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#include "tensorflow/core/public/tensor_shape.h"
#include "tensorflow/core/lib/strings/str_util.h"
#include "tensorflow/core/lib/strings/strcat.h"
#include "tensorflow/core/platform/logging.h"
namespace tensorflow {
// An upper limit of the total number of elements in a tensor.
static const int64 kMaxElements = (1LL << 40);
bool TensorShape::IsValid(const TensorShapeProto& proto) {
int64 num_elements = 1;
for (const auto& d : proto.dim()) {
if (d.size() < 0) return false;
num_elements *= d.size();
if (num_elements > kMaxElements) return false;
}
return true;
}
TensorShape::TensorShape(const TensorShapeProto& proto) {
dim_sizes_.reserve(proto.dim_size());
num_elements_ = 1;
for (const auto& d : proto.dim()) {
AddDim(d.size());
}
}
TensorShape::TensorShape(gtl::ArraySlice<int64> dim_sizes) {
dim_sizes_.reserve(dim_sizes.size());
num_elements_ = 1;
for (auto s : dim_sizes) {
AddDim(s);
}
}
TensorShape::TensorShape() : num_elements_(1) {}
void TensorShape::Clear() {
dim_sizes_.clear();
num_elements_ = 1;
}
void TensorShape::AddDim(int64 size) {
CHECK_GE(size, 0);
dim_sizes_.push_back(size);
num_elements_ *= size;
CHECK_LE(0, num_elements_);
CHECK_LE(num_elements_, kMaxElements);
}
void TensorShape::AppendShape(const TensorShape& shape) {
for (auto d : shape) AddDim(d.size);
}
void TensorShape::InsertDim(int d, int64 size) {
CHECK_GE(d, 0);
CHECK_LE(d, dims());
CHECK_GE(size, 0);
dim_sizes_.insert(dim_sizes_.begin() + d, size);
num_elements_ *= size;
CHECK_LE(0, num_elements_);
CHECK_LE(num_elements_, kMaxElements);
}
void TensorShape::set_dim(int d, int64 size) {
CHECK_GE(d, 0);
CHECK_LT(d, dims());
CHECK_GE(size, 0);
// Update the number of elements. num_elements_ is int64.
dim_sizes_[d] = size;
recompute_dims();
}
void TensorShape::RemoveDim(int d) {
CHECK_GE(d, 0);
CHECK_LT(d, dims());
// Update the number of elements and remove the dimension from the
// sizes.
dim_sizes_.erase(dim_sizes_.begin() + d);
recompute_dims();
}
void TensorShape::recompute_dims() {
num_elements_ = 1;
for (auto s : dim_sizes_) {
num_elements_ *= s;
CHECK_LE(0, num_elements_);
CHECK_LE(num_elements_, kMaxElements);
}
}
bool TensorShape::IsSameSize(const TensorShape& b) const {
if (b.dims() != dims()) return false;
for (int d = 0; d < dims(); d++) {
if (dim_size(d) != b.dim_size(d)) return false;
}
return true;
}
void TensorShape::AsProto(TensorShapeProto* proto) const {
proto->Clear();
for (size_t d = 0; d < dim_sizes_.size(); ++d) {
auto* dim = proto->add_dim();
dim->set_size(dim_sizes_[d]);
}
}
TensorShapeIter TensorShape::begin() const { return TensorShapeIter(this, 0); }
TensorShapeIter TensorShape::end() const {
return TensorShapeIter(this, dims());
}
string TensorShape::DebugString() const {
TensorShapeProto proto;
AsProto(&proto);
return proto.ShortDebugString();
}
string TensorShape::ShortDebugString() const {
return strings::StrCat(
"[", str_util::Join(gtl::ArraySlice<int64>(dim_sizes_), ","), "]");
}
bool TensorShapeUtils::StartsWith(const TensorShape& shape,
const TensorShape& prefix) {
if (shape.dims() < prefix.dims()) return false;
for (int i = 0; i < prefix.dims(); i++) {
if (shape.dim_size(i) != prefix.dim_size(i)) return false;
}
return true;
}
} // namespace tensorflow
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