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CPU Upgrade
| import gradio as gr | |
| import tensorflow as tf | |
| from huggingface_hub import from_pretrained_keras | |
| import numpy as np | |
| adamatch_model = from_pretrained_keras("keras-io/adamatch-domain-adaption") | |
| base_model = from_pretrained_keras("johko/wideresnet28-2-mnist") | |
| labels = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"] | |
| def predict_image(image, model): | |
| image = tf.constant(image) | |
| image = tf.reshape(image, [-1, 32, 32, 3]) | |
| probs_ada_mnist = model.predict(image)[0,:] | |
| top_pred = probs_ada_mnist.tolist() | |
| return {labels[i]: top_pred[i] for i in range(10)} | |
| def infer(mnist_img, svhn_img, model): | |
| labels_out = [] | |
| for im in [mnist_img, svhn_img]: | |
| labels_out.append(predict_image(im, model)) | |
| return labels_out | |
| def infer_ada(mnist_image, svhn_image): | |
| return infer(mnist_image, svhn_image, adamatch_model) | |
| def infer_base(mnist_image, svhn_image): | |
| return infer(mnist_image, svhn_image, base_model) | |
| def infer_all(mnist_image, svhn_image): | |
| base_res = infer_base(mnist_image, svhn_image) | |
| ada_res = infer_ada(mnist_image, svhn_image) | |
| return base_res.extend(ada_res) | |
| article = """<center> | |
| Authors: <a href='https://twitter.com/johko990' target='_blank'>Johannes Kolbe</a> based on an example by [Sayak Paul](https://twitter.com/RisingSayak) on | |
| <a href='https://keras.io/examples/vision/adamatch/' target='_blank'>**keras.io**</a>""" | |
| description = """<center> | |
| This space lets you compare image classification results of identical architecture (WideResNet-2-28) models. The training of one of the models was improved | |
| by using AdaMatch as seen in the example on [keras.io](https://keras.io/examples/vision/adamatch/). | |
| The base model was only trained on the MNIST dataset and shows a low classification accuracy (8.96%) for a different domain dataset like SVHN. The AdaMatch model | |
| uses a semi-supervised domain adaption approach to adapt to the SVHN dataset and shows a significantly higher accuracy (26.51%). | |
| """ | |
| mnist_image_base = gr.inputs.Image(shape=(32, 32)) | |
| svhn_image_base = gr.inputs.Image(shape=(32, 32)) | |
| mnist_image_ada = gr.inputs.Image(shape=(32, 32)) | |
| svhn_image_ada = gr.inputs.Image(shape=(32, 32)) | |
| label_mnist_base = gr.outputs.Label(num_top_classes=3, label="MNIST Prediction Base") | |
| label_svhn_base = gr.outputs.Label(num_top_classes=3, label="SVHN Prediction Base") | |
| label_mnist_ada = gr.outputs.Label(num_top_classes=3, label="MNIST Prediction AdaMatch") | |
| label_svhn_ada = gr.outputs.Label(num_top_classes=3, label="SVHN Prediction AdaMatch") | |
| base_iface = gr.Interface( | |
| fn=infer_base, | |
| inputs=[mnist_image_base, svhn_image_base], | |
| outputs=[label_mnist_base,label_svhn_base] | |
| ) | |
| ada_iface = gr.Interface( | |
| fn=infer_ada, | |
| inputs=[mnist_image_ada, svhn_image_ada], | |
| outputs=[label_mnist_ada,label_svhn_ada] | |
| ) | |
| gr.Parallel(base_iface, | |
| ada_iface, | |
| examples=[ | |
| ["examples/mnist_3.jpg", "examples/svhn_3.jpeg"], | |
| ["examples/mnist_8.jpg", "examples/svhn_8.jpg"] | |
| ], | |
| title="Semi-Supervised Domain Adaption with AdaMatch", | |
| article=article, | |
| description=description, | |
| ).launch() | |