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#pragma once
#include "caffe2/core/context.h"
#include "caffe2/core/logging.h"
#include "caffe2/core/operator.h"
#include "caffe2/operators/filler_op.h"
#include "caffe2/utils/cast.h"
#include "caffe2/utils/math.h"
namespace caffe2 {
template <typename T, class Context>
class GivenTensorFillOp final : public FillerOp<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
explicit GivenTensorFillOp(const OperatorDef& operator_def, Workspace* ws)
: FillerOp<Context>(operator_def, ws) {
const ArgumentHelper helper(operator_def);
// GivenTensorFillOp can be provided with a "dtype" arg if float is
// is specified as T. Otherwise, "dtype" is ignored.
// In the ideal world, we would get rid of templating of T at all, but we
// need to provide backwards compatibility.
if (!std::is_same<T, float>::value || !helper.HasArgument("dtype")) {
ExtractValues<T>();
} else {
auto dtype = cast::GetCastDataType(helper, "dtype");
switch (dtype) {
case TensorProto_DataType_FLOAT:
ExtractValues<float>();
break;
case TensorProto_DataType_DOUBLE:
ExtractValues<double>();
break;
case TensorProto_DataType_BOOL:
ExtractValues<bool>();
break;
case TensorProto_DataType_INT16:
ExtractValues<int16_t>();
break;
case TensorProto_DataType_INT32:
ExtractValues<int>();
break;
case TensorProto_DataType_INT64:
ExtractValues<int64_t>();
break;
case TensorProto_DataType_STRING:
ExtractValues<std::string>();
break;
case TensorProto_DataType_UNDEFINED:
CAFFE_THROW("Cannot have undefined 'dtype' argument");
default:
CAFFE_THROW("Unexpected 'dtype' argument value: ", dtype);
}
}
}
bool Fill(Tensor* output) override {
return (this->*body_)(output);
}
private:
template <typename Type>
void ExtractValues() {
auto source_values = this->template GetRepeatedArgument<Type>("values");
ReinitializeTensor(
&values_,
{static_cast<int64_t>(source_values.size())},
at::dtype<Type>().device(CPU));
Type* values_data = values_.template mutable_data<Type>();
// NOLINTNEXTLINE(clang-diagnostic-sign-compare)
for (const auto i : c10::irange(source_values.size())) {
values_data[i] = static_cast<Type>(source_values[i]);
}
body_ = &GivenTensorFillOp::FillWithType<Type>;
}
template <typename Type>
bool FillWithType(Tensor* output) {
CAFFE_ENFORCE_EQ(output->numel(), values_.numel());
auto* data = output->template mutable_data<Type>();
const Type* values_data = values_.template data<Type>();
if (output->numel()) {
context_.CopyItemsFromCPU(
TypeMeta::Make<Type>(), output->numel(), values_data, data);
}
return true;
}
bool (GivenTensorFillOp::*body_)(Tensor* output);
Tensor values_;
};
} // namespace caffe2
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