本文整理汇总了C++中Net::getBlob方法的典型用法代码示例。如果您正苦于以下问题：C++ Net::getBlob方法的具体用法？C++ Net::getBlob怎么用？C++ Net::getBlob使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Net的用法示例。
在下文中一共展示了Net::getBlob方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: runTorchNet
static void runTorchNet(String prefix, String outLayerName = "",
bool check2ndBlob = false, bool isBinary = false)
{
String suffix = (isBinary) ? ".dat" : ".txt";
Net net;
Ptr<Importer> importer = createTorchImporter(_tf(prefix + "_net" + suffix), isBinary);
ASSERT_TRUE(importer != NULL);
importer->populateNet(net);
Blob inp, outRef;
ASSERT_NO_THROW( inp = readTorchBlob(_tf(prefix + "_input" + suffix), isBinary) );
ASSERT_NO_THROW( outRef = readTorchBlob(_tf(prefix + "_output" + suffix), isBinary) );
net.setBlob(".0", inp);
net.forward();
if (outLayerName.empty())
outLayerName = net.getLayerNames().back();
Blob out = net.getBlob(outLayerName);
normAssert(outRef, out);
if (check2ndBlob)
{
Blob out2 = net.getBlob(outLayerName + ".1");
Blob ref2 = readTorchBlob(_tf(prefix + "_output_2" + suffix), isBinary);
normAssert(out2, ref2);
}
}示例2: inputSize
TEST(Reproducibility_AlexNet, Accuracy)
{
Net net;
{
const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
Ptr<Importer> importer = createCaffeImporter(proto, model);
ASSERT_TRUE(importer != NULL);
importer->populateNet(net);
}
Mat sample = imread(_tf("grace_hopper_227.png"));
ASSERT_TRUE(!sample.empty());
Size inputSize(227, 227);
if (sample.size() != inputSize)
resize(sample, sample, inputSize);
net.setBlob(".data", blobFromImage(sample, 1.));
net.forward();
Mat out = net.getBlob("prob");
Mat ref = blobFromNPY(_tf("caffe_alexnet_prob.npy"));
normAssert(ref, out);
}示例3: inputSize
TEST(Reproducibility_AlexNet, Accuracy)
{
Net net;
{
Ptr<Importer> importer = createCaffeImporter(_tf("bvlc_alexnet.prototxt"), _tf("bvlc_alexnet.caffemodel"));
ASSERT_TRUE(importer != NULL);
importer->populateNet(net);
}
Mat sample = imread(_tf("grace_hopper_227.png"));
ASSERT_TRUE(!sample.empty());
cv::cvtColor(sample, sample, cv::COLOR_BGR2RGB);
Size inputSize(227, 227);
if (sample.size() != inputSize)
resize(sample, sample, inputSize);
net.setBlob(".data", dnn::Blob::fromImages(sample));
net.forward();
Blob out = net.getBlob("prob");
Blob ref = blobFromNPY(_tf("caffe_alexnet_prob.npy"));
normAssert(ref, out);
}示例4: createTorchImporter
TEST(Torch_Importer, ENet_accuracy)
{
Net net;
{
Ptr<Importer> importer = createTorchImporter(_tf("Enet-model-best.net", false));
ASSERT_TRUE(importer != NULL);
importer->populateNet(net);
}
Mat sample = imread(_tf("street.png", false));
cv::cvtColor(sample, sample, cv::COLOR_BGR2RGB);
sample.convertTo(sample, CV_32F, 1/255.0);
dnn::Blob inputBlob = dnn::Blob::fromImages(sample);
net.setBlob("", inputBlob);
net.forward();
dnn::Blob out = net.getBlob(net.getLayerNames().back());
Blob ref = blobFromNPY(_tf("torch_enet_prob.npy", false));
normAssert(ref, out);
}