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Remove unused LoDTensor methods (#7247)

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* Remove unused LoDTensor methods

* Update
detection_output_fixbug
Yu Yang 7 years ago committed by GitHub
parent 274bfcb6c6
commit 1dad4bb2e9
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3 changed files with 0 additions and 172 deletions
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49
paddle/framework/lod_tensor.cc
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@ -59,18 +59,6 @@ std::ostream &operator<<(std::ostream &os, const LoDTensor &t) {
return os;
}
LoD SliceLevels(const LoD &in, size_t level_begin, size_t level_end) {
LoD new_lod;
new_lod.reserve(level_end - level_begin);
for (size_t i = level_begin; i < level_end; i++) {
new_lod.emplace_back(in.at(i));
}
// transform the lowest level to absolute offset.
LoD abs_offset_lod = ToAbsOffset(in);
new_lod.back() = abs_offset_lod[level_end - 1];
return new_lod;
}
LoD SliceInLevel(const LoD &in, size_t level, size_t elem_begin,
size_t elem_end) {
PADDLE_ENFORCE_LT(level, in.size());
@ -131,43 +119,6 @@ bool operator==(const LoD &a, const LoD &b) {
return true;
}
size_t LoDTensor::NumElements(size_t level, size_t idx) const {
PADDLE_ENFORCE_LT(level, NumLevels());
PADDLE_ENFORCE_LT(idx, NumElements(level));
return lod_[level][idx + 1] - lod_[level][idx];
}
size_t LoDTensor::NumInstancesInElement(size_t level, size_t idx) const {
PADDLE_ENFORCE_LT(level, NumLevels());
PADDLE_ENFORCE_LT(idx, NumElements(level));
auto abs_lod = ToAbsOffset(lod());
size_t begin = abs_lod[level][idx];
size_t end = abs_lod[level][idx + 1];
return end - begin;
}
void LoDTensor::ShrinkLevels(size_t level_begin, size_t level_end) {
auto new_lod = framework::SliceLevels(lod_, level_begin, level_end);
lod_ = new_lod;
}
void LoDTensor::ShrinkInLevel(size_t level, size_t elem_begin,
size_t elem_end) {
PADDLE_ENFORCE_LT(level, NumLevels());
PADDLE_ENFORCE_LT(elem_begin, NumElements(level));
PADDLE_ENFORCE_LT(elem_end, NumElements(level) + 1);
auto abs_lod = framework::ToAbsOffset(lod());
auto new_lod = framework::SliceInLevel(lod_, level, elem_begin, elem_end);
lod_ = new_lod;
// slice the underlying tensor
size_t begin = abs_lod[level][elem_begin];
size_t end = abs_lod[level][elem_end];
PADDLE_ENFORCE_LT(begin, end, "Cannot shrink, the result tensor is empty.");
ShareDataWith(Slice(begin, end));
}
using LoDAndOffset = std::pair<LoD, std::pair<size_t, size_t>>;
LoDAndOffset GetSubLoDAndAbsoluteOffset(const LoD &lod, size_t start_idx,
size_t end_idx, size_t start_level) {

37
paddle/framework/lod_tensor.h
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@ -60,14 +60,6 @@ using LoD = std::vector<Vector<size_t>>;
std::ostream& operator<<(std::ostream& os, const LoD& lod);
std::ostream& operator<<(std::ostream& os, const LoDTensor& t);
/*
* Slice levels from a LoD.
* NOTE the lowest level should always be the absolute offsets of the underlying
* tensor instances. So if higher layers are sliced without the lowest level,
* the lower level of the sliced LoD will be transformed to the absolute offset.
*/
LoD SliceLevels(const LoD& in, size_t level_begin, size_t level_end);
LoD SliceInLevel(const LoD& in, size_t level, size_t elem_begin,
size_t elem_end);
/*
@ -116,35 +108,6 @@ class LoDTensor : public Tensor {
return (lod_)[level].size() - 1;
}
/*
* Number of lower-level elements.
* For example, a 2-level lod-tensor
*
* 0-th level | |
* 1-th level || |||
*
* NumElements(0, 0) get 2
* NumElements(0, 1) get 3
*/
size_t NumElements(size_t level, size_t idx) const;
/*
* Get the number of instances in the underlying tensor in the `idx`-th
* element.
*/
size_t NumInstancesInElement(size_t level, size_t idx) const;
/*
* Shrink levels[level_begin:level_end]
*/
void ShrinkLevels(size_t level_begin, size_t level_end);
/*
* Shrink elements of a level, [elem_begin: elem_end]
* @note: low performance in slice lod_.
*/
void ShrinkInLevel(size_t level, size_t elem_begin, size_t elem_end);
std::vector<LoDTensor> SplitLoDTensor(
const std::vector<platform::Place> places) const;

86
paddle/framework/lod_tensor_test.cc
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@ -54,92 +54,6 @@ class LoDTensorTester : public ::testing::Test {
LoDTensor lod_tensor_;
};
TEST_F(LoDTensorTester, NumLevels) { ASSERT_EQ(lod_tensor_.NumLevels(), 3UL); }
TEST_F(LoDTensorTester, NumElements) {
ASSERT_EQ(lod_tensor_.NumElements(0), 2UL);
ASSERT_EQ(lod_tensor_.NumElements(1), 3UL);
ASSERT_EQ(lod_tensor_.NumElements(2), 8UL);
}
TEST_F(LoDTensorTester, NumElements2) {
ASSERT_EQ(lod_tensor_.NumElements(0, 0), 2UL);
ASSERT_EQ(lod_tensor_.NumElements(0, 1), 1UL);
ASSERT_EQ(lod_tensor_.NumElements(1, 1), 3UL);
}
TEST_F(LoDTensorTester, ShrinkLevels) {
// slice 1 level
for (size_t level = 0; level < 3UL; ++level) {
LoDTensor new_lod_tensor = lod_tensor_;
new_lod_tensor.ShrinkLevels(level, level + 1);
ASSERT_EQ(new_lod_tensor.NumLevels(), 1UL);
ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor_.data<float>());
}
// shrink 2 level
for (size_t level = 0; level < 2UL; ++level) {
LoDTensor new_lod_tensor = lod_tensor_;
new_lod_tensor.ShrinkLevels(level, level + 2);
// the lowest level's last element should be the tensor's batch_size.
ASSERT_EQ(new_lod_tensor.lod().back().back(),
lod_tensor_.lod().back().back());
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor_.data<float>());
}
}
TEST_F(LoDTensorTester, ShrinkInLevel) {
size_t level = 0;
LoDTensor new_lod_tensor = lod_tensor_;
new_lod_tensor.ShrinkInLevel(level, 0, 1);
ASSERT_EQ(new_lod_tensor.NumLevels(), 3UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), 1UL);
ASSERT_EQ(new_lod_tensor.NumElements(1), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(2), 5UL);
ASSERT_EQ(new_lod_tensor.dims()[0], 12);
for (int i = 0; i < 12 * 128; i++) {
ASSERT_EQ(new_lod_tensor.data<float>()[i], i);
}
level = 1;
new_lod_tensor = lod_tensor_;
new_lod_tensor.ShrinkInLevel(level, 1, 2);
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), 1UL);
ASSERT_EQ(new_lod_tensor.NumElements(1), 3UL);
ASSERT_EQ(new_lod_tensor.dims()[0], 7);
for (int i = 5 * 128; i < 12 * 128; i++) {
ASSERT_EQ(new_lod_tensor.data<float>()[i - 5 * 128], i);
}
LoDTensor t1;
t1.set_lod(lod_tensor_.lod());
t1.ShareDataWith(lod_tensor_);
LoDTensor t2;
t2.set_lod(lod_tensor_.lod());
t2.ShareDataWith(lod_tensor_);
t1.ShrinkInLevel(0, 1, 2);
t2.ShrinkInLevel(0, 0, 1);
EXPECT_NE(t1.data<float>(), t2.data<float>());
EXPECT_NE(t1.data<float>(), lod_tensor_.data<float>());
}
TEST_F(LoDTensorTester, SerializeAndDeserialize) {
LoDTensor dst_tensor;
platform::CPUDeviceContext cpu_ctx((platform::CPUPlace()));
std::ostringstream oss;
SerializeToStream(oss, lod_tensor_, cpu_ctx);
std::istringstream iss(oss.str());
DeserializeFromStream(iss, &dst_tensor, cpu_ctx);
float* dst_ptr = dst_tensor.mutable_data<float>(platform::CPUPlace());
for (int i = 0; i < kLodTensorSize; ++i) {
EXPECT_EQ(dst_ptr[i], i);
}
EXPECT_EQ(dst_tensor.lod(), lod_tensor_.lod());
}
TEST(LodExpand, test) {
LoD lod{{0, 2}};
LoDTensor tensor;

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