mmr/tkDNN
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Shortcat ok

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This commit is contained in:
Davide Sapienza
2019-10-29 15:08:44 +01:00
parent 058f9b795f
commit e9ec582223
5 changed files with 87 additions and 29 deletions
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src/LayerWgs.cpp
+19 -1
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@@ -19,7 +19,14 @@ LayerWgs::LayerWgs(Network *net, int inputs, int outputs,
readBinaryFile(weights_path.c_str(), inputs*outputs*kh*kw*kl, &data_h, &data_d, seek);
seek += inputs*outputs*kh*kw*kl;
readBinaryFile(weights_path.c_str(), outputs, &bias_h, &bias_d, seek);
std::cout<<"w:\n";
printDeviceVector(64, data_d, true);
std::cout<<"b:\n";
printDeviceVector(64, bias_d, true);
std::cout<<"step----------------------------------------\n";
this->batchnorm = batchnorm;
if(batchnorm) {
seek += outputs;
@@ -29,6 +36,17 @@ LayerWgs::LayerWgs(Network *net, int inputs, int outputs,
seek += outputs;
readBinaryFile(weights_path.c_str(), outputs, &variance_h, &variance_d, seek);
std::cout<<"s:\n";
printDeviceVector(64, scales_d, true);
std::cout<<"m:\n";
printDeviceVector(64, mean_d, true);
std::cout<<"v:\n";
printDeviceVector(64, variance_d, true);
std::cout<<"END----------------------------------------\n";
float eps = CUDNN_BN_MIN_EPSILON;
power_h = new dnnType[outputs];