tf.lite.experimental.Analyzer

Provides a collection of TFLite model analyzer tools.

Used in the notebooks

Used in the guide

Example:

model = tf.keras.applications.MobileNetV3Large()
fb_model = tf.lite.TFLiteConverterV2.from_keras_model(model).convert()
tf.lite.experimental.Analyzer.analyze(model_content=fb_model)
# === TFLite ModelAnalyzer ===
#
# Your TFLite model has ‘1’ subgraph(s). In the subgraph description below,
# T# represents the Tensor numbers. For example, in Subgraph#0, the MUL op
# takes tensor #0 and tensor #19 as input and produces tensor #136 as output.
#
# Subgraph#0 main(T#0) -> [T#263]
#   Op#0 MUL(T#0, T#19) -> [T#136]
#   Op#1 ADD(T#136, T#18) -> [T#137]
#   Op#2 CONV_2D(T#137, T#44, T#93) -> [T#138]
#   Op#3 HARD_SWISH(T#138) -> [T#139]
#   Op#4 DEPTHWISE_CONV_2D(T#139, T#94, T#24) -> [T#140]
#   ...

Methods

analyze

View source

@staticmethod
analyze(
    model_path=None, model_content=None, gpu_compatibility=False, **kwargs
)

Analyzes the given tflite_model with dumping model structure.

This tool provides a way to understand users' TFLite flatbuffer model by dumping internal graph structure. It also provides additional features like checking GPU delegate compatibility.

Args
model_path TFLite flatbuffer model path.
model_content TFLite flatbuffer model object.
gpu_compatibility Whether to check GPU delegate compatibility.
**kwargs Experimental keyword arguments to analyze API.

Returns
Print analyzed report via console output.