom_count; j++)
{
int bottom_blob_index = layer->bottoms[j];
blob_names.insert(blobs[bottom_blob_index].name);
}
int top_count = layer->tops.size();
for (int j=0; j<top_count; j++)
{
int top_blob_index = layer->tops[j];
blob_names.insert(blobs[top_blob_index].name);
}
}
int blob_count_fused = blob_names.size();
fprintf(pp, "%d %d\n", layer_count_fused, blob_count_fused);
for (int i=0; i<layer_count; i++)
{
const ncnn::Layer* layer = layers[i];
if (layer->type == "ncnnfused")
continue;
int bottom_count = layer->bottoms.size();
int top_count = layer->tops.size();
fprintf(pp, "%-24s %-24s %d %d", layer->type.c_str(), layer->name.c_str(), bottom_count, top_count);
for (int j=0; j<bottom_count; j++)
{
int bottom_blob_index = layer->bottoms[j];
fprintf(pp, " %s", blobs[bottom_blob_index].name.c_str());
}
for (int j=0; j<top_count; j++)
{
int top_blob_index = layer->tops[j];
fprintf(pp, " %s", blobs[top_blob_index].name.c_str());
}
ncnn::Layer* layer_default = ncnn::create_layer(layer->typeindex);
ncnn::ParamDict pd;
layer_default->load_param(pd);
#define fprintf_param_value(format, phase) \
{ if (op->phase != op_default->phase) fprintf(pp, format, op->phase); }
if (layer->type == "BatchNorm")
{
ncnn::BatchNorm* op = (ncnn::BatchNorm*)layer;
ncnn::BatchNorm* op_default = (ncnn::BatchNorm*)layer_default;
fprintf_param_value(" 0=%d", channels)
fprintf_param_value(" 1=%f", eps)
fwrite_weight_data(op->slope_data, bp);
fwrite_weight_data(op->mean_data, bp);
fwrite_weight_data(op->var_data, bp);
fwrite_weight_data(op->bias_data, bp);
}
else if (layer->type == "Bias")
{
ncnn::Bias* op = (ncnn::Bias*)layer;
ncnn::Bias* op_default = (ncnn::Bias*)layer_default;
fprintf_param_value(" 0=%d", bias_data_size)
fwrite_weight_data(op->bias_data, bp);
}
else if (layer->type == "BinaryOp")
{
ncnn::BinaryOp* op = (ncnn::BinaryOp*)layer;
ncnn::BinaryOp* op_default = (ncnn::BinaryOp*)layer_default;
fprintf_param_value(" 0=%d", op_type)
fprintf_param_value(" 1=%d", with_scalar)
fprintf_param_value(" 2=%f", b)
}
else if (layer->type == "Clip")
{
ncnn::Clip* op = (ncnn::Clip*)layer;
ncnn::Clip* op_default = (ncnn::Clip*)layer_default;
fprintf_param_value(" 0=%f", min)
fprintf_param_value(" 1=%f", max)
}
else if (layer->type == "Concat")
{
ncnn::Concat* op = (ncnn::Concat*)layer;
ncnn::Concat* op_default = (ncnn::Concat*)layer_default;
fprintf_param_value(" 0=%d", axis)
}
else if (layer->type == "Convolution")
{
ncnn::Convolution* op = (ncnn::Convolution*)layer;
ncnn::Convolution* op_default = (ncnn::Convolution*)layer_default;
fprintf_param_value(" 0=%d", num_output)
fprintf_param_value(" 1=%d", kernel_w)
{ if (op->kernel_h != op->kernel_w) fprintf(pp, " 11=%d", op->kernel_h); }
fprintf_param_value(" 2=%d", dilation_w)
{ if (op->dilation_h != op->dilation_w) fprintf(pp, " 12=%d", op->dilation_h); }
fprintf_param_value(" 3=%d", stride_w)
{ if (op->stride_h != op->stride_w) fprintf(pp, " 13=%d", op->stride_h); }
fprintf_param_value(" 4=%d", pad_w)
{ if (op->pad_h != op->pad_w) fprintf(pp, " 14=%d", op->pad_h); }
fprintf_param_value(" 5=%d", bias_term)
fprintf_param_value(" 6=%d", weight_data_size)
fprintf_param_value(" 8=%d", int8_scale_term)
fwrite_weight_tag(0, bp);
fwrite_weight_data(op->weight_data, bp);
fwrite_weight_data(op->bias_data, bp);
}
else if (layer->type == "ConvolutionDepthWise")
{
ncnn::ConvolutionDepthWise* op = (ncnn::ConvolutionDepthWise*)layer;
ncnn::ConvolutionDepthWise* op_default = (ncnn::ConvolutionDepthWise*)layer_default;
fprintf_param_value(" 0=%d", num_output)
fprintf_param_value(" 1=%d", kernel_w)
{ if (op->kernel_h != op->kernel_w) fprintf(pp, " 11=%d", op->kernel_h); }
fprintf_param_value(" 2=%d", dilation_w)
{ if (op->dilation_h != op->dilation_w) fprintf(pp, " 12=%d", op->dilation_h); }
fprintf_param_value(" 3=%d", stride_w)
{ if (op->stride_h != op->stride_w) fprintf(pp, " 13=%d", op->stride_h); }
fprintf_param_value(" 4=%d", pad_w)
{ if (op->pad_h != op->pad_w) fprintf(pp, " 14=%d", op->pad_h); }
fprintf_param_value(" 5=%d", bias_term)
fprintf_param_value(" 6=%d", weight_data_size)
fprintf_param_value(" 7=%d", group)
fprintf_param_value(" 8=%d", int8_scale_term)
fwrite_weight_tag(0, bp);
fwrite_weight_data(op->weight_data, bp);
fwrite_weight_data(op->bias_data, bp);
}
else if (layer->type == "Crop")
{
ncnn::Crop* op = (ncnn::Crop*)layer;
ncnn::Crop* op_default = (ncnn::Crop*)layer_default;
fprintf_param_value(" 0=%d", woffset)
fprintf_param_value(" 1=%d", hoffset)
fprintf_param_value(" 2=%d", coffset)
fprintf_param_value(" 3=%d", outw)
fprintf_param_value(" 4=%d", outh)
fprintf_param_value(" 5=%d", outc)
}
else if (layer->type == "Deconvolution")
{
ncnn::Deconvolution* op = (ncnn::Deconvolution*)layer;
ncnn::Deconvolution* op_default = (ncnn::Deconvolution*)layer_default;
fprintf_param_value(" 0=%d", num_output)
fprintf_param_value(" 1=%d", kernel_w)
{ if (op->kernel_h != op->kernel_w) fprintf(pp, " 11=%d", op->kernel_h); }
fprintf_param_value(" 2=%d", dilation_w)
{ if (op->dilation_h != op->dilation_w) fprintf(pp, " 12=%d", op->dilation_h); }
fprintf_param_value(" 3=%d", stride_w)
{ if (op->stride_h != op->stride_w) fprintf(pp, " 13=%d", op->stride_h); }
fprintf_param_value(" 4=%d", pad_w)
{ if (op->pad_h != op->pad_w) fprintf(pp, " 14=%d", op->pad_h); }
fprintf_param_value(" 5=%d", bias_term)
fprintf_param_value(" 6=%d", weight_data_size)
fwrite_weight_tag(0, bp);
fwrite_weight_data(op->weight_data, bp);
fwrite_weight_data(op->bias_data, bp);
}
else if (layer->type == "DeconvolutionDepthWise")
{
ncnn::DeconvolutionDepthWise* op = (ncnn::DeconvolutionDepthWise*)layer;
ncnn::DeconvolutionDepthWise* op_default = (ncnn::DeconvolutionDepthWise*)layer_default;
fprintf_param_value(" 0=%d", num_output)
fprintf_param_value(" 1=%d", kernel_w)
{ if (op->kernel_h != op->kernel_w) fprintf(pp, " 11=%d", op->kernel_h); }
fprintf_param_value(" 2=%d", dilation_w)
{ if (op->dilation_h != op->dilation_w) fprintf(pp, " 12=%d", op->dilation_h); }
fprintf_param_value(" 3=%d", stride_w)
{ if (op->stride_h != op->stride_w) fprintf(pp, " 13=%d", op->stride_h); }
fprintf_param_value(" 4=%d", pad_w)
{ if (op->pad_h != op->pad_w) fprintf(pp, " 14=%d", op->pad_h); }
fprintf_param_value(" 5=%d", bias_term)
fprintf_param_value(" 6=%d", weight_data_size)
fprintf_param_value(" 7=%d", group)
fwrite_weight_tag(0, bp);
fwrite_weight_data(op->weight_data, bp);
fwrite_weight_data(op->bias_data, bp);
}
else if (layer->type == "DetectionOutput")
{
ncnn::DetectionOutput* op = (ncnn::DetectionOutput*)layer;
ncnn::DetectionOutput* op_default = (ncnn::DetectionOutput*)layer_default;
fprintf_param_value(" 0=%d", num_class)
fprintf_param_value(" 1=%f", nms_threshold)
fprintf_param_value(" 2=%d", nms_top_k)
fprintf_param_value(" 3=%d", keep_top_k)
fprintf_param_value(" 4=%f", confidence_threshold)
fprintf_param_value(" 5=%f", variances[0])
fprintf_param_value(" 6=%f", variances[1])
fprintf_param_value(" 7=%f", variances[2])
fprintf_param_value(" 8=%f", variances[3])
}
else if (layer->type == "Dropout")
{
ncnn::Dropout* op = (ncnn::Dropout*)layer;
ncnn::Dropout* op_default = (ncnn::Dropout*)layer_default;
fprintf_param_value(" 0=%f", scale)
}
else if (layer->type == "Eltwise")
{
ncnn::Eltwise* op = (ncnn::Eltwise*)layer;
ncnn::Eltwise* op_default = (ncnn::Eltwise*)layer_default;
fprintf_param_value(" 0=%d", op_type)
{ if (!op->coeffs.empty()) fprintf_param_int_array(1, op->coeffs, pp); }
}
else if (layer->type == "ELU")
{
ncnn::ELU* op = (ncnn::ELU*)layer;
ncnn::ELU* op_default = (ncnn::ELU*)layer_default;
fprintf_param_value(" 0=%f", alpha)
}
else if (layer->type == "Exp")
{
ncnn::Exp* op = (ncnn::Exp*)layer;
ncnn::Exp* op_default = (ncnn::Exp*)layer_default;
fprintf_param_value(" 0=%f", base)
fprintf_param_value(" 1=%f", scale)
fprintf_param_value(" 2=%f", shift)
}
else if (layer->type == "InnerProduct")
{
ncnn::InnerProduct* op = (ncnn::InnerProduct*)layer;
ncnn::InnerProduct* op_default = (ncnn::InnerProduct*)layer_default;
fprintf_param_value(" 0=%d", num_output)
fprintf_param_value(" 1=%d", bias_term)
fprintf_param_value(" 2=%d", weight_data_size)
fprintf_param_value(" 8=%d", int8_scale_term)
fwrite_weight_tag(0, bp);
fwrite_weight_data(op->weight_data, bp);
fwrite_weight_data(op->bias_data, bp);
}
else if (layer->type == "Input")
{
ncnn::Input* op = (ncnn::Input*)layer;
ncnn::Input* op_default = (ncnn::Input*)layer_default;
fprintf_param_value(" 0=%d", w)
fprintf_param_value(" 1=%d", h)
fprintf_param_value(" 2=%d", c)
}
else if (layer->type == "InstanceNorm")
{
ncnn::InstanceNorm* op = (ncnn::InstanceNorm*)layer;
ncnn::InstanceNorm* op_default = (ncnn::InstanceNorm*)layer_default;
fprintf_param_value(" 0=%d", channels)
fprintf_param_value(" 1=%f", eps)
}
else if (layer->type == "Interp")
{
ncnn::Interp* op = (ncnn::Interp*)layer;
ncnn::Interp* op_default = (ncnn::Interp*)layer_default;
fprintf_param_value(" 0=%d", resize_type)
fprintf_param_value(" 1=%f", height_scale)
fprintf_param_value(" 2=%f", width_scale)
fprintf_param_value(" 3=%d", output_height)
fprintf_param_value(" 4=%d", output_width)
}
else if (layer->type == "Log")
{
ncnn::Log* op = (ncnn::Log*)layer;
ncnn::Log* op_default = (ncnn::Log*)layer_default;
fprintf_param_value(" 0=%f", base)
fprintf_param_value(" 1=%f", scale)
fprintf_param_value(" 2=%f", shift)
}
else if (layer->type == "LRN")
{
ncnn::LRN* op = (ncnn::LRN*)layer;
ncnn::LRN* op_default = (ncnn::LRN*)layer_default;
fprintf_param_value(" 0=%d", region_type)
fprintf_param_value(" 1=%d", local_size)
fprintf_param_value(" 2=%f", alpha)
fprintf_param_value(" 3=%f", beta)
fprintf_param_value(" 4=%f", bias)
}
else if (layer->type == "MVN")
{
ncnn::MVN* op = (ncnn::MVN*)layer;
ncnn::MVN* op_default = (ncnn::MVN*)layer_default;
fprintf_param_value(" 0=%d", normalize_variance)
fprintf_param_value(" 1=%d", across_channels)
fprintf_param_value(" 2=%f", eps)
}
else if (layer->type == "Normalize")
{
ncnn::Normalize* op = (ncnn::Normalize*)layer;
ncnn::Normalize* op_default = (ncnn::Normalize*)layer_default;
fprintf_param_value(" 0=%d", across_spatial)
fprintf_param_value(" 1=%d", channel_shared)
fprintf_param_value(" 2=%f", eps)
fprintf_param_value(" 3=%d", scale_data_size)
fprintf_param_value(" 4=%d", across_channel)
fwrite_weight_data(op->scale_data, bp);
}
else if (layer->type == "Padding")
{
ncnn::Padding* op = (ncnn::Padding*)layer;
ncnn::Padding* op_default = (ncnn::Padding*)layer_default;
fprintf_param_value(" 0=%d", top)
fprintf_param_value(" 1=%d", bottom)
fprintf_param_value(" 2=%d", left)
fprintf_param_value(" 3=%d", right)
fprintf_param_value(" 4=%d", type)
fprintf_param_value(" 5=%f", value)
}
else if (layer->type == "Permute")
{
ncnn::Permute* op = (ncnn::Permute*)layer;
ncnn::Permute* op_default = (ncnn::Permute*)layer_default;
fprintf_param_value(" 0=%d", order_type)
}
else if (layer->type == "Pooling")
{
ncnn::Pooling* op = (ncnn::Pooling*)layer;
ncnn::Pooling* op_default = (ncnn::Pooling*)layer_default;
fprintf_param_value(" 0=%d", pooling_type)
fprintf_param_value(" 1=%d", kernel_w)
{ if (op->kernel_h != op->kernel_w) fprintf(pp, " 11=%d", op->kernel_h); }
fprintf_param_value(" 2=%d", stride_w)
{ if (op->stride_h != op->stride_w) fprintf(pp, " 12=%d", op->stride_h); }
fprintf_param_value(" 3=%d", pad_left)
{ if (op->pad_top != op->pad_left) fprintf(pp, " 13=%d", op->pad_top); }
{ if (op->pad_right != op->pad_left) fprintf(pp, " 14=%d", op->pad_right); }
{ if (op->pad_bottom != op->pad_top) fprintf(pp, " 15=%d", op->pad_bottom); }
fprintf_param_value(" 4=%d", global_pooling)
fprintf_param_value(" 5=%d", pad_mode)
}
else if (layer->type == "Power")
{
ncnn::Power* op = (ncnn::Power*)layer;
ncnn::Power* op_default = (ncnn::Power*)layer_default;
fprintf_param_value(" 0=%f", power)
fprintf_param_value(" 1=%f", scale)
fprintf_param_value(" 2=%f", shift)
}
else if (layer->type == "PReLU")
{
ncnn::PReLU* op = (ncnn::PReLU*)layer;
ncnn::PReLU* op_default = (ncnn::PReLU*)layer_default;
fprintf_param_value(" 0=%d", num_slope)
fwrite_weight_data(op->slope_data, bp);
}
else if (layer->type == "PriorBox")
{
ncnn::PriorBox* op = (ncnn::PriorBox*)layer;
ncnn::PriorBox* op_default = (ncnn::PriorBox*)layer_default;
{ if (!op->min_sizes.empty()) fprintf_param_int_array(0, op->min_sizes, pp); }
{ if (!op->max_sizes.empty()) fprintf_param_int_array(1, op->max_sizes, pp); }
{ if (!op->aspect_ratios.empty()) fprintf_param_int_array(2, op->aspect_ratios, pp); }
fprintf_param_value(" 3=%f", variances[0])
fprintf_param_value(" 4=%f", variances[1])
fprintf_param_value(" 5=%f", variances[2])
fprintf_param_value(" 6=%f", variances[3])
fprintf_param_value(" 7=%d", flip)
fprintf_param_value(" 8=%d", clip)
fprintf_param_value(" 9=%d", image_width)
fprintf_param_value(" 10=%d", image_height)
fprintf_param_value(" 11=%f", step_width)
fprintf_param_value(" 12=%f", step_height)
fprintf_param_value(" 13=%f", offset)
}
else if (layer->type == "Proposal")
{
ncnn::Proposal* op = (ncnn::Proposal*)layer;
ncnn::Proposal* op_default = (ncnn::Proposal*)layer_default;
fprintf_param_value(" 0=%d", feat_stride)
fprintf_param_value(" 1=%d", base_size)
fprintf_param_value(" 2=%d", pre_nms_topN)
fprintf_param_value(" 3=%d", after_nms_topN)
fprintf_param_value(" 4=%f", nms_thresh)
fprintf_param_value(" 5=%d", min_size)
}
else if (layer->type == "PSROIPooling")
{
ncnn::PSROIPooling* op = (ncnn::PSROIPooling*)layer;
ncnn::PSROIPooling* op_default = (ncnn::PSROIPooling*)layer_default;
fprintf_param_value(" 0=%d", pooled_width)
fprintf_param_value(" 1=%d", pooled_height)
fprintf_param_value(" 2=%f", spatial_scale)
fprintf_param_value(" 3=%d", output_dim)
}
else if (layer->type == "Quantize")
{
ncnn::Quantize* op = (ncnn::Quantize*)layer;
ncnn::Quantize* op_default = (ncnn::Quantize*)layer_default;
fprintf_param_value(" 0=%f", scale)
}
else if (layer->type == "Reduction")
{
ncnn::Reduction* op = (ncnn::Reduction*)layer;
ncnn::Reduction* op_default = (ncnn::Reduction*)layer_default;
fprintf_param_value(" 0=%d", operation)
fprintf_param_value(" 1=%d", dim)
fprintf_param_value(" 2=%f", coeff)
}
else if (layer->type == "ReLU")
{
ncnn::ReLU* op = (ncnn::ReLU*)layer;
ncnn::ReLU* op_default = (ncnn::ReLU*)layer_default;
fprintf_param_value(" 0=%f", slope)
}
else if (layer->type == "Reorg")
{
ncnn::Reorg* op = (ncnn::Reorg*)layer;
ncnn::Reorg* op_default = (ncnn::Reorg*)layer_default;
fprintf_param_value(" 0=%d", stride)
}
else if (layer->type == "Requantize")
{
ncnn::Requantize* op = (ncnn::Requantize*)layer;
ncnn::Requantize* op_default = (ncnn::Requantize*)layer_default;
fprintf_param_value(" 0=%f", scale_in)
fprintf_param_value(" 1=%f", scale_out)
fprintf_param_value(" 2=%d", bias_term)
fprintf_param_value(" 3=%d", bias_data_size)
fprintf_param_value(" 4=%d", fusion_relu)
}
else if (layer->type == "Reshape")
{
ncnn::Reshape* op = (ncnn::Reshape*)layer;
ncnn::Reshape* op_default = (ncnn::Reshape*)layer_default;
fprintf_param_value(" 0=%d", w)
fprintf_param_value(" 1=%d", h)
fprintf_param_value(" 2=%d", c)
fprintf_param_value(" 3=%d", permute)
}
else if (layer->type == "ROIAlign")
{
ncnn::ROIAlign* op = (ncnn::ROIAlign*)layer;
ncnn::ROIAlign* op_default = (ncnn::ROIAlign*)layer_default;
fprintf_param_value(" 0=%d", pooled_width)
fprintf_param_value(" 1=%d", pooled_height)
fprintf_param_value(" 2=%f", spatial_scale)
}
else if (layer->type == "ROIPooling")
{
ncnn::ROIPooling* op = (ncnn::ROIPooling*)layer;
ncnn::ROIPooling* op_default = (ncnn::ROIPooling*)layer_default;
fprintf_param_value(" 0=%d", pooled_width)
fprintf_param_value(" 1=%d", pooled_height)
fprintf_param_value(" 2=%f", spatial_scale)
}
else if (layer->type == "Scale")
{
ncnn::Scale* op = (ncnn::Scale*)layer;
ncnn::Scale* op_default = (ncnn::Scale*)layer_default;
fprintf_param_value(" 0=%d", scale_data_size)
fprintf_param_value(" 1=%d", bias_term)
fwrite_weight_data(op->scale_data, bp);
fwrite_weight_data(op->bias_data, bp);
}
else if (layer->type == "ShuffleChannel")
{
ncnn::ShuffleChannel* op = (ncnn::ShuffleChannel*)layer;
ncnn::ShuffleChannel* op_default = (ncnn::ShuffleChannel*)layer_default;
fprintf_param_value(" 0=%d", group)
}
else if (layer->type == "Slice")
{
ncnn::Slice* op = (ncnn::Slice*)layer;
ncnn::Slice* op_default = (ncnn::Slice*)layer_default;
{ if (!op->slices.empty()) fprintf_param_int_array(0, op->slices, pp); }
fprintf_param_value(" 1=%d", axis)
}
else if (layer->type == "Softmax")
{
ncnn::Softmax* op = (ncnn::Softmax*)layer;
ncnn::Softmax* op_default = (ncnn::Softmax*)layer_default;
fprintf_param_value(" 0=%d", axis)
// HACK
if (op->axis != 0)
{
int fixbug0 = 1;
fprintf(pp, " 1=%d", fixbug0);
}
}
else if (layer->type == "Threshold")
{
ncnn::Threshold* op = (ncnn::Threshold*)layer;
ncnn::Threshold* op_default = (ncnn::Threshold*)layer_default;
fprintf_param_value(" 0=%f", threshold)
}
else if (layer->type == "UnaryOp")
{
ncnn::UnaryOp* op = (ncnn::UnaryOp*)layer;
ncnn::UnaryOp* op_default = (ncnn::UnaryOp*)layer_default;
fprintf_param_value(" 0=%d", op_type)
}
else if (layer->type == "YoloDetectionOutput")
{
ncnn::YoloDetectionOutput* op = (ncnn::YoloDetectionOutput*)layer;
ncnn::YoloDetectionOutput* op_default = (ncnn::YoloDetectionOutput*)layer_default;
fprintf_param_value(" 0=%d", num_class)
fprintf_param_value(" 1=%d", num_box)
fprintf_param_value(" 2=%f", confidence_threshold)
fprintf_param_value(" 3=%f", nms_threshold)
{ if (!op->biases.empty()) fprintf_param_int_array(4, op->biases, pp); }
}
else if (layer->type == "Yolov3DetectionOutput")
{
ncnn::Yolov3DetectionOutput* op = (ncnn::Yolov3DetectionOutput*)layer;
ncnn::Yolov3DetectionOutput* op_default = (ncnn::Yolov3DetectionOutput*)layer_default;
fprintf_param_value(" 0=%d", num_class)
fprintf_param_value(" 1=%d", num_box)
fprintf_param_value(" 2=%f", confidence_threshold)
fprintf_param_value(" 3=%f", nms_threshold)
{ if (!op->biases.empty()) fprintf_param_int_array(4, op->biases, pp); }
{ if (!op->mask.empty()) fprintf_param_int_array(5, op->mask, pp); }
{ if (!op->anchors_scale.empty()) fprintf_param_int_array(6, op->anchors_scale, pp); }
}
#undef fprintf_param_value
fprintf(pp, "\n");
delete layer_default;
}
fclose(pp);
fclose(bp);
return 0;
}
int main(int argc, char** argv)
{
// in in out out 65535
const char* inparam = argv[1];
const char* inbin = argv[2];
const char* outparam = argv[3];
const char* outbin = argv[4];
int flag = atoi(argv[5]);
NetOptimize optimizer;
optimizer.load_param(inparam);
optimizer.load_model(inbin);
optimizer.fuse_batchnorm_scale();
optimizer.fuse_convolution_batchnorm();
optimizer.fuse_convolutiondepthwise_batchnorm();
optimizer.fuse_innerproduct_batchnorm();
// optimizer.fuse_convolution_relu();
// optimizer.fuse_convolutiondepthwise_relu();
// optimizer.fuse_innerproduct_relu();
optimizer.save(outparam, outbin);
return 0;
}
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