tflite_runtime, but easier.
Having to select a platform-specific url from here?
Uh no. certainly not 🔥.
What this does:
- Detects your platform + Python version so you don't have to pick the right url and you can add
tflite_runtimeas a dependency without having to pick a single platform to support.
- Creates a familiar
keras-like interface for models, so you can do
tflit.Model(path).predict(X)without ever having to think about tensor indexes or three step predictions, or batching.
pip install tflit
I tried to provide an interface as similar to Keras as possible.
import tflit model = tflit.Model('path/to/model.tflite') model.summary() # prints input and output details print(model.input_shape) # (10, 30) - a single input print(model.output_shape) # [(5, 2), (1, 2)] - two outputs print(model.dtype) # 'float32' # *see notes below print(model.input_names) # may not preserve names (based on how you export) print(model.output_names) # doesn't preserve names atm # predict over batches of outputs. y_pred = model.predict(np.random.randn(32, 10, 30)) # predict single output at a time y_pred = model.predict_batch(np.random.randn(1, 10, 30))
Additional tflite stuff not in Keras
# remember, you can access the tflite_runtime interpreter directly # so if something is being weird, please submit an issue, but also # there's not that much code in here so just look here to figure out # the right way: # https://www.tensorflow.org/lite/api_docs/python/tf/lite/Interpreter interpreter = model.interpreter # change the model's batch size model.set_batch_size(64) # reset the model variables model.reset() # get tensor by index model.input(1) # 2nd input model.output(0) # 1st output # get tensor value copy by index model.input_value(1) # 2nd input model.output_value(0) # 1st output
Just for reference, this is how I used to do it:
def load_tflite_model_function(model_path, **kw): import tflite_runtime.interpreter as tflite compute = prepare_model_function(tflite.Interpreter(model_path), **kw) compute.model_path = model_path return compute def prepare_model_function(model, verbose=False): # assumes a single input and output in_dets = model.get_input_details() out_dets = model.get_output_details() model.allocate_tensors() def compute(x): # set inputs model.set_tensor(in_dets['index'], X.astype(in_dets['dtype'])) # compute outputs model.invoke() # get outputs return model.get_tensor(out_dets['index']) if verbose: print('-- Input details --') print(in_dets, '\n') print('-- Output details --') print(out_dets, '\n') # set input and output shapes so they're easily accessible compute.input_shape = in_dets['shape'][1:] compute.output_shape = out_dets['shape'][1:] return compute
This was cleaner than the code that I factored it out from, but it is still unnecessarily complex and I got tired after copying it over to my 3rd project. This also doesn't handle things like multiple inputs/outputs or batching.
Update 7/8/21: Tensorflow has gotten a bit better about being able to pip install tflite_runtime - however, I don't understand why they won't just release it to PyPI, like I'm sure it's just like one or two devs at google, but like if you're already pushing to google coral, why can't you just install twine too? So now you can install it with:
pip install --index-url https://google-coral.github.io/py-repo/ tflite_runtime, except, you have to specify the index url specifically (UGH!)
I was having trouble getting tflite_runtime to install as a dependency in
setup.pyso right now, it's just installing on first run if it's not already installed. I'll probably fix that at some point... but I have other things that I need to be doing and this is working atm. Hopefully tensorflow will just start deploying to pypi and this will all be resolved. Not sure what's going on there...
It's possible that
tflite_runtimemay not have a build for your system. Check this link to verify.
There's a bug with the current
tfliteconverter where it doesn't copy over the input and output names.
However, if you do this when you're exporting, the input names will be saved:
converter = tf.lite.TFLiteConverter.from_keras_model(model) converter.experimental_new_converter = True # <<< this tflite_model = converter.convert()
But still no luck with the output names :/. To be clear, this is a tensorflow issue and I have no control over this.
I intended to have a
model.set_batch_sizemethod to change the batch size at runtime, but it doesn't currently work because tflite freaks out about there being an increased tensor size (it doesn't know how to broadcast). This is also a tensorflow issue.
For the time being, we just compute one batch at a time and concatenate them at the end. If the model's fixed batch size doesn't divide evenly, it will throw an error. By default, tflite converts
Nonebatch sizes to
1so most of the time it won't be a problem. To compute a single frame, it is more efficient to use
I would love to get both of these resolved, but they are out of my control and I don't really have the bandwidth or the urgent need to have these resolved.
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