Add image and audio converter classes

keras_nlp/api/__init__.py

@@ -22,7 +22,6 @@
 from keras_nlp.api import models
 from keras_nlp.api import samplers
 from keras_nlp.api import tokenizers
-from keras_nlp.api import utils
 from keras_nlp.src.utils.preset_utils import upload_preset
 from keras_nlp.src.version_utils import __version__
 from keras_nlp.src.version_utils import version

keras_nlp/api/layers/__init__.py

@@ -36,6 +36,8 @@
 )
 from keras_nlp.src.layers.modeling.transformer_decoder import TransformerDecoder
 from keras_nlp.src.layers.modeling.transformer_encoder import TransformerEncoder
+from keras_nlp.src.layers.preprocessing.audio_converter import AudioConverter
+from keras_nlp.src.layers.preprocessing.image_converter import ImageConverter
 from keras_nlp.src.layers.preprocessing.masked_lm_mask_generator import (
     MaskedLMMaskGenerator,
 )
@@ -44,4 +46,13 @@
 )
 from keras_nlp.src.layers.preprocessing.random_deletion import RandomDeletion
 from keras_nlp.src.layers.preprocessing.random_swap import RandomSwap
+from keras_nlp.src.layers.preprocessing.resizing_image_converter import (
+    ResizingImageConverter,
+)
 from keras_nlp.src.layers.preprocessing.start_end_packer import StartEndPacker
+from keras_nlp.src.models.pali_gemma.pali_gemma_image_converter import (
+    PaliGemmaImageConverter,
+)
+from keras_nlp.src.models.whisper.whisper_audio_converter import (
+    WhisperAudioConverter,
+)

keras_nlp/api/models/__init__.py

@@ -228,13 +228,7 @@
 from keras_nlp.src.models.text_classifier_preprocessor import (
     TextClassifierPreprocessor,
 )
-from keras_nlp.src.models.whisper.whisper_audio_feature_extractor import (
-    WhisperAudioFeatureExtractor,
-)
 from keras_nlp.src.models.whisper.whisper_backbone import WhisperBackbone
-from keras_nlp.src.models.whisper.whisper_preprocessor import (
-    WhisperPreprocessor,
-)
 from keras_nlp.src.models.whisper.whisper_tokenizer import WhisperTokenizer
 from keras_nlp.src.models.xlm_roberta.xlm_roberta_backbone import (
     XLMRobertaBackbone,

keras_nlp/src/layers/preprocessing/audio_converter.py

@@ -0,0 +1,121 @@
+# Copyright 2024 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.layers.preprocessing.preprocessing_layer import (
+    PreprocessingLayer,
+)
+from keras_nlp.src.utils.preset_utils import AUDIO_CONVERTER_CONFIG_FILE
+from keras_nlp.src.utils.preset_utils import find_subclass
+from keras_nlp.src.utils.preset_utils import get_preset_loader
+from keras_nlp.src.utils.preset_utils import list_presets
+from keras_nlp.src.utils.preset_utils import list_subclasses
+from keras_nlp.src.utils.preset_utils import save_serialized_object
+from keras_nlp.src.utils.python_utils import classproperty
+
+
+@keras_nlp_export("keras_nlp.layers.AudioConverter")
+class AudioConverter(PreprocessingLayer):
+    """Convert raw audio for models that support audio input.
+
+    This class converts from raw audio tensors of any length, to preprocessed
+    audio for pretrained model inputs. It is meant to be a convenient way to
+    write custom preprocessing code that is not model specific. This layer
+    should be instantiated via the `from_preset()` constructor, which will
+    create the correct subclass of this layer for the model preset.
+
+    The layer will take as input a raw audio tensor with shape `(batch_size,
+    num_samples)`, and output a preprocessed audio input for modeling. The exact
+    structure of the preprocessed input will vary per model. Preprocessing
+    will often include computing a spectogram of the raw audio signal.
+
+    Examples:
+    ```python
+    # Load an audio converter from a preset.
+    converter = keras_nlp.layers.AudioConverter.from_preset("whisper_base_en")
+    # Convert some raw audio input.
+    converter(np.ones(2, 1_000))
+    ```
+    """
+
+    backbone_cls = None
+
+    @classproperty
+    def presets(cls):
+        """List built-in presets for a `Task` subclass."""
+        presets = list_presets(cls)
+        for subclass in list_subclasses(cls):
+            presets.update(subclass.presets)
+        return presets
+
+    @classmethod
+    def from_preset(
+        cls,
+        preset,
+        **kwargs,
+    ):
+        """Instantiate a `keras_nlp.layers.AudioConverter` from a model preset.
+
+        A preset is a directory of configs, weights and other file assets used
+        to save and load a pre-trained model. The `preset` can be passed as
+        one of:
+
+        1. a built-in preset identifier like `'whisper_base_en'`
+        2. a Kaggle Models handle like
+           `'kaggle://user/whisper/keras/whisper_base_en'`
+        3. a Hugging Face handle like `'hf://user/whisper_base_en'`
+        4. a path to a local preset directory like `'./whisper_base_en'`
+
+        You can run `cls.presets.keys()` to list all built-in presets available
+        on the class.
+
+        This constructor can be called in one of two ways. Either from the base
+        class like `keras_nlp.models.AudioConverter.from_preset()`, or from a
+        model class like `keras_nlp.models.WhisperAudioConverter.from_preset()`.
+        If calling from the base class, the subclass of the returning object
+        will be inferred from the config in the preset directory.
+
+        Args:
+            preset: string. A built-in preset identifier, a Kaggle Models
+                handle, a Hugging Face handle, or a path to a local directory.
+            load_weights: bool. If `True`, the weights will be loaded into the
+                model architecture. If `False`, the weights will be randomly
+                initialized.
+
+        Examples:
+        ```python
+        # Load an audio converter from a preset.
+        converter = keras_nlp.layers.AudioConverter.from_preset(
+            "whisper_base_en"
+        )
+        # Convert some raw mono channel audio input.
+        converter(np.ones(2, 1_000))
+        ```
+        """
+        loader = get_preset_loader(preset)
+        backbone_cls = loader.check_backbone_class()
+        if cls.backbone_cls != backbone_cls:
+            cls = find_subclass(preset, cls, backbone_cls)
+        return loader.load_audio_converter(cls, **kwargs)
+
+    def save_to_preset(self, preset_dir):
+        """Save audio converter to a preset directory.
+
+        Args:
+            preset_dir: The path to the local model preset directory.
+        """
+        save_serialized_object(
+            self,
+            preset_dir,
+            config_file=AUDIO_CONVERTER_CONFIG_FILE,
+        )

keras_nlp/src/layers/preprocessing/audio_converter_test.py

@@ -0,0 +1,69 @@
+# Copyright 2024 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import pathlib
+
+import numpy as np
+import pytest
+
+from keras_nlp.src.layers.preprocessing.audio_converter import AudioConverter
+from keras_nlp.src.models.backbone import Backbone
+from keras_nlp.src.models.whisper.whisper_audio_converter import (
+    WhisperAudioConverter,
+)
+from keras_nlp.src.tests.test_case import TestCase
+
+
+class AudioConverterTest(TestCase):
+    def test_preset_accessors(self):
+        pali_gemma_presets = set(WhisperAudioConverter.presets.keys())
+        all_presets = set(AudioConverter.presets.keys())
+        self.assertContainsSubset(pali_gemma_presets, all_presets)
+
+    @pytest.mark.large
+    def test_from_preset(self):
+        self.assertIsInstance(
+            AudioConverter.from_preset("whisper_tiny_en"),
+            WhisperAudioConverter,
+        )
+
+    @pytest.mark.large
+    def test_from_preset_errors(self):
+        with self.assertRaises(ValueError):
+            AudioConverter.from_preset("bert_tiny_en_uncased")
+        with self.assertRaises(ValueError):
+            # No loading on a non-keras model.
+            AudioConverter.from_preset("hf://spacy/en_core_web_sm")
+
+    @pytest.mark.large
+    def test_save_to_preset(self):
+        save_dir = self.get_temp_dir()
+        converter = AudioConverter.from_preset(
+            "whisper_tiny_en",
+            num_mels=40,
+        )
+        converter.save_to_preset(save_dir)
+        # Save a backbone so the preset is valid.
+        backbone = Backbone.from_preset("whisper_tiny_en", load_weights=False)
+        backbone.save_to_preset(save_dir)
+
+        # Check existence of files.
+        path = pathlib.Path(save_dir)
+        self.assertTrue(os.path.exists(path / "audio_converter.json"))
+
+        # Check loading.
+        restored = AudioConverter.from_preset(save_dir)
+        test_audio = np.random.rand(1_000)
+        self.assertAllClose(restored(test_audio), converter(test_audio))

keras_nlp/src/layers/preprocessing/image_converter.py

@@ -0,0 +1,130 @@
+# Copyright 2024 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.layers.preprocessing.preprocessing_layer import (
+    PreprocessingLayer,
+)
+from keras_nlp.src.utils.preset_utils import IMAGE_CONVERTER_CONFIG_FILE
+from keras_nlp.src.utils.preset_utils import find_subclass
+from keras_nlp.src.utils.preset_utils import get_preset_loader
+from keras_nlp.src.utils.preset_utils import list_presets
+from keras_nlp.src.utils.preset_utils import list_subclasses
+from keras_nlp.src.utils.preset_utils import save_serialized_object
+from keras_nlp.src.utils.python_utils import classproperty
+
+
+@keras_nlp_export("keras_nlp.layers.ImageConverter")
+class ImageConverter(PreprocessingLayer):
+    """Convert raw image for models that support image input.
+
+    This class converts from raw images of any size, to preprocessed
+    images for pretrained model inputs. It is meant to be a convenient way to
+    write custom preprocessing code that is not model specific. This layer
+    should be instantiated via the `from_preset()` constructor, which will
+    create the correct subclass of this layer for the model preset.
+
+    The layer will take as input a raw image tensor in the channels last or
+    channels first format, and output a preprocessed image input for modeling.
+    The exact structure of the output will vary per model, though in most cases
+    this layer will simply resize the image to the size needed by the model
+    input.
+
+    Examples:
+    ```python
+    # Resize images for `"pali_gemma_3b_224"`.
+    converter = keras_nlp.layers.ImageConverter.from_preset("pali_gemma_3b_224")
+    converter(np.ones(2, 512, 512, 3)) # Output shape: (2, 224, 224, 3)
+    # Resize images for `"pali_gemma_3b_448"`.
+    converter = keras_nlp.layers.ImageConverter.from_preset("pali_gemma_3b_448")
+    converter(np.ones(2, 512, 512, 3)) # Output shape: (2, 448, 448, 3)
+    ```
+    """
+
+    backbone_cls = None
+
+    @classproperty
+    def presets(cls):
+        """List built-in presets for a `Task` subclass."""
+        presets = list_presets(cls)
+        for subclass in list_subclasses(cls):
+            presets.update(subclass.presets)
+        return presets
+
+    @classmethod
+    def from_preset(
+        cls,
+        preset,
+        **kwargs,
+    ):
+        """Instantiate a `keras_nlp.layers.ImageConverter` from a model preset.
+
+        A preset is a directory of configs, weights and other file assets used
+        to save and load a pre-trained model. The `preset` can be passed as
+        one of:
+
+        1. a built-in preset identifier like `'pali_gemma_3b_224'`
+        2. a Kaggle Models handle like
+           `'kaggle://user/paligemma/keras/pali_gemma_3b_224'`
+        3. a Hugging Face handle like `'hf://user/pali_gemma_3b_224'`
+        4. a path to a local preset directory like `'./pali_gemma_3b_224'`
+
+        You can run `cls.presets.keys()` to list all built-in presets available
+        on the class.
+
+        This constructor can be called in one of two ways. Either from the base
+        class like `keras_nlp.models.ImageConverter.from_preset()`, or from a
+        model class like
+        `keras_nlp.models.PaliGemmaImageConverter.from_preset()`. If calling
+        from the base class, the subclass of the returning object will be
+        inferred from the config in the preset directory.
+
+        Args:
+            preset: string. A built-in preset identifier, a Kaggle Models
+                handle, a Hugging Face handle, or a path to a local directory.
+            load_weights: bool. If `True`, the weights will be loaded into the
+                model architecture. If `False`, the weights will be randomly
+                initialized.
+
+        Examples:
+        ```python
+        # Resize images for `"pali_gemma_3b_224"`.
+        converter = keras_nlp.layers.ImageConverter.from_preset(
+            "pali_gemma_3b_224"
+        )
+        converter(np.ones(2, 512, 512, 3)) # Output shape: (2, 224, 224, 3)
+        # Override arguments on the base class.
+        converter = keras_nlp.layers.ImageConverter.from_preset(
+            "pali_gemma_3b_448",
+            crop_to_aspect_ratio=False,
+        )
+        converter(np.ones(2, 512, 512, 3)) # (2, 448, 448, 3)
+        ```
+        """
+        loader = get_preset_loader(preset)
+        backbone_cls = loader.check_backbone_class()
+        if cls.backbone_cls != backbone_cls:
+            cls = find_subclass(preset, cls, backbone_cls)
+        return loader.load_image_converter(cls, **kwargs)
+
+    def save_to_preset(self, preset_dir):
+        """Save image converter to a preset directory.
+
+        Args:
+            preset_dir: The path to the local model preset directory.
+        """
+        save_serialized_object(
+            self,
+            preset_dir,
+            config_file=IMAGE_CONVERTER_CONFIG_FILE,
+        )