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#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/lib/strings/str_util.h"
#include "tensorflow/core/lib/strings/strcat.h"
#include "tensorflow/core/platform/logging.h"
namespace tensorflow {
bool DeviceType::operator<(const DeviceType& other) const {
return type_ < other.type_;
}
bool DeviceType::operator==(const DeviceType& other) const {
return type_ == other.type_;
}
std::ostream& operator<<(std::ostream& os, const DeviceType& d) {
os << d.type();
return os;
}
const char* const DEVICE_CPU = "CPU";
const char* const DEVICE_GPU = "GPU";
string DataTypeString(DataType dtype) {
if (IsRefType(dtype)) {
DataType non_ref = static_cast<DataType>(dtype - kDataTypeRefOffset);
return strings::StrCat(DataTypeString(non_ref), "_ref");
}
switch (dtype) {
case DT_INVALID:
return "INVALID";
case DT_FLOAT:
return "float";
case DT_DOUBLE:
return "double";
case DT_INT32:
return "int32";
case DT_UINT8:
return "uint8";
case DT_INT16:
return "int16";
case DT_INT8:
return "int8";
case DT_STRING:
return "string";
case DT_COMPLEX64:
return "complex64";
case DT_INT64:
return "int64";
case DT_BOOL:
return "bool";
case DT_QINT8:
return "qint8";
case DT_QUINT8:
return "quint8";
case DT_QINT32:
return "qint32";
case DT_BFLOAT16:
return "bfloat16";
default:
LOG(FATAL) << "Unrecognized DataType enum value " << dtype;
return "";
}
}
bool DataTypeFromString(StringPiece sp, DataType* dt) {
if (sp.ends_with("_ref")) {
sp.remove_suffix(4);
DataType non_ref;
if (DataTypeFromString(sp, &non_ref) && !IsRefType(non_ref)) {
*dt = static_cast<DataType>(non_ref + kDataTypeRefOffset);
return true;
} else {
return false;
}
}
if (sp == "float" || sp == "float32") {
*dt = DT_FLOAT;
return true;
} else if (sp == "double" || sp == "float64") {
*dt = DT_DOUBLE;
return true;
} else if (sp == "int32") {
*dt = DT_INT32;
return true;
} else if (sp == "uint8") {
*dt = DT_UINT8;
return true;
} else if (sp == "int16") {
*dt = DT_INT16;
return true;
} else if (sp == "int8") {
*dt = DT_INT8;
return true;
} else if (sp == "string") {
*dt = DT_STRING;
return true;
} else if (sp == "complex64") {
*dt = DT_COMPLEX64;
return true;
} else if (sp == "int64") {
*dt = DT_INT64;
return true;
} else if (sp == "bool") {
*dt = DT_BOOL;
return true;
} else if (sp == "qint8") {
*dt = DT_QINT8;
return true;
} else if (sp == "quint8") {
*dt = DT_QUINT8;
return true;
} else if (sp == "qint32") {
*dt = DT_QINT32;
return true;
} else if (sp == "bfloat16") {
*dt = DT_BFLOAT16;
return true;
}
return false;
}
string DeviceTypeString(DeviceType device_type) { return device_type.type(); }
string DataTypeSliceString(const DataTypeSlice types) {
string out;
for (auto it = types.begin(); it != types.end(); ++it) {
strings::StrAppend(&out, ((it == types.begin()) ? "" : ", "),
DataTypeString(*it));
}
return out;
}
DataTypeVector AllTypes() {
return {DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16,
DT_INT8, DT_STRING, DT_COMPLEX64, DT_INT64, DT_BOOL,
DT_QINT8, DT_QUINT8, DT_QINT32};
}
#ifndef __ANDROID__
DataTypeVector RealNumberTypes() {
return {DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64, DT_UINT8, DT_INT16, DT_INT8};
}
DataTypeVector QuantizedTypes() { return {DT_QINT8, DT_QUINT8, DT_QINT32}; }
DataTypeVector RealAndQuantizedTypes() {
return {DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64, DT_UINT8,
DT_INT16, DT_INT8, DT_QINT8, DT_QUINT8, DT_QINT32};
}
DataTypeVector NumberTypes() {
return {DT_FLOAT, DT_DOUBLE, DT_INT64, DT_INT32, DT_UINT8, DT_INT16,
DT_INT8, DT_COMPLEX64, DT_QINT8, DT_QUINT8, DT_QINT32};
}
#else // __ANDROID__
DataTypeVector RealNumberTypes() { return {DT_FLOAT, DT_INT32}; }
DataTypeVector NumberTypes() {
return {DT_FLOAT, DT_INT32, DT_QINT8, DT_QUINT8, DT_QINT32};
}
DataTypeVector QuantizedTypes() { return {DT_QINT8, DT_QUINT8, DT_QINT32}; }
DataTypeVector RealAndQuantizedTypes() {
return {DT_FLOAT, DT_INT32, DT_QINT8, DT_QUINT8, DT_QINT32};
}
#endif // __ANDROID__
// TODO(jeff): Maybe unify this with Tensor::CanUseDMA, or the underlying
// is_simple<T> in tensor.cc (and possible choose a more general name?)
bool DataTypeCanUseMemcpy(DataType dt) {
switch (dt) {
case DT_FLOAT:
case DT_DOUBLE:
case DT_INT32:
case DT_UINT8:
case DT_INT16:
case DT_INT8:
case DT_COMPLEX64:
case DT_INT64:
case DT_BOOL:
case DT_QINT8:
case DT_QUINT8:
case DT_QINT32:
case DT_BFLOAT16:
return true;
default:
return false;
}
}
bool DataTypeIsQuantized(DataType dt) {
switch (dt) {
case DT_QINT8:
case DT_QUINT8:
case DT_QINT32:
return true;
default:
return false;
}
}
} // namespace tensorflow
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