void convertProtoToLua(void** handle, const char* lua_name, const char* cuda_package)
{
const caffe::NetParameter netparam = *(const caffe::NetParameter*)handle[1];
if (netparam.layers_size() > 0)
convertProtoToLuaV1(netparam, lua_name, cuda_package);
else
convertProtoToLuaV2(netparam, lua_name, cuda_package);
}
void convertProtoToLua(void** handle, const char* lua_name, const char* cuda_package)
{
const caffe::NetParameter netparam = *(const caffe::NetParameter*)handle[1];
std::ofstream ofs (lua_name);
ofs << "require '" << cuda_package << "'\n";
ofs << "require 'cunn'\n";
ofs << "model = {}\n";
if(std::string(cuda_package)=="ccn2")
ofs<< "table.insert(model, {'torch_transpose_dwhb', nn.Transpose({1,4},{1,3},{1,2})})\n";
int num_output = netparam.input_dim_size();
for (int i=0; i<netparam.layers_size(); ++i)
{
std::vector<std::pair<std::string, std::string>> lines;
auto& layer = netparam.layers(i);
switch(layer.type())
{
case caffe::LayerParameter::CONVOLUTION:
{
auto ¶m = layer.convolution_param();
int groups = param.group() == 0 ? 1 : param.group();
int nInputPlane = layer.blobs(0).channels()*groups;
int nOutputPlane = param.num_output();
num_output = nOutputPlane;
int kW = param.kernel_w();
int kH = param.kernel_h();
int dW = param.stride_w();
int dH = param.stride_h();
if(kW==0 || kH==0)
{
kW = param.kernel_size();
kH = kW;
}
if(dW==0 || dH==0)
{
dW = param.stride();
dH = dW;
}
int pad_w = param.pad_w();
int pad_h = param.pad_h();
if(pad_w==0 || pad_h==0)
{
pad_w = param.pad();
pad_h = pad_w;
}
if(std::string(cuda_package) == "ccn2")
{
if(kW != kH || dW != dH || pad_w != pad_h)
{
std::cout << "ccn2 only supports square images!\n";
break;
}
char buf[1024];
sprintf(buf, "ccn2.SpatialConvolution(%d, %d, %d, %d, %d, %d)",
nInputPlane, nOutputPlane, kW, dW, pad_w, groups);
lines.emplace_back(layer.name(), buf);
}
else
{
char buf[1024];
const char* mm_or_not = std::string(cuda_package)=="nn" ? "MM" : "";
sprintf(buf, "%s.SpatialConvolution%s(%d, %d, %d, %d, %d, %d, %d, %d)",
cuda_package, mm_or_not, nInputPlane, nOutputPlane, kW, kH, dW, dH, pad_w, pad_h);
lines.emplace_back(layer.name(), buf);
}
break;
}
case caffe::LayerParameter::POOLING:
{
auto ¶m = layer.pooling_param();
std::string ptype = param.pool() == caffe::PoolingParameter::MAX ? "Max" : "Avg";
int kW = param.kernel_w();
int kH = param.kernel_h();
int dW = param.stride_w();
int dH = param.stride_h();
if(kW==0 || kH==0)
{
kW = param.kernel_size();
kH = kW;
}
if(dW==0 || dH==0)
{
dW = param.stride();
dH = dW;
}
if(std::string(cuda_package) == "ccn2")
{
char buf[1024];
sprintf(buf, "ccn2.Spatial%sPooling(%d, %d)", ptype.c_str(), kW, dW);
lines.emplace_back(layer.name(), buf);
}
else if(std::string(cuda_package) == "cudnn")
{
char buf[1024];
sprintf(buf, "%s.Spatial%sPooling(%d, %d, %d, %d):ceil()", cuda_package, ptype=="Avg" ? "Average" : "Max", kW, kH, dW, dH);
lines.emplace_back(layer.name(), buf);
}
break;
}
case caffe::LayerParameter::RELU:
{
lines.emplace_back(layer.name(), "nn.ReLU()");
break;
}
case caffe::LayerParameter::LRN:
{
if(std::string(cuda_package) == "ccn2")
{
auto ¶m = layer.lrn_param();
int local_size = param.local_size();
float alpha = param.alpha();
float beta = param.beta();
char buf[1024];
sprintf(buf, "ccn2.SpatialCrossResponseNormalization(%d, %.6f, %.4f)", local_size, alpha, beta);
lines.emplace_back(layer.name(), buf);
}
break;
}
case caffe::LayerParameter::INNER_PRODUCT:
{
auto ¶m = layer.inner_product_param();
int nInputPlane = layer.blobs(0).width();
int nOutputPlane = param.num_output();
char buf[1024];
sprintf(buf, "nn.Linear(%d, %d)", nInputPlane, nOutputPlane);
if(num_output != nInputPlane)
{
if(std::string(cuda_package) == "ccn2")
lines.emplace_back("torch_transpose_bdwh", "nn.Transpose({4,1},{4,2},{4,3})");
lines.emplace_back("torch_view", "nn.View(-1):setNumInputDims(3)");
}
lines.emplace_back(layer.name(), buf);
num_output = nOutputPlane;
break;
}
case caffe::LayerParameter::DROPOUT:
{
char buf[1024];
sprintf(buf, "nn.Dropout(%f)", netparam.layers(i).dropout_param().dropout_ratio());
lines.emplace_back(layer.name(), buf);
break;
}
case caffe::LayerParameter::SOFTMAX_LOSS:
{
lines.emplace_back(layer.name(), "nn.SoftMax()");
break;
}
case caffe::LayerParameter::SOFTMAX:
{
lines.emplace_back(layer.name(), "nn.SoftMax()");
break;
}
default:
{
std::cout << "MODULE " << netparam.layers(i).name() << " UNDEFINED\n";
break;
}
}
if(!lines.empty())
for(auto& it: lines)
ofs << "table.insert(model, {'" << it.first << "', " << it.second << "})\n";
else
ofs << "-- module '" << layer.name() << "' not found\n";
}
}
void convertProtoToLuaV1(const caffe::NetParameter &netparam, const char* lua_name, const char* cuda_package)
{
PACKAGE_TYPE cuda_package_type = CCN2;
if(std::string(cuda_package) == "ccn2")
cuda_package_type = CCN2;
else if(std::string(cuda_package) == "nn")
cuda_package_type = NN;
else if(std::string(cuda_package) == "cudnn")
cuda_package_type = CUDNN;
std::ofstream ofs (lua_name);
ofs << "require '" << cuda_package << "'\n";
ofs << "require 'cunn'\n";
ofs << "local model = {}\n";
if(std::string(cuda_package)=="ccn2")
ofs<< "table.insert(model, {'torch_transpose_dwhb', nn.Transpose({1,4},{1,3},{1,2})})\n";
else if(std::string(cuda_package)=="nn" || std::string(cuda_package)=="cudnn")
ofs<< "require 'inn'\n";
int num_output = netparam.input_dim_size();
for (int i=0; i<netparam.layers_size(); ++i)
{
std::vector<std::pair<std::string, std::string>> lines;
auto& layer = netparam.layers(i);
switch(layer.type())
{
case caffe::V1LayerParameter::CONVOLUTION:
{
auto ¶m = layer.convolution_param();
int groups = param.group() == 0 ? 1 : param.group();
int nInputPlane = layer.blobs(0).channels()*groups;
int nOutputPlane = layer.blobs(0).num();
//int nOutputPlane = param.num_output();
num_output = nOutputPlane;
int kW = param.kernel_w();
int kH = param.kernel_h();
int dW = param.stride_w();
int dH = param.stride_h();
if(kW==0 || kH==0)
{
kW = param.kernel_size();
kH = kW;
}
if(dW==0 || dH==0)
{
dW = param.stride();
dH = dW;
}
int pad_w = param.pad_w();
int pad_h = param.pad_h();
if(pad_w==0 || pad_h==0)
{
pad_w = param.pad();
pad_h = pad_w;
}
if(cuda_package_type == CCN2)
{
if(kW != kH || dW != dH || pad_w != pad_h)
{
std::cout << "ccn2 only supports square images!\n";
break;
}
char buf[1024];
sprintf(buf, "ccn2.SpatialConvolution(%d, %d, %d, %d, %d, %d)",
nInputPlane, nOutputPlane, kW, dW, pad_w, groups);
lines.emplace_back(layer.name(), buf);
}
else if(cuda_package_type == NN)
{
if(groups != 1)
{
std::cout << "nn supports no groups!\n";
break;
}
char buf[1024];
sprintf(buf, "nn.SpatialConvolutionMM(%d, %d, %d, %d, %d, %d, %d, %d)",
nInputPlane, nOutputPlane, kW, kH, dW, dH, pad_w, pad_h);
lines.emplace_back(layer.name(), buf);
}
else
{
char buf[1024];
sprintf(buf, "cudnn.SpatialConvolution(%d, %d, %d, %d, %d, %d, %d, %d, %d)",
nInputPlane, nOutputPlane, kW, kH, dW, dH, pad_w, pad_h, groups);
lines.emplace_back(layer.name(), buf);
}
break;
}
case caffe::V1LayerParameter::POOLING:
{
auto ¶m = layer.pooling_param();
int kW = param.kernel_w();
int kH = param.kernel_h();
int dW = param.stride_w();
int dH = param.stride_h();
int padW = param.pad_w();
int padH = param.pad_h();
if(kW==0 || kH==0)
{
kW = param.kernel_size();
kH = kW;
}
if(dW==0 || dH==0)
{
dW = param.stride();
dH = dW;
}
char buf[1024];
switch(cuda_package_type)
{
case CCN2: // ceil mode by default
if(param.pool() == caffe::PoolingParameter::MAX)
sprintf(buf, "ccn2.SpatialMaxPooling(%d, %d)", kW, dW);
else if(param.pool() == caffe::PoolingParameter::AVE)
sprintf(buf, "ccn2.SpatialAvgPooling(%d, %d)", kW, dW);
else if(param.pool() == caffe::PoolingParameter::STOCHASTIC)
THError("Stochastic pooling is not implemented in DHWB format");
break;
case CUDNN:
if(param.pool() == caffe::PoolingParameter::MAX)
sprintf(buf, "cudnn.SpatialMaxPooling(%d, %d, %d, %d, %d, %d):ceil()", kW, kH, dW, dH, padW, padH);
else if(param.pool() == caffe::PoolingParameter::AVE)
sprintf(buf, "cudnn.SpatialAveragePooling(%d, %d, %d, %d, %d, %d):ceil()", kW, kH, dW, dH, padW, padH);
else if(param.pool() == caffe::PoolingParameter::STOCHASTIC)
sprintf(buf, "inn.SpatialStochasticPooling(%d, %d, %d, %d)", kW, kH, dW, dH);
break;
case NN:
if(param.pool() == caffe::PoolingParameter::MAX)
//sprintf(buf, "nn.SpatialMaxPooling(%d, %d, %d, %d, %d, %d):ceil()", kW, kH, dW, dH, padW, padH);
sprintf(buf, "nn.SpatialMaxPooling(%d, %d, %d, %d, %d, %d)", kW, kH, dW, dH, padW, padH);
else if(param.pool() == caffe::PoolingParameter::AVE)
sprintf(buf, "inn.SpatialAveragePooling(%d, %d, %d, %d)", kW, kH, dW, dH); // padding is not supported yet
else if(param.pool() == caffe::PoolingParameter::STOCHASTIC)
sprintf(buf, "inn.SpatialStochasticPooling(%d, %d, %d, %d)", kW, kH, dW, dH);
break;
}
lines.emplace_back(layer.name(), buf);
break;
}
case caffe::V1LayerParameter::RELU:
{
switch(cuda_package_type)
{
case CUDNN:
lines.emplace_back(layer.name(), "cudnn.ReLU(true)");
break;
default:
lines.emplace_back(layer.name(), "nn.ReLU(true)");
break;
}
break;
}
case caffe::V1LayerParameter::TANH:
{
switch(cuda_package_type)
{
case CUDNN:
lines.emplace_back(layer.name(), "cudnn.Tanh(true)");
break;
default:
lines.emplace_back(layer.name(), "nn.Tanh()");
break;
}
break;
}
case caffe::V1LayerParameter::SIGMOID:
{
switch(cuda_package_type)
{
case CUDNN:
lines.emplace_back(layer.name(), "cudnn.Sigmoid(true)");
break;
default:
lines.emplace_back(layer.name(), "nn.Sigmoid()");
break;
}
break;
}
case caffe::V1LayerParameter::LRN:
{
auto ¶m = layer.lrn_param();
int local_size = param.local_size();
float alpha = param.alpha();
float beta = param.beta();
float k = param.k();
char buf[1024];
if(std::string(cuda_package) == "ccn2")
sprintf(buf, "ccn2.SpatialCrossResponseNormalization(%d, %.6f, %.4f, %f)", local_size, alpha, beta, k);
else
sprintf(buf, "inn.SpatialCrossResponseNormalization(%d, %.6f, %.4f, %f)", local_size, alpha, beta, k);
lines.emplace_back(layer.name(), buf);
break;
}
case caffe::V1LayerParameter::INNER_PRODUCT:
{
auto ¶m = layer.inner_product_param();
int nInputPlane = layer.blobs(0).width();
int nOutputPlane = param.num_output();
char buf[1024];
sprintf(buf, "nn.Linear(%d, %d)", nInputPlane, nOutputPlane);
if(num_output != nInputPlane)
{
if(std::string(cuda_package) == "ccn2")
lines.emplace_back("torch_transpose_bdwh", "nn.Transpose({4,1},{4,2},{4,3})");
lines.emplace_back("torch_view", "nn.View(-1):setNumInputDims(3)");
}
lines.emplace_back(layer.name(), buf);
num_output = nOutputPlane;
break;
}
case caffe::V1LayerParameter::DROPOUT:
{
char buf[1024];
sprintf(buf, "nn.Dropout(%f)", layer.dropout_param().dropout_ratio());
lines.emplace_back(layer.name(), buf);
break;
}
case caffe::V1LayerParameter::SOFTMAX_LOSS:
{
lines.emplace_back(layer.name(), "nn.SoftMax()");
break;
}
case caffe::V1LayerParameter::SOFTMAX:
{
lines.emplace_back(layer.name(), "nn.SoftMax()");
break;
}
default:
{
std::cout << "MODULE " << layer.name() << " UNDEFINED\n";
break;
}
}
if(!lines.empty())
for(auto& it: lines)
ofs << "table.insert(model, {'" << it.first << "', " << it.second << "})\n";
else
{
ofs << "-- warning: module '" << layer.name() << "' not found\n";
std::cout << "warning: module '" << layer.name() << "' not found\n";
}
}
ofs << "return model";
}
