def load(self, path, shape_dict=None):
# pylint: disable=C0415
import tflite.Model as model
with open(path, "rb") as tf_graph:
content = tf_graph.read()
# tflite.Model.Model is tflite.Model in 1.14 and 2.1.0
try:
tflite_model = model.Model.GetRootAsModel(content, 0)
except AttributeError:
tflite_model = model.GetRootAsModel(content, 0)
try:
version = tflite_model.Version()
logger.debug("tflite version %s", version)
except Exception:
raise TVMCException("input file not tflite")
if version != 3:
raise TVMCException("input file not tflite version 3")
logger.debug("tflite_input_type")
input_shapes, dtype_dict = TFLiteFrontend._input_type(tflite_model)
if shape_dict is not None:
input_shapes.update(shape_dict)
logger.debug(
"parse TFLite model and convert into Relay computation graph")
mod, params = relay.frontend.from_tflite(tflite_model,
shape_dict=input_shapes,
dtype_dict=dtype_dict)
return mod, params
from tflite import Model
import numpy as np
from collections import OrderedDict
from facial_lm_model import FacialLM_Model
from utils import GetKeysDict
import torch
data = open("model_weights/face_landmark.tflite", "rb").read()
model = Model.GetRootAsModel(data, 0)
tflite_graph = model.Subgraphs(0)
tflite_graph.Name()
# Tensor name to index mapping
tflite_tensor_dict = {}
for i in range(tflite_graph.TensorsLength()):
tflite_tensor_dict[tflite_graph.Tensors(i).Name().decode("utf8")] = i
def get_weights(tensor_name):
index = tflite_tensor_dict[tensor_name]
tensor = tflite_graph.Tensors(index)
buffer = tensor.Buffer()
shape = [tensor.Shape(i) for i in range(tensor.ShapeLength())]
weights = model.Buffers(buffer).DataAsNumpy()
weights = weights.view(dtype=np.float32)
weights = weights.reshape(shape)
return weights
import tflite
from tflite import Model
import pdb
buf = open('weights/yolov3_quant.tflite', 'rb').read()
model = Model.GetRootAsModel(buf, 0)
subgraph = model.Subgraphs(0)
# print(dir(subgraph))
print('subgraph.OperatorsLength()', subgraph.OperatorsLength())
n_ops = subgraph.OperatorsLength()
print('n_ops', n_ops)
i_op = 0
# op = subgraph.Operators(i_op)
# print("Inputs", op.Inputs(0), op.Inputs(1))
# print(op.BuiltinOptionsType())
# assert(op.BuiltinOptionsType() == tflite.BuiltinOptions.ReshapeOptions)
for i_op in range(n_ops):
op = subgraph.Operators(i_op)
if op.BuiltinOptionsType() == tflite.BuiltinOptions.ReshapeOptions:
print(i_op)
i_out = op.Outputs(0)
out = subgraph.Tensors(i_out)
print('out', out.Name())
op_opt = op.BuiltinOptions()
opt = tflite.ReshapeOptions()
opt.Init(op_opt.Bytes, op_opt.Pos)
print(opt.NewShapeAsNumpy())
# print(op.BuiltinOptions())