ImageDeserializerBase::ImageDeserializerBase(CorpusDescriptorPtr corpus, const ConfigParameters& config, bool primary)
: DataDeserializerBase(primary),
m_corpus(corpus)
{
assert(m_corpus);
ConfigParameters inputs = config("input");
std::vector<std::string> featureNames = GetSectionsWithParameter("ImageDeserializerBase", inputs, "transforms");
std::vector<std::string> labelNames = GetSectionsWithParameter("ImageDeserializerBase", inputs, "labelDim");
if (featureNames.size() != 1 || labelNames.size() != 1)
RuntimeError(
"Please specify a single feature and label stream. '%d' features , '%d' labels found.",
static_cast<int>(featureNames.size()),
static_cast<int>(labelNames.size()));
string precision = config("precision", "float");
m_precision = AreEqualIgnoreCase(precision, "float") ? ElementType::tfloat : ElementType::tdouble;
m_verbosity = config(L"verbosity", 0);
// Feature stream.
ConfigParameters featureSection = inputs(featureNames[0]);
auto features = std::make_shared<StreamDescription>();
features->m_id = 0;
features->m_name = msra::strfun::utf16(featureSection.ConfigName());
features->m_storageType = StorageType::dense;
// Due to performance, now we support images of different types.
features->m_elementType = ElementType::tvariant;
m_streams.push_back(features);
// Label stream.
ConfigParameters label = inputs(labelNames[0]);
size_t labelDimension = label("labelDim");
auto labels = std::make_shared<StreamDescription>();
labels->m_id = 1;
labels->m_name = msra::strfun::utf16(label.ConfigName());
labels->m_sampleLayout = std::make_shared<TensorShape>(labelDimension);
labels->m_storageType = StorageType::sparse_csc;
labels->m_elementType = m_precision;
m_streams.push_back(labels);
m_labelGenerator = labels->m_elementType == ElementType::tfloat ?
(LabelGeneratorPtr)std::make_shared<TypedLabelGenerator<float>>(labelDimension) :
std::make_shared<TypedLabelGenerator<double>>(labelDimension);
m_grayscale = config(L"grayscale", false);
// TODO: multiview should be done on the level of randomizer/transformers - it is responsiblity of the
// TODO: randomizer to collect how many copies each transform needs and request same sequence several times.
m_multiViewCrop = config(L"multiViewCrop", false);
}
ImageConfigHelper::ImageConfigHelper(const ConfigParameters& config)
: m_dataFormat(CHW)
{
std::vector<std::string> featureNames = GetSectionsWithParameter(config, "width");
std::vector<std::string> labelNames = GetSectionsWithParameter(config, "labelDim");
// REVIEW alexeyk: currently support only one feature and label section.
if (featureNames.size() != 1 || labelNames.size() != 1)
{
RuntimeError(
"ImageReader currently supports a single feature and label stream. '%d' features , '%d' labels found.",
static_cast<int>(featureNames.size()),
static_cast<int>(labelNames.size()));
}
ConfigParameters featureSection = config(featureNames[0]);
size_t w = featureSection("width");
size_t h = featureSection("height");
size_t c = featureSection("channels");
std::string mbFmt = featureSection("mbFormat", "nchw");
if (AreEqualIgnoreCase(mbFmt, "nhwc") || AreEqualIgnoreCase(mbFmt, "legacy"))
{
m_dataFormat = HWC;
}
else if (!AreEqualIgnoreCase(mbFmt, "nchw") || AreEqualIgnoreCase(mbFmt, "cudnn"))
{
RuntimeError("ImageReader does not support the sample format '%s', only 'nchw' and 'nhwc' are supported.", mbFmt.c_str());
}
auto features = std::make_shared<StreamDescription>();
features->m_id = 0;
features->m_name = msra::strfun::utf16(featureSection.ConfigName());
features->m_sampleLayout = std::make_shared<TensorShape>(ImageDimensions(w, h, c).AsTensorShape(m_dataFormat));
m_streams.push_back(features);
ConfigParameters label = config(labelNames[0]);
size_t labelDimension = label("labelDim");
auto labelSection = std::make_shared<StreamDescription>();
labelSection->m_id = 1;
labelSection->m_name = msra::strfun::utf16(label.ConfigName());
labelSection->m_sampleLayout = std::make_shared<TensorShape>(labelDimension);
m_streams.push_back(labelSection);
m_mapPath = config(L"file");
std::string rand = config(L"randomize", "auto");
if (AreEqualIgnoreCase(rand, "auto"))
{
m_randomize = true;
}
else if (AreEqualIgnoreCase(rand, "none"))
{
m_randomize = false;
}
else
{
RuntimeError("'randomize' parameter must be set to 'auto' or 'none'");
}
// Identify precision
string precision = config.Find("precision", "float");
if (AreEqualIgnoreCase(precision, "float"))
{
features->m_elementType = ElementType::tfloat;
labelSection->m_elementType = ElementType::tfloat;
}
else if (AreEqualIgnoreCase(precision, "double"))
{
features->m_elementType = ElementType::tdouble;
labelSection->m_elementType = ElementType::tdouble;
}
else
{
RuntimeError("Not supported precision '%s'. Expected 'double' or 'float'.", precision.c_str());
}
m_cpuThreadCount = config(L"numCPUThreads", 0);
}
