I've been trying to follow this process to run an object detector (SSD MobileNet) on the Google Coral Edge TPU: Edge TPU model workflow
I've successfully trained and evaluated my model with the Object Detection API. I have the model both in checkpoint format as well as tf SavedModel format. As per the documentation, the next step is to convert to .tflite format using post-training quantization.
I am to attempting to follow this example. The export_tflite_graph_tf2.py script and the conversion code that comes after run without errors, but I see some weird behavior when I try to actually use the model to run inference.
- I am unable to use the saved_model generated by export_tflite_graph_tf2.py. When running the following code, I get an error:
print('loading model...')
model = tf.saved_model.load(tflite_base)
print('model loaded!')
results = model(image_np)
TypeError: '_UserObject' object is not callable --> results = model(image_np)
As a result, I have no way to tell if the script broke my model or not before I even convert it to tflite. Why would model not be callable in this way? I have even verified that the type returned by tf.saved_model.load() is the same when I pass in a saved_model before it went through the export_tflite_graph_tf2.py script and after. The only possible explanation I can think of is that the script alters the object in some way that causes it to break.
- I convert to tflite with post-training quantization with the following code:
def representative_data_gen():
dataset_list = tf.data.Dataset.list_files(images_dir + '/*')
for i in range(100):
image = next(iter(dataset_list))
image = tf.io.read_file(image)
# supports PNG as well
image = tf.io.decode_image(image, channels=3)
image = tf.image.resize(image, [IMAGE_SIZE, IMAGE_SIZE])
image = tf.cast(image / 255., tf.float32)
image = tf.expand_dims(image, 0)
if i == 0:
print(image.dtype)
yield [image]
# This enables quantization
# This sets the representative dataset for quantization
converter = tf.lite.TFLiteConverter.from_saved_model(base_saved_model)
# converter = tf.lite.TFLiteConverter.from_keras(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT] # issue here?
converter.representative_dataset = representative_data_gen
converter.target_spec.supported_ops = [
# tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.
# tf.lite.OpsSet.SELECT_TF_OPS, # enable TensorFlow ops.
tf.lite.OpsSet.TFLITE_BUILTINS_INT8 # This ensures that if any ops can't be quantized, the converter throws an error
]
# This ensures that if any ops can't be quantized, the converter throws an error
# For full integer quantization, though supported types defaults to int8 only, we explicitly declare it for clarity.
converter.target_spec.supported_types = [tf.int8]
converter.target_spec.supported_ops += [tf.lite.OpsSet.TFLITE_BUILTINS]
# These set the input and output tensors to uint8 (added in r2.3)
converter.inference_input_type = tf.uint8
converter.inference_output_type = tf.uint8
tflite_model_quantized = converter.convert()
Everything runs with no errors, but when I try to actually run an image through the model, it returns garbage. I tried removing the quantization to see if that was the issue, but even without quantization it returns seemingly random bounding boxes that are completely off from the model's performance prior to conversion. The shape of the output tensors look fine, it's just the content is all wrong.
What's the right way to get this model converted to a quantized tflite form? I should note that I can't use the tflite_convert utility because I need to quantize the model, and it appears according to the source code that the quantize_weights flag is deprecated? There are a bunch of conflicting resources I see from TF1 and TF2 about this conversion process so I'm pretty confused.
Note: I'm using a retrained SSD MobileNet from the model zoo. I have not made any changes to the architecture in my training workflow. I've confirmed that the errors persist even on the base model pulled directly from the object detection model zoo.