int main(int argc, char** argv) {
cudaSetDevice(1);
Caffe::set_mode(Caffe::GPU);
Caffe::set_phase(Caffe::TEST);
CHECK(argc == 4);
LOG(INFO) << "Reading trained parameters";
NetParameter trained_net_param;
ReadProtoFromBinaryFile(argv[1], &trained_net_param);
int quant_size = atoi(argv[3]);
LOG(INFO) << "Quantizing trained parameters";
NetParameter quantized_net_param = trained_net_param;
for (int i = 0; i < quantized_net_param.layers_size(); ++i) {
LayerParameter* layer = quantized_net_param.mutable_layers(i)->mutable_layer();
for (int blob_id = 0; blob_id < layer->blobs_size(); ++blob_id) {
quantizeBlob(layer->mutable_blobs(blob_id), quant_size);
}
}
LOG(INFO) << "Outputting quantized parameters";
WriteProtoToBinaryFile(quantized_net_param, argv[2]);
LOG(INFO) << "Finish writing quantized parameters";
return 0;
}
bool ConvolutionLayer::copy_trained_layer(const LayerParameter& param) {
if(param.blobs_size() < 1) return true;
if(layer_param_.blobs_size() < 1)
layer_param_.add_blobs()->CopyFrom(param.blobs(0));
else
layer_param_.mutable_blobs(0)->CopyFrom(param.blobs(0));
if(layer_param_.convolution_param().bias_term() &&
param.convolution_param().bias_term()) {
if(layer_param_.blobs_size() < 2)
layer_param_.add_blobs()->CopyFrom(param.blobs(1));
else
layer_param_.mutable_blobs(1)->CopyFrom(param.blobs(1));
}
initialized = false;
init();
return true;
}