/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "tensorflow/core/util/saved_tensor_slice_util.h"
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#include "tensorflow/core/lib/core/errors.h"
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#include "tensorflow/core/lib/strings/ordered_code.h"
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#include "tensorflow/core/lib/strings/str_util.h"
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namespace tensorflow {
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namespace checkpoint {
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const char kSavedTensorSlicesKey[] = "";
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string EncodeTensorNameSlice(const string& name, const TensorSlice& slice) {
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string buffer;
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// All the tensor slice keys will start with a 0
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tensorflow::strings::OrderedCode::WriteNumIncreasing(&buffer, 0);
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tensorflow::strings::OrderedCode::WriteString(&buffer, name);
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tensorflow::strings::OrderedCode::WriteNumIncreasing(&buffer, slice.dims());
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for (int d = 0; d < slice.dims(); ++d) {
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// A trivial extent (meaning we take EVERYTHING) will default to -1 for both
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// start and end. These will be properly parsed.
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tensorflow::strings::OrderedCode::WriteSignedNumIncreasing(&buffer,
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slice.start(d));
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tensorflow::strings::OrderedCode::WriteSignedNumIncreasing(&buffer,
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slice.length(d));
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}
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return buffer;
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}
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Status DecodeTensorNameSlice(const string& code, string* name,
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tensorflow::TensorSlice* slice) {
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StringPiece src(code);
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uint64 x;
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if (!tensorflow::strings::OrderedCode::ReadNumIncreasing(&src, &x)) {
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return errors::Internal("Failed to parse the leading number: src = ", src);
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}
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if (x != 0) {
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return errors::Internal(
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"The leading number should always be 0 for any valid key: src = ", src);
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}
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if (!tensorflow::strings::OrderedCode::ReadString(&src, name)) {
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return errors::Internal("Failed to parse the tensor name: src = ", src);
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}
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if (!tensorflow::strings::OrderedCode::ReadNumIncreasing(&src, &x)) {
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return errors::Internal("Failed to parse the tensor rank: src = ", src);
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}
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if (x == 0) {
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return errors::Internal("Expecting positive rank of the tensor, got ", x,
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", src = ", src);
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}
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if (x >= kint32max) {
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return errors::Internal("Too many elements ", x);
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}
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slice->SetFullSlice(x);
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for (int d = 0; d < static_cast<int32>(x); ++d) {
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// We expected 2x integers
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int64 start, length;
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if (!tensorflow::strings::OrderedCode::ReadSignedNumIncreasing(&src,
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&start)) {
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return errors::Internal("Failed to parse start: src = ", src);
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}
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if (!tensorflow::strings::OrderedCode::ReadSignedNumIncreasing(&src,
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&length)) {
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return errors::Internal("Failed to parse length: src = ", src);
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}
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if (length >= 0) {
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// a non-trivial extent
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slice->set_start(d, start);
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slice->set_length(d, length);
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}
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}
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return Status::OK();
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}
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Status ParseShapeAndSlice(const string& shape_and_slice, TensorShape* shape,
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TensorSlice* slice, TensorShape* shape_slice) {
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CHECK(!shape_and_slice.empty());
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// Syntax: dim0 dim1 dim2 ... <slice string>
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// Where slice string is defined in core/framework/tensor_slice.h
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std::vector<string> splits = str_util::Split(shape_and_slice, ' ');
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// Must have at least 2 strings.
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if (splits.size() < 2) {
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return errors::InvalidArgument(
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"Need least two elements in shape_and_slice specification: ",
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shape_and_slice);
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}
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// The last split is the slice specification.
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slice->Clear();
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auto status = slice->Parse(splits.back(), slice);
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if (!status.ok()) return status;
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// The first n-1 are the shape specification.
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splits.pop_back();
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shape->Clear();
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for (const auto& s : splits) {
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int64 dim;
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if (!strings::safe_strto64(s, &dim)) {
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return errors::InvalidArgument(
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"Non numerical dimension in shape_and_slice: ", shape_and_slice);
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}
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shape->AddDim(dim);
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}
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// The specified slice must be compatible with the specified shape.
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return slice->SliceTensorShape(*shape, shape_slice);
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}
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} // namespace checkpoint
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} // namespace tensorflow
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