Fix misc formatting and spelling errors. (#435)

* Fix misc formatting and spelling errors.

* Re-enable layering check.
Co-authored-by: NAdvait Jain <advaitjain@users.noreply.github.com>

tensorflow/lite/micro/build_def.bzl

 def micro_copts():
-  return [
-      "-Wall",
-      "-Werror",
-      "-DFLATBUFFERS_LOCALE_INDEPENDENT=0",
-  ]
+    return [
+        "-Wall",
+        "-Werror",
+        "-DFLATBUFFERS_LOCALE_INDEPENDENT=0",
+    ]
 
 def generate_cc_arrays(name, src, out, visibility = None):
-  native.genrule(
-    name = name,
-    srcs = [
-      src,
-    ],
-    outs = [
-      out,
-    ],
-    cmd = "$(location //tensorflow/lite/micro/tools:generate_cc_arrays) $@ $<",
-    tools = ["//tensorflow/lite/micro/tools:generate_cc_arrays"],
-    visibility=visibility,
-  )
+    native.genrule(
+        name = name,
+        srcs = [
+            src,
+        ],
+        outs = [
+            out,
+        ],
+        cmd = "$(location //tensorflow/lite/micro/tools:generate_cc_arrays) $@ $<",
+        tools = ["//tensorflow/lite/micro/tools:generate_cc_arrays"],
+        visibility = visibility,
+    )

tensorflow/lite/micro/examples/hello_world/BUILD

@@ -2,13 +2,12 @@
 #   TensorFlow Lite for Microcontrollers "hello world" example.
 load(
     "//tensorflow/lite/micro:build_def.bzl",
+    "generate_cc_arrays",
     "micro_copts",
 )
-load("//tensorflow/lite/micro:build_def.bzl","generate_cc_arrays")
 
 package(
     default_visibility = ["//visibility:public"],
-    features = ["-layering_check"],
     licenses = ["notice"],
 )
 

tensorflow/lite/micro/examples/magic_wand/BUILD

 # Description:
 #   TensorFlow Lite for Microcontrollers "gesture recognition" example.
+load("//tensorflow/lite/micro:build_def.bzl","generate_cc_arrays")
+
 package(
     default_visibility = ["//visibility:public"],
-    features = ["-layering_check"],
     licenses = ["notice"],
 )
 
-load("//tensorflow/lite/micro:build_def.bzl","generate_cc_arrays")
-
 generate_cc_arrays(
     name="generated_magic_wand_model_cc",
     src="magic_wand.tflite",

tensorflow/lite/micro/examples/person_detection/BUILD

 # Description:
 #   TensorFlow Lite for Microcontrollers Vision Example.
+load("//tensorflow/lite/micro:build_def.bzl","generate_cc_arrays")
+
 package(
     default_visibility = ["//visibility:public"],
-    features = ["-layering_check"],
     licenses = ["notice"],
 )
 
-load("//tensorflow/lite/micro:build_def.bzl","generate_cc_arrays")
-
 cc_library(
     name = "model_settings",
     srcs = [

tensorflow/lite/micro/examples/person_detection/training_a_model.md

@@ -119,7 +119,7 @@ fed into the model. It reduces each image to the size specified by `--train_imag
 - `--train_dir` will contain the trained checkpoints and summaries.
 - The `--learning_rate`, `--label_smoothing`, `--learning_rate_decay_factor`,
 `--num_epochs_per_decay`, `--moving_average_decay` and `--batch_size` are all
-parameters that control how weights are updated during the the training
+parameters that control how weights are updated during the training
 process. Training deep networks is still a bit of a dark art, so these exact
 values we found through experimentation for this particular model. You can try
 tweaking them to speed up training or gain a small boost in accuracy, but we

tensorflow/lite/micro/kernels/xtensa/lstm/kernels/lstm_eval.cc

@@ -692,7 +692,7 @@ void UpdateLstmCellInteger(int n_batch, int n_cell, int16_t* cell_state,
 //      constant inputs, describing projection matrix and bias.
 //  - output_state_zp: zero point of output_state. (Input, calibrated value.)
 //  - quantized_proj_clip: if > 0, clip the output of the projection.
-//  - output_state: output vector, size n_batch*n_output. Must be contigous.
+//  - output_state: output vector, size n_batch*n_output. Must be contiguous.
 //  - context: data for optimized MatrixBatchVectorMultiplyAccumulate.
 //  - scratch0: scratch area of size n_batch*n_cell
 //  - scratch1: scratch area of size n_batch*n_cell
@@ -843,7 +843,7 @@ void CalculateLstmGateInteger8x8_8(
 //      constant inputs, describing projection matrix and bias.
 //  - output_state_zp: zero point of the output state.
 //  - quantized_proj_clip: if > 0, clip the output of the projection.
-//  - output_state: output vector, size n_batch*n_output. Must be contigous.
+//  - output_state: output vector, size n_batch*n_output. Must be contiguous.
 //  - scratch: scratch area of size n_batch*n_cell
 void CalculateLstmOutputInteger8x8_8(
     int n_batch, int n_cell, int n_output, const int16_t* cell_state,
@@ -1324,7 +1324,7 @@ inline void LstmStepInteger8x8_8(
       projection_bias_ptr, output_state_zp, quantized_proj_clip,
       output_state_ptr, scratch2);
   // Copy output state to the output. Note that unlike float or hybrid, output
-  // is always contigous.
+  // is always contiguous.
   std::copy_n(output_state_ptr, n_batch * n_output, output_ptr);
 }
 

tensorflow/lite/micro/kernels/xtensa/lstm/kernels/unidirectional_sequence_lstm.cc

@@ -587,7 +587,6 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context,
   // 1) If projection weight is not present, then projection bias should not be
   // present.
   // 2) If projection weight is present, then projection bias is optional.
-  // TODO(ghodrat): make sure this is correct.
   const bool projecton_tensors_consistent =
       ((projection_weights != nullptr) || (projection_bias == nullptr));
   TF_LITE_ENSURE(context, projecton_tensors_consistent == true);

tensorflow/lite/micro/models/BUILD

 # Description:
 #   TensorFlow Lite for Microcontrollers Vision Example.
+load("//tensorflow/lite/micro:build_def.bzl","generate_cc_arrays")
+
 package(
     default_visibility = ["//visibility:public"],
     features = ["-layering_check"],
     licenses = ["notice"],
 )
 
-load("//tensorflow/lite/micro:build_def.bzl","generate_cc_arrays")
-
 generate_cc_arrays(
     name="generated_person_detect_model_cc",
     src="person_detect.tflite",

tensorflow/lite/micro/tools/ci_build/test_xtensa_vision_p6.sh

@@ -36,7 +36,7 @@ readable_run make -f tensorflow/lite/micro/tools/make/Makefile \
   build -j$(nproc)
 
 
-# Since we currently do not have optimized kernel implemetations for vision_p6,
+# Since we currently do not have optimized kernel implementations for vision_p6,
 # running the tests (in particular person_detection_int8) takes a very long
 # time. So, we have changed the default for this script to only perform a build
 # and added an option to run all the tests when that is feasible.