From bec38f2829a81dc512d5062070d50516039bea0a Mon Sep 17 00:00:00 2001
From: Robert David <lrdx@google.com>
Date: Tue, 8 Sep 2020 17:37:10 -0700
Subject: [PATCH] Separate AddVariableInput and AddInput functions in
test_util.h/.cc
Some of the usages were wrong before; the bool input was invoked as {}.
PiperOrigin-RevId: 330623020
Change-Id: I9505c199297d6a1e7465c0610184652168f61430
---
.../delegates/nnapi/nnapi_delegate_test.cc | 11 +++----
.../unidirectional_sequence_gru_test.cc | 2 +-
tensorflow/lite/kernels/basic_rnn_test.cc | 2 +-
.../bidirectional_sequence_lstm_test.cc | 20 +++++-------
.../bidirectional_sequence_rnn_test.cc | 4 +--
tensorflow/lite/kernels/lstm_test.cc | 32 ++++++++-----------
.../lite/kernels/non_max_suppression_test.cc | 4 +--
.../lite/kernels/optional_tensor_test.cc | 6 ++--
.../lite/kernels/quant_basic_lstm_test.cc | 10 +++---
tensorflow/lite/kernels/svdf_test.cc | 10 +++---
tensorflow/lite/kernels/test_util.cc | 15 +++++++--
tensorflow/lite/kernels/test_util.h | 7 ++--
.../unidirectional_sequence_lstm_test.cc | 9 +++---
.../unidirectional_sequence_rnn_test.cc | 2 +-
14 files changed, 64 insertions(+), 70 deletions(-)
diff --git a/tensorflow/lite/delegates/nnapi/nnapi_delegate_test.cc b/tensorflow/lite/delegates/nnapi/nnapi_delegate_test.cc
index fe022199dd6..205a44991dc 100644
--- a/tensorflow/lite/delegates/nnapi/nnapi_delegate_test.cc
+++ b/tensorflow/lite/delegates/nnapi/nnapi_delegate_test.cc
@@ -2682,7 +2682,7 @@ class RNNOpModel : public SingleOpModelWithNNAPI {
weights_ = AddInput(weights);
recurrent_weights_ = AddInput(recurrent_weights);
bias_ = AddInput(TensorType_FLOAT32);
- hidden_state_ = AddInput(TensorType_FLOAT32, true);
+ hidden_state_ = AddVariableInput(TensorType_FLOAT32);
output_ = AddOutput(TensorType_FLOAT32);
SetBuiltinOp(
BuiltinOperator_RNN, BuiltinOptions_RNNOptions,
@@ -2872,9 +2872,8 @@ class BaseSVDFOpModel : public SingleOpModelWithNNAPI {
// when using NNAPI delegate.
bias_ = AddInput(TensorType_FLOAT32);
const int num_filters = units * rank;
- activation_state_ = AddInput(
- TensorData{TensorType_FLOAT32, {batches, memory_size * num_filters}},
- /*is_variable=*/true);
+ activation_state_ = AddVariableInput(
+ TensorData{TensorType_FLOAT32, {batches, memory_size * num_filters}});
output_ = AddOutput(TensorType_FLOAT32);
SetBuiltinOp(
BuiltinOperator_SVDF, BuiltinOptions_SVDFOptions,
@@ -3098,8 +3097,8 @@ class LSTMOpModel : public SingleOpModelWithNNAPI {
}
// Adding the 2 input state tensors.
- input_activation_state_ = AddInput(TensorType_FLOAT32, true);
- input_cell_state_ = AddInput(TensorType_FLOAT32, true);
+ input_activation_state_ = AddVariableInput(TensorType_FLOAT32);
+ input_cell_state_ = AddVariableInput(TensorType_FLOAT32);
const bool use_layer_norm = input_shapes.size() > 20;
// Layer norm weights.
diff --git a/tensorflow/lite/experimental/kernels/unidirectional_sequence_gru_test.cc b/tensorflow/lite/experimental/kernels/unidirectional_sequence_gru_test.cc
index 593d714e557..0436636a0af 100644
--- a/tensorflow/lite/experimental/kernels/unidirectional_sequence_gru_test.cc
+++ b/tensorflow/lite/experimental/kernels/unidirectional_sequence_gru_test.cc
@@ -37,7 +37,7 @@ class GRUOpModel : public SingleOpModel {
: n_batch_(n_batch), n_input_(n_input), n_output_(n_output) {
input_ = AddInput(TensorType_FLOAT32);
input_state_ =
- AddInput(TensorData{TensorType_FLOAT32, {n_batch, n_output}}, true);
+ AddVariableInput(TensorData{TensorType_FLOAT32, {n_batch, n_output}});
gate_weight_ = AddInput(TensorType_FLOAT32);
gate_bias_ = AddInput(TensorType_FLOAT32);
candidate_weight_ = AddInput(TensorType_FLOAT32);
diff --git a/tensorflow/lite/kernels/basic_rnn_test.cc b/tensorflow/lite/kernels/basic_rnn_test.cc
index 2146d086c9a..675c7dcbf5b 100644
--- a/tensorflow/lite/kernels/basic_rnn_test.cc
+++ b/tensorflow/lite/kernels/basic_rnn_test.cc
@@ -179,7 +179,7 @@ class RNNOpModel : public SingleOpModel {
weights_ = AddInput(weights);
recurrent_weights_ = AddInput(recurrent_weights);
bias_ = AddInput(TensorType_FLOAT32);
- hidden_state_ = AddInput(TensorType_FLOAT32, true);
+ hidden_state_ = AddVariableInput(TensorType_FLOAT32);
output_ = AddOutput(TensorType_FLOAT32);
SetBuiltinOp(BuiltinOperator_RNN, BuiltinOptions_RNNOptions,
CreateRNNOptions(builder_, ActivationFunctionType_RELU,
diff --git a/tensorflow/lite/kernels/bidirectional_sequence_lstm_test.cc b/tensorflow/lite/kernels/bidirectional_sequence_lstm_test.cc
index 778751aa04b..6f47fd0d315 100644
--- a/tensorflow/lite/kernels/bidirectional_sequence_lstm_test.cc
+++ b/tensorflow/lite/kernels/bidirectional_sequence_lstm_test.cc
@@ -160,20 +160,16 @@ class BidirectionalLSTMOpModel : public SingleOpModel {
}
// Adding the 2 input state tensors.
- fw_input_activation_state_ =
- AddInput(TensorData{TensorType_FLOAT32, {n_fw_output_ * n_batch_}},
- /*is_variable=*/true);
- fw_input_cell_state_ =
- AddInput(TensorData{TensorType_FLOAT32, {n_fw_cell_ * n_batch_}},
- /*is_variable=*/true);
+ fw_input_activation_state_ = AddVariableInput(
+ TensorData{TensorType_FLOAT32, {n_fw_output_ * n_batch_}});
+ fw_input_cell_state_ = AddVariableInput(
+ TensorData{TensorType_FLOAT32, {n_fw_cell_ * n_batch_}});
// Adding the 2 input state tensors.
- bw_input_activation_state_ =
- AddInput(TensorData{TensorType_FLOAT32, {n_bw_output_ * n_batch_}},
- /*is_variable=*/true);
- bw_input_cell_state_ =
- AddInput(TensorData{TensorType_FLOAT32, {n_bw_cell_ * n_batch_}},
- /*is_variable=*/true);
+ bw_input_activation_state_ = AddVariableInput(
+ TensorData{TensorType_FLOAT32, {n_bw_output_ * n_batch_}});
+ bw_input_cell_state_ = AddVariableInput(
+ TensorData{TensorType_FLOAT32, {n_bw_cell_ * n_batch_}});
fw_output_ = AddOutput(TensorType_FLOAT32);
diff --git a/tensorflow/lite/kernels/bidirectional_sequence_rnn_test.cc b/tensorflow/lite/kernels/bidirectional_sequence_rnn_test.cc
index 870b99d7437..e683a2a2271 100644
--- a/tensorflow/lite/kernels/bidirectional_sequence_rnn_test.cc
+++ b/tensorflow/lite/kernels/bidirectional_sequence_rnn_test.cc
@@ -680,11 +680,11 @@ class BidirectionalRNNOpModel : public SingleOpModel {
fw_weights_ = AddInput(tensor_type);
fw_recurrent_weights_ = AddInput(tensor_type);
fw_bias_ = AddInput(TensorType_FLOAT32);
- fw_hidden_state_ = AddInput(TensorType_FLOAT32, true);
+ fw_hidden_state_ = AddVariableInput(TensorType_FLOAT32);
bw_weights_ = AddInput(tensor_type);
bw_recurrent_weights_ = AddInput(tensor_type);
bw_bias_ = AddInput(TensorType_FLOAT32);
- bw_hidden_state_ = AddInput(TensorType_FLOAT32, true);
+ bw_hidden_state_ = AddVariableInput(TensorType_FLOAT32);
const auto input_shape =
(time_major) ? std::vector<int>({sequence_len_, batches_, input_size_})
diff --git a/tensorflow/lite/kernels/lstm_test.cc b/tensorflow/lite/kernels/lstm_test.cc
index edd6002cfcb..ccdc8193f09 100644
--- a/tensorflow/lite/kernels/lstm_test.cc
+++ b/tensorflow/lite/kernels/lstm_test.cc
@@ -101,8 +101,8 @@ class LSTMOpModel : public SingleOpModel {
}
// Adding the 2 state tensors.
- AddInput({TensorType_FLOAT32, {n_batch, n_output}}, true);
- AddInput({TensorType_FLOAT32, {n_batch, n_cell}}, true);
+ AddVariableInput({TensorType_FLOAT32, {n_batch, n_output}});
+ AddVariableInput({TensorType_FLOAT32, {n_batch, n_cell}});
// Layer norm weights.
if (!model_has_legacy_20_inputs) {
@@ -1412,16 +1412,14 @@ class LSTMIntegerOpModel : public SingleOpModel {
}
// Adding the 2 state tensors.
- AddInput({TensorType_INT16,
- {n_batch, n_output},
- ranges[18].first,
- ranges[18].second},
- true);
- AddInput({TensorType_INT16,
- {n_batch, n_cell},
- ranges[19].first,
- ranges[19].second},
- true);
+ AddVariableInput({TensorType_INT16,
+ {n_batch, n_output},
+ ranges[18].first,
+ ranges[18].second});
+ AddVariableInput({TensorType_INT16,
+ {n_batch, n_cell},
+ ranges[19].first,
+ ranges[19].second});
// Layer norm weights.
if (use_layer_norm) {
@@ -2204,12 +2202,10 @@ class HybridSparseLSTMOpModel : public ::tflite::SingleOpModel {
}
// Adding the 2 state tensors.
- output_state_ = AddInput(::tflite::TensorData{::tflite::TensorType_FLOAT32,
- {n_output_ * n_batch_}},
- true);
- cell_state_ = AddInput(::tflite::TensorData{::tflite::TensorType_FLOAT32,
- {n_cell_ * n_batch_}},
- true);
+ output_state_ = AddVariableInput(::tflite::TensorData{
+ ::tflite::TensorType_FLOAT32, {n_output_ * n_batch_}});
+ cell_state_ = AddVariableInput(::tflite::TensorData{
+ ::tflite::TensorType_FLOAT32, {n_cell_ * n_batch_}});
if (use_cifg) {
input_layer_norm_weights_ = AddNullInput();
diff --git a/tensorflow/lite/kernels/non_max_suppression_test.cc b/tensorflow/lite/kernels/non_max_suppression_test.cc
index 9b7baa147e5..3ca54010fcd 100644
--- a/tensorflow/lite/kernels/non_max_suppression_test.cc
+++ b/tensorflow/lite/kernels/non_max_suppression_test.cc
@@ -77,7 +77,7 @@ class NonMaxSuppressionV4OpModel : public BaseNMSOp {
input_max_output_size_ =
AddConstInput(TensorType_INT32, {max_output_size});
} else {
- input_max_output_size_ = AddInput(TensorType_INT32, {});
+ input_max_output_size_ = AddInput(TensorType_INT32);
}
input_iou_threshold_ = AddConstInput(TensorType_FLOAT32, {iou_threshold});
input_score_threshold_ = AddInput({TensorType_FLOAT32, {}});
@@ -168,7 +168,7 @@ class NonMaxSuppressionV5OpModel : public BaseNMSOp {
input_max_output_size_ =
AddConstInput(TensorType_INT32, {max_output_size});
} else {
- input_max_output_size_ = AddInput(TensorType_INT32, {});
+ input_max_output_size_ = AddInput(TensorType_INT32);
}
input_iou_threshold_ = AddConstInput(TensorType_FLOAT32, {iou_threshold});
input_score_threshold_ = AddInput({TensorType_FLOAT32, {}});
diff --git a/tensorflow/lite/kernels/optional_tensor_test.cc b/tensorflow/lite/kernels/optional_tensor_test.cc
index 9e83c74da8d..1468c89e375 100644
--- a/tensorflow/lite/kernels/optional_tensor_test.cc
+++ b/tensorflow/lite/kernels/optional_tensor_test.cc
@@ -94,10 +94,10 @@ class LSTMOpModel : public SingleOpModel {
}
// Adding the 2 input state tensors.
- input_activation_state_ =
- AddInput(TensorData{TensorType_FLOAT32, {n_output_ * n_batch_}}, true);
+ input_activation_state_ = AddVariableInput(
+ TensorData{TensorType_FLOAT32, {n_output_ * n_batch_}});
input_cell_state_ =
- AddInput(TensorData{TensorType_FLOAT32, {n_cell_ * n_batch_}}, true);
+ AddVariableInput(TensorData{TensorType_FLOAT32, {n_cell_ * n_batch_}});
output_ = AddOutput(TensorType_FLOAT32);
diff --git a/tensorflow/lite/kernels/quant_basic_lstm_test.cc b/tensorflow/lite/kernels/quant_basic_lstm_test.cc
index 3e081c221c5..787cb6d2e97 100644
--- a/tensorflow/lite/kernels/quant_basic_lstm_test.cc
+++ b/tensorflow/lite/kernels/quant_basic_lstm_test.cc
@@ -61,9 +61,8 @@ class QuantizedLSTMOpModel : public MultiOpModel {
input_ = AddInput(input_tensor_data);
- prev_output_ =
- AddInput({TensorType_UINT8, output_shape, 0.0f, 0.0f, 1. / 128., 128},
- /*is_variable=*/true);
+ prev_output_ = AddVariableInput(
+ {TensorType_UINT8, output_shape, 0.0f, 0.0f, 1. / 128., 128});
// Biases and Weights have to be constant in order to allow NNAPI
// delegation
weights_ = AddConstInput<uint8_t>({TensorType_UINT8, weight_shape, 0.0f,
@@ -72,9 +71,8 @@ class QuantizedLSTMOpModel : public MultiOpModel {
biases_ = AddConstInput<int32_t>(
{TensorType_INT32, bias_shape, 0.0f, 0.0f, weightsScale / 128, 0},
biases);
- prev_cell_state_ =
- AddInput({TensorType_INT16, state_shape, 0.0f, 0.0f, 1. / 2048., 0},
- /*is_variable=*/true);
+ prev_cell_state_ = AddVariableInput(
+ {TensorType_INT16, state_shape, 0.0f, 0.0f, 1. / 2048., 0});
sum_out_ = AddOutput(input_tensor_data);
diff --git a/tensorflow/lite/kernels/svdf_test.cc b/tensorflow/lite/kernels/svdf_test.cc
index b0ac2011948..c13810d3b75 100644
--- a/tensorflow/lite/kernels/svdf_test.cc
+++ b/tensorflow/lite/kernels/svdf_test.cc
@@ -143,9 +143,8 @@ class BaseSVDFOpModel : public SingleOpModel {
weights_time_ = AddInput(weights_time_type);
bias_ = AddNullInput();
const int num_filters = units * rank;
- activation_state_ = AddInput(
- TensorData{TensorType_FLOAT32, {batches, memory_size * num_filters}},
- /*is_variable=*/true);
+ activation_state_ = AddVariableInput(
+ TensorData{TensorType_FLOAT32, {batches, memory_size * num_filters}});
output_ = AddOutput(TensorType_FLOAT32);
SetBuiltinOp(BuiltinOperator_SVDF, BuiltinOptions_SVDFOptions,
CreateSVDFOptions(builder_, rank, ActivationFunctionType_NONE,
@@ -482,9 +481,8 @@ class IntegerSVDFOpModel : public SingleOpModel {
weights_time_ =
AddInput({TensorType_INT16, {num_filters, memory_size}, -1, 1});
bias_ = AddInput({TensorType_INT32, {units}, -512, 512});
- activation_state_ = AddInput(
- {TensorType_INT16, {batches, memory_size * num_filters}, -16, 16},
- /*is_variable=*/true);
+ activation_state_ = AddVariableInput(
+ {TensorType_INT16, {batches, memory_size * num_filters}, -16, 16});
output_ = AddOutput({TensorType_INT8, {batches, units}, -0.5, 0.5});
SetBuiltinOp(
BuiltinOperator_SVDF, BuiltinOptions_SVDFOptions,
diff --git a/tensorflow/lite/kernels/test_util.cc b/tensorflow/lite/kernels/test_util.cc
index cfa55575604..d8e1e84f938 100644
--- a/tensorflow/lite/kernels/test_util.cc
+++ b/tensorflow/lite/kernels/test_util.cc
@@ -79,12 +79,23 @@ std::vector<Matcher<std::complex<float>>> ArrayComplex64Near(
return matchers;
}
-int SingleOpModel::AddInput(const TensorData& t, bool is_variable) {
+int SingleOpModel::AddInput(const TensorData& t) {
int id = 0;
if (t.per_channel_quantization) {
id = AddTensorPerChannelQuant(t);
} else {
- id = AddTensor<float>(t, {}, is_variable);
+ id = AddTensor<float>(t, {});
+ }
+ inputs_.push_back(id);
+ return id;
+}
+
+int SingleOpModel::AddVariableInput(const TensorData& t) {
+ int id = 0;
+ if (t.per_channel_quantization) {
+ id = AddTensorPerChannelQuant(t);
+ } else {
+ id = AddTensor<float>(t, {}, true);
}
inputs_.push_back(id);
return id;
diff --git a/tensorflow/lite/kernels/test_util.h b/tensorflow/lite/kernels/test_util.h
index 63193b021aa..4f4195cf4de 100644
--- a/tensorflow/lite/kernels/test_util.h
+++ b/tensorflow/lite/kernels/test_util.h
@@ -188,10 +188,8 @@ class SingleOpModel {
SingleOpModel& operator=(const SingleOpModel&) = delete;
// Add a TensorType input tensor and return its index.
- int AddInput(TensorType type, bool is_variable = false) {
- return AddInput(TensorData{type}, is_variable);
- }
- int AddInput(const TensorData& t, bool is_variable = false);
+ int AddInput(const TensorData& t);
+ int AddVariableInput(const TensorData& t);
int AddIntermediate(TensorType type, const std::vector<float>& scale,
const std::vector<int64_t>& zero_point);
@@ -378,7 +376,6 @@ class SingleOpModel {
int AddNullInput();
// Add a TensorType output tensor and return its index.
- int AddOutput(TensorType type) { return AddOutput(TensorData{type}); }
int AddOutput(const TensorData& t);
template <typename T>
diff --git a/tensorflow/lite/kernels/unidirectional_sequence_lstm_test.cc b/tensorflow/lite/kernels/unidirectional_sequence_lstm_test.cc
index 74584ec9e85..90a96ca98fe 100644
--- a/tensorflow/lite/kernels/unidirectional_sequence_lstm_test.cc
+++ b/tensorflow/lite/kernels/unidirectional_sequence_lstm_test.cc
@@ -101,11 +101,10 @@ class UnidirectionalLSTMOpModel : public SingleOpModel {
}
// Adding the 2 state tensors.
- output_state_ =
- AddInput(TensorData{TensorType_FLOAT32, {n_output_ * n_batch_}},
- /*is_variable=*/true);
- cell_state_ = AddInput(TensorData{TensorType_FLOAT32, {n_cell_ * n_batch_}},
- /*is_variable=*/true);
+ output_state_ = AddVariableInput(
+ TensorData{TensorType_FLOAT32, {n_output_ * n_batch_}});
+ cell_state_ =
+ AddVariableInput(TensorData{TensorType_FLOAT32, {n_cell_ * n_batch_}});
// Layer norm weights.
if (is_layer_norm) {
diff --git a/tensorflow/lite/kernels/unidirectional_sequence_rnn_test.cc b/tensorflow/lite/kernels/unidirectional_sequence_rnn_test.cc
index f1486267c17..8ae562ea0b0 100644
--- a/tensorflow/lite/kernels/unidirectional_sequence_rnn_test.cc
+++ b/tensorflow/lite/kernels/unidirectional_sequence_rnn_test.cc
@@ -183,7 +183,7 @@ class UnidirectionalRNNOpModel : public SingleOpModel {
weights_ = AddInput(weights);
recurrent_weights_ = AddInput(recurrent_weights);
bias_ = AddInput(TensorType_FLOAT32);
- hidden_state_ = AddInput(TensorType_FLOAT32, true);
+ hidden_state_ = AddVariableInput(TensorType_FLOAT32);
output_ = AddOutput(TensorType_FLOAT32);
SetBuiltinOp(BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN,
BuiltinOptions_SequenceRNNOptions,