blob: 73d3a0ee6a8475897cad508993560bab3571d34c [file] [log] [blame]
#ifndef CAFFE2_OPERATORS_MAP_OPS_H_
#define CAFFE2_OPERATORS_MAP_OPS_H_
#include <algorithm>
#include <iterator>
#include <string>
#include <typeinfo>
#include <unordered_map>
#include <utility>
#include <vector>
#include "caffe2/core/blob_serialization.h"
#include "caffe2/core/context.h"
#include "caffe2/core/operator.h"
namespace caffe2 {
template <typename T>
struct TypeNameTraits {
static constexpr const char* name = "unknown";
};
template <>
struct TypeNameTraits<int64_t> {
static constexpr const char* name = "int64_t";
};
template <>
struct TypeNameTraits<int32_t> {
static constexpr const char* name = "int32_t";
};
template <typename KEY_T, typename VALUE_T>
struct MapTypeTraits {
using MapType = std::unordered_map<KEY_T, VALUE_T>;
static string MapTypeName() {
return string("(std::unordered_map<") + TypeNameTraits<KEY_T>::name + ", " +
TypeNameTraits<VALUE_T>::name + ">)";
}
};
using MapType64To64 = MapTypeTraits<int64_t, int64_t>::MapType;
using MapType64To32 = MapTypeTraits<int64_t, int32_t>::MapType;
using MapType32To32 = MapTypeTraits<int32_t, int32_t>::MapType;
using MapType32To64 = MapTypeTraits<int32_t, int64_t>::MapType;
template <class Context>
class KeyValueToMapOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
KeyValueToMapOp(const OperatorDef& operator_def, Workspace* ws)
: Operator<Context>(operator_def, ws) {}
~KeyValueToMapOp() {}
bool RunOnDevice() override {
return DispatchHelper<TensorTypes<int32_t, int64_t>>::call(
this, Input(KEYS));
}
template <typename KEY_T>
bool DoRunWithType() {
return DispatchHelper<
TensorTypes2<int32_t, int64_t, GenericTensorImplementation>,
KEY_T>::call(this, Input(VALUES));
}
template <typename KEY_T, typename VALUE_T>
bool DoRunWithType2() {
using MapType = typename MapTypeTraits<KEY_T, VALUE_T>::MapType;
const auto& key_input = Input(KEYS);
const auto& value_input = Input(VALUES);
CAFFE_ENFORCE_EQ(key_input.size(), value_input.size());
auto* key_data = key_input.template data<KEY_T>();
auto* value_data = value_input.template data<VALUE_T>();
auto* map_data = OperatorBase::Output<MapType>(MAP);
for (int i = 0; i < key_input.size(); ++i) {
map_data->emplace(key_data[i], value_data[i]);
}
return true;
}
template <typename KEY_T>
bool DoRunWithOtherType2() {
CAFFE_THROW(
"KeyValueToMap is not implemented on value tensor of type ",
Input(VALUES).meta().name(),
"Consider adding it a type in the list DispatchHelper");
}
INPUT_TAGS(KEYS, VALUES);
OUTPUT_TAGS(MAP);
};
template <class Context>
class MapToKeyValueOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
MapToKeyValueOp(const OperatorDef& operator_def, Workspace* ws)
: Operator<Context>(operator_def, ws) {}
~MapToKeyValueOp() {}
bool RunOnDevice() override {
return DispatchHelper<TensorTypes<
MapType64To64,
MapType64To32,
MapType32To32,
MapType32To64>>::call(this, OperatorBase::InputBlob(MAP));
}
template <typename MAP_T>
bool DoRunWithType() {
using key_type = typename MAP_T::key_type;
using mapped_type = typename MAP_T::mapped_type;
auto& map_data = OperatorBase::Input<MAP_T>(MAP);
auto* key_output = Output(KEYS);
auto* value_output = Output(VALUES);
key_output->Resize(map_data.size());
value_output->Resize(map_data.size());
auto* key_data = key_output->template mutable_data<key_type>();
auto* value_data = value_output->template mutable_data<mapped_type>();
for (const auto& it : map_data) {
*key_data = it.first;
*value_data = it.second;
key_data++;
value_data++;
}
return true;
}
INPUT_TAGS(MAP);
OUTPUT_TAGS(KEYS, VALUES);
};
template <typename KEY_T, typename VALUE_T>
class MapSerializer : public BlobSerializerBase {
public:
using MapType = typename MapTypeTraits<KEY_T, VALUE_T>::MapType;
void Serialize(
const Blob& blob,
const string& name,
BlobSerializerBase::SerializationAcceptor acceptor) override {
CAFFE_ENFORCE(blob.IsType<MapType>());
const MapType& map_data = blob.template Get<MapType>();
TIndex sz = map_data.size();
Tensor<CPUContext> key_tensor;
key_tensor.Resize(sz);
Tensor<CPUContext> value_tensor;
value_tensor.Resize(sz);
auto* key_data = key_tensor.mutable_data<KEY_T>();
auto* value_data = value_tensor.mutable_data<VALUE_T>();
for (const auto& it : map_data) {
*key_data = it.first;
*value_data = it.second;
key_data++;
value_data++;
}
TensorProtos tensor_protos;
TensorSerializer<CPUContext> ser;
ser.Serialize(
key_tensor, name, tensor_protos.add_protos(), 0, key_tensor.size());
ser.Serialize(
value_tensor, name, tensor_protos.add_protos(), 0, value_tensor.size());
BlobProto blob_proto;
blob_proto.set_name(name);
blob_proto.set_type(MapTypeTraits<KEY_T, VALUE_T>::MapTypeName());
blob_proto.set_content(tensor_protos.SerializeAsString());
acceptor(name, blob_proto.SerializeAsString());
}
};
template <typename KEY_T, typename VALUE_T>
class MapDeserializer : public BlobDeserializerBase {
public:
using MapType = typename MapTypeTraits<KEY_T, VALUE_T>::MapType;
void Deserialize(const BlobProto& proto, Blob* blob) override {
TensorProtos tensor_protos;
CAFFE_ENFORCE(
tensor_protos.ParseFromString(proto.content()),
"Fail to parse TensorProtos");
TensorDeserializer<CPUContext> deser;
Tensor<CPUContext> key_tensor, value_tensor;
deser.Deserialize(tensor_protos.protos(0), &key_tensor);
deser.Deserialize(tensor_protos.protos(1), &value_tensor);
auto* key_data = key_tensor.data<KEY_T>();
auto* value_data = value_tensor.data<VALUE_T>();
auto* map_ptr = blob->template GetMutable<MapType>();
for (int i = 0; i < key_tensor.size(); ++i) {
map_ptr->emplace(key_data[i], value_data[i]);
}
}
};
} // namespace caffe2
#endif // CAFFE2_OPERATORS_MAP_OPS_H_