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# SPDX-FileCopyrightText: Copyright 2023, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
import json
import os
import random
import tempfile
from collections import defaultdict
import numpy as np
from mlia.nn.rewrite.core.utils.utils import load
from mlia.nn.rewrite.core.utils.utils import save
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
import tensorflow as tf
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
from tensorflow.lite.python import interpreter as interpreter_wrapper
def make_decode_fn(filename):
def decode_fn(record_bytes, type_map):
parse_dict = {
name: tf.io.FixedLenFeature([], tf.string) for name in type_map.keys()
}
example = tf.io.parse_single_example(record_bytes, parse_dict)
features = {
n: tf.io.parse_tensor(example[n], tf.as_dtype(t))
for n, t in type_map.items()
}
return features
meta_filename = filename + ".meta"
with open(meta_filename) as f:
type_map = json.load(f)["type_map"]
return lambda record_bytes: decode_fn(record_bytes, type_map)
def NumpyTFReader(filename):
decode_fn = make_decode_fn(filename)
dataset = tf.data.TFRecordDataset(filename)
return dataset.map(decode_fn)
def numpytf_count(filename):
meta_filename = filename + ".meta"
with open(meta_filename) as f:
return json.load(f)["count"]
class NumpyTFWriter:
def __init__(self, filename):
self.filename = filename
self.meta_filename = filename + ".meta"
self.writer = tf.io.TFRecordWriter(filename)
self.type_map = {}
self.count = 0
def __enter__(self):
return self
def __exit__(self, type, value, traceback):
self.close()
def __del__(self):
self.close()
def write(self, array_dict):
type_map = {n: str(a.dtype.name) for n, a in array_dict.items()}
self.type_map.update(type_map)
self.count += 1
feature = {
n: tf.train.Feature(
bytes_list=tf.train.BytesList(value=[tf.io.serialize_tensor(a).numpy()])
)
for n, a in array_dict.items()
}
example = tf.train.Example(features=tf.train.Features(feature=feature))
self.writer.write(example.SerializeToString())
def close(self):
with open(self.meta_filename, "w") as f:
meta = {"type_map": self.type_map, "count": self.count}
json.dump(meta, f)
self.writer.close()
class TFLiteModel:
def __init__(self, filename, batch_size=None, num_threads=None):
if num_threads == 0:
num_threads = None
if batch_size == None:
self.interpreter = interpreter_wrapper.Interpreter(
model_path=filename, num_threads=num_threads
)
else: # if a batch size is specified, modify the TFLite model to use this size
with tempfile.TemporaryDirectory() as tmp:
fb = load(filename)
for sg in fb.subgraphs:
for t in list(sg.inputs) + list(sg.outputs):
sg.tensors[t].shape = np.array(
[batch_size] + list(sg.tensors[t].shape[1:]), dtype=np.int32
)
tempname = os.path.join(tmp, "rewrite_tmp.tflite")
save(fb, tempname)
self.interpreter = interpreter_wrapper.Interpreter(
model_path=tempname, num_threads=num_threads
)
try:
self.interpreter.allocate_tensors()
except RuntimeError:
self.interpreter = interpreter_wrapper.Interpreter(
model_path=filename, 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):
"""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 = x_batch[i : i + self.batch_size]
self.interpreter.set_tensor(self.handle_from_name[name], x)
self.interpreter.invoke()
for d in self.output_details:
named_ys[d["name"]].append(self.interpreter.get_tensor(d["index"]))
if remainder:
for name, x_batch in named_input.items():
x = np.zeros(self.shape_from_name[name]).astype(x_batch.dtype)
x[:remainder] = x_batch[-remainder:]
self.interpreter.set_tensor(self.handle_from_name[name], x)
self.interpreter.invoke()
for d in self.output_details:
named_ys[d["name"]].append(
self.interpreter.get_tensor(d["index"])[:remainder]
)
return {k: np.concatenate(v) for k, v in named_ys.items()}
def input_tensors(self):
return [d["name"] for d in self.input_details]
def output_tensors(self):
return [d["name"] for d in self.output_details]
def sample_tfrec(input_file, k, output_file):
total = numpytf_count(input_file)
next = sorted(random.sample(range(total), k=k), reverse=True)
reader = NumpyTFReader(input_file)
with NumpyTFWriter(output_file) as writer:
for i, data in enumerate(reader):
if i == next[-1]:
next.pop()
writer.write(data)
if not next:
break
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