Merge branch 'master' into port_mobilenet

.devcontainer/devcontainer.json

@@ -3,26 +3,21 @@
     "customizations": {
         "vscode": {
             "settings": {
-                "python.linting.enabled": true,
-                "python.linting.flake8Enabled": true,
-                "python.linting.pylintEnabled": false,
                 "python.testing.pytestEnabled": true,
                 "editor.formatOnSave": true,
                 "editor.codeActionsOnSave": {
                     "source.organizeImports": true
                 },
                 "[python]": {
-                    "editor.defaultFormatter": "ms-python.black-formatter"
+                    "editor.defaultFormatter": "charliermarsh.ruff"
                 },
                 "editor.rulers": [
                     80
                 ]
             },
             "extensions": [
-                "ms-python.python",
-                "ms-python.isort",
-                "ms-python.flake8",
-                "ms-python.black-formatter"
+                "charliermarsh.ruff",
+                "ms-python.python"
             ]
         }
     },

.github/workflows/actions.yml

@@ -20,7 +20,7 @@ jobs:
         version: [keras-stable]
         include:
           - backend: jax
-            version: keras-3.1
+            version: keras-3.5
           - backend: jax
             version: keras-nightly
     runs-on: ubuntu-latest
@@ -48,11 +48,11 @@ jobs:
       run: |
           pip install -r requirements.txt --progress-bar off
           pip install --no-deps -e "." --progress-bar off
-    - name: Pin Keras 3.1
-      if: ${{ matrix.version == 'keras-3.1'}}
+    - name: Pin Keras 3.5
+      if: ${{ matrix.version == 'keras-3.5'}}
       run: |
         pip uninstall -y keras
-        pip install keras==3.1.0 --progress-bar off
+        pip install keras==3.5.0 --progress-bar off
     - name: Pin Keras Nightly
       if: ${{ matrix.version == 'keras-nightly'}}
       run: |

.gitignore

@@ -20,4 +20,4 @@ build/
 .idea/
 
 venv/
-
+.DS_Store

CONTRIBUTING.md

@@ -216,12 +216,11 @@ so you aren't waiting around forever!
 
 ## Formatting Code
 
-We use `flake8`, `isort` and `black` for code formatting.  You can run
-the following commands manually every time you want to format your code:
+KerasHub uses [Ruff](https://docs.astral.sh/ruff/) to format the code. You can
+run `the following commands manually every time you want to format your code:
 
 - Run `shell/format.sh` to format your code
 - Run `shell/lint.sh` to check the result.
 
-If after running these the CI flow is still failing, try updating `flake8`,
-`isort` and `black`. This can be done by running `pip install --upgrade black`,
-`pip install --upgrade flake8`, and `pip install --upgrade isort`.
+If after running these the CI flow is still failing, try updating `ruff`
+with `pip install --upgrade ruff`.

README.md

@@ -1,107 +1,121 @@
-# KerasHub: Multi-framework Models
+# KerasHub: Multi-framework Pretrained Models
 [![](https://github.com/keras-team/keras-hub/workflows/Tests/badge.svg?branch=master)](https://github.com/keras-team/keras-hub/actions?query=workflow%3ATests+branch%3Amaster)
 ![Python](https://img.shields.io/badge/python-v3.9.0+-success.svg)
 [![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/keras-team/keras-hub/issues)
 
 > [!IMPORTANT]
 > 📢 KerasNLP is now KerasHub! 📢 Read
 > [the announcement](https://github.com/keras-team/keras-hub/issues/1831).
->
-> We have renamed the repo to KerasHub in preparation for the release, but have not yet
-> released the new package. Follow the announcement for news.
-
-KerasHub is a library that supports natural language processing, computer
-vision, audio, and multimodal backbones and task models, working natively with
-TensorFlow, JAX, or PyTorch. KerasHub provides a repository of pre-trained
-models and a collection of lower-level building blocks for these tasks. Built
-on Keras 3, models can be trained and serialized in any framework and re-used
-in another without costly migrations.
-
-This library is an extension of the core Keras API; all high-level modules are
-Layers and Models that receive that same level of polish as core Keras.
-If you are familiar with Keras, congratulations! You already understand most of
-KerasHub.
+
+**KerasHub** is a pretrained modeling library that aims to be simple, flexible,
+and fast. The library provides [Keras 3](https://keras.io/keras_3/)
+implementations of popular model architectures, paired with a collection of
+pretrained checkpoints available on [Kaggle Models](https://kaggle.com/models/).
+Models can be used with text, image, and audio data for generation, classification,
+and many other built in tasks.
+
+KerasHub is an extension of the core Keras API; KerasHub components are provided
+as `Layer` and `Model` implementations. If you are  familiar with Keras,
+congratulations! You already understand most of KerasHub.
 
 All models support JAX, TensorFlow, and PyTorch from a single model
 definition and can be fine-tuned on GPUs and TPUs out of the box. Models can
 be trained on individual accelerators with built-in PEFT techniques, or
 fine-tuned at scale with model and data parallel training. See our
 [Getting Started guide](https://keras.io/guides/keras_hub/getting_started)
-to start learning our API. Browse our models on
-[Kaggle](https://www.kaggle.com/organizations/keras/models).
-We welcome contributions.
+to start learning our API.
 
 ## Quick Links
 
 ### For everyone
 
-- [Home Page](https://keras.io/keras_hub)
-- [Developer Guides](https://keras.io/guides/keras_hub)
-- [API Reference](https://keras.io/api/keras_hub)
-- [Pre-trained Models](https://www.kaggle.com/organizations/keras/models)
+- [Home page](https://keras.io/keras_hub)
+- [Getting started](https://keras.io/keras_hub/getting_started)
+- [Guides](https://keras.io/keras_hub/guides)
+- [API documentation](https://keras.io/keras_hub/api)
+- [Pre-trained models](https://keras.io/keras_hub/presets/)
 
 ### For contributors
 
+- [Call for Contributions](https://github.com/keras-team/keras-hub/issues/1835)
+- [Roadmap](https://github.com/keras-team/keras-hub/issues/1836)
 - [Contributing Guide](CONTRIBUTING.md)
-- [Roadmap](ROADMAP.md)
 - [Style Guide](STYLE_GUIDE.md)
 - [API Design Guide](API_DESIGN_GUIDE.md)
-- [Call for Contributions](https://github.com/keras-team/keras-hub/issues?q=is%3Aissue+is%3Aopen+label%3A%22contributions+welcome%22)
 
 ## Quickstart
 
-Fine-tune a BERT classifier on IMDb movie reviews:
+Choose a backend:
 
 ```python
 import os
 os.environ["KERAS_BACKEND"] = "jax"  # Or "tensorflow" or "torch"!
+```
+
+Import KerasHub and other libraries:
 
+```python
+import keras
 import keras_hub
+import numpy as np
 import tensorflow_datasets as tfds
+```
+
+Load a resnet model and use it to predict a label for an image:
 
+```python
+classifier = keras_hub.models.ImageClassifier.from_preset(
+    "resnet_50_imagenet",
+    activation="softmax",
+)
+url = "https://upload.wikimedia.org/wikipedia/commons/a/aa/California_quail.jpg"
+path = keras.utils.get_file(origin=url)
+image = keras.utils.load_img(path)
+preds = classifier.predict(np.array([image]))
+print(keras_hub.utils.decode_imagenet_predictions(preds))
+```
+
+Load a Bert model and fine-tune it on IMDb movie reviews:
+
+```python
+classifier = keras_hub.models.BertClassifier.from_preset(
+    "bert_base_en_uncased",
+    activation="softmax",
+    num_classes=2,
+)
 imdb_train, imdb_test = tfds.load(
     "imdb_reviews",
     split=["train", "test"],
     as_supervised=True,
     batch_size=16,
 )
-
-# Load a BERT model.
-classifier = keras_hub.models.Classifier.from_preset(
-    "bert_base_en",
-    num_classes=2,
-    activation="softmax",
-)
-
-# Fine-tune on IMDb movie reviews.
 classifier.fit(imdb_train, validation_data=imdb_test)
-# Predict two new examples.
-classifier.predict(["What an amazing movie!", "A total waste of my time."])
+preds = classifier.predict(["What an amazing movie!", "A total waste of time."])
+print(preds)
 ```
 
-Try it out [in a colab](https://colab.research.google.com/drive/1gSWkh3yOLwmKAaNh2dQQ6kQIlnGte7P2?usp=sharing).
-For more in depth guides and examples, visit
-[keras.io/keras_hub](https://keras.io/keras_hub/).
-
 ## Installation
 
-To try out the latest version of KerasHub, you can use
-our nightly package:
+To install the latest KerasHub release with Keras 3, simply run:
 
-```bash
-pip install keras-hub
+```
+pip install --upgrade keras-hub
 ```
 
-KerasHub currently requires TensorFlow to be installed for use of the
-`tf.data` API for preprocessing. Even when pre-processing with `tf.data`,
-training can still happen on any backend.
+To install the latest nightly changes for both KerasHub and Keras, you can use
+our nightly package.
 
-Read [Getting started with Keras](https://keras.io/getting_started/) for more
-information on installing Keras 3 and compatibility with different frameworks.
+```
+pip install --upgrade keras-hub-nightly
+```
 
-> [!IMPORTANT]
-> We recommend using KerasHub with TensorFlow 2.16 or later, as TF 2.16 packages
-> Keras 3 by default.
+Currently, installing KerasHub will always pull in TensorFlow for use of the
+`tf.data` API for preprocessing. When pre-processing with `tf.data`, training
+can still happen on any backend.
+
+Visit the [core Keras getting started page](https://keras.io/getting_started/)
+for more information on installing Keras 3, accelerator support, and
+compatibility with different frameworks.
 
 ## Configuring your backend
 

benchmarks/text_generation.py

@@ -150,7 +150,7 @@ def next(prompt, state, index):
                 )
                 print("Time taken: ", time_taken)
                 res_handler.write(
-                    f"{sampler},{execution_method}," f"{time_taken}\n"
+                    f"{sampler},{execution_method},{time_taken}\n"
                 )
                 print()
             print("*************************************")

keras_hub/api/layers/__init__.py

@@ -14,6 +14,7 @@
 from keras_hub.src.layers.modeling.reversible_embedding import (
     ReversibleEmbedding,
 )
+from keras_hub.src.layers.modeling.rms_normalization import RMSNormalization
 from keras_hub.src.layers.modeling.rotary_embedding import RotaryEmbedding
 from keras_hub.src.layers.modeling.sine_position_encoding import (
     SinePositionEncoding,
@@ -34,12 +35,19 @@
 from keras_hub.src.layers.preprocessing.random_deletion import RandomDeletion
 from keras_hub.src.layers.preprocessing.random_swap import RandomSwap
 from keras_hub.src.layers.preprocessing.start_end_packer import StartEndPacker
+from keras_hub.src.models.basnet.basnet_image_converter import (
+    BASNetImageConverter,
+)
+from keras_hub.src.models.clip.clip_image_converter import CLIPImageConverter
 from keras_hub.src.models.deeplab_v3.deeplab_v3_image_converter import (
     DeepLabV3ImageConverter,
 )
 from keras_hub.src.models.densenet.densenet_image_converter import (
     DenseNetImageConverter,
 )
+from keras_hub.src.models.efficientnet.efficientnet_image_converter import (
+    EfficientNetImageConverter,
+)
 from keras_hub.src.models.mit.mit_image_converter import MiTImageConverter
 from keras_hub.src.models.mobilenet.mobilenet_image_converter import (
     MobileNetImageConverter,
@@ -50,13 +58,18 @@
 from keras_hub.src.models.resnet.resnet_image_converter import (
     ResNetImageConverter,
 )
+from keras_hub.src.models.retinanet.anchor_generator import AnchorGenerator
+from keras_hub.src.models.retinanet.retinanet_image_converter import (
+    RetinaNetImageConverter,
+)
 from keras_hub.src.models.sam.sam_image_converter import SAMImageConverter
 from keras_hub.src.models.sam.sam_mask_decoder import SAMMaskDecoder
 from keras_hub.src.models.sam.sam_prompt_encoder import SAMPromptEncoder
 from keras_hub.src.models.segformer.segformer_image_converter import (
     SegFormerImageConverter,
 )
 from keras_hub.src.models.vgg.vgg_image_converter import VGGImageConverter
+from keras_hub.src.models.vit.vit_image_converter import ViTImageConverter
 from keras_hub.src.models.whisper.whisper_audio_converter import (
     WhisperAudioConverter,
 )