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ncnn/tools/ncnnoptimize.cpp
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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 <algorithm>
#include <set>
#include <vector>
// ncnn public header
#include "datareader.h"
#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/expanddims.h"
#include "layer/flatten.h"
#include "layer/hardsigmoid.h"
#include "layer/hardswish.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/memorydata.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/squeeze.h"
#include "layer/threshold.h"
#include "layer/unaryop.h"
#include "layer/yolodetectionoutput.h"
#include "layer/yolov3detectionoutput.h"
#if defined(__aarch64__) && defined(LINUX)
#include <locale>
#include <chrono>
#include <random>
#include <limits>
#include <cassert>
#define TEXT_GREEN "\033[32m"
#define TEXT_YELLOW "\033[33m"
#define TEXT_RED "\033[31m"
#define CLR "\033[0m"
#endif // defined(__aarch64__) && defined(LINUX)
class DataReaderFromEmpty : public ncnn::DataReader
{
public:
virtual int scan(const char* format, void* p) const { return 0; }
virtual size_t read(void* /*buf*/, size_t size) const { return size; }
};
class NetOptimize : public ncnn::Net
{
public:
// 0=fp32 1=fp16
int storage_type;
public:
int fuse_batchnorm_scale();
int fuse_convolution_batchnorm();
int fuse_convolutiondepthwise_batchnorm();
int fuse_deconvolution_batchnorm();
int fuse_deconvolutiondepthwise_batchnorm();
int fuse_innerproduct_batchnorm();
int fuse_innerproduct_dropout();
int fuse_convolution_activation();
int fuse_convolutiondepthwise_activation();
int fuse_deconvolution_activation();
int fuse_deconvolutiondepthwise_activation();
int fuse_innerproduct_activation();
int eliminate_dropout();
int eliminate_pooling1x1();
int eliminate_noop();
int eliminate_orphaned_memorydata();
int eliminate_flatten_after_global_pooling();
int eliminate_reshape_after_global_pooling();
int eliminate_flatten_after_innerproduct();
int eliminate_reshape_before_binaryop();
int replace_convolution_with_innerproduct_after_global_pooling();
int replace_convolution_with_innerproduct_after_innerproduct();
int shape_inference();
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_data(int tag, const ncnn::Mat& data, FILE* bp);
int fwrite_weight_data(const ncnn::Mat& data, FILE* bp);
int save(const char* parampath, const char* binpath);
#if defined(__aarch64__) && defined(LINUX)
void gauss_random(ncnn::Mat &m);
void find_fastest_fp32_conv(const char* name, int w, int h, int c);
int support_fp32_conv_type(const ncnn::Convolution* op, const ncnn::Mat& mat, const int type);
#endif
};
#if defined(__aarch64__) && defined(LINUX)
void NetOptimize::gauss_random(ncnn::Mat &m)
{
std::random_device rd;
std::mt19937 gen(rd());
std::normal_distribution<float> d(1.0f, 1.0f);
int size = m.total();
for (int i = 0; i < size; ++i)
{
m[i] = d(gen);
}
}
void NetOptimize::find_fastest_fp32_conv(const char* dataname, int w, int h, int c)
{
ncnn::PoolAllocator allocator;
allocator.clear();
ncnn::Option opt;
// embeded system generally use single thread
opt.num_threads = 1;
const size_t layer_count = layers.size();
ncnn::Extractor ex = create_extractor();
ncnn::Mat input(w, h, c);
if (ex.input(dataname, input) < 0)
{
fprintf(stderr, "set input failed, check dataname.\n");
return;
}
const char* IMPL_NAME[6] = {"baseline", "winograd", "pointwise", "im2col", "direct", "conv3x3s2"};
for (int i = 0; i < layer_count; ++i)
{
ncnn::Layer* layer = layers[i];
if (layer->type == "Convolution")
{
ncnn::Convolution* op = (ncnn::Convolution*)layer;
ncnn::Mat bottom_blob;
ncnn::Mat top_blob;
ex.extract(layer->bottoms[0], bottom_blob);
ex.extract(layer->tops[0], top_blob);
if (bottom_blob.empty() || top_blob.empty())
{
continue;
}
ncnn::Mat weight_blob(op->kernel_w, op->kernel_h, bottom_blob.c * top_blob.c);
fprintf(stdout, TEXT_GREEN "Input [w h nc]: %d %d %d\n" CLR, bottom_blob.w, bottom_blob.h, bottom_blob.c);
fprintf(stdout, TEXT_GREEN "Kernel [w h nc]: %d %d %d\n" CLR, op->kernel_w, op->kernel_h, bottom_blob.c * top_blob.c);
fprintf(stdout, TEXT_GREEN "Output [w h nc]: %d %d %d\n" CLR, top_blob.w, top_blob.h, top_blob.c);
// randomize input and kernel
gauss_random(bottom_blob);
// try every implementation
double min_cost = std::numeric_limits<double>::max();
int best_type = 0;
// how much conv implementation type ncnn has ?
for (int type = 1; type <= 5; ++type)
{
int support = support_fp32_conv_type(op, bottom_blob, type);
if (support < 1)
{
// implementation type mismatch convolution configuration, skip
continue;
}
op->impl_type = type;
auto start = std::chrono::high_resolution_clock::now();
const int NREPEATS = 20;
op->create_pipeline(opt);
for (int repeat = 0; repeat < NREPEATS; ++repeat)
{
op->forward(top_blob, bottom_blob, opt);
}
op->destroy_pipeline(opt);
auto stop = std::chrono::high_resolution_clock::now();
double cur_cost = std::chrono::duration<double, std::micro>(stop-start).count() / NREPEATS;
fprintf(stdout, TEXT_GREEN "%s cost %0.3lfms \n" CLR, IMPL_NAME[type], cur_cost/1000);
if (cur_cost < min_cost)
{
min_cost = cur_cost;
best_type = type;
}
}
op->impl_type = best_type;
fprintf(stdout, TEXT_YELLOW "%d: %s use %s \n\n" CLR, i, layer->name.c_str(), IMPL_NAME[op->impl_type]);
}
}
}
int NetOptimize::support_fp32_conv_type(const ncnn::Convolution* op, const ncnn::Mat& bottom, const int type)
{
// not baseline, then k_h == k_w and s_h == s_w
// no dilation conv shall be allowed
if (op->kernel_w != op->kernel_h ||
op->stride_w != op->stride_h ||
op->dilation_w != op->dilation_h ||
op->dilation_h != 1)
{
return -1;
}
// (kernel, stride) in {(1, 1), (1, 2), (2, 1), (3, 1), (3, 2), (4, 4), (5, 1), (5, 2), (7, 1), (7, 2)}
const int support_table[7][4] =
{
{1, 1, 0, 0},
{1, 0, 0, 0},
{1, 1, 0, 0},
{0, 0, 0, 1},
{1, 1, 0, 0},
{0, 0, 0, 0},
{1, 1, 0, 0}
};
// kernel_size x stride
const int kernel = op->kernel_h,
stride = op->stride_h;
// if match prequisation
switch(type)
{
case 1:
// winograd
if (kernel != 3 || stride != 1){
return -1;
}
break;
case 2:
// pointwise
// input_h == 1, input_w == 1, dilation == 1, stride == 1
if (bottom.h != 1 || bottom.w != 1 || stride != 1)
{
return -1;
}
break;
case 3:
// im2col
break;
case 4:
// direct conv
if (support_table[kernel-1][stride-1] == 0)
{
return -1;
}
break;
case 5:
// conv3x3s2
// kernel == 3 and stride == 2
if (kernel != 3 || stride != 2)
{
return -1;
}
break;
default:
fprintf(stderr, TEXT_RED "unrecognize convolution impl type: %d" CLR, type);
break;
}
return 1;
}
#endif // defined(__aarch64__) && defined(LINUX)
int NetOptimize::fuse_batchnorm_scale()
{
const size_t 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 size_t 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 = static_cast<float>(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] = bias[i] * b[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 size_t 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 = static_cast<float>(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] = bias[i] * b[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_deconvolution_batchnorm()
{
const size_t layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "Deconvolution")
continue;
// Deconvolution - 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 Deconvolution - BatchNorm to Deconvolution
ncnn::Deconvolution* deconvolution = (ncnn::Deconvolution*)layers[i];
ncnn::BatchNorm* batchnorm = (ncnn::BatchNorm*)layers[j];
fprintf(stderr, "fuse_deconvolution_batchnorm %s %s\n", deconvolution->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 = static_cast<float>(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 (deconvolution->bias_term == 0)
{
// init bias as zero
deconvolution->bias_term = 1;
deconvolution->bias_data = ncnn::Mat(channels);
deconvolution->bias_data.fill(0.f);
}
const int weight_per_outch = deconvolution->weight_data_size / channels;
float* weight = deconvolution->weight_data;
float* bias = deconvolution->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] = bias[i] * b[i] + a[i];
}
}
int top_blob_index_final = batchnorm->tops[0];
deconvolution->tops[0] = top_blob_index_final;
blobs[top_blob_index_final].producer = i;
batchnorm->type = "ncnnfused";
}
return 0;
}
int NetOptimize::fuse_deconvolutiondepthwise_batchnorm()
{
const size_t layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "DeconvolutionDepthWise")
continue;
// DeconvolutionDepthWise - 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 DeconvolutionDepthWise - BatchNorm to DeconvolutionDepthWise
ncnn::DeconvolutionDepthWise* deconvolutiondepthwise = (ncnn::DeconvolutionDepthWise*)layers[i];
ncnn::BatchNorm* batchnorm = (ncnn::BatchNorm*)layers[j];
fprintf(stderr, "fuse_deconvolutiondepthwise_batchnorm %s %s\n", deconvolutiondepthwise->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 = static_cast<float>(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 (deconvolutiondepthwise->bias_term == 0)
{
// init bias as zero
deconvolutiondepthwise->bias_term = 1;
deconvolutiondepthwise->bias_data = ncnn::Mat(channels);
deconvolutiondepthwise->bias_data.fill(0.f);
}
const int weight_per_outch = deconvolutiondepthwise->weight_data_size / channels;
float* weight = deconvolutiondepthwise->weight_data;
float* bias = deconvolutiondepthwise->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] = bias[i] * b[i] + a[i];
}
}
int top_blob_index_final = batchnorm->tops[0];
deconvolutiondepthwise->tops[0] = top_blob_index_final;
blobs[top_blob_index_final].producer = i;
batchnorm->type = "ncnnfused";
}
return 0;
}
int NetOptimize::fuse_innerproduct_batchnorm()
{
const size_t 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 = static_cast<float>(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] = bias[i] * b[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_innerproduct_dropout()
{
const size_t layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "InnerProduct")
continue;
// InnerProduct - Dropout
int top_blob_index = layers[i]->tops[0];
int j = i + 1;
for (; j<layer_count; j++)
{
if (layers[j]->type != "Dropout")
continue;
if (layers[j]->bottoms.size() != 1)
continue;
if (layers[j]->bottoms[0] == top_blob_index)
break;
}
if (j == layer_count)
continue;
// fuse InnerProduct - Dropout to InnerProduct
ncnn::InnerProduct* innerproduct = (ncnn::InnerProduct*)layers[i];
ncnn::Dropout* dropout = (ncnn::Dropout*)layers[j];
fprintf(stderr, "fuse_innerproduct_dropout %s %s\n", innerproduct->name.c_str(), dropout->name.c_str());
float scale = dropout->scale;
if (scale != 1.f)
{
const int num_output = innerproduct->num_output;
const int weight_per_outch = innerproduct->weight_data_size / num_output;
float* weight = innerproduct->weight_data;
for (int i=0; i<num_output; i++)
{
float* conv_weight_outch = weight + weight_per_outch * i;
for (int j=0; j<weight_per_outch; j++)
{
conv_weight_outch[j] *= scale;
}
}
if (innerproduct->bias_term)
{
float* bias = innerproduct->bias_data;
for (int i=0; i<num_output; i++)
{
bias[i] *= scale;
}
}
}
int top_blob_index_final = dropout->tops[0];
innerproduct->tops[0] = top_blob_index_final;
blobs[top_blob_index_final].producer = i;
dropout->type = "ncnnfused";
}
return 0;
}
int NetOptimize::fuse_convolution_activation()
{
const size_t layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "Convolution")
continue;
// Convolution - Activation
int top_blob_index = layers[i]->tops[0];
int j = i + 1;
for (; j<layer_count; j++)
{
if (layers[j]->type != "ReLU" && layers[j]->type != "Clip" && layers[j]->type != "Sigmoid")
continue;
if (layers[j]->bottoms.size() != 1)
continue;
if (layers[j]->bottoms[0] == top_blob_index)
break;
}
if (j == layer_count)
continue;
// fuse Convolution - Activation to Convolution
ncnn::Convolution* convolution = (ncnn::Convolution*)layers[i];
ncnn::Layer* activation = layers[j];
fprintf(stderr, "fuse_convolution_activation %s %s\n", convolution->name.c_str(), activation->name.c_str());
if (activation->type == "ReLU")
{
ncnn::ReLU* relu = (ncnn::ReLU*)activation;
if (relu->slope == 0.f)
{
convolution->activation_type = 1;
}
else
{
convolution->activation_type = 2;
convolution->activation_params = ncnn::Mat(1);
convolution->activation_params[0] = relu->slope;
}
}
else if (activation->type == "Clip")
{
ncnn::Clip* clip = (ncnn::Clip*)activation;
convolution->activation_type = 3;
convolution->activation_params = ncnn::Mat(2);
convolution->activation_params[0] = clip->min;
convolution->activation_params[1] = clip->max;
}
else if (activation->type == "Sigmoid")
{
convolution->activation_type = 4;
}
int top_blob_index_final = activation->tops[0];
convolution->tops[0] = top_blob_index_final;
blobs[top_blob_index_final].producer = i;
activation->type = "ncnnfused";
}
return 0;
}
int NetOptimize::fuse_convolutiondepthwise_activation()
{
const size_t layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "ConvolutionDepthWise")
continue;
// ConvolutionDepthWise - Activation
int top_blob_index = layers[i]->tops[0];
int j = i + 1;
for (; j<layer_count; j++)
{
if (layers[j]->type != "ReLU" && layers[j]->type != "Clip" && layers[j]->type != "Sigmoid")
continue;
if (layers[j]->bottoms.size() != 1)
continue;
if (layers[j]->bottoms[0] == top_blob_index)
break;
}
if (j == layer_count)
continue;
// fuse ConvolutionDepthWise - Activation to ConvolutionDepthWise
ncnn::ConvolutionDepthWise* convolutiondepthwise = (ncnn::ConvolutionDepthWise*)layers[i];
ncnn::Layer* activation = layers[j];
fprintf(stderr, "fuse_convolutiondepthwise_activation %s %s\n", convolutiondepthwise->name.c_str(), activation->name.c_str());
if (activation->type == "ReLU")
{
ncnn::ReLU* relu = (ncnn::ReLU*)activation;
if (relu->slope == 0.f)
{
convolutiondepthwise->activation_type = 1;
}
else
{
convolutiondepthwise->activation_type = 2;
convolutiondepthwise->activation_params = ncnn::Mat(1);
convolutiondepthwise->activation_params[0] = relu->slope;
}
}
else if (activation->type == "Clip")
{
ncnn::Clip* clip = (ncnn::Clip*)activation;
convolutiondepthwise->activation_type = 3;
convolutiondepthwise->activation_params = ncnn::Mat(2);
convolutiondepthwise->activation_params[0] = clip->min;
convolutiondepthwise->activation_params[1] = clip->max;
}
else if (activation->type == "Sigmoid")
{
convolutiondepthwise->activation_type = 4;
}
int top_blob_index_final = activation->tops[0];
convolutiondepthwise->tops[0] = top_blob_index_final;
blobs[top_blob_index_final].producer = i;
activation->type = "ncnnfused";
}
return 0;
}
int NetOptimize::fuse_deconvolution_activation()
{
const size_t layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "Deconvolution")
continue;
// Deconvolution - Activation
int top_blob_index = layers[i]->tops[0];
int j = i + 1;
for (; j<layer_count; j++)
{
if (layers[j]->type != "ReLU" && layers[j]->type != "Clip" && layers[j]->type != "Sigmoid")
continue;
if (layers[j]->bottoms.size() != 1)
continue;
if (layers[j]->bottoms[0] == top_blob_index)
break;
}
if (j == layer_count)
continue;
// fuse Deconvolution - Activation to Deconvolution
ncnn::Deconvolution* deconvolution = (ncnn::Deconvolution*)layers[i];
ncnn::Layer* activation = layers[j];
fprintf(stderr, "fuse_deconvolution_activation %s %s\n", deconvolution->name.c_str(), activation->name.c_str());
if (activation->type == "ReLU")
{
ncnn::ReLU* relu = (ncnn::ReLU*)activation;
if (relu->slope == 0.f)
{
deconvolution->activation_type = 1;
}
else
{
deconvolution->activation_type = 2;
deconvolution->activation_params = ncnn::Mat(1);
deconvolution->activation_params[0] = relu->slope;
}
}
else if (activation->type == "Clip")
{
ncnn::Clip* clip = (ncnn::Clip*)activation;
deconvolution->activation_type = 3;
deconvolution->activation_params = ncnn::Mat(2);
deconvolution->activation_params[0] = clip->min;
deconvolution->activation_params[1] = clip->max;
}
else if (activation->type == "Sigmoid")
{
deconvolution->activation_type = 4;
}
int top_blob_index_final = activation->tops[0];
deconvolution->tops[0] = top_blob_index_final;
blobs[top_blob_index_final].producer = i;
activation->type = "ncnnfused";
}
return 0;
}
int NetOptimize::fuse_deconvolutiondepthwise_activation()
{
const size_t layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "DeconvolutionDepthWise")
continue;
// DeconvolutionDepthWise - Activation
int top_blob_index = layers[i]->tops[0];
int j = i + 1;
for (; j<layer_count; j++)
{
if (layers[j]->type != "ReLU" && layers[j]->type != "Clip" && layers[j]->type != "Sigmoid")
continue;
if (layers[j]->bottoms.size() != 1)
continue;
if (layers[j]->bottoms[0] == top_blob_index)
break;
}
if (j == layer_count)
continue;
// fuse DeconvolutionDepthWise - Activation to DeconvolutionDepthWise
ncnn::DeconvolutionDepthWise* deconvolutiondepthwise = (ncnn::DeconvolutionDepthWise*)layers[i];
ncnn::Layer* activation = layers[j];
fprintf(stderr, "fuse_deconvolutiondepthwise_activation %s %s\n", deconvolutiondepthwise->name.c_str(), activation->name.c_str());
if (activation->type == "ReLU")
{
ncnn::ReLU* relu = (ncnn::ReLU*)activation;
if (relu->slope == 0.f)
{
deconvolutiondepthwise->activation_type = 1;
}
else
{
deconvolutiondepthwise->activation_type = 2;
deconvolutiondepthwise->activation_params = ncnn::Mat(1);
deconvolutiondepthwise->activation_params[0] = relu->slope;
}
}
else if (activation->type == "Clip")
{
ncnn::Clip* clip = (ncnn::Clip*)activation;
deconvolutiondepthwise->activation_type = 3;
deconvolutiondepthwise->activation_params = ncnn::Mat(2);
deconvolutiondepthwise->activation_params[0] = clip->min;
deconvolutiondepthwise->activation_params[1] = clip->max;
}
else if (activation->type == "Sigmoid")
{
deconvolutiondepthwise->activation_type = 4;
}
int top_blob_index_final = activation->tops[0];
deconvolutiondepthwise->tops[0] = top_blob_index_final;
blobs[top_blob_index_final].producer = i;
activation->type = "ncnnfused";
}
return 0;
}
int NetOptimize::fuse_innerproduct_activation()
{
const size_t layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "InnerProduct")
continue;
// InnerProduct - Activation
int top_blob_index = layers[i]->tops[0];
int j = i + 1;
for (; j<layer_count; j++)
{
if (layers[j]->type != "ReLU" && layers[j]->type != "Clip" && layers[j]->type != "Sigmoid")
continue;
if (layers[j]->bottoms.size() != 1)
continue;
if (layers[j]->bottoms[0] == top_blob_index)
break;
}
if (j == layer_count)
continue;
// fuse InnerProduct - Activation to InnerProduct
ncnn::InnerProduct* innerproduct = (ncnn::InnerProduct*)layers[i];
ncnn::Layer* activation = layers[j];
fprintf(stderr, "fuse_innerproduct_activation %s %s\n", innerproduct->name.c_str(), activation->name.c_str());
if (activation->type == "ReLU")
{
ncnn::ReLU* relu = (ncnn::ReLU*)activation;
if (relu->slope == 0.f)
{
innerproduct->activation_type = 1;
}
else
{
innerproduct->activation_type = 2;
innerproduct->activation_params = ncnn::Mat(1);
innerproduct->activation_params[0] = relu->slope;
}
}
else if (activation->type == "Clip")
{
ncnn::Clip* clip = (ncnn::Clip*)activation;
innerproduct->activation_type = 3;
innerproduct->activation_params = ncnn::Mat(2);
innerproduct->activation_params[0] = clip->min;
innerproduct->activation_params[1] = clip->max;
}
else if (activation->type == "Sigmoid")
{
innerproduct->activation_type = 4;
}
int top_blob_index_final = activation->tops[0];
innerproduct->tops[0] = top_blob_index_final;
blobs[top_blob_index_final].producer = i;
activation->type = "ncnnfused";
}
return 0;
}
int NetOptimize::eliminate_dropout()
{
const size_t layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "Dropout")
continue;
ncnn::Dropout* dropout = (ncnn::Dropout*)layers[i];
if (dropout->scale != 1.f)
continue;
// Any - Dropout
int bottom_blob_index = layers[i]->bottoms[0];
int j = i - 1;
for (; j>=0; j--)
{
if (layers[j]->type == "ncnnfused")
continue;
if (layers[j]->tops.size() != 1)
continue;
if (layers[j]->tops[0] == bottom_blob_index)
break;
}
if (j == -1)
continue;
ncnn::Layer* any = layers[j];
fprintf(stderr, "eliminate_dropout %s %s\n", any->name.c_str(), dropout->name.c_str());
int top_blob_index_final = dropout->tops[0];
any->tops[0] = top_blob_index_final;
blobs[top_blob_index_final].producer = j;
dropout->type = "ncnnfused";
}
return 0;
}
int NetOptimize::eliminate_pooling1x1()
{
const size_t layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "Pooling")
continue;
ncnn::Pooling* pooling = (ncnn::Pooling*)layers[i];
if (pooling->pad_left != 0 || pooling->pad_right != 0 || pooling->pad_top != 0 || pooling->pad_bottom != 0)
continue;
if (pooling->kernel_w != 1 || pooling->kernel_h != 1 || pooling->stride_w != 1 || pooling->stride_h != 1)
continue;
if (pooling->global_pooling != 0)
continue;
// Any - Pooling
int bottom_blob_index = layers[i]->bottoms[0];
int top_i = -1;
int j = i - 1;
for (; j>=0; j--)
{
if (layers[j]->type == "ncnnfused")
continue;
for (int k=0; k<layers[j]->tops.size(); k++)
{
if (layers[j]->tops[k] == bottom_blob_index)
{
top_i = k;
break;
}
}
if (top_i != -1)
break;
}
if (j == -1)
continue;
ncnn::Layer* any = layers[j];
fprintf(stderr, "eliminate_pooling1x1 %s %s\n", any->name.c_str(), pooling->name.c_str());
int top_blob_index_final = pooling->tops[0];
any->tops[top_i] = top_blob_index_final;
blobs[top_blob_index_final].producer = j;
pooling->type = "ncnnfused";
}
return 0;
}
int NetOptimize::eliminate_noop()
{
const size_t layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "Noop")
continue;
ncnn::Layer* noop = layers[i];
if (noop->bottoms.empty())
{
// Noop
fprintf(stderr, "eliminate_noop %s\n", noop->name.c_str());
size_t top_blob_count = noop->tops.size();
for (int k=0; k<top_blob_count; k++)
{
int top_blob_index_final = noop->tops[k];
blobs[top_blob_index_final].producer = -1;
}
noop->type = "ncnnfused";
continue;
}
// Any - Noop
int bottom_blob_index = layers[i]->bottoms[0];
int j = i - 1;
for (; j>=0; j--)
{
if (layers[j]->type == "ncnnfused")
continue;
if (layers[j]->tops.size() != 1)
continue;
if (layers[j]->tops[0] == bottom_blob_index)
break;
}
if (j == -1)
continue;
ncnn::Layer* any = layers[j];
fprintf(stderr, "eliminate_noop %s %s\n", any->name.c_str(), noop->name.c_str());
size_t top_blob_count = std::min(noop->tops.size(), any->tops.size());
for (int k=0; k<top_blob_count; k++)
{
int top_blob_index_final = noop->tops[k];
any->tops[k] = top_blob_index_final;
blobs[top_blob_index_final].producer = j;
}
noop->type = "ncnnfused";
}
return 0;
}
int NetOptimize::eliminate_orphaned_memorydata()
{
const size_t layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "MemoryData")
continue;
// MemoryData - X
int top_blob_index = layers[i]->tops[0];
int j = i + 1;
for (; j<layer_count; j++)
{
if (layers[j]->type == "ncnnfused")
continue;
bool orphaned = true;
for (int k=0; k<layers[j]->bottoms.size(); k++)
{
if (layers[j]->bottoms[k] == top_blob_index)
{
orphaned = false;
break;
}
}
if (!orphaned)
break;
}
if (j < layer_count)
continue;
// assert orphaned == true
fprintf(stderr, "eliminate_orphaned_memorydata %s\n", layers[i]->name.c_str());
layers[i]->type = "ncnnfused";
}
return 0;
}
int NetOptimize::eliminate_reshape_after_global_pooling()
{
const size_t layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "Pooling")
continue;
ncnn::Pooling* pooling = (ncnn::Pooling*)layers[i];
if (pooling->global_pooling == 0)
continue;
// Pooling - Reshape
int top_blob_index = layers[i]->tops[0];
int j = i + 1;
for (; j<layer_count; j++)
{
if (layers[j]->type != "Reshape")
continue;
if (layers[j]->bottoms.size() != 1)
continue;
if (layers[j]->bottoms[0] == top_blob_index)
break;
}
if (j == layer_count)
continue;
ncnn::Reshape* reshape = (ncnn::Reshape*)layers[j];
if (reshape->h != -233 || reshape->c != -233 || reshape->permute != 0)
continue;
fprintf(stderr, "eliminate_reshape_after_global_pooling %s %s\n", pooling->name.c_str(), reshape->name.c_str());
int top_blob_index_final = reshape->tops[0];
pooling->tops[0] = top_blob_index_final;
blobs[top_blob_index_final].producer = i;
reshape->type = "ncnnfused";
}
return 0;
}
int NetOptimize::eliminate_flatten_after_global_pooling()
{
const size_t layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "Pooling")
continue;
ncnn::Pooling* pooling = (ncnn::Pooling*)layers[i];
if (pooling->global_pooling == 0)
continue;
// Pooling - Flatten
int top_blob_index = layers[i]->tops[0];
int j = i + 1;
for (; j<layer_count; j++)
{
if (layers[j]->type != "Flatten")
continue;
if (layers[j]->bottoms.size() != 1)
continue;
if (layers[j]->bottoms[0] == top_blob_index)
break;
}
if (j == layer_count)
continue;
ncnn::Flatten* flatten = (ncnn::Flatten*)layers[j];
fprintf(stderr, "eliminate_flatten_after_global_pooling %s %s\n", pooling->name.c_str(), flatten->name.c_str());
int top_blob_index_final = flatten->tops[0];
pooling->tops[0] = top_blob_index_final;
blobs[top_blob_index_final].producer = i;
flatten->type = "ncnnfused";
}
return 0;
}
int NetOptimize::eliminate_flatten_after_innerproduct()
{
const size_t layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "InnerProduct")
continue;
// InnerProduct - Flatten
int top_blob_index = layers[i]->tops[0];
int j = i + 1;
for (; j<layer_count; j++)
{
if (layers[j]->type != "Flatten")
continue;
if (layers[j]->bottoms.size() != 1)
continue;
if (layers[j]->bottoms[0] == top_blob_index)
break;
}
if (j == layer_count)
continue;
ncnn::InnerProduct* innerproduct = (ncnn::InnerProduct*)layers[i];
ncnn::Flatten* flatten = (ncnn::Flatten*)layers[j];
fprintf(stderr, "eliminate_flatten_after_innerproduct %s %s\n", innerproduct->name.c_str(), flatten->name.c_str());
int top_blob_index_final = flatten->tops[0];
innerproduct->tops[0] = top_blob_index_final;
blobs[top_blob_index_final].producer = i;
flatten->type = "ncnnfused";
}
return 0;
}
int NetOptimize::eliminate_reshape_before_binaryop()
{
const size_t layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "Reshape")
continue;
ncnn::Reshape* reshape = (ncnn::Reshape*)layers[i];
if (reshape->w != 1 || reshape->h != 1 || reshape->permute != 0)
continue;
// Reshape - BinaryOp
int top_blob_index = layers[i]->tops[0];
int j = i + 1;
for (; j<layer_count; j++)
{
if (layers[j]->type != "BinaryOp")
continue;
if (layers[j]->bottoms.size() != 2)
continue;
if (layers[j]->bottoms[0] == top_blob_index || layers[j]->bottoms[1] == top_blob_index)
break;
}
if (j == layer_count)
continue;
ncnn::BinaryOp* binaryop = (ncnn::BinaryOp*)layers[j];
fprintf(stderr, "eliminate_reshape_before_binaryop %s %s\n", reshape->name.c_str(), binaryop->name.c_str());
int bottom_blob_index_final = reshape->bottoms[0];
if (layers[j]->bottoms[0] == top_blob_index)
binaryop->bottoms[0] = bottom_blob_index_final;
if (layers[j]->bottoms[1] == top_blob_index)
binaryop->bottoms[1] = bottom_blob_index_final;
blobs[bottom_blob_index_final].consumers.erase(std::find(blobs[bottom_blob_index_final].consumers.begin(), blobs[bottom_blob_index_final].consumers.end(), i));
blobs[bottom_blob_index_final].consumers.push_back(j);
reshape->type = "ncnnfused";
}
return 0;
}
int NetOptimize::replace_convolution_with_innerproduct_after_global_pooling()
{
const size_t layer_count = layers.size();
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "Pooling")
continue;
ncnn::Pooling* pooling = (ncnn::Pooling*)layers[i];
if (pooling->global_pooling == 0)
continue;
// Pooling - Convolution
int top_blob_index = layers[i]->tops[0];
int j = i + 1;
for (; j<layer_count; j++)
{
if (layers[j]->type != "Convolution")
continue;
if (layers[j]->bottoms.size() != 1)
continue;
if (layers[j]->bottoms[0] == top_blob_index)
break;
}
if (j == layer_count)
continue;
ncnn::Convolution* convolution = (ncnn::Convolution*)layers[j];
fprintf(stderr, "replace_convolution_with_innerproduct_after_global_pooling %s %s\n", pooling->name.c_str(), convolution->name.c_str());
ncnn::InnerProduct* innerproduct = (ncnn::InnerProduct*)ncnn::create_layer("InnerProduct");
innerproduct->type = "InnerProduct";
innerproduct->name = convolution->name;
innerproduct->bottoms = convolution->bottoms;
innerproduct->tops = convolution->tops;
ncnn::ParamDict pd;
innerproduct->load_param(pd);
innerproduct->num_output = convolution->num_output;
innerproduct->bias_term = convolution->bias_term;
innerproduct->weight_data_size = convolution->weight_data_size;
innerproduct->weight_data = convolution->weight_data;
innerproduct->bias_data = convolution->bias_data;
innerproduct->activation_type = convolution->activation_type;
innerproduct->activation_params = convolution->activation_params;
layers[j] = innerproduct;
delete convolution;
}
return 0;
}
int NetOptimize::replace_convolution_with_innerproduct_after_innerproduct()
{
const size_t layer_count = layers.size();
for (;;)
{
bool replaced = false;
for (int i=0; i<layer_count; i++)
{
if (layers[i]->type != "InnerProduct")
continue;
// InnerProduct - Convolution
int top_blob_index = layers[i]->tops[0];
int j = i + 1;
for (; j<layer_count; j++)
{
if (layers[j]->type != "Convolution")
continue;
if (layers[j]->bottoms.size() != 1)
continue;
if (layers[j]->bottoms[0] == top_blob_index)
break;
}
if (j == layer_count)
continue;
ncnn::InnerProduct* innerproduct = (ncnn::InnerProduct*)layers[i];
ncnn::Convolution* convolution = (ncnn::Convolution*)layers[j];
fprintf(stderr, "replace_convolution_with_innerproduct_after_innerproduct %s %s\n", innerproduct->name.c_str(), convolution->name.c_str());
ncnn::InnerProduct* innerproduct2 = (ncnn::InnerProduct*)ncnn::create_layer("InnerProduct");
innerproduct2->type = "InnerProduct";
innerproduct2->name = convolution->name;
innerproduct2->bottoms = convolution->bottoms;
innerproduct2->tops = convolution->tops;
ncnn::ParamDict pd;
innerproduct2->load_param(pd);
innerproduct2->num_output = convolution->num_output;
innerproduct2->bias_term = convolution->bias_term;
innerproduct2->weight_data_size = convolution->weight_data_size;
innerproduct2->weight_data = convolution->weight_data;
innerproduct2->bias_data = convolution->bias_data;
innerproduct2->activation_type = convolution->activation_type;
innerproduct2->activation_params = convolution->activation_params;
layers[j] = innerproduct2;
delete convolution;
replaced = true;
}
if (!replaced)
break;
}
return 0;
}
int NetOptimize::shape_inference()
{
const size_t layer_count = layers.size();
const size_t blob_count = blobs.size();
ncnn::Extractor ex = create_extractor();
// prepare Input blobs
for (size_t i=0; i<layer_count; i++)
{
const ncnn::Layer* layer = layers[i];
if (layer->type == "ncnnfused")
continue;
if (layer->type != "Input")
continue;
ncnn::Input* input = (ncnn::Input*)layer;
int w = input->w;
int h = input->h;
int c = input->c;
int dims = 0;
if (w == 0 && h == 0 && c == 0) dims = 0;
if (w != 0 && h == 0 && c == 0) dims = 1;
if (w != 0 && h != 0 && c == 0) dims = 2;
if (w != 0 && h != 0 && c != 0) dims = 3;
if (dims == 0)
{
fprintf(stderr, "Input layer %s without shape info, shape_inference aborted\n", layer->name.c_str());
return -1;
}
ncnn::Mat m;
if (dims == 1) m.create(w);
if (dims == 2) m.create(w, h);
if (dims == 3) m.create(w, h, c);
ex.input(layer->tops[0], m);
}
// prepare blobs with predefined shape
for (size_t i=0; i<blob_count; i++)
{
const ncnn::Blob blob = blobs[i];
int dims = blob.shape.dims;
int w = blob.shape.w;
int h = blob.shape.h;
int c = blob.shape.c;
if (dims == 0)
continue;
ncnn::Mat m;
if (dims == 1) m.create(w);
if (dims == 2) m.create(w, h);
if (dims == 3) m.create(w, h, c);
ex.input(i, m);
}
fprintf(stderr, "shape_inference\n");
// resolve all layer output blob shape
for (size_t i=0; i<layer_count; i++)
{
const ncnn::Layer* layer = layers[i];
if (layer->type == "ncnnfused")
continue;
for (size_t j=0; j<layer->tops.size(); j++)
{
int top_blob_index = layer->tops[j];
ncnn::Mat m;
ex.extract(top_blob_index, m);
blobs[top_blob_index].shape = m.shape();
}
}
// assign all layer blob shape
for (size_t i=0; i<layer_count; i++)
{
ncnn::Layer* layer = layers[i];
if (layer->type == "ncnnfused")
continue;
layer->bottom_shapes.resize(layer->bottoms.size());
for (size_t j=0; j<layer->bottoms.size(); j++)
{
int bottom_blob_index = layer->bottoms[j];
layer->bottom_shapes[j] = blobs[bottom_blob_index].shape;
}
layer->top_shapes.resize(layer->tops.size());
for (size_t j=0; j<layer->tops.size(); j++)
{
int top_blob_index = layer->tops[j];
layer->top_shapes[j] = blobs[top_blob_index].shape;
// fprintf(stderr, "%d %4d %4d %4d | %2d %s\n", blobs[top_blob_index].shape.dims, blobs[top_blob_index].shape.w, blobs[top_blob_index].shape.h, blobs[top_blob_index].shape.c, top_blob_index, blobs[top_blob_index].name.c_str());
}
}
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, ",%e", ptr[i]);
}
return 0;
}
static inline size_t alignSize(size_t sz, int n)
{
return (sz + n-1) & -n;
}
int NetOptimize::fwrite_weight_tag_data(int tag, const ncnn::Mat& data, FILE* bp)
{
int p0 = ftell(bp);
ncnn::Mat data_flattened = data.reshape(data.w * data.h * data.c);
if (storage_type == 1 && tag == 0)
{
tag = 0x01306B47; // fp16 magic
fwrite(&tag, sizeof(int), 1, bp);
ncnn::Mat data_flattened_fp16;
ncnn::cast_float32_to_float16(data_flattened, data_flattened_fp16);
fwrite(data_flattened_fp16.data, data_flattened_fp16.elemsize, data_flattened_fp16.w, bp);
}
else
{
fwrite(&tag, sizeof(int), 1, bp);
fwrite(data_flattened.data, data_flattened.elemsize, data_flattened.w, bp);
}
// padding to 32bit align
int nwrite = ftell(bp) - p0;
size_t nalign = alignSize(nwrite, 4);
unsigned char padding[4] = {0x00, 0x00, 0x00, 0x00};
fwrite(padding, sizeof(unsigned char), nalign - nwrite, bp);
return 0;
}
int NetOptimize::fwrite_weight_data(const ncnn::Mat& data, FILE* bp)
{
int p0 = ftell(bp);
ncnn::Mat data_flattened = data.reshape(data.w * data.h * data.c);
fwrite(data_flattened.data, data_flattened.elemsize, data_flattened.w, bp);
// padding to 32bit align
int nwrite = ftell(bp) - p0;
size_t nalign = alignSize(nwrite, 4);
unsigned char padding[4] = {0x00, 0x00, 0x00, 0x00};
fwrite(padding, sizeof(unsigned char), nalign - nwrite, 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 size_t 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++;
size_t 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);
}
size_t 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);
}
}
size_t blob_count_fused = blob_names.size();
fprintf(pp, "%d %zd\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;
size_t bottom_count = layer->bottoms.size();
size_t top_count = layer->tops.size();
fprintf(pp, "%-24s %-24s %zd %zd", 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());
}
// write shape hints
int shape_hint_array_size = 0;
for (int j=0; j<top_count; j++)
{
int top_blob_index = layer->tops[j];
int dims = blobs[top_blob_index].shape.dims;
if (dims == 0)
{
shape_hint_array_size = 0;
break;
}
shape_hint_array_size += dims + 1;
}
if (shape_hint_array_size)
{
fprintf(pp, " -23330=%d", shape_hint_array_size);
for (int j=0; j<top_count; j++)
{
int top_blob_index = layer->tops[j];
int dims = blobs[top_blob_index].shape.dims;
int w = blobs[top_blob_index].shape.w;
int h = blobs[top_blob_index].shape.h;
int c = blobs[top_blob_index].shape.c;
fprintf(pp, ",%d", dims);
if (dims == 1)
{
fprintf(pp, ",%d", w);
}
if (dims == 2)
{
fprintf(pp, ",%d,%d", w, h);
}
if (dims == 3)
{
fprintf(pp, ",%d,%d,%d", w, h, c);
}
}
}
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=%e", 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=%e", b)
}
else if (layer->type == "Clip")
{
ncnn::Clip* op = (ncnn::Clip*)layer;
ncnn::Clip* op_default = (ncnn::Clip*)layer_default;
fprintf_param_value(" 0=%e", min)
fprintf_param_value(" 1=%e", 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_left)
{ if (op->pad_top != op->pad_left) fprintf(pp, " 14=%d", op->pad_top); }
{ if (op->pad_right != op->pad_left) fprintf(pp, " 15=%d", op->pad_right); }
{ if (op->pad_bottom != op->pad_top) fprintf(pp, " 16=%d", op->pad_bottom); }
fprintf_param_value(" 18=%e", pad_value)
fprintf_param_value(" 5=%d", bias_term)
fprintf_param_value(" 6=%d", weight_data_size)
fprintf_param_value(" 8=%d", int8_scale_term)
fprintf_param_value(" 9=%d", activation_type)
{ if (!op->activation_params.empty()) fprintf_param_float_array(10, op->activation_params, pp); }
fprintf_param_value(" 17=%d", impl_type)
fwrite_weight_tag_data(0, 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_left)
{ if (op->pad_top != op->pad_left) fprintf(pp, " 14=%d", op->pad_top); }
{ if (op->pad_right != op->pad_left) fprintf(pp, " 15=%d", op->pad_right); }
{ if (op->pad_bottom != op->pad_top) fprintf(pp, " 16=%d", op->pad_bottom); }
fprintf_param_value(" 18=%e", pad_value)
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)
fprintf_param_value(" 9=%d", activation_type)
{ if (!op->activation_params.empty()) fprintf_param_float_array(10, op->activation_params, pp); }
fwrite_weight_tag_data(0, 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)
fprintf_param_value(" 6=%d", woffset2)
fprintf_param_value(" 7=%d", hoffset2)
fprintf_param_value(" 8=%d", coffset2)
{ if (!op->starts.empty()) fprintf_param_int_array(9, op->starts, pp); }
{ if (!op->ends.empty()) fprintf_param_int_array(10, op->ends, pp); }
{ if (!op->axes.empty()) fprintf_param_int_array(11, op->axes, pp); }
}
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_left)
{ if (op->pad_top != op->pad_left) fprintf(pp, " 14=%d", op->pad_top); }
{ if (op->pad_right != op->pad_left) fprintf(pp, " 15=%d", op->pad_right); }
{ if (op->pad_bottom != op->pad_top) fprintf(pp, " 16=%d", op->pad_bottom); }
fprintf_param_value(" 18=%d", output_pad_right)
{ if (op->output_pad_bottom != op->output_pad_right) fprintf(pp, " 19=%d", op->output_pad_bottom); }
fprintf_param_value(" 20=%d", output_w)
{ if (op->output_h != op->output_w) fprintf(pp, " 21=%d", op->output_h); }
fprintf_param_value(" 5=%d", bias_term)
fprintf_param_value(" 6=%d", weight_data_size)
fprintf_param_value(" 9=%d", activation_type)
{ if (!op->activation_params.empty()) fprintf_param_float_array(10, op->activation_params, pp); }
fwrite_weight_tag_data(0, 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_left)
{ if (op->pad_top != op->pad_left) fprintf(pp, " 14=%d", op->pad_top); }
{ if (op->pad_right != op->pad_left) fprintf(pp, " 15=%d", op->pad_right); }
{ if (op->pad_bottom != op->pad_top) fprintf(pp, " 16=%d", op->pad_bottom); }
fprintf_param_value(" 18=%d", output_pad_right)
{ if (op->output_pad_bottom != op->output_pad_right) fprintf(pp, " 19=%d", op->output_pad_bottom); }
fprintf_param_value(" 20=%d", output_w)
{ if (op->output_h != op->output_w) fprintf(pp, " 21=%d", op->output_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(" 9=%d", activation_type)
{ if (!op->activation_params.empty()) fprintf_param_float_array(10, op->activation_params, pp); }
fwrite_weight_tag_data(0, 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=%e", nms_threshold)
fprintf_param_value(" 2=%d", nms_top_k)
fprintf_param_value(" 3=%d", keep_top_k)
fprintf_param_value(" 4=%e", confidence_threshold)
fprintf_param_value(" 5=%e", variances[0])
fprintf_param_value(" 6=%e", variances[1])
fprintf_param_value(" 7=%e", variances[2])
fprintf_param_value(" 8=%e", 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=%e", 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_float_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=%e", alpha)
}
else if (layer->type == "Exp")
{
ncnn::Exp* op = (ncnn::Exp*)layer;
ncnn::Exp* op_default = (ncnn::Exp*)layer_default;
fprintf_param_value(" 0=%e", base)
fprintf_param_value(" 1=%e", scale)
fprintf_param_value(" 2=%e", shift)
}
else if (layer->type == "ExpandDims")
{
ncnn::ExpandDims* op = (ncnn::ExpandDims*)layer;
ncnn::ExpandDims* op_default = (ncnn::ExpandDims*)layer_default;
fprintf_param_value(" 0=%d", expand_w)
fprintf_param_value(" 1=%d", expand_h)
fprintf_param_value(" 2=%d", expand_c)
{ if (!op->axes.empty()) fprintf_param_int_array(0, op->axes, pp); }
}
else if (layer->type == "HardSigmoid")
{
ncnn::HardSigmoid* op = (ncnn::HardSigmoid*)layer;
ncnn::HardSigmoid* op_default = (ncnn::HardSigmoid*)layer_default;
fprintf_param_value(" 0=%e", alpha)
fprintf_param_value(" 1=%e", beta)
}
else if (layer->type == "HardSwish")
{
ncnn::HardSwish* op = (ncnn::HardSwish*)layer;
ncnn::HardSwish* op_default = (ncnn::HardSwish*)layer_default;
fprintf_param_value(" 0=%e", alpha)
fprintf_param_value(" 1=%e", beta)
}
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)
fprintf_param_value(" 9=%d", activation_type)
{ if (!op->activation_params.empty()) fprintf_param_float_array(10, op->activation_params, pp); }
fwrite_weight_tag_data(0, 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=%e", eps)
fwrite_weight_data(op->gamma_data, bp);
fwrite_weight_data(op->beta_data, bp);
}
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=%e", height_scale)
fprintf_param_value(" 2=%e", 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=%e", base)
fprintf_param_value(" 1=%e", scale)
fprintf_param_value(" 2=%e", 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=%e", alpha)
fprintf_param_value(" 3=%e", beta)
fprintf_param_value(" 4=%e", bias)
}
else if (layer->type == "MemoryData")
{
ncnn::MemoryData* op = (ncnn::MemoryData*)layer;
ncnn::MemoryData* op_default = (ncnn::MemoryData*)layer_default;
fprintf_param_value(" 0=%d", w)
fprintf_param_value(" 1=%d", h)
fprintf_param_value(" 2=%d", c)
fwrite_weight_data(op->data, bp);
}
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=%e", 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=%e", eps)
fprintf_param_value(" 3=%d", scale_data_size)
fprintf_param_value(" 4=%d", across_channel)
fprintf_param_value(" 9=%d", eps_mode)
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=%e", 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=%e", power)
fprintf_param_value(" 1=%e", scale)
fprintf_param_value(" 2=%e", 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_float_array(0, op->min_sizes, pp); }
{ if (!op->max_sizes.empty()) fprintf_param_float_array(1, op->max_sizes, pp); }
{ if (!op->aspect_ratios.empty()) fprintf_param_float_array(2, op->aspect_ratios, pp); }
fprintf_param_value(" 3=%e", variances[0])
fprintf_param_value(" 4=%e", variances[1])
fprintf_param_value(" 5=%e", variances[2])
fprintf_param_value(" 6=%e", 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=%e", step_width)
fprintf_param_value(" 12=%e", step_height)
fprintf_param_value(" 13=%e", 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=%e", 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=%e", 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=%e", 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", reduce_all)
fprintf_param_value(" 2=%e", coeff)
{ if (!op->axes.empty()) fprintf_param_int_array(3, op->axes, pp); }
fprintf_param_value(" 4=%d", keepdims)
}
else if (layer->type == "ReLU")
{
ncnn::ReLU* op = (ncnn::ReLU*)layer;
ncnn::ReLU* op_default = (ncnn::ReLU*)layer_default;
fprintf_param_value(" 0=%e", 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=%e", scale_in)
fprintf_param_value(" 1=%e", 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=%e", 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=%e", 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 == "Squeeze")
{
ncnn::Squeeze* op = (ncnn::Squeeze*)layer;
ncnn::Squeeze* op_default = (ncnn::Squeeze*)layer_default;
fprintf_param_value(" 0=%d", squeeze_w)
fprintf_param_value(" 1=%d", squeeze_h)
fprintf_param_value(" 2=%d", squeeze_c)
{ if (!op->axes.empty()) fprintf_param_int_array(0, op->axes, pp); }
}
else if (layer->type == "Threshold")
{
ncnn::Threshold* op = (ncnn::Threshold*)layer;
ncnn::Threshold* op_default = (ncnn::Threshold*)layer_default;
fprintf_param_value(" 0=%e", 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=%e", confidence_threshold)
fprintf_param_value(" 3=%e", nms_threshold)
{ if (!op->biases.empty()) fprintf_param_float_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=%e", confidence_threshold)
fprintf_param_value(" 3=%e", nms_threshold)
{ if (!op->biases.empty()) fprintf_param_float_array(4, op->biases, pp); }
{ if (!op->mask.empty()) fprintf_param_int_array(5, op->mask, pp); }
{ if (!op->anchors_scale.empty()) fprintf_param_float_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)
{
#if defined(__aarch64__) && defined(LINUX)
if (argc != 10)
{
fprintf(stderr, "usage: %s [inparam] [inbin] [outparam] [outbin] [flag] [dataname] [w] [h] [c]\n", argv[0]);
return -1;
}
const char* dataname = argv[6];
int inw = atoi(argv[7]);
int inh = atoi(argv[8]);
int inc = atoi(argv[9]);
#else
if (argc != 6)
{
fprintf(stderr, "usage: %s [inparam] [inbin] [outparam] [outbin] [flag]\n", argv[0]);
return -1;
}
#endif // defined(__aarch64__) && defined(LINUX)
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;
if (flag == 65536)
{
optimizer.storage_type = 1;
}
else
{
optimizer.storage_type = 0;
}
optimizer.load_param(inparam);
if (strcmp(inbin, "null") == 0)
{
DataReaderFromEmpty dr;
optimizer.load_model(dr);
}
else
optimizer.load_model(inbin);
#if defined(__aarch64__) && defined(LINUX)
optimizer.find_fastest_fp32_conv(dataname, inw, inh, inc);
#endif // defined(__aarch64__) && defined(LINUX)
optimizer.fuse_batchnorm_scale();
optimizer.fuse_convolution_batchnorm();
optimizer.fuse_convolutiondepthwise_batchnorm();
optimizer.fuse_deconvolution_batchnorm();
optimizer.fuse_deconvolutiondepthwise_batchnorm();
optimizer.fuse_innerproduct_batchnorm();
optimizer.fuse_innerproduct_dropout();
optimizer.fuse_convolution_activation();
optimizer.fuse_convolutiondepthwise_activation();
optimizer.fuse_deconvolution_activation();
optimizer.fuse_deconvolutiondepthwise_activation();
optimizer.fuse_innerproduct_activation();
optimizer.eliminate_dropout();
optimizer.eliminate_pooling1x1();
optimizer.eliminate_noop();
optimizer.eliminate_flatten_after_global_pooling();
optimizer.eliminate_reshape_after_global_pooling();
optimizer.eliminate_reshape_before_binaryop();
optimizer.replace_convolution_with_innerproduct_after_global_pooling();
optimizer.replace_convolution_with_innerproduct_after_innerproduct();
optimizer.eliminate_flatten_after_innerproduct();
optimizer.eliminate_orphaned_memorydata();
optimizer.shape_inference();
optimizer.save(outparam, outbin);
return 0;
}
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