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#include "tensorflow/core/framework/tensor_slice.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/strings/strcat.h"
#include "tensorflow/core/lib/strings/str_util.h"
namespace tensorflow {
TensorSlice::TensorSlice(int dim) { SetFullSlice(dim); }
TensorSlice::TensorSlice(const TensorSliceProto& proto) {
starts_.reserve(proto.extent_size());
lengths_.reserve(proto.extent_size());
for (const auto& e : proto.extent()) {
starts_.push_back(e.start());
lengths_.push_back(GetExtentLength(e));
}
}
TensorSlice::TensorSlice(std::initializer_list<std::pair<int, int>> extents) {
starts_.reserve(extents.size());
lengths_.reserve(extents.size());
for (const auto& e : extents) {
starts_.push_back(e.first);
lengths_.push_back(e.second);
}
}
Status TensorSlice::Parse(const string& str, TensorSlice* slice) {
std::vector<string> items = str_util::Split(str, ':', str_util::SkipEmpty());
slice->starts_.reserve(items.size());
slice->lengths_.reserve(items.size());
for (const string& x : items) {
int s, l;
if (x == "-") {
// "everything"
s = 0;
l = kFullExtent;
} else {
char junk;
if (sscanf(x.c_str(), "%d,%d%c", &s, &l, &junk) != 2) {
return errors::InvalidArgument(
"Expected a pair of numbers or '-' "
"but got '",
x, "': string = ", str);
}
if (s < 0 || l <= 0) {
return errors::InvalidArgument(
"Expected non-negative start and "
"positive length but got start = ",
s, ", length = ", l, ": string = ", str);
}
}
slice->starts_.push_back(s);
slice->lengths_.push_back(l);
}
return Status::OK();
}
void TensorSlice::Clear() {
starts_.clear();
lengths_.clear();
}
void TensorSlice::SetFullSlice(int dim) {
Clear();
starts_.reserve(dim);
lengths_.reserve(dim);
for (int d = 0; d < dim; ++d) {
starts_.push_back(0);
lengths_.push_back(kFullExtent);
}
}
void TensorSlice::Extend(int dim) {
int old_dim = dims();
DCHECK_LE(old_dim, dim);
starts_.resize(dim);
lengths_.resize(dim);
for (int d = old_dim; d < dim; ++d) {
starts_[d] = 0;
lengths_[d] = kFullExtent;
}
}
void TensorSlice::AsProto(TensorSliceProto* proto) const {
for (int d = 0; d < dims(); ++d) {
TensorSliceProto::Extent* e = proto->add_extent();
// We only need to record the explicit slice for non-full slices
if (!IsFullAt(d)) {
e->set_start(starts_[d]);
e->set_length(lengths_[d]);
}
}
}
string TensorSlice::DebugString() const {
string buffer;
bool first = true;
for (int d = 0; d < dims(); ++d) {
if (!first) {
buffer.append(":");
}
string s;
if (IsFullAt(d)) {
buffer.append("-");
} else {
strings::StrAppend(&buffer, starts_[d], ",", lengths_[d]);
}
first = false;
}
return buffer;
}
bool TensorSlice::Intersect(const TensorSlice& other,
TensorSlice* result) const {
// First, if two slices have different ranks, they obviously don't overlap
// -- in fact they are not compatible.
if (dims() != other.dims()) {
return false;
}
// Setting the result to the right dimension
if (result) {
result->SetFullSlice(dims());
}
// The two slices overlap if they overlap in all dimensions.
for (int d = 0; d < dims(); ++d) {
if (IsFullAt(d)) {
if (result) {
result->set_start(d, other.start(d));
result->set_length(d, other.length(d));
}
} else if (other.IsFullAt(d)) {
if (result) {
result->set_start(d, start(d));
result->set_length(d, length(d));
}
} else {
// If we have an intersection here, it should have a start that is the
// max of the two starts and an end that is the min of the two ends.
int s = std::max(start(d), other.start(d));
int l = std::min(end(d), other.end(d)) - s;
if (l > 0) {
// We have a real intersection
if (result) {
result->set_start(d, s);
result->set_length(d, l);
}
} else {
// We don't have an intersection for this dimension -- thus we don't
// have any intersection at all.
if (result) {
result->Clear();
}
return false;
}
}
}
// If we are here, we know there is overlap in every dimension.
return true;
}
void TensorSlice::ComputeRelative(const TensorSlice& sub,
TensorSlice* relative) const {
DCHECK_EQ(dims(), sub.dims());
relative->SetFullSlice(dims());
for (int d = 0; d < dims(); ++d) {
if (IsFullAt(d)) {
relative->set_start(d, sub.start(d));
relative->set_length(d, sub.length(d));
} else {
// Otherwise the relative start is the difference between the start of
// sub and the start of base
relative->set_start(d, sub.start(d) - start(d));
relative->set_length(d, sub.length(d));
}
}
}
// static
bool TensorSlice::HasExtentLength(const TensorSliceProto::Extent& extent) {
return extent.has_length_case() == TensorSliceProto::Extent::kLength;
}
// static
int64 TensorSlice::GetExtentLength(const TensorSliceProto::Extent& extent) {
if (!HasExtentLength(extent)) return -1;
return extent.length();
}
Status TensorSlice::SliceTensorShape(const TensorShape& shape,
TensorShape* result_shape) const {
result_shape->Clear();
// Mismatching ranks: we can't apply the slice at all.
if (shape.dims() != dims()) {
return errors::Internal("Mismatching ranks: shape = ", shape.DebugString(),
", slice = ", DebugString());
}
for (int d = 0; d < dims(); ++d) {
if (IsFullAt(d)) {
result_shape->AddDim(shape.dim_size(d));
} else {
// Check if the extent applies to the dimension
if (end(d) <= shape.dim_size(d)) {
// Yes: the end is within the range of the dim -- we adjust the result
// shape so that its size along this dimension is the length of the
// slice.
result_shape->AddDim(length(d));
} else {
// The extent doesn't apply to the dimension
result_shape->Clear();
return errors::Internal("Extent in dimension ", d,
" out of bounds: shape = ", shape.DebugString(),
", slice = ", DebugString());
}
}
}
// If we are here, we have successfully applied the shape.
return Status::OK();
}
const int TensorSlice::kFullExtent = -1;
} // namespace tensorflow
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