Just a simple script to inspect TF Lite metadata
simple use:
python metadata_viewer.py \
model_file=./some_tf_lite_model.tfliteto visualize metadata
python metadata_viewer.py \
model_file=./some_tf_lite_model.tflite \
appended_resource_id=0where appended_resource_id should be the index (0 based) of the appended resource file
If you just want to get the label names that are embedded within the .tflite file (i.e like for edge models trained in Google Cloud AutoML) then you can use this helper function:
import zipfile
from tflite_support import metadata
def get_labels_from_tflite(model_file_name, format=0):
'''
Unpack the metadata from a tflite model and return and parse the first
associated file which is assumed to be the label file.
input : str (model file name), int (format 0=list, 1=dict)
output : str (text from labels file)
'''
displayer = metadata.MetadataDisplayer.with_model_file(model_file_name)
associate_files= displayer.get_packed_associated_file_list()
resource_file = associate_files[0]
archive = zipfile.ZipFile(model_file_name, 'r')
labels = archive.read(resource_file).decode().split('\n')
if format == 1:
labels = {i:label for (i, label) in enumerate(labels)}
return labels
# Example usage
# get labels as list >> ["cat", "dog", "mouse"]
labels_list = get_labels_from_tflite('my_model.tflite', format=0)
# get labels as dict >> {0:"cat", 1:"dog", 2:"mouse"}
labels_dict = get_labels_from_tflite('my_model.tflite', format=1)
Oficial documentation for TF Lite metadata