NFC - minor spelling tweaks under lite/testing directory

tensorflow/lite/testing/op_tests/hardswish.py

@@ -33,7 +33,7 @@ def _tflite_convert_verify_num_ops(tflite_convert_function, *args, **kwargs):
   tflite_model_binary = result[0]
   if not result[0]:
     tf.compat.v1.logging.error(result[1])  # stderr from running tflite_convert.
-    raise RuntimeError("Failed to bulid model: \n\n" + result[1])
+    raise RuntimeError("Failed to build model: \n\n" + result[1])
   interpreter = tf.lite.Interpreter(model_content=tflite_model_binary)
   interpreter.allocate_tensors()
   if len(interpreter.get_tensor_details()) != num_ops:

tensorflow/lite/testing/parse_testdata.cc

@@ -63,7 +63,7 @@ std::vector<std::string> ParseLine(const std::string& line) {
 
 }  // namespace
 
-// Given a `filename`, produce a vector of Examples corresopnding
+// Given a `filename`, produce a vector of Examples corresponding
 // to test cases that can be applied to a tflite model.
 TfLiteStatus ParseExamples(const char* filename,
                            std::vector<Example>* examples) {

tensorflow/lite/testing/tf_driver_test.cc

@@ -42,7 +42,7 @@ class TestDriver : public TfDriver {
   }
 };
 
-TEST(TfDriverTest, ReadingAndWrintingValues) {
+TEST(TfDriverTest, ReadingAndWritingValues) {
   TestDriver driver;
   ASSERT_EQ(driver.WriteAndReadBack(tensorflow::DT_FLOAT, {1, 2, 2},
                                     "0.10,0.20,0.30,0.40"),
@@ -55,7 +55,7 @@ TEST(TfDriverTest, ReadingAndWrintingValues) {
             "0,1,y,z");
 }
 
-TEST(TfDriverTest, ReadingAndWrintingValuesStrings) {
+TEST(TfDriverTest, ReadingAndWritingValuesStrings) {
   TestDriver driver;
 
   auto set_buffer = [](const std::vector<string>& values, string* buffer) {

tensorflow/lite/testing/zip_test_utils.py

@@ -94,7 +94,7 @@ def __init__(self):
     self.allow_custom_ops = False
     # Rnn states that are used to support rnn / lstm cells.
     self.rnn_states = None
-    # Split the LSTM inputs from 5 inoputs to 18 inputs for TFLite.
+    # Split the LSTM inputs from 5 inputs to 18 inputs for TFLite.
     self.split_tflite_lstm_inputs = None
     # The inference input type passed to TFLiteConvert.
     self.inference_input_type = None
@@ -168,7 +168,7 @@ def write_examples(fp, examples):
 
   Args:
     fp: File-like object to write to.
-    examples: Example dictionary consiting of keys "inputs" and "outputs"
+    examples: Example dictionary consisting of keys "inputs" and "outputs"
   """
 
   def write_tensor(fp, x):
@@ -196,7 +196,7 @@ def write_test_cases(fp, model_name, examples):
   Args:
     fp: File-like object to write to.
     model_name: Filename where the model was written to, relative to filename.
-    examples: Example dictionary consiting of keys "inputs" and "outputs"
+    examples: Example dictionary consisting of keys "inputs" and "outputs"
   """
 
   fp.write("load_model: %s\n" % os.path.basename(model_name))
@@ -262,8 +262,8 @@ def make_zip_of_tests(options,
                       expected_tf_failures=0):
   """Helper to make a zip file of a bunch of TensorFlow models.
 
-  This does a cartestian product of the dictionary of test_parameters and
-  calls make_graph() for each item in the cartestian product set.
+  This does a cartesian product of the dictionary of test_parameters and
+  calls make_graph() for each item in the cartesian product set.
   If the graph is built successfully, then make_test_inputs() is called to
   build expected input/output value pairs. The model is then converted to tflite
   with toco, and the examples are serialized with the tflite model into a zip