Update tflite-model-maker APIs to 0.3.2.

PiperOrigin-RevId: 374055079

tensorflow_examples/lite/model_maker/README.md

@@ -42,39 +42,44 @@ cd examples/tensorflow_examples/lite/model_maker/pip_package
 pip install -e .
 ```
 
+TensorFlow Lite Model Maker depends on TensorFlow
+[pip package](https://www.tensorflow.org/install/pip). For GPU support, please
+refer to TensorFlow's [GPU guide](https://www.tensorflow.org/install/gpu) or
+[installation guide](https://www.tensorflow.org/install).
+
 ## End-to-End Example
 
 For instance, it could have an end-to-end image classification example that
 utilizes this library with just 4 lines of code, each of which representing one
 step of the overall process. For more detail, you could refer to
 [Colab for image classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_image_classification.ipynb).
 
-*   1. Import the required modules.
+*   Step 1. Import the required modules.
 
 ```python
 from tflite_model_maker import image_classifier
 from tflite_model_maker.image_classifier import DataLoader
 ```
 
-*   2. Load input data specific to an on-device ML app.
+*   Step 2. Load input data specific to an on-device ML app.
 
 ```python
 data = DataLoader.from_folder('flower_photos/')
 ```
 
-*   3. Customize the TensorFlow model.
+*   Step 3. Customize the TensorFlow model.
 
 ```python
 model = image_classifier.create(data)
 ```
 
-*   4. Evaluate the model.
+*   Step 4. Evaluate the model.
 
 ```python
 loss, accuracy = model.evaluate()
 ```
 
-*   5. Export to Tensorflow Lite model and label file in `export_dir`.
+*   Step 5. Export to Tensorflow Lite model and label file in `export_dir`.
 
 ```python
 model.export(export_dir='/tmp/')
@@ -86,6 +91,8 @@ Currently, we support image classification, text classification and question
 answer tasks. Meanwhile, we provide demo code for each of them in demo folder.
 
 *   [Overview for TensorFlow Lite Model Maker](https://www.tensorflow.org/lite/guide/model_maker)
+*   [Python API Reference](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker)
 *   [Colab for image classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_image_classification.ipynb)
 *   [Colab for text classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_text_classification.ipynb)
 *   [Colab for BERT question answer](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_question_answer.ipynb)
+*   [Colab for object detection](https://www.tensorflow.org/lite/tutorials/model_maker_object_detection)

tensorflow_examples/lite/model_maker/RELEASE.md

@@ -1,5 +1,9 @@
 # Release 0.3
 
+## 0.3.2
+
+*   Minor fix: Move librosa to an optional library for audio task.
+
 ## 0.3.1
 
 *   Polish document showing in

tensorflow_examples/lite/model_maker/pip_package/setup.py

@@ -57,14 +57,14 @@
 # Set package version.
 if nightly:
   project_name = '{}-nightly'.format(project_name)
-  version = '0.3.2'  # Version prefix, usually major.minor.micro.
+  version = '0.3.3'  # Version prefix, usually major.minor.micro.
   version = '{:s}.dev{:s}'.format(version, datestring)
   classifiers += [
       'Development Status :: 4 - Beta',
   ]
 else:
   # LINT.IfChange(model_maker_pip_version)
-  version = '0.3.1'
+  version = '0.3.2'
   # LINT.ThenChange(../public/__init__.py, ../RELEASE.md)
 
 # Path to model_maker dir: <repo>/tensorflow_examples/lite/model_maker

tensorflow_examples/lite/model_maker/public/__init__.py

@@ -58,4 +58,4 @@
 from tensorflow_examples.lite.model_maker.core.task import configs
 # pylint: enable=g-bad-import-order
 
-__version__ = '0.3.1'
+__version__ = '0.3.2'