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# SPDX-FileCopyrightText: Copyright 2022-2023, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Model configuration."""
from __future__ import annotations
import logging
import tempfile
from collections import defaultdict
from pathlib import Path
import numpy as np
import tensorflow as tf
from mlia.core.context import Context
from mlia.nn.tensorflow.tflite_graph import load_fb
from mlia.nn.tensorflow.tflite_graph import save_fb
from mlia.nn.tensorflow.utils import convert_to_tflite
from mlia.nn.tensorflow.utils import is_keras_model
from mlia.nn.tensorflow.utils import is_saved_model
from mlia.nn.tensorflow.utils import is_tflite_model
from mlia.nn.tensorflow.utils import save_tflite_model
from mlia.utils.logging import log_action
logger = logging.getLogger(__name__)
class ModelConfiguration:
"""Base class for model configuration."""
def __init__(self, model_path: str | Path) -> None:
"""Init model configuration instance."""
self.model_path = str(model_path)
def convert_to_tflite(
self, tflite_model_path: str | Path, quantized: bool = False
) -> TFLiteModel:
"""Convert model to TensorFlow Lite format."""
raise NotImplementedError()
def convert_to_keras(self, keras_model_path: str | Path) -> KerasModel:
"""Convert model to Keras format."""
raise NotImplementedError()
class KerasModel(ModelConfiguration):
"""Keras model configuration.
Supports all models supported by Keras API: saved model, H5, HDF5
"""
def get_keras_model(self) -> tf.keras.Model:
"""Return associated Keras model."""
return tf.keras.models.load_model(self.model_path)
def convert_to_tflite(
self, tflite_model_path: str | Path, quantized: bool = False
) -> TFLiteModel:
"""Convert model to TensorFlow Lite format."""
with log_action("Converting Keras to TensorFlow Lite ..."):
converted_model = convert_to_tflite(self.get_keras_model(), quantized)
save_tflite_model(converted_model, tflite_model_path)
logger.debug(
"Model %s converted and saved to %s", self.model_path, tflite_model_path
)
return TFLiteModel(tflite_model_path)
def convert_to_keras(self, keras_model_path: str | Path) -> KerasModel:
"""Convert model to Keras format."""
return self
class TFLiteModel(ModelConfiguration): # pylint: disable=abstract-method
"""TensorFlow Lite model configuration."""
def __init__(
self,
model_path: str | Path,
batch_size: int | None = None,
num_threads: int | None = None,
) -> None:
"""Initiate a TFLite Model."""
super().__init__(model_path)
if not num_threads:
num_threads = None
if not batch_size:
self.interpreter = tf.lite.Interpreter(
model_path=self.model_path, num_threads=num_threads
)
else: # if a batch size is specified, modify the TFLite model to use this size
with tempfile.TemporaryDirectory() as tmp:
flatbuffer = load_fb(self.model_path)
for subgraph in flatbuffer.subgraphs:
for tensor in list(subgraph.inputs) + list(subgraph.outputs):
subgraph.tensors[tensor].shape = np.array(
[batch_size] + list(subgraph.tensors[tensor].shape[1:]),
dtype=np.int32,
)
tempname = Path(tmp, "rewrite_tmp.tflite")
save_fb(flatbuffer, tempname)
self.interpreter = tf.lite.Interpreter(
model_path=str(tempname), num_threads=num_threads
)
try:
self.interpreter.allocate_tensors()
except RuntimeError:
self.interpreter = tf.lite.Interpreter(
model_path=self.model_path, num_threads=num_threads
)
self.interpreter.allocate_tensors()
# Get input and output tensors.
self.input_details = self.interpreter.get_input_details()
self.output_details = self.interpreter.get_output_details()
details = list(self.input_details) + list(self.output_details)
self.handle_from_name = {d["name"]: d["index"] for d in details}
self.shape_from_name = {d["name"]: d["shape"] for d in details}
self.batch_size = next(iter(self.shape_from_name.values()))[0]
def __call__(self, named_input: dict) -> dict:
"""Execute the model on one or a batch of named inputs \
(a dict of name: numpy array)."""
input_len = next(iter(named_input.values())).shape[0]
full_steps = input_len // self.batch_size
remainder = input_len % self.batch_size
named_ys = defaultdict(list)
for i in range(full_steps):
for name, x_batch in named_input.items():
x_tensor = x_batch[i : i + self.batch_size] # noqa: E203
self.interpreter.set_tensor(self.handle_from_name[name], x_tensor)
self.interpreter.invoke()
for output_detail in self.output_details:
named_ys[output_detail["name"]].append(
self.interpreter.get_tensor(output_detail["index"])
)
if remainder:
for name, x_batch in named_input.items():
x_tensor = np.zeros( # pylint: disable=invalid-name
self.shape_from_name[name]
).astype(x_batch.dtype)
x_tensor[:remainder] = x_batch[-remainder:]
self.interpreter.set_tensor(self.handle_from_name[name], x_tensor)
self.interpreter.invoke()
for output_detail in self.output_details:
named_ys[output_detail["name"]].append(
self.interpreter.get_tensor(output_detail["index"])[:remainder]
)
return {k: np.concatenate(v) for k, v in named_ys.items()}
def input_tensors(self) -> list:
"""Return name from input details."""
return [d["name"] for d in self.input_details]
def output_tensors(self) -> list:
"""Return name from output details."""
return [d["name"] for d in self.output_details]
def convert_to_tflite(
self, tflite_model_path: str | Path, quantized: bool = False
) -> TFLiteModel:
"""Convert model to TensorFlow Lite format."""
return self
class TfModel(ModelConfiguration): # pylint: disable=abstract-method
"""TensorFlow model configuration.
Supports models supported by TensorFlow API (not Keras)
"""
def convert_to_tflite(
self, tflite_model_path: str | Path, quantized: bool = False
) -> TFLiteModel:
"""Convert model to TensorFlow Lite format."""
converted_model = convert_to_tflite(self.model_path, quantized)
save_tflite_model(converted_model, tflite_model_path)
return TFLiteModel(tflite_model_path)
def get_model(model: str | Path) -> ModelConfiguration:
"""Return the model object."""
if is_tflite_model(model):
return TFLiteModel(model)
if is_keras_model(model):
return KerasModel(model)
if is_saved_model(model):
return TfModel(model)
raise ValueError(
"The input model format is not supported "
"(supported formats: TensorFlow Lite, Keras, TensorFlow saved model)!"
)
def get_tflite_model(model: str | Path, ctx: Context) -> TFLiteModel:
"""Convert input model to TensorFlow Lite and returns TFLiteModel object."""
dst_model_path = ctx.get_model_path("converted_model.tflite")
src_model = get_model(model)
return src_model.convert_to_tflite(dst_model_path, quantized=True)
def get_keras_model(model: str | Path, ctx: Context) -> KerasModel:
"""Convert input model to Keras and returns KerasModel object."""
keras_model_path = ctx.get_model_path("converted_model.h5")
converted_model = get_model(model)
return converted_model.convert_to_keras(keras_model_path)
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