/usr/local/lib64/python3.6/site-packages/torch/include/caffe2/operators
Edit: /usr/local/lib64/python3.6/site-packages/torch/include/caffe2/operators/given_tensor_fill_op.h (3002B)
#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
class GivenTensorFillOp final : public FillerOp {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
explicit GivenTensorFillOp(const OperatorDef& operator_def, Workspace* ws)
: FillerOp(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::value || !helper.HasArgument("dtype")) {
ExtractValues();
} else {
auto dtype = cast::GetCastDataType(helper, "dtype");
switch (dtype) {
case TensorProto_DataType_FLOAT:
ExtractValues();
break;
case TensorProto_DataType_DOUBLE:
ExtractValues();
break;
case TensorProto_DataType_BOOL:
ExtractValues();
break;
case TensorProto_DataType_INT16:
ExtractValues();
break;
case TensorProto_DataType_INT32:
ExtractValues();
break;
case TensorProto_DataType_INT64:
ExtractValues();
break;
case TensorProto_DataType_STRING:
ExtractValues();
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
void ExtractValues() {
auto source_values = this->template GetRepeatedArgument("values");
ReinitializeTensor(
&values_,
{static_cast(source_values.size())},
at::dtype().device(CPU));
Type* values_data = values_.template mutable_data();
// NOLINTNEXTLINE(clang-diagnostic-sign-compare)
for (int i = 0; i < source_values.size(); i++) {
values_data[i] = static_cast(source_values[i]);
}
body_ = &GivenTensorFillOp::FillWithType;
}
template
bool FillWithType(Tensor* output) {
CAFFE_ENFORCE_EQ(output->numel(), values_.numel());
auto* data = output->template mutable_data();
const Type* values_data = values_.template data();
if (output->numel()) {
context_.CopyItemsFromCPU(
TypeMeta::Make(), output->numel(), values_data, data);
}
return true;
}
bool (GivenTensorFillOp::*body_)(Tensor* output);
Tensor values_;
};
} // namespace caffe2