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
def guess_frontend(path):
"""
This function will try to imply which framework is being used,
based on the extension of the file provided in the path parameter.
Parameters
----------
path : str
The path to the model file.
Returns
-------
frontend : tvm.driver.tvmc.Frontend
An instance of the frontend that matches with
the file extension provided in `path`.
"""
suffix = Path(path).suffix.lower()
if suffix.startswith("."):
suffix = suffix[1:]
for frontend in ALL_FRONTENDS:
if suffix in frontend.suffixes():
return frontend()
raise TVMCException(
"failed to infer the model format. Please specify --model-format")
def get_frontend_by_name(name: str):
"""
This function will try to get a frontend instance, based
on the name provided.
Parameters
----------
name : str
the name of a given frontend
Returns
-------
frontend : tvm.driver.tvmc.Frontend
An instance of the frontend that matches with
the file extension provided in `path`.
"""
for frontend in ALL_FRONTENDS:
if name == frontend.name():
return frontend()
raise TVMCException(
"unrecognized frontend '{0}'. Choose from: {1}".format(name, get_frontend_names())
)
def load(self, path, shape_dict=None, **kwargs):
# pylint: disable=C0103
tf, keras = import_keras()
# tvm build currently imports keras directly instead of tensorflow.keras
try:
model = keras.models.load_model(path)
except ValueError as err:
raise TVMCException(str(err))
# There are two flavours of keras model, sequential and
# functional, TVM expects a functional model, so convert
# if required:
if self.is_sequential_p(model):
model = self.sequential_to_functional(model)
in_shapes = []
for layer in model._input_layers:
if tf.executing_eagerly():
in_shapes.append(tuple(dim if dim is not None else 1 for dim in layer.input.shape))
else:
in_shapes.append(
tuple(dim.value if dim.value is not None else 1 for dim in layer.input.shape)
)
inputs = [np.random.uniform(size=shape, low=-1.0, high=1.0) for shape in in_shapes]
input_shapes = {name: x.shape for (name, x) in zip(model.input_names, inputs)}
if shape_dict is not None:
input_shapes.update(shape_dict)
kwargs.setdefault("layout", "NHWC")
return relay.frontend.from_keras(model, input_shapes, **kwargs)
def load(self, path, shape_dict=None, **kwargs):
# pylint: disable=C0415
import torch
if shape_dict is None:
raise TVMCException("--input-shapes must be specified for %s" % self.name())
traced_model = torch.jit.load(path)
traced_model.eval() # Switch to inference mode
# Convert shape dictionary to list for Pytorch frontend compatibility
input_shapes = list(shape_dict.items())
logger.debug("parse Torch model and convert into Relay computation graph")
return relay.frontend.from_pytorch(traced_model, input_shapes, **kwargs)
def load(self, path, shape_dict=None, **kwargs):
model = lazy_import("tflite.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("parse TFLite model and convert into Relay computation graph")
mod, params = relay.frontend.from_tflite(tflite_model, shape_dict=shape_dict, **kwargs)
return mod, params