fix: vision/audio argv builders silently drop or duplicate transform flags

tinyml-modelmaker/tests/test_argv_builder_transform_bugs.py

@@ -0,0 +1,120 @@
+"""Regression tests for three argv-builder bugs in vision/audio ai_modules:
+
+1. image_base.py's train argv rendered data_proc_transforms as a stringified
+   list while test argv passed the raw list -- prepare_transforms() (in
+   tinyml-tinyverse) only combines it into args.transforms when
+   isinstance(args.data_proc_transforms, list) is true, so the stringified
+   form silently skipped it during training while testing still applied it.
+
+2. audio_base.py's train and test argv builders each declared
+   --data-proc-transforms/--feat-ext-transform twice (once raw, once
+   stringified); argparse's last-occurrence-wins semantics meant the earlier,
+   correct raw declaration was always shadowed by the later, stringified one.
+
+3. Fixing (1) by making data_proc_transforms a raw list exposed a second bug
+   an independent peer review caught: prepare_transforms() does
+   `args.data_proc_transforms + args.feat_ext_transform` whenever
+   data_proc_transforms is a list. image_base.py's train argv builder still
+   stringified --feat-ext-transform (and --augmentation-transform), so this
+   became `list + str`, raising TypeError on every image-classification
+   training run. test_image_train_argv_feat_ext_transform_survives_
+   prepare_transforms below exercises the REAL prepare_transforms() against
+   the argv builder's actual output -- the shape-only MagicMock tests below
+   couldn't catch this since they never fed argv through it.
+
+Most of these are pure unit tests against the argv-building methods in
+isolation (constructed via unittest.mock.MagicMock for self, rather than a
+full ModelRunner instance) -- these methods only read attributes off
+self.params and don't need real training infrastructure.
+"""
+from argparse import Namespace
+from unittest.mock import MagicMock
+
+from tinyml_modelmaker.ai_modules.vision.training.tinyml_tinyverse.image_base import (
+    BaseImageModelTraining,
+)
+from tinyml_modelmaker.ai_modules.audio.training.tinyml_tinyverse.audio_base import (
+    BaseAudioModelTraining,
+)
+from tinyml_tinyverse.references.common.train_base import prepare_transforms
+
+
+def _argv_value_after(argv, flag):
+    return argv[argv.index(flag) + 1]
+
+
+def _count_occurrences(argv, flag):
+    return argv.count(flag)
+
+
+def test_image_train_argv_passes_data_proc_transforms_as_a_raw_list():
+    fake_self = MagicMock()
+    fake_self.params.data_processing_feature_extraction.data_proc_transforms = ["BINARIZE"]
+
+    argv = BaseImageModelTraining._build_common_train_argv(fake_self, device="cpu", distributed=0)
+
+    value = _argv_value_after(argv, "--data-proc-transforms")
+    assert value == ["BINARIZE"]
+    assert isinstance(value, list)
+
+
+def test_image_train_argv_feat_ext_transform_survives_prepare_transforms():
+    """Drives the REAL prepare_transforms() (tinyml-tinyverse) against the
+    actual argv the train-argv builder produces -- data_proc_transforms and
+    feat_ext_transform must both come out as lists, or the `+` inside
+    prepare_transforms raises TypeError."""
+    fake_self = MagicMock()
+    fake_self.params.data_processing_feature_extraction.data_proc_transforms = ["BINARIZE"]
+    fake_self.params.data_processing_feature_extraction.feat_ext_transform = ["MFCC"]
+
+    argv = BaseImageModelTraining._build_common_train_argv(fake_self, device="cpu", distributed=0)
+
+    args = Namespace(
+        data_proc_transforms=_argv_value_after(argv, "--data-proc-transforms"),
+        feat_ext_transform=_argv_value_after(argv, "--feat-ext-transform"),
+    )
+
+    prepare_transforms(args)
+
+    assert args.transforms == ["BINARIZE", "MFCC"]
+
+
+def test_image_train_and_test_argv_agree_on_data_proc_transforms_form():
+    fake_self = MagicMock()
+    fake_self.params.data_processing_feature_extraction.data_proc_transforms = ["BINARIZE", "RESIZE"]
+
+    train_argv = BaseImageModelTraining._build_common_train_argv(fake_self, device="cpu", distributed=0)
+    test_argv = BaseImageModelTraining._build_common_test_argv(
+        fake_self, device="cpu", data_path="/tmp/data", model_path="/tmp/model.onnx", output_dir="/tmp/out"
+    )
+
+    assert _argv_value_after(train_argv, "--data-proc-transforms") == \
+        _argv_value_after(test_argv, "--data-proc-transforms")
+
+
+def test_audio_train_argv_declares_data_proc_transforms_exactly_once():
+    fake_self = MagicMock()
+    fake_self.params.data_processing_feature_extraction.data_proc_transforms = ["NORMALIZE"]
+    fake_self.params.data_processing_feature_extraction.feat_ext_transform = ["MFCC"]
+
+    argv = BaseAudioModelTraining._build_common_train_argv(fake_self, device="cpu", distributed=0)
+
+    assert _count_occurrences(argv, "--data-proc-transforms") == 1
+    assert _count_occurrences(argv, "--feat-ext-transform") == 1
+    assert _argv_value_after(argv, "--data-proc-transforms") == ["NORMALIZE"]
+    assert _argv_value_after(argv, "--feat-ext-transform") == ["MFCC"]
+
+
+def test_audio_test_argv_declares_data_proc_transforms_exactly_once():
+    fake_self = MagicMock()
+    fake_self.params.data_processing_feature_extraction.data_proc_transforms = ["NORMALIZE"]
+    fake_self.params.data_processing_feature_extraction.feat_ext_transform = ["MFCC"]
+
+    argv = BaseAudioModelTraining._build_common_test_argv(
+        fake_self, device="cpu", data_path="/tmp/data", model_path="/tmp/model.onnx", output_dir="/tmp/out"
+    )
+
+    assert _count_occurrences(argv, "--data-proc-transforms") == 1
+    assert _count_occurrences(argv, "--feat-ext-transform") == 1
+    assert _argv_value_after(argv, "--data-proc-transforms") == ["NORMALIZE"]
+    assert _argv_value_after(argv, "--feat-ext-transform") == ["MFCC"]

tinyml-modelmaker/tinyml_modelmaker/ai_modules/audio/training/tinyml_tinyverse/audio_base.py

@@ -341,9 +341,6 @@ def _build_common_train_argv(self, device, distributed):
             '--normalize-audio', f'{self.params.data_processing_feature_extraction.normalize_audio}',
             '--mono', f'{self.params.data_processing_feature_extraction.mono}',
 
-            '--data-proc-transforms', f'{self.params.data_processing_feature_extraction.data_proc_transforms}',
-            '--feat-ext-transform', f'{self.params.data_processing_feature_extraction.feat_ext_transform}',
-
             '--output-int', f'{self.params.training.output_int}',
             '--variables', f'{self.params.data_processing_feature_extraction.variables}',
             '--lis', f'{self.params.training.log_file_path}',
@@ -392,8 +389,6 @@ def _build_common_test_argv(self, device, data_path, model_path, output_dir):
             '--normalize-audio', f'{self.params.data_processing_feature_extraction.normalize_audio}',
             '--mono', f'{self.params.data_processing_feature_extraction.mono}',
 
-            '--data-proc-transforms', f'{self.params.data_processing_feature_extraction.data_proc_transforms}',
-            '--feat-ext-transform', f'{self.params.data_processing_feature_extraction.feat_ext_transform}',
             '--nn-for-feature-extraction', f'{self.params.data_processing_feature_extraction.nn_for_feature_extraction}',
             '--output-int', f'{self.params.training.output_int}',
 

tinyml-modelmaker/tinyml_modelmaker/ai_modules/vision/training/tinyml_tinyverse/image_base.py

@@ -339,9 +339,18 @@ def _build_common_train_argv(self, device, distributed):
             '--generic-model', f'{self.params.common.generic_model}',
             '--sampling-rate', f'{self.params.data_processing_feature_extraction.sampling_rate}',
             # Transform
-            '--data-proc-transforms', f'{self.params.data_processing_feature_extraction.data_proc_transforms}',
-            '--feat-ext-transform', f'{self.params.data_processing_feature_extraction.feat_ext_transform}',
-            '--augmentation-transform', f'{self.params.data_processing_feature_extraction.augmentation_transform}',
+            # Pass raw lists (not stringified) -- matches the test argv builder
+            # below and timeseries_base.py's reference implementation.
+            # prepare_transforms() (train_base.py) does
+            # `args.data_proc_transforms + args.feat_ext_transform` whenever
+            # isinstance(args.data_proc_transforms, list) is true. Both operands
+            # must therefore be actual lists at that point, not just
+            # data_proc_transforms: a stringified feat_ext_transform (e.g. "[]")
+            # makes this `list + str`, raising TypeError on every training run
+            # once data_proc_transforms itself was made a raw list.
+            '--data-proc-transforms', self.params.data_processing_feature_extraction.data_proc_transforms,
+            '--feat-ext-transform', self.params.data_processing_feature_extraction.feat_ext_transform,
+            '--augmentation-transform', self.params.data_processing_feature_extraction.augmentation_transform,
             '--feat-ext-store-dir', f'{self.params.data_processing_feature_extraction.feat_ext_store_dir}',
             '--dont-train-just-feat-ext', f'{self.params.data_processing_feature_extraction.dont_train_just_feat_ext}',
             '--store-feat-ext-data', f'{self.params.data_processing_feature_extraction.store_feat_ext_data}',