experiments/STT-tensorflow
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minor spelling tweaks

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This commit is contained in:
Kazuaki Ishizaki 2020-03-19 02:24:31 +09:00
parent 56944a8148
commit c4a4aab605
12 changed files with 46 additions and 46 deletions

2
tensorflow/lite/tools/accuracy/ilsvrc/generate_validation_labels.py
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@ -88,7 +88,7 @@ def main():
parser.add_argument(
'--ilsvrc_devkit_dir',
type=str,
help='Full path to ILSVRC 2012 devikit directory.')
help='Full path to ILSVRC 2012 devkit directory.')
args = parser.parse_args()
try:
_check_arguments(args)

8
tensorflow/lite/tools/benchmark/benchmark_test.cc
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@ -325,10 +325,10 @@ TEST(BenchmarkTest, DoesntCrashWithExplicitInputValueFilesStringModel) {
class MaxDurationWorksTestListener : public BenchmarkListener {
void OnBenchmarkEnd(const BenchmarkResults& results) override {
const int64_t num_actul_runs = results.inference_time_us().count();
TFLITE_LOG(INFO) << "number of actual runs: " << num_actul_runs;
EXPECT_GE(num_actul_runs, 1);
EXPECT_LT(num_actul_runs, 100000000);
const int64_t num_actual_runs = results.inference_time_us().count();
TFLITE_LOG(INFO) << "number of actual runs: " << num_actual_runs;
EXPECT_GE(num_actual_runs, 1);
EXPECT_LT(num_actual_runs, 100000000);
}
};

2
tensorflow/lite/tools/benchmark/benchmark_tflite_model.cc
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@ -488,7 +488,7 @@ BenchmarkTfLiteModel::CreateRandomTensorData(const TfLiteTensor& t,
#else
// You need to build with -DTFLITE_ENABLE_FP16_CPU_BENCHMARKS=1 using a
// compiler that supports __fp16 type. Note: when using Clang and *not*
// linking with compiler-rt, a defintion of __gnu_h2f_ieee and
// linking with compiler-rt, a definition of __gnu_h2f_ieee and
// __gnu_f2h_ieee must be supplied.
TFLITE_LOG(FATAL) << "Populating the tensor " << t.name
<< " of type FLOAT16 is disabled.";

4
tensorflow/lite/tools/evaluation/evaluation_stage.h
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@ -24,8 +24,8 @@ namespace evaluation {
// Superclass for a single stage of an EvaluationPipeline.
// Defines basic skeleton for sub-classes to implement.
//
// Ideally EvaluationStages should obtain access to initilizer/input objects via
// Get/Set methods on pointers, and not take ownership unless necessary.
// Ideally EvaluationStages should obtain access to initializer/input objects
// via Get/Set methods on pointers, and not take ownership unless necessary.
class EvaluationStage {
public:
// Initializes an EvaluationStage, including verifying the

2
tensorflow/lite/tools/evaluation/stages/topk_accuracy_eval_stage.h
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@ -62,7 +62,7 @@ class TopkAccuracyEvalStage : public EvaluationStage {
private:
// Updates accuracy_counts_ based on comparing top k labels and the
// groundtruth one. Using string comparision since there are some duplicate
// groundtruth one. Using string comparison since there are some duplicate
// labels in the imagenet dataset.
void UpdateCounts(const std::vector<int>& topk_indices);

2
tensorflow/lite/tools/evaluation/stages/utils/image_metrics_test.cc
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@ -128,7 +128,7 @@ TEST(ImageMetricsTest, BBoxAPwithIgnoredGroundTruth) {
pd.push_back({false, 100, 0.95, {{0.9, 1.9}, {0.9, 1.9}}});
// Two gt and three pd, one pair get ignored. So it's actuallly one gt with
// Two gt and three pd, one pair get ignored. So it's actually one gt with
// two pd.
EXPECT_NEAR(0.5, AveragePrecision().FromBoxes(gt, pd), 1e-6);
gt[0].ignore = kIgnoreAllMatches;

2
tensorflow/lite/tools/gen_op_registration_test.cc
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@ -36,7 +36,7 @@ class GenOpRegistrationTest : public ::testing::Test {
std::map<string, std::pair<int, int>> custom_ops_;
};
TEST_F(GenOpRegistrationTest, TestNonExistantFiles) {
TEST_F(GenOpRegistrationTest, TestNonExistentFiles) {
ReadOps("/tmp/tflite_model_1234");
EXPECT_EQ(builtin_ops_.size(), 0);
EXPECT_EQ(custom_ops_.size(), 0);

24
tensorflow/lite/tools/optimize/calibration/builtin_logging_ops/lstm.cc
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@ -65,7 +65,7 @@ inline void LstmStepWithAuxInput(
float* output_state_ptr, float* cell_state_ptr, float* input_gate_scratch,
float* forget_gate_scratch, float* cell_scratch, float* output_gate_scratch,
float* output_ptr, Logger* logger,
const std::vector<int>& intemediate_tensor_indexes,
const std::vector<int>& intermediate_tensor_indexes,
ErrorReporter* error_reporter) {
// Since we have already checked that weights are all there or none, we can
// check the existence of only one to the get the condition.
@ -155,7 +155,7 @@ inline void LstmStepWithAuxInput(
input_gate_scratch);
}
if (use_layer_norm) {
logger->LogTensorValue(intemediate_tensor_indexes[0], input_gate_scratch,
logger->LogTensorValue(intermediate_tensor_indexes[0], input_gate_scratch,
n_cell * n_batch, error_reporter);
tensor_utils::MeanStddevNormalization(
input_gate_scratch, input_gate_scratch, n_cell, n_batch);
@ -176,7 +176,7 @@ inline void LstmStepWithAuxInput(
forget_gate_scratch);
}
if (use_layer_norm) {
logger->LogTensorValue(intemediate_tensor_indexes[1], forget_gate_scratch,
logger->LogTensorValue(intermediate_tensor_indexes[1], forget_gate_scratch,
n_cell * n_batch, error_reporter);
tensor_utils::MeanStddevNormalization(forget_gate_scratch,
forget_gate_scratch, n_cell, n_batch);
@ -193,7 +193,7 @@ inline void LstmStepWithAuxInput(
tensor_utils::VectorVectorCwiseProduct(forget_gate_scratch, cell_state_ptr,
n_batch * n_cell, cell_state_ptr);
if (use_layer_norm) {
logger->LogTensorValue(intemediate_tensor_indexes[2], cell_scratch,
logger->LogTensorValue(intermediate_tensor_indexes[2], cell_scratch,
n_cell * n_batch, error_reporter);
tensor_utils::MeanStddevNormalization(cell_scratch, cell_scratch, n_cell,
n_batch);
@ -226,7 +226,7 @@ inline void LstmStepWithAuxInput(
output_gate_scratch);
}
if (use_layer_norm) {
logger->LogTensorValue(intemediate_tensor_indexes[3], output_gate_scratch,
logger->LogTensorValue(intermediate_tensor_indexes[3], output_gate_scratch,
n_cell * n_batch, error_reporter);
tensor_utils::MeanStddevNormalization(output_gate_scratch,
output_gate_scratch, n_cell, n_batch);
@ -243,7 +243,7 @@ inline void LstmStepWithAuxInput(
tensor_utils::VectorVectorCwiseProduct(output_gate_scratch, cell_scratch,
n_batch * n_cell, output_gate_scratch);
logger->LogTensorValue(intemediate_tensor_indexes[4], output_gate_scratch,
logger->LogTensorValue(intermediate_tensor_indexes[4], output_gate_scratch,
n_cell * n_batch, error_reporter);
const bool use_projection_weight = (projection_weights_ptr != nullptr);
@ -314,7 +314,7 @@ TfLiteStatus EvalFloat(
int output_offset, TfLiteTensor* scratch_buffer,
TfLiteTensor* activation_state, TfLiteTensor* cell_state,
TfLiteTensor* output, Logger* logger,
const std::vector<int>& intemediate_tensor_indexes,
const std::vector<int>& intermediate_tensor_indexes,
ErrorReporter* error_reporter) {
TF_LITE_ASSERT(input->dims->size >= 2 && input->dims->size <= 3);
int max_time, n_batch;
@ -402,7 +402,7 @@ TfLiteStatus EvalFloat(
GetTensorData<float>(activation_state),
GetTensorData<float>(cell_state), input_gate_scratch,
forget_gate_scratch, cell_scratch, output_gate_scratch,
output_ptr_time, logger, intemediate_tensor_indexes, error_reporter);
output_ptr_time, logger, intermediate_tensor_indexes, error_reporter);
}
} else {
for (int b = 0; b < n_batch; b++) {
@ -463,7 +463,7 @@ TfLiteStatus EvalFloat(
n_cell, n_input, aux_input_size, n_output, output_batch_leading_dim,
activation_state_ptr, cell_state_ptr, input_gate_scratch_ptr,
forget_gate_scratch_ptr, cell_scratch_ptr, output_gate_scratch_ptr,
output_ptr, logger, intemediate_tensor_indexes, error_reporter);
output_ptr, logger, intermediate_tensor_indexes, error_reporter);
}
}
}
@ -559,9 +559,9 @@ TfLiteStatus lstm_eval(TfLiteContext* context, TfLiteNode* node, Logger* logger,
TfLiteTensor* output =
GetOutput(context, node, ops::builtin::lstm::full::kOutputTensor);
std::vector<int> intemediate_tensor_indexes(node->intermediates->size);
std::vector<int> intermediate_tensor_indexes(node->intermediates->size);
for (int i = 0; i < node->intermediates->size; ++i) {
intemediate_tensor_indexes[i] = node->intermediates->data[i];
intermediate_tensor_indexes[i] = node->intermediates->data[i];
}
switch (input_to_output_weights->type) {
@ -583,7 +583,7 @@ TfLiteStatus lstm_eval(TfLiteContext* context, TfLiteNode* node, Logger* logger,
projection_bias, params, /*forward_sequence=*/true,
/*time_major=*/true,
/*output_offset=*/0, scratch_buffer, activation_state, cell_state,
output, logger, intemediate_tensor_indexes, error_reporter);
output, logger, intermediate_tensor_indexes, error_reporter);
}
case kTfLiteUInt8:
case kTfLiteInt8:

2
tensorflow/lite/tools/optimize/calibration/calibrator_test.cc
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@ -165,7 +165,7 @@ TEST(CalibratorTest, MultipleInvokes) {
EXPECT_NEAR(stats.at(tensor_idx).max, expected_values[tensor_idx], eps);
}
// Set input[0][0] = 1.5 and input[0][1] = 0.5 this should change the values
// only for input[0] and tensor 4 and ouputs 5, 6.
// only for input[0] and tensor 4 and outputs 5, 6.
TfLiteTensor* input0 = interpreter->tensor(0);
input0->data.f[0] = 1.5f;
input0->data.f[1] = 0.5f;

12
tensorflow/lite/tools/optimize/calibration/logging_op_resolver.cc
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@ -29,20 +29,20 @@ LoggingOpResolver::LoggingOpResolver(
base_resolver.FindOp(op_and_version.first, op_and_version.second);
BuiltinOperatorKey key = op_and_version;
builtin_op_evalfn_map_[key] = base_registration->invoke;
auto logging_registation =
auto logging_registration =
absl::make_unique<TfLiteRegistration>(*base_registration);
logging_registation->invoke = logging_eval_fn;
builtin_op_registration_map_[key] = std::move(logging_registation);
logging_registration->invoke = logging_eval_fn;
builtin_op_registration_map_[key] = std::move(logging_registration);
}
for (const auto& op_and_version : custom_ops_to_replace) {
const TfLiteRegistration* base_registration = base_resolver.FindOp(
op_and_version.first.c_str(), op_and_version.second);
CustomOperatorKey key = op_and_version;
custom_op_evalfn_map_[key] = base_registration->invoke;
auto logging_registation =
auto logging_registration =
absl::make_unique<TfLiteRegistration>(*base_registration);
logging_registation->invoke = logging_eval_fn;
custom_op_registration_map_[key] = std::move(logging_registation);
logging_registration->invoke = logging_eval_fn;
custom_op_registration_map_[key] = std::move(logging_registration);
}
}

24
tensorflow/lite/tools/optimize/operator_property.cc
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@ -496,8 +496,8 @@ OperatorProperty GetOperatorProperty(const ModelT* model, int subgraph_index,
tensor_property_9.number_of_bits = 16;
tensor_property_9.symmetric = true;
// Without layer norm, we choose to quantize bias with the scale of
// input and its correpsonding weight. The other choice will
// be to ues the scale of recurrent and its correpsonding weight but we
// input and its corresponding weight. The other choice will
// be to ues the scale of recurrent and its corresponding weight but we
// choose to use the smaller scale, which means higher resolution.
TensorProperty tensor_property_12;
tensor_property_12.use_derived_scale = true;
@ -548,7 +548,7 @@ OperatorProperty GetOperatorProperty(const ModelT* model, int subgraph_index,
};
property.outputs = {{0, {}}};
property.intermediates = {
// Without layer normliazation, intermediate tensors 0, 1, 2, 3 are
// Without layer normalization, intermediate tensors 0, 1, 2, 3 are
// not used and and their quantization parameters are ignored.
{0, {}},
{1, {}},
@ -563,8 +563,8 @@ OperatorProperty GetOperatorProperty(const ModelT* model, int subgraph_index,
if (!op_variant.use_layer_norm && op_variant.use_projection &&
!op_variant.use_peephole) {
// Without layer norm, we choose to quantize bias with the scale of
// input and its correpsonding weight. The other choice will
// be to ues the scale of recurrent and its correpsonding weight but we
// input and its corresponding weight. The other choice will
// be to ues the scale of recurrent and its corresponding weight but we
// choose to use the smaller scale, which means higher resolution.
TensorProperty tensor_property_12;
tensor_property_12.use_derived_scale = true;
@ -612,7 +612,7 @@ OperatorProperty GetOperatorProperty(const ModelT* model, int subgraph_index,
};
property.outputs = {{0, {}}};
property.intermediates = {
// Without layer normliazation, intermediate tensors 0, 1, 2, 3 are
// Without layer normalization, intermediate tensors 0, 1, 2, 3 are
// not used and their quantization parameters are ignored.
{0, {}},
{1, {}},
@ -630,8 +630,8 @@ OperatorProperty GetOperatorProperty(const ModelT* model, int subgraph_index,
tensor_property_9.number_of_bits = 16;
tensor_property_9.symmetric = true;
// Without layer norm, we choose to quantize bias with the scale of
// input and its correpsonding weight. The other choice will
// be to ues the scale of recurrent and its correpsonding weight but we
// input and its corresponding weight. The other choice will
// be to ues the scale of recurrent and its corresponding weight but we
// choose to use the smaller scale, which means higher resolution.
TensorProperty tensor_property_12;
tensor_property_12.use_derived_scale = true;
@ -676,7 +676,7 @@ OperatorProperty GetOperatorProperty(const ModelT* model, int subgraph_index,
};
property.outputs = {{0, {}}};
property.intermediates = {
// Without layer normliazation, intermediate tensors 0, 1, 2, 3 are
// Without layer normalization, intermediate tensors 0, 1, 2, 3 are
// not used and their quantization parameters are ignored.
{0, {}},
{1, {}},
@ -696,8 +696,8 @@ OperatorProperty GetOperatorProperty(const ModelT* model, int subgraph_index,
if (!op_variant.use_layer_norm && !op_variant.use_projection &&
!op_variant.use_peephole) {
// Without layer norm, we choose to quantize bias with the scale of
// input and its correpsonding weight. The other choice will
// be to ues the scale of recurrent and its correpsonding weight but we
// input and its corresponding weight. The other choice will
// be to ues the scale of recurrent and its corresponding weight but we
// choose to use the smaller scale, which means higher resolution.
TensorProperty tensor_property_12;
tensor_property_12.use_derived_scale = true;
@ -739,7 +739,7 @@ OperatorProperty GetOperatorProperty(const ModelT* model, int subgraph_index,
};
property.outputs = {{0, {}}};
property.intermediates = {
// Without layer normliazation, intermediate tensors 0, 1, 2, 3 are
// Without layer normalization, intermediate tensors 0, 1, 2, 3 are
// not used and their quantization parameters are ignored.
{0, {}},
{1, {}},

8
tensorflow/lite/tools/optimize/quantize_model.cc
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@ -347,7 +347,7 @@ TfLiteStatus ApplyConstraints(ModelT* model,
// Add requant op before this input.
// There are better ways to handle this, which is to try to push the
// rescale upwards recurrsively and hope all upstream ops can absort
// rescale upwards recursively and hope all upstream ops can absort
// this rescale.and only add requant when there is no other way.
std::unique_ptr<OperatorT> requant_op;
utils::MakeQuantizeOperator(model, &requant_op, op->inputs[input_idx],
@ -747,9 +747,9 @@ TfLiteStatus QuantizeIntemediateTensors(ModelT* model,
// Quantize tensros that have shared range. For example, in LSTM, the output
// tensor and input state tensor should share the same range because they are
// using the same scale and zero point.
// We have to model this explicitely because the output is modeled as an extra
// We have to model this explicitly because the output is modeled as an extra
// tensor in LSTM. In calibrator, state tensors are logged both before and after
// the inferece so the range is fully captured. But output, although it is
// the inference so the range is fully captured. But output, although it is
// identical to activation, is not a state tensor the input value (range) of the
// very first inference is not captured.
TfLiteStatus QuantizeSharedRange(ModelT* model, ErrorReporter* error_reporter) {
@ -1073,7 +1073,7 @@ TfLiteStatus EnsureBiasScaleCompatibility(
return kTfLiteError;
}
// Get input scale for assymmetric quantization.
// Get input scale for asymmetric quantization.
QuantizationParametersT temp_quant_params = QuantizationParametersT();
utils::GetAsymmetricQuantizationParams(
input_tensor->quantization->min[0],