Consolidate search and small-classifier code under inference.

PiperOrigin-RevId: 615067551

.github/workflows/ci_no_jaxtrain.yml

@@ -32,4 +32,4 @@ jobs:
           poetry install --without jaxtrain
       - name: Test with unittest
         # TODO: Group together jaxtrain tests so they can be easily excluded.
-        run: poetry run python -m unittest discover -s chirp/tests -p "*inference_test.py"
+        run: poetry run python -m unittest discover -s chirp/inference/tests -p "*test.py"

chirp/projects/multicluster/classify.py → chirp/inference/classify/classify.py

@@ -18,8 +18,8 @@
 import dataclasses
 from typing import Sequence
 
+from chirp.inference.classify import data_lib
 from chirp.models import metrics
-from chirp.projects.multicluster import data_lib
 import numpy as np
 import tensorflow as tf
 
@@ -131,9 +131,7 @@ def train_embedding_model(
 ) -> ClassifierMetrics:
   """Trains a classification model over embeddings and labels."""
   train_locs, test_locs, _ = merged.create_random_train_test_split(
-      train_ratio,
-      train_examples_per_class,
-      random_seed,
+      train_ratio, train_examples_per_class, random_seed,
       exclude_eval_classes=exclude_eval_classes,
   )
   test_metrics = train_from_locs(

chirp/projects/bootstrap/bootstrap.py → chirp/inference/search/bootstrap.py

@@ -91,12 +91,12 @@ class BootstrapConfig:
   tensor_dtype: str
 
   # The following are populated automatically from the embedding config.
-  embedding_hop_size_s: float | None = None
-  file_id_depth: int | None = None
-  audio_globs: Sequence[str] | None = None
-  model_key: str | None = None
-  model_config: config_dict.ConfigDict | None = None
-  tf_record_shards: int | None = None
+  embedding_hop_size_s: float
+  file_id_depth: int
+  audio_globs: Sequence[str]
+  model_key: str
+  model_config: config_dict.ConfigDict
+  tf_record_shards: int
 
   @classmethod
   def load_from_embedding_config(

chirp/projects/bootstrap/display.py → chirp/inference/search/display.py

@@ -19,8 +19,8 @@
 from typing import Sequence
 
 from chirp import audio_utils
+from chirp.inference.search import search
 from chirp.models import frontend
-from chirp.projects.bootstrap import search
 import IPython
 from IPython.display import display as ipy_display
 import ipywidgets

chirp/tests/inference_test.py → chirp/inference/tests/embed_test.py

@@ -13,7 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""Tests for inference library."""
+"""Tests for mass-embedding functionality."""
 
 import os
 import tempfile
@@ -28,12 +28,12 @@
 from chirp.inference import interface
 from chirp.inference import models
 from chirp.inference import tf_examples
+from chirp.inference.classify import classify
+from chirp.inference.classify import data_lib
+from chirp.inference.search import bootstrap
+from chirp.inference.search import display
+from chirp.inference.search import search
 from chirp.models import metrics
-from chirp.projects.bootstrap import bootstrap
-from chirp.projects.bootstrap import display
-from chirp.projects.bootstrap import search
-from chirp.projects.multicluster import classify
-from chirp.projects.multicluster import data_lib
 from chirp.taxonomy import namespace
 from etils import epath
 from ml_collections import config_dict
@@ -56,7 +56,7 @@ def _make_output_head_model(model_path: str, embedding_dim: int = 1280):
   )
 
 
-class InferenceTest(parameterized.TestCase):
+class EmbedTest(parameterized.TestCase):
 
   def test_imports(self):
     # Test that imports work in external github environment.
@@ -139,9 +139,7 @@ def test_embed_fn(
     self.assertIsNotNone(embed_fn.embedding_model)
 
     test_wav_path = os.fspath(
-        path_utils.get_absolute_path(
-            'tests/testdata/tfds_builder_wav_directory_test/clap.wav'
-        )
+        path_utils.get_absolute_path('inference/tests/testdata/clap.wav')
     )
 
     source_info = embed_lib.SourceInfo(test_wav_path, 0, 10)
@@ -205,17 +203,17 @@ def test_embed_fn(
   def test_embed_fn_from_config(self, config_filename):
     # Test that we can load a model from a golden config and compute embeddings.
     test_config_path = os.fspath(
-        path_utils.get_absolute_path(f'tests/testdata/{config_filename}.json')
+        path_utils.get_absolute_path(
+            f'inference/tests/testdata/{config_filename}.json'
+        )
     )
     embed_config = embed_lib.load_embedding_config(test_config_path, '')
     embed_fn = embed_lib.EmbedFn(**embed_config)
     embed_fn.setup()
     self.assertIsNotNone(embed_fn.embedding_model)
 
     test_wav_path = os.fspath(
-        path_utils.get_absolute_path(
-            'tests/testdata/tfds_builder_wav_directory_test/clap.wav'
-        )
+        path_utils.get_absolute_path('inference/tests/testdata/clap.wav')
     )
     source_info = embed_lib.SourceInfo(test_wav_path, 0, 10)
     example = embed_fn.process(source_info, crop_s=10.0)[0]
@@ -253,9 +251,7 @@ def test_embed_fn_source_variations(self):
     parser = tf_examples.get_example_parser()
 
     test_wav_path = os.fspath(
-        path_utils.get_absolute_path(
-            'tests/testdata/tfds_builder_wav_directory_test/clap.wav'
-        )
+        path_utils.get_absolute_path('inference/tests/testdata/clap.wav')
     )
 
     # Check that a SourceInfo with window_size_s <= 0 embeds the entire file.
@@ -315,9 +311,7 @@ def test_keyed_write_logits(self):
     self.assertIsNotNone(embed_fn.embedding_model)
 
     test_wav_path = os.fspath(
-        path_utils.get_absolute_path(
-            'tests/testdata/tfds_builder_wav_directory_test/clap.wav'
-        )
+        path_utils.get_absolute_path('inference/tests/testdata/clap.wav')
     )
 
     source_info = embed_lib.SourceInfo(test_wav_path, 0, 10)
@@ -396,9 +390,7 @@ def test_embed_short_audio(self):
     self.assertIsNotNone(embed_fn.embedding_model)
 
     test_wav_path = os.fspath(
-        path_utils.get_absolute_path(
-            'tests/testdata/tfds_builder_wav_directory_test/clap.wav'
-        )
+        path_utils.get_absolute_path('inference/tests/testdata/clap.wav')
     )
     source_info = embed_lib.SourceInfo(test_wav_path, 0, 10)
     # Crop to 3.0s to ensure we can handle short audio examples.
@@ -443,11 +435,7 @@ def test_frame_audio(self):
 
   def test_create_source_infos(self):
     # Just one file, but it's all good.
-    globs = [
-        path_utils.get_absolute_path(
-            'tests/testdata/tfds_builder_wav_directory_test/clap.wav'
-        )
-    ]
+    globs = [path_utils.get_absolute_path('inference/tests/testdata/clap.wav')]
     # Disable sharding by setting shard_len_s <= 0.
     got_infos = embed_lib.create_source_infos(
         globs, shard_len_s=-1, num_shards_per_file=100
@@ -672,9 +660,7 @@ def test_pooled_embeddings(self):
   def test_beam_pipeline(self):
     """Check that we can write embeddings to TFRecord file."""
     test_wav_path = os.fspath(
-        path_utils.get_absolute_path(
-            'tests/testdata/tfds_builder_wav_directory_test/clap.wav'
-        )
+        path_utils.get_absolute_path('inference/tests/testdata/clap.wav')
     )
     source_infos = [embed_lib.SourceInfo(test_wav_path, 0, 10)]
     base_pipeline = test_pipeline.TestPipeline()

chirp/projects/bootstrap/tests/search_test.py → chirp/inference/tests/search_test.py

@@ -15,7 +15,7 @@
 
 """Tests for the bootstrap search component."""
 
-from chirp.projects.bootstrap import search
+from chirp.inference.search import search
 import numpy as np
 
 from absl.testing import absltest