Python库 | finetuner-0.3.1.dev16.tar.gz

<p align="center"> <img src="https://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" alt="Finetuner logo: Finetuner allows one to finetune any deep Neural Network for better embedding on search tasks. It accompanies Jina to deliver the last mile of performance-tuning for neural search applications." width="150px"> </p> <p align="center"> <b>Finetuning any deep neural network for better embedding on neural search tasks</b> </p> <p align=center> <a href="https://pypi.org/project/finetuner/"><img src="https://github.com/jina-ai/jina/blob/master/.github/badges/python-badge.svg?raw=true" alt="Python 3.7 3.8 3.9" title="Finetuner supports Python 3.7 and above"></a> <a href="https://pypi.org/project/finetuner/"><img src="https://img.shields.io/pypi/v/finetuner?color=%23099cec&amp;label=PyPI&amp;logo=pypi&amp;logoColor=white" alt="PyPI"></a> <a href="https://codecov.io/gh/jina-ai/finetuner"><img src="https://codecov.io/gh/jina-ai/finetuner/branch/main/graph/badge.svg?token=xSs4acAEaJ"/></a> <a href="https://slack.jina.ai"><img src="https://img.shields.io/badge/Slack-2.2k%2B-blueviolet?logo=slack&amp;logoColor=white"></a> </p> <!-- start elevator-pitch --> Finetuner allows one to tune the weights of any deep neural network for better embeddings on search tasks. It accompanies [Jina](https://github.com/jina-ai/jina) to deliver the last mile of performance for domain-specific neural search applications. 🎛 **Designed for finetuning**: a human-in-the-loop deep learning tool for leveling up your pretrained models in domain-specific neural search applications. 🔱 **Powerful yet intuitive**: all you need is `finetuner.fit()` - a one-liner that unlocks rich features such as siamese/triplet network, interactive labeling, layer pruning, weights freezing, dimensionality reduction. ⚛️ **Framework-agnostic**: promise an identical API & user experience on PyTorch, Tensorflow/Keras and PaddlePaddle deep learning backends. 🧈 **Jina integration**: buttery smooth integration with Jina, reducing the cost of context-switch between experiment and production. <!-- end elevator-pitch --> ## How does it work <img src="https://github.com/jina-ai/finetuner/blob/main/docs/img/finetuner-journey.svg?raw=true" alt="Python 3.7 3.8 3.9" title="Finetuner supports Python 3.7 and above"> ## Install Requires Python 3.7+ and *one of* [PyTorch](https://pytorch.org/)(>=1.9) or [Tensorflow](https://tensorflow.org/)(>=2.5) or [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) installed on Linux/MacOS. ```bash pip install finetuner ``` ## [Documentation](https://finetuner.jina.ai) ## Usage <table> <thead> <tr> <th colspan="2" rowspan="2">Usage</th> <th colspan="2">Do you have an <a href="https://finetuner.jina.ai/basics/glossary/#term-Embedding-model">embedding model</a>?</th> </tr> <tr> <th>Yes</th> <th>No</th> </tr> </thead> <tbody> <tr> <td rowspan="2"><b>Do you have <a href="https://finetuner.jina.ai/basics/glossary/#term-Labeled-dataset">labeled data</a>?</b></td> <td><b>Yes</b></td> <td align="center">🟠</td> <td align="center">🟡</td> </tr> <tr> <td><b>No</b></td> <td align="center">🟢</td> <td align="center">🔵</td> </tr> </tbody> </table> ### 🟠 Have embedding model and labeled data Perfect! Now `embed_model` and `labeled_data` are given by you already, simply do: ```python import finetuner tuned_model = finetuner.fit( embed_model, train_data=labeled_data ) ``` ### 🟢 Have embedding model and unlabeled data You have an `embed_model` to use, but no labeled data for finetuning this model. No worry, that's good enough already! You can use Finetuner to interactive label data and train `embed_model` as below: ```python import finetuner tuned_model = finetuner.fit( embed_model, train_data=unlabeled_data, interactive=True ) ``` ### 🟡 Have general model and labeled data You have a `general_model` which does not output embeddings. Luckily you provide some `labeled_data` for training. No worries, Finetuner can convert your model into an embedding model and train it via: ```python import finetuner tuned_model = finetuner.fit( general_model, train_data=labeled_data, to_embedding_model=True, layer_name='my_embedding_layer', freeze=['layer_1', 'layer_2'], ) ``` ### 🔵 Have general model and unlabeled data You have a `general_model` which is not for embeddings. Meanwhile, you don't have labeled data for training. But no worries, Finetuner can help you train an embedding model with interactive labeling on-the-fly: ```python import finetuner tuned_model = finetuner.fit( general_model, train_data=unlabeled_data, interactive=True, to_embedding_model=True, layer_name='my_embedding_layer', freeze=['layer_1', 'layer_2'], ) ``` ## Finetuning ResNet50 on CelebA > ⚡ To get the best experience, you will need a GPU-machine for this example. For CPU users, we provide [finetuning a MLP on FashionMNIST](https://finetuner.jina.ai/get-started/fashion-mnist/) and [finetuning a Bi-LSTM on CovidQA](https://finetuner.jina.ai/get-started/covid-qa/) that run out the box on low-profile machines. Check out more examples in [our docs](https://finetuner.jina.ai)! 1. Download [CelebA-small dataset (7.7MB)](https://static.jina.ai/celeba/celeba-img.zip) and decompress it to `'./img_align_celeba'`. [Full dataset can be found here.](https://drive.google.com/drive/folders/0B7EVK8r0v71pWEZsZE9oNnFzTm8?resourcekey=0-5BR16BdXnb8hVj6CNHKzLg) 2. Finetuner accepts Jina `DocumentArray`/`DocumentArrayMemmap`, so we load CelebA image into this format using a generator: ```python from jina.types.document.generators import from_files # please change the file path to your data path data = list(from_files('img_align_celeba/*.jpg', size=100, to_dataturi=True)) for doc in data: doc.load_uri_to_image_blob( height=224, width=224 ).set_image_blob_normalization().set_image_blob_channel_axis( -1, 0 ) # No need for changing channel axes line if you are using tf/keras ``` 3. Load pretrained ResNet50 using PyTorch/Keras/Paddle: - PyTorch ```python import torchvision model = torchvision.models.resnet50(pretrained=True) ``` - Keras ```python import tensorflow as tf model = tf.keras.applications.resnet50.ResNet50(weights='imagenet') ``` - Paddle ```python import paddle model = paddle.vision.models.resnet50(pretrained=True) ``` 4. Start the Finetuner: ```python import finetuner finetuner.fit( model=model, interactive=True, train_data=data, freeze=True, to_embedding_model=True, input_size=(3, 224, 224), layer_name='my_embedding_layer', freeze=['layer_1', 'layer_2'], ) ``` 5. After downloading the model and loading the data (takes ~20s depending on your network/CPU/GPU), your browser will open the Labeler UI as below. You can now label the relevance of celebrity faces via mouse/keyboard. The ResNet50 model will get finetuned and improved as you are labeling. If you are running this example on a CPU machine, it may take up to 20 seconds for each labeling round.  <!-- start support-pitch --> ## Support - Use [Discussions](https://github.com/jina-ai/finetuner/discussions) to talk about your use cases, questions, and support queries. - Join our [Slack community](https://slack.jina.ai) and chat with other Jina community members about ideas. - Join our [Engineering All Hands](https://youtube.com/playlist?list=PL3UBBWOUVhFYRUa_gpYYKBqEAkO4sxmne) meet-up to discuss your use case and learn Jina's new features. - **When?** The second Tuesday of every month - **Where?** Zoom ([see our publi