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#include "tensorflow/core/framework/tensor_util.h"
#include <gtest/gtest.h>
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/public/tensor.h"
namespace tensorflow {
namespace {
TEST(TensorUtil, DeepCopy0d) {
Tensor x(DT_FLOAT, TensorShape({}));
x.scalar<float>()() = 10.0;
// Make y a deep copy of x and then change it.
Tensor y = tensor::DeepCopy(x);
y.scalar<float>()() = 20.0;
// x doesn't change
EXPECT_EQ(10.0, x.scalar<float>()());
// Change x.
x.scalar<float>()() = 30.0;
// Y doesn't change.
EXPECT_EQ(20.0, y.scalar<float>()());
Tensor z = tensor::DeepCopy(y);
// Change y.
y.scalar<float>()() = 40.0;
// The final states should all be different.
EXPECT_EQ(20.0, z.scalar<float>()());
EXPECT_EQ(30.0, x.scalar<float>()());
EXPECT_EQ(40.0, y.scalar<float>()());
// Should have the same shape and type.
EXPECT_EQ(TensorShape({}), x.shape());
EXPECT_EQ(TensorShape({}), y.shape());
EXPECT_EQ(TensorShape({}), z.shape());
EXPECT_EQ(DT_FLOAT, x.dtype());
EXPECT_EQ(DT_FLOAT, y.dtype());
EXPECT_EQ(DT_FLOAT, z.dtype());
}
TEST(TensorUtil, DeepCopy) {
Tensor x(DT_FLOAT, TensorShape({1}));
x.flat<float>()(0) = 10.0;
// Make y a deep copy of x and then change it.
Tensor y = tensor::DeepCopy(x);
y.flat<float>()(0) = 20.0;
// x doesn't change
EXPECT_EQ(10.0, x.flat<float>()(0));
// Change x.
x.flat<float>()(0) = 30.0;
// Y doesn't change.
EXPECT_EQ(20.0, y.flat<float>()(0));
Tensor z = tensor::DeepCopy(y);
// Change y.
y.flat<float>()(0) = 40.0;
// The final states should all be different.
EXPECT_EQ(20.0, z.flat<float>()(0));
EXPECT_EQ(30.0, x.flat<float>()(0));
EXPECT_EQ(40.0, y.flat<float>()(0));
// Should have the same shape and type.
EXPECT_EQ(TensorShape({1}), x.shape());
EXPECT_EQ(TensorShape({1}), y.shape());
EXPECT_EQ(TensorShape({1}), z.shape());
EXPECT_EQ(DT_FLOAT, x.dtype());
EXPECT_EQ(DT_FLOAT, y.dtype());
EXPECT_EQ(DT_FLOAT, z.dtype());
// Test string deep copy
Tensor str1(DT_STRING, TensorShape({2}));
str1.flat<string>()(0) = "foo1";
str1.flat<string>()(1) = "foo2";
Tensor str2 = tensor::DeepCopy(str1);
str2.flat<string>()(0) = "bar1";
str2.flat<string>()(1) = "bar2";
EXPECT_NE(str2.flat<string>()(0), str1.flat<string>()(0));
}
TEST(TensorUtil, DeepCopySlice) {
Tensor x(DT_INT32, TensorShape({10}));
x.flat<int32>().setConstant(1);
// Slice 'x' -- y still refers to the same buffer.
Tensor y = x.Slice(2, 6);
// Do a deep copy of y, which is a slice.
Tensor z = tensor::DeepCopy(y);
// Set x to be different.
x.flat<int32>().setConstant(2);
EXPECT_EQ(TensorShape({10}), x.shape());
EXPECT_EQ(TensorShape({4}), y.shape());
EXPECT_EQ(TensorShape({4}), z.shape());
EXPECT_EQ(DT_INT32, x.dtype());
EXPECT_EQ(DT_INT32, y.dtype());
EXPECT_EQ(DT_INT32, z.dtype());
// x and y should now all be '2', but z should be '1'.
for (int i = 0; i < 10; ++i) {
EXPECT_EQ(2, x.flat<int32>()(i));
}
for (int i = 0; i < 4; ++i) {
EXPECT_EQ(2, y.unaligned_flat<int32>()(i));
EXPECT_EQ(1, z.flat<int32>()(i));
}
}
TEST(TensorUtil, Concat) {
std::vector<int64> sizes = {1, 4, 5};
std::vector<Tensor> to_concat;
int64 total_size = 0;
int offset = 0;
for (int entry = 0; entry < sizes.size(); ++entry) {
const int64 size = sizes[entry];
Tensor tensor(DT_INT32, TensorShape({size, 2}));
for (int i = offset; i < offset + size; ++i) {
for (int j = 0; j < 2; ++j) {
tensor.matrix<int32>()(i - offset, j) = 2 * i + j;
}
}
to_concat.push_back(tensor);
total_size += size;
offset += size;
}
Tensor concated = tensor::Concat(to_concat);
ASSERT_EQ(TensorShape({total_size, 2}), concated.shape());
for (int i = 0; i < total_size; ++i) {
for (int j = 0; j < 2; ++j) {
EXPECT_EQ(2 * i + j, concated.matrix<int32>()(i, j));
}
}
}
TEST(TensorUtil, Split) {
Tensor to_split(DT_INT64, TensorShape({10, 2}));
for (int i = 0; i < 10; ++i) {
for (int j = 0; j < 2; ++j) {
to_split.matrix<int64>()(i, j) = 2 * i + j;
}
}
std::vector<int64> sizes = {1, 4, 5};
std::vector<Tensor> splits = tensor::Split(to_split, sizes);
ASSERT_EQ(sizes.size(), splits.size());
int offset = 0;
for (int entry = 0; entry < splits.size(); ++entry) {
const int64 size = sizes[entry];
const Tensor& split = splits[entry];
ASSERT_EQ(TensorShape({size, 2}), split.shape());
for (int i = offset; i < offset + size; ++i) {
for (int j = 0; j < 2; ++j) {
EXPECT_EQ(2 * i + j, split.matrix<int64>()(i - offset, j));
}
}
offset += size;
}
}
TEST(TensorUtil, ConcatSplitStrings) {
Tensor x(DT_STRING, TensorShape({4, 3}));
for (int i = 0; i < 4 * 3; ++i) {
x.flat<string>()(i) = strings::StrCat("foo_", i);
}
Tensor x_round_tripped = tensor::Concat(tensor::Split(x, {2, 1, 1}));
ASSERT_EQ(x.shape(), x_round_tripped.shape());
for (int i = 0; i < 4 * 3; ++i) {
EXPECT_EQ(x.flat<string>()(i), x_round_tripped.flat<string>()(i));
}
// Ensure that no memory is being shared between 'x' and 'x_round_tripped'.
for (int i = 0; i < 4 * 3; ++i) {
x_round_tripped.flat<string>()(i) = strings::StrCat("bar_", i);
}
for (int i = 0; i < 4 * 3; ++i) {
EXPECT_NE(x.flat<string>()(i), x_round_tripped.flat<string>()(i));
}
}
} // namespace
} // namespace tensorflow
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