experiments/STT-tensorflow
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minor spelling tweaks

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This commit is contained in:
Kazuaki Ishizaki 2020-03-19 01:52:50 +09:00
parent 56944a8148
commit 70efc8c05c
20 changed files with 34 additions and 33 deletions

2
tensorflow/lite/toco/dump_graphviz.cc
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@ -647,7 +647,7 @@ void DumpNode(const Model& model, string* output_file, const string& node_name,
for (const auto& child : node.children) {
if (!child.second->array_id.empty()) {
// Dump array if this node posesses one.
// Dump array if this node possesses one.
DumpArray(model, output_file, child.second->array_id);
}
// Note that it is always possible to have children. Unlike a filesystem,

2
tensorflow/lite/toco/graph_transformations/convert_trivial_transpose_to_reshape.cc
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@ -67,7 +67,7 @@ bool TransposeAffectsMemoryOrder(std::vector<int> perm,
}
// Note: We can assume we have error checked inputs in PropagateFixedSizes.
// Check that the permutation has propogated.
// Check that the permutation has propagated.
std::vector<int> const& perm = transpose_op->perm;
if (perm.empty()) {
return ::tensorflow::Status::OK();

2
tensorflow/lite/toco/graph_transformations/graph_transformations.h
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@ -159,7 +159,7 @@ DECLARE_GRAPH_TRANSFORMATION(PropagateFixedSizes)
DECLARE_GRAPH_TRANSFORMATION(HardcodeMinMax)
DECLARE_GRAPH_TRANSFORMATION(Quantize)
DECLARE_GRAPH_TRANSFORMATION(RemoveFinalDequantizeOp)
DECLARE_GRAPH_TRANSFORMATION(RemoveSuccesiveTranspose)
DECLARE_GRAPH_TRANSFORMATION(RemoveSuccessiveTranspose)
DECLARE_GRAPH_TRANSFORMATION(RemoveTensorFlowAssert)
DECLARE_GRAPH_TRANSFORMATION(RemoveTensorFlowIdentity)
DECLARE_GRAPH_TRANSFORMATION(RemoveTrivialBinaryOperator)

4
tensorflow/lite/toco/graph_transformations/identify_dilated_conv.cc
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@ -225,8 +225,8 @@ bool ResolveDilatedConv(Model* model, Operator* conv_base_op, Operator* stb_op,
dilation_factor);
if (changed) {
LOG(INFO)
<< "Replaced sub-netork with Dilated DepthwiseConv2D op outputting \""
<< conv_base_op->outputs[0] << "\".";
<< "Replaced sub-network with Dilated DepthwiseConv2D op outputting "
<< "\"" << conv_base_op->outputs[0] << "\".";
}
}

2
tensorflow/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc
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@ -245,7 +245,7 @@ bool RecursivelyForwardPropagateDataType(GraphTransformation* transformation,
// This can be thought of as a bidirectional flood-fill of the num_bits implied
// final_data_type that terminates at other FakeQuant ops (and a few others as
// determined by DoesOpBlockBackwardPropagation/DoesOpBlockForwardPropagation).
// Once all FakeQuant ops have been visted the arrays should all have
// Once all FakeQuant ops have been visited the arrays should all have
// appropriate final_data_types if the source graph was annotated with the
// proper FakeQuant ops.
//

10
tensorflow/lite/toco/graph_transformations/propagate_fixed_sizes.cc
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@ -671,7 +671,7 @@ void ProcessConcatenationOperator(Model* model, ConcatenationOperator* op) {
break;
}
}
// Determine the concat size, and enfore that all inputs have
// Determine the concat size, and enforce that all inputs have
// the same dimensions count.
int concat_size = 0;
for (const auto& input_name : op->inputs) {
@ -1098,7 +1098,7 @@ void ProcessUnidirectionalSequenceLstmOperator(
constexpr int kInputActivationStateTensor = 18;
constexpr int kInputCellStateTensor = 19;
// TFlite intepreter does not support array which is variable and contains a
// TFlite interpreter does not support array which is variable and contains a
// buffer (see b/115961645 for more discussion).
// The follow block remove buffer from the array to work around the
// restriction, as a consequence, downstream applications should not
@ -1142,7 +1142,7 @@ void ProcessUnidirectionalSequenceRnnOperator(
}
constexpr int kHiddenStateTensor = 4;
// TFlite intepreter does not support array which is variable and contains a
// TFlite interpreter does not support array which is variable and contains a
// buffer (see b/115961645 for more discussion).
// The follow block remove buffer from the array to work around the
// restriction, as a consequence, downstream applications should not
@ -1658,7 +1658,7 @@ void ProcessStridedSliceOperator(Model* model, StridedSliceOperator* op) {
}
if (op->ellipsis_mask != 0) {
// Something like LOG_FIRST_N(WARNING, 10) would be prefferable to reduce
// Something like LOG_FIRST_N(WARNING, 10) would be preferable to reduce
// log noise. However, the TensorFlow logging library does not appear to
// support this.
LOG(WARNING) << "Skipping StridedSlice op with output \"" << op->outputs[0]
@ -2434,7 +2434,7 @@ void ProcessMatrixSetDiagOperator(Model* model, MatrixSetDiagOperator* op) {
break;
case OperatorType::kCTCBeamSearchDecoder:
// The sizes of the outputs are only known in runtime based on the input.
// Ignore shape progapation here and defer that to the interpreter.
// Ignore shape propagation here and defer that to the interpreter.
break;
case OperatorType::kMatrixSetDiagV2:
// MatrixSetDiagV2 operators are converted to MatrixSetDiag,

2
tensorflow/lite/toco/graph_transformations/quantization_util.cc
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@ -229,7 +229,7 @@ bool IsArrayQuantizedRangeSubset(GraphTransformation* transformation,
ChooseQuantizationParamsForArrayAndQuantizedDataType(
array, quantized_data_type, &quantization_params);
transformation->AddMessageF(
"No quantization params - infering from data type %s with minmax "
"No quantization params - inferring from data type %s with minmax "
"%g,%g as zero_point=%g, scale=%g",
ArrayDataTypeName(quantized_data_type), array.minmax->min,
array.minmax->max, quantization_params.zero_point,

6
tensorflow/lite/toco/graph_transformations/remove_successive_transpose.cc
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@ -56,9 +56,9 @@ void ReplaceOpInputsWith(Model* model, const string& lookfor,
} // namespace
::tensorflow::Status RemoveSuccesiveTranspose::Run(Model* model,
std::size_t op_index,
bool* modified) {
::tensorflow::Status RemoveSuccessiveTranspose::Run(Model* model,
std::size_t op_index,
bool* modified) {
*modified = false;
auto op = model->operators.begin() + op_index;
if (op->get()->type != OperatorType::kTranspose) {

6
tensorflow/lite/toco/graph_transformations/tests/remove_successive_transpose_test.cc
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@ -94,7 +94,7 @@ TEST_F(RemoveSuccessiveTransposeTest, RemoveTranspose) {
// Creating a model.
CreateGraph({1, 0}, {1, 0});
toco::RemoveSuccesiveTranspose transformation;
toco::RemoveSuccessiveTranspose transformation;
bool modified;
ASSERT_TRUE(transformation.Run(model_.get(), /*op_index=*/1, &modified).ok());
EXPECT_TRUE(modified);
@ -109,7 +109,7 @@ TEST_F(RemoveSuccessiveTransposeTest, DontRemoveNotIdentityTranspose) {
// Creating a model.
CreateGraph({0, 2, 1}, {1, 0, 2});
toco::RemoveSuccesiveTranspose transformation;
toco::RemoveSuccessiveTranspose transformation;
bool modified;
ASSERT_TRUE(transformation.Run(model_.get(), /*op_index=*/1, &modified).ok());
EXPECT_FALSE(modified);
@ -139,7 +139,7 @@ TEST_F(RemoveSuccessiveTransposeTest, DontRemoveTransposeOutputUnused) {
transpose2_op->outputs = {"InputTransposeTranspose"};
model_->operators.push_back(std::unique_ptr<toco::Operator>(transpose2_op));
toco::RemoveSuccesiveTranspose transformation;
toco::RemoveSuccessiveTranspose transformation;
bool modified;
ASSERT_TRUE(transformation.Run(model_.get(), /*op_index=*/1, &modified).ok());
EXPECT_FALSE(modified);

2
tensorflow/lite/toco/graph_transformations/unroll_batch_matmul.cc
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@ -177,7 +177,7 @@ TransposeOperator* TransposeInput(const string& input, Model* model) {
CHECK_EQ(input_array_a.shape().dims(dims_a - 1),
input_array_b.shape().dims(dims_b - 2))
<< "Input dimensions must be compatible for multipication. shape a = ["
<< "Input dimensions must be compatible for multiplication. shape a = ["
<< absl::StrJoin(input_array_a.shape().dims(), ", ") << "], shape b = ["
<< absl::StrJoin(input_array_b.shape().dims(), ", ") << "]";

2
tensorflow/lite/toco/logging/gen_html.py
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@ -136,7 +136,7 @@ class HTMLGenerator(object):
dot_after: A string, the dot representation of the model after
the conversion.
toco_err_log: A string, the logs emitted by TOCO during conversion. Caller
need to ensure that this string is properly anoynimized (any kind of
need to ensure that this string is properly anonymized (any kind of
user data should be eliminated).
tflite_graph_path: A string, the filepath to the converted TFLite model.

4
tensorflow/lite/toco/model.h
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@ -490,7 +490,7 @@ struct ConvOperator : Operator {
// inputs[4]: optional: merge repeated.
//
// Outputs:
// outputs[0]: deocoded.
// outputs[0]: decoded.
// outputs[1]: log probability.
//
// TensorFlow equivalent: CTCBeamSearchDecoder
@ -1258,7 +1258,7 @@ struct ExpandDimsOperator : Operator {
ExpandDimsOperator() : Operator(OperatorType::kExpandDims) {}
};
// Ceates a tensor of shape dims and fills it with the given scalar value.
// Creates a tensor of shape dims and fills it with the given scalar value.
// Output type will be the same as the given scalar value.
//
// Inputs:

2
tensorflow/lite/toco/python/toco_from_protos.py
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@ -21,7 +21,7 @@ import argparse
import sys
# We need to import pywrap_tensorflow prior to the toco wrapper.
# pylint: disable=invalud-import-order,g-bad-import-order
# pylint: disable=invalid-import-order,g-bad-import-order
from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import
from tensorflow.python import _pywrap_toco_api
from tensorflow.python.platform import app

2
tensorflow/lite/toco/python/toco_from_protos_test.py
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@ -85,7 +85,7 @@ class TocoFromProtosTest(googletest.TestCase):
val = img + tf.constant([1., 2., 3.]) + tf.constant([1., 4., 4.])
out = tf.identity(val, name="out")
out2 = tf.sin(val, name="out2")
# This is a valid mdoel
# This is a valid model
self._run(sess, img, out, True)
# This uses an invalid function.
# TODO(aselle): Check to make sure a warning is included.

5
tensorflow/lite/toco/tflite/export.cc
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@ -53,7 +53,7 @@ namespace {
// Check if a TensorFlow Op is a control flow op by its name.
bool IsControlFlowOp(const string& tensorflow_op) {
// Technically this is equalivent to `::tensorflow::Node::IsControlFlow()`.
// Technically this is equivalent to `::tensorflow::Node::IsControlFlow()`.
// It requires to construct a `::tensorflow::Graph` to use that helper
// function, so we simply hardcode the list of control flow ops here.
if (tensorflow_op == "Switch" || tensorflow_op == "RefSwitch" ||
@ -477,7 +477,8 @@ tensorflow::Status Export(
for (const string& input_array : model.GetInvalidInputArrays()) {
if (model.HasArray(input_array)) {
return tensorflow::errors::InvalidArgument(absl::StrCat(
"Placeholder ", input_array, " should be specied by input_arrays."));
"Placeholder ", input_array, " should be specified by "
"input_arrays."));
}
}

2
tensorflow/lite/toco/tflite/operator_test.cc
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@ -37,7 +37,7 @@ class OperatorTest : public ::testing::Test {
static auto* by_name = new OpsByName(BuildOperatorByNameMap());
static auto* by_type = new OpsByType(BuildOperatorByTypeMap());
// Make sure the two maps were consitently built.
// Make sure the two maps were consistently built.
CHECK(by_name->count(name)) << "No operator for '" << name << "'.";
BaseOperator* op1 = by_name->at(name).get();
CHECK(op1->type() == type) << "while verifying '" << name << "'.";

2
tensorflow/lite/toco/toco_cmdline_flags.cc
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@ -171,7 +171,7 @@ bool ParseTocoFlagsFromCommandLineFlags(
"Ignored if the output format is not TFLite."),
Flag("quantize_to_float16", parsed_flags.quantize_to_float16.bind(),
parsed_flags.quantize_to_float16.default_value(),
"Used in conjuction with post_training_quantize. Specifies that "
"Used in conjunction with post_training_quantize. Specifies that "
"the weights should be quantized to fp16 instead of the default "
"(int8)"),
Flag("quantize_weights", parsed_flags.quantize_weights.bind(),

2
tensorflow/lite/toco/toco_convert_test.cc
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@ -39,7 +39,7 @@ TEST(TocoTest, BadInputFormat) {
"Unhandled input_format='FILE_FORMAT_UNKNOWN'");
}
TEST(TocoTest, MissingOuputArrays) {
TEST(TocoTest, MissingOutputArrays) {
TocoFlags toco_flags;
ModelFlags model_flags;

6
tensorflow/lite/toco/toco_tooling.cc
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@ -67,7 +67,7 @@ void MakeGeneralGraphTransformationsSet(
transformations->Add(new PropagateActivationFunctionIntoConstants);
transformations->Add(new PropagateArrayDataTypes);
transformations->Add(new PropagateFixedSizes);
transformations->Add(new RemoveSuccesiveTranspose);
transformations->Add(new RemoveSuccessiveTranspose);
transformations->Add(new RemoveTensorFlowAssert);
transformations->Add(new RemoveTensorFlowIdentity);
transformations->Add(new RemoveTrivialConcatenation);
@ -415,10 +415,10 @@ tensorflow::Status TransformWithStatus(const TocoFlags& toco_flags,
// is:
// Input [1, 20, 1, 20, 1, 64] * ones [1, 3, 1, 3, 1, 1]
// The problem is if the input is quantized, then the quantization parameters
// will be slightly different for the input and the output. (althought the
// will be slightly different for the input and the output. (although the
// difference is really small).
// But, since we're changing this pattern to be pack-based which enforce
// the quantization paramters to be exactly the same.
// the quantization parameters to be exactly the same.
// So we have to wait for all quantization parameters being resolved and
// propagated and create our own.
// We may need to revisit this logic later.

2
tensorflow/lite/toco/tooling_util.cc
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@ -929,7 +929,7 @@ void CheckNonExistentIOArrays(const Model& model) {
}
static constexpr char general_comment[] =
"Is it a typo? This should not happen. If you trigger this error "
"please send a bug report (with code to reporduce this error), to the "
"please send a bug report (with code to reproduce this error), to the "
"TensorFlow Lite team.";
for (const string& output_array : model.flags.output_arrays()) {
if (IsConstantParameterArray(model, output_array)) {