In this article, we are going to convert the TensorFlow model to tflite model and will use it in a real-time Sign language detection app. we will not cover the training part in this article btw I used the TensorFlow object detection API for that. first, we will save the inference model from the checkpoint which we created while training our model. this article is helpful for those who are starting the TensorFlow lite. I hope it will be helpful. Application link at the end of the article.
- First we will freeze the inference graph using TensorFlow od API.
Freezing is the process to identify and save all of the required things(graph, weights, etc) in a single file that you can easily use.
python models/research/object_detection/exporter_main_v2.py \ --input_type image_tensor \ --pipeline_config_path /output/exported_models/training/001/pipeline.config \ --trained_checkpoint_dir output/exported_models/training/001/ \ --output_directory output/exported_models/inference_model
2.Then we will convert the model to the tflite inference graph.
python models/research/object_detection/export_tflite_graph_tf2.py \ --pipeline_config_path output/exported_models/inference_model/inference_modelsaved_model/pipeline.config \ --trained_checkpoint_dir output/exported_models/inference_model/saved_model/checkpint \ --output_directory output/exported_models/tflite_infernce
3.Then we will post quantize the graph and save the tflite model.
# save this file as postQuantization.py def representative_dataset(): for _ in range(100): data = np.random.rand(1, 320, 320, 3) yield [data.astype(np.float32)] import numpy as np import tensorflow as tf saved_model_dir = "output/exported_models/tflite_infernce/saved_model" converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir) converter.allow_custom_ops = True converter.optimizations = [tf.lite.Optimize.DEFAULT] converter.representative_dataset = representative_dataset converter.inference_input_type = tf.uint8 # or tf.uint8 converter.inference_output_type = tf.uint8 # or tf.uint8 tflite_quant_model = converter.convert() with tf.io.gfile.GFile(tf_lite_model_path, 'wb') as f: f.write(tflite_quant_model)
4.Write metadata into the tflite model to use with the android.
''' Writing MetaData to TfLite Model save it as MetaDataWriterFile.py ''' from tflite_support.metadata_writers import object_detector from tflite_support.metadata_writers import writer_utils from tflite_support import metadata ObjectDetectorWriter = object_detector.MetadataWriter _MODEL_PATH = <tf_lite_model_path> _LABEL_FILE = <label_path> _SAVE_TO_PATH = <path_to_tflite_path/tflite_with_metadata.tflite> writer = ObjectDetectorWriter.create_for_inference( writer_utils.load_file(_MODEL_PATH), [127.5], [127.5], [_LABEL_FILE]) writer_utils.save_file(writer.populate(), _SAVE_TO_PATH) # Verify the populated metadata and associated files. displayer = metadata.MetadataDisplayer.with_model_file(_SAVE_TO_PATH) print("Metadata populated:") print(displayer.get_metadata_json()) print("Associated file(s) populated:") print(displayer.get_packed_associated_file_list())
Tensorflow-examples repository from the TensorFlow GitHub account.
Download the Android studio and SDK file to use the android app for detection.
git clone https://github.com/tensorflow/examples
tflite_with_metadata.tflite file and rename it as 'detect.tflite
and save it in theapp/src/main/assets/detect.tflite`
TF_OD_API_INPUT_SIZE to the model in the
app\src\main\java\org\tensorflow\lite\examples\detection\DetectorActivity.java to 320.
Create the virtual device or connect your phone and run the object detection application successfully.
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GitHub Link with all data including android app
Download the APK for testing from Google Drive
Thanks to David Lee and Roboflow for the Dataset.
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