1 files changed, 28 insertions, 23 deletions
diff --git a/src/mlia/nn/rewrite/core/graph_edit/record.py b/src/mlia/nn/rewrite/core/graph_edit/record.py
index ae13313..90f3db8 100644
--- a/src/mlia/nn/rewrite/core/graph_edit/record.py
+++ b/src/mlia/nn/rewrite/core/graph_edit/record.py
@@ -1,34 +1,39 @@
 # SPDX-FileCopyrightText: Copyright 2023, Arm Limited and/or its affiliates.
 # SPDX-License-Identifier: Apache-2.0
+"""Save subgraph data."""
+# pylint: disable=too-many-locals
+from __future__ import annotations
+
 import math
 import os
+from pathlib import Path
 
-os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 import tensorflow as tf
+from rich.progress import track
 
-tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
-
-from tqdm import tqdm
-from mlia.nn.rewrite.core.utils.numpy_tfrecord import (
-    NumpyTFReader,
-    NumpyTFWriter,
-    TFLiteModel,
-    numpytf_count,
-)
+from mlia.nn.rewrite.core.utils.numpy_tfrecord import numpytf_count
+from mlia.nn.rewrite.core.utils.numpy_tfrecord import numpytf_read
+from mlia.nn.rewrite.core.utils.numpy_tfrecord import NumpyTFWriter
 from mlia.nn.rewrite.core.utils.parallel import ParallelTFLiteModel
 
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+
 
 def record_model(
-    input_filename,
-    model_filename,
-    output_filename,
-    batch_size=None,
-    show_progress=False,
-    num_procs=1,
-    num_threads=0,
-):
-    """num_procs: 0 => detect real cores on system
-    num_threads: 0 => TFLite impl. specific setting, usually 3"""
+    input_filename: str | Path,
+    model_filename: str | Path,
+    output_filename: str | Path,
+    batch_size: int = 0,
+    show_progress: bool = False,
+    num_procs: int = 1,
+    num_threads: int = 0,
+) -> None:
+    """Model recorder.
+
+    num_procs: 0 => detect real cores on system
+    num_threads: 0 => TFLite impl. specific setting, usually 3
+    """
     model = ParallelTFLiteModel(model_filename, num_procs, num_threads, batch_size)
     if not batch_size:
         batch_size = (
@@ -36,10 +41,10 @@ def record_model(
         )  # automatically batch to the minimum effective size if not specified
 
     total = numpytf_count(input_filename)
-    dataset = NumpyTFReader(input_filename)
+    dataset = numpytf_read(input_filename)
 
     if batch_size > 1:
-        # Collapse batch-size 1 items into batch-size n. I regret using batch-size 1 items in tfrecs now.
+        # Collapse batch-size 1 items into batch-size n.
         dataset = dataset.map(
             lambda d: {k: tf.squeeze(v, axis=0) for k, v in d.items()}
         )
@@ -48,7 +53,7 @@ def record_model(
 
     with NumpyTFWriter(output_filename) as writer:
         for _, named_x in enumerate(
-            tqdm(dataset.as_numpy_iterator(), total=total, disable=not show_progress)
+            track(dataset.as_numpy_iterator(), total=total, disable=not show_progress)
         ):
             named_y = model(named_x)
             if batch_size > 1: