experiments/STT-tensorflow
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Merge pull request #26641 from amitsrivastava78:interpreter_tc_2

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PiperOrigin-RevId: 242214108
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TensorFlower Gardener 2019-04-05 17:04:35 -07:00
commit aa63d2feb9
1 changed files with 81 additions and 1 deletions
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82
tensorflow/lite/interpreter_test.cc
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@ -1162,9 +1162,16 @@ class TestDelegate : public ::testing::Test {
[](TfLiteContext* context, TfLiteDelegate* delegate,
TfLiteBufferHandle buffer_handle,
TfLiteTensor* output) -> TfLiteStatus {
// TODO(ycling): Implement tests to test buffer copying logic.
TFLITE_CHECK_GE(buffer_handle, -1);
TFLITE_CHECK_EQ(output->buffer_handle, buffer_handle);
const float floats[] = {6., 6., 6.};
int num = output->dims->data[0];
for (int i = 0; i < num; i++) {
output->data.f[i] = floats[i];
}
return kTfLiteOk;
};
delegate_.FreeBufferHandle =
[](TfLiteContext* context, TfLiteDelegate* delegate,
TfLiteBufferHandle* handle) { *handle = kTfLiteNullBufferHandle; };
@ -1176,6 +1183,21 @@ class TestDelegate : public ::testing::Test {
static TfLiteRegistration FakeFusedRegistration() {
TfLiteRegistration reg = {nullptr};
reg.custom_name = "fake_fused_op";
reg.invoke = [](TfLiteContext* context,
TfLiteNode* node) -> TfLiteStatus {
// Copy input data to output data.
TfLiteTensor* a0 = &context->tensors[node->inputs->data[0]];
TfLiteTensor* a1 = &context->tensors[node->inputs->data[1]];
TfLiteTensor* out = &context->tensors[node->outputs->data[0]];
int num = a0->dims->data[0];
for (int i = 0; i < num; i++) {
out->data.f[i] = a0->data.f[i] + a1->data.f[i];
}
// Make the data stale so that CopyFromBufferHandle can be invoked
out->data_is_stale = true;
return kTfLiteOk;
};
return reg;
}
@ -1332,6 +1354,64 @@ TEST_F(TestDelegate, ResizeInputWithNonDynamicDelegateShouldFail) {
ASSERT_EQ(interpreter_->ResizeInputTensor(0, {1, 2}), kTfLiteError);
}
TEST_F(TestDelegate, TestCopyFromBufferInvoke) {
delegate_ = std::unique_ptr<SimpleDelegate>(new SimpleDelegate({0, 1, 2}));
TfLiteDelegate* delegate = delegate_->get_tf_lite_delegate();
interpreter_->ModifyGraphWithDelegate(delegate);
constexpr int kOutputTensorIndex = 3;
TfLiteTensor* tensor = interpreter_->tensor(kOutputTensorIndex);
std::vector<float> floats = {1.0f, 2.0f, 3.0f};
memcpy(interpreter_->typed_tensor<float>(0), floats.data(),
floats.size() * sizeof(float));
memcpy(interpreter_->typed_tensor<float>(1), floats.data(),
floats.size() * sizeof(float));
// Before setting the buffer handle, the tensor's `delegate` is already set
// because it will be written by the delegate.
ASSERT_EQ(tensor->delegate, delegate);
ASSERT_EQ(tensor->buffer_handle, kTfLiteNullBufferHandle);
// Called Invoke without setting the buffer will not call the CopyFromBuffer
interpreter_->Invoke();
std::vector<float> res = {2.0f, 4.0f, 6.0f};
for (int i = 0; i < tensor->dims->data[0]; ++i) {
ASSERT_EQ(tensor->data.f[i], res[i]);
}
}
TEST_F(TestDelegate, TestCopyFromBuffer) {
delegate_ = std::unique_ptr<SimpleDelegate>(new SimpleDelegate({0, 1, 2}));
TfLiteDelegate* delegate = delegate_->get_tf_lite_delegate();
interpreter_->ModifyGraphWithDelegate(delegate);
constexpr int kOutputTensorIndex = 3;
TfLiteTensor* tensor = interpreter_->tensor(kOutputTensorIndex);
std::vector<float> floats = {1.0f, 2.0f, 3.0f};
memcpy(interpreter_->typed_tensor<float>(0), floats.data(),
floats.size() * sizeof(float));
memcpy(interpreter_->typed_tensor<float>(1), floats.data(),
floats.size() * sizeof(float));
// Before setting the buffer handle, the tensor's `delegate` is already set
// because it will be written by the delegate.
ASSERT_EQ(tensor->delegate, delegate);
ASSERT_EQ(tensor->buffer_handle, kTfLiteNullBufferHandle);
TfLiteBufferHandle handle = AllocateBufferHandle();
TfLiteStatus status =
interpreter_->SetBufferHandle(kOutputTensorIndex, handle, delegate);
interpreter_->Invoke();
ASSERT_EQ(status, kTfLiteOk);
EXPECT_EQ(tensor->delegate, delegate);
EXPECT_EQ(tensor->buffer_handle, handle);
for (int i = 0; i < tensor->dims->data[0]; ++i) {
ASSERT_EQ(tensor->data.f[i], 6.0f);
}
}
class TestDelegateWithDynamicTensors : public ::testing::Test {
protected:
void SetUp() override {