How to read parameters of layers of .tflite model in python - tensorflow

Question

I was trying to read tflite model and pull all the parameters of the layers out.
My steps:
I generated flatbuffers model representation by running (please build flatc before):
flatc -python tensorflow/tensorflow/lite/schema/schema.fbs
Result is tflite/ folder that contains layer description files (*.py) and some utilitarian files.
I successfully loaded model:
in case of import Error: set PYTHONPATH to point to the folder where tflite/ is
from tflite.Model import Model
def read_tflite_model(file):
buf = open(file, "rb").read()
buf = bytearray(buf)
model = Model.GetRootAsModel(buf, 0)
return model
I partly pulled model and node parameters out and stacked in iterating over nodes:
Model part:
def print_model_info(model):
version = model.Version()
print("Model version:", version)
description = model.Description().decode('utf-8')
print("Description:", description)
subgraph_len = model.SubgraphsLength()
print("Subgraph length:", subgraph_len)
Nodes part:
def print_nodes_info(model):
# what does this 0 mean? should it always be zero?
subgraph = model.Subgraphs(0)
operators_len = subgraph.OperatorsLength()
print('Operators length:', operators_len)
from collections import deque
nodes = deque(subgraph.InputsAsNumpy())
STEP_N = 0
MAX_STEPS = operators_len
print("Nodes info:")
while len(nodes) != 0 and STEP_N <= MAX_STEPS:
print("MAX_STEPS={} STEP_N={}".format(MAX_STEPS, STEP_N))
print("-" * 60)
node_id = nodes.pop()
print("Node id:", node_id)
tensor = subgraph.Tensors(node_id)
print("Node name:", tensor.Name().decode('utf-8'))
print("Node shape:", tensor.ShapeAsNumpy())
# which type is it? what does it mean?
type_of_tensor = tensor.Type()
print("Tensor type:", type_of_tensor)
quantization = tensor.Quantization()
min = quantization.MinAsNumpy()
max = quantization.MaxAsNumpy()
scale = quantization.ScaleAsNumpy()
zero_point = quantization.ZeroPointAsNumpy()
print("Quantization: ({}, {}), s={}, z={}".format(min, max, scale, zero_point))
# I do not understand it again. what is j, that I set to 0 here?
operator = subgraph.Operators(0)
for i in operator.OutputsAsNumpy():
nodes.appendleft(i)
STEP_N += 1
print("-"*60)
Please point me to documentation or some example of using this API.
My problems are:
I can not get documentation on this API
Iterating over Tensor objects seems not possible for me, as it doesn't have Inputs and Outputs methods. + subgraph.Operators(j=0) I do not understand what j means in here. Because of that my cycle goes through two nodes: input (once) and the next one over and over again.
Iterating over Operator objects is surely possible:
Here we iterate over them all but I can not get how to map Operator and Tensor.
def print_in_out_info_of_all_operators(model):
# what does this 0 mean? should it always be zero?
subgraph = model.Subgraphs(0)
for i in range(subgraph.OperatorsLength()):
operator = subgraph.Operators(i)
print('Outputs', operator.OutputsAsNumpy())
print('Inputs', operator.InputsAsNumpy())
I do not understand how to pull parameters out Operator object. BuiltinOptions method gives me Table object, that I do not know what to map at.
subgraph = model.Subgraphs(0)
What does this 0 mean? should it always be zero? obviously no, but what is it? Id of the subgraph? If so - I'm happy. If no, please try to explain it.