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import numpy as np |
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import tensorflow as tf |
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from tensorflow import keras |
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from tensorflow.keras import layers |
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from matplotlib import pyplot as plt |
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import numpy as np |
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import tensorflow as tf |
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import gradio as gr |
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from huggingface_hub import from_pretrained_keras |
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model = from_pretrained_keras('keras-io/attention_mil') |
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IMG_SIZE = 28 |
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def plot(input_images=None, predictions=None, attention_weights=None): |
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bag_class = np.argmax(predictions) |
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bag_class = 'This set of image does not contain number 8' if bag_class == 0 else 'This set of image contains number 8' |
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prob_str = f"Each image probability: {attention_weights[0]:.2f}, {attention_weights[1]:.2f}, {attention_weights[2]:.2f}" |
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if input_images is not None: |
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figure = plt.figure(figsize=(8, 8)) |
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for j in range(len(input_images)): |
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image = input_images[j] |
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figure.add_subplot(1, len(input_images), j + 1) |
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plt.grid(False) |
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if attention_weights is not None: |
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plt.title(f"prob={attention_weights[j]:.2f}") |
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plt.imshow(np.squeeze(input_images[j])) |
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return [bag_class, plt.gcf()] |
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return [bag_class, prob_str] |
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def preprocess_image(image): |
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image = image / 255.0 |
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image = np.expand_dims(image, axis = 0) |
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return image |
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def infer(input_images_1, input_images_2, input_images_3): |
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if (input_images_1 is not None) & (input_images_2 is not None) & (input_images_3 is not None): |
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input_images_1 = preprocess_image(input_images_1) |
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input_images_2 = preprocess_image(input_images_2) |
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input_images_3 = preprocess_image(input_images_3) |
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prediction = model.predict([input_images_1, input_images_2, input_images_3]) |
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prediction = np.squeeze(np.swapaxes(prediction, 1, 0)) |
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intermediate_model = keras.Model(model.input, model.get_layer("alpha").output) |
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intermediate_predictions = intermediate_model.predict([input_images_1, input_images_2, input_images_3]) |
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attention_weights = np.squeeze(np.swapaxes(intermediate_predictions, 1, 0)) |
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return plot( |
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[input_images_1, input_images_2, input_images_3], |
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predictions=prediction, |
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attention_weights=attention_weights |
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) |
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input1 = gr.Image(shape=(28, 28), type='numpy', image_mode='L', label='First image', show_label=True, visible=True) |
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input2 = gr.Image(shape=(28, 28), type='numpy', image_mode='L', label='Second image', show_label=True, visible=True) |
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input3 = gr.Image(shape=(28, 28), type='numpy', image_mode='L', label='Third image', show_label=True, visible=True) |
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output = [gr.Label(), gr.Plot()] |
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title = 'Bag of Image Classification' |
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description = 'This is the demo for Keras Implementation of Classification using Attention-based Deep Multiple Instance Learning (MIL). The model will try to predict whether number 8 is within the set of input images. As it was trained on MNIST dataset, please use MNIST image for precise result.' |
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article = "Author: <a href=\"https://huggingface.co/geninhu\">Nhu Hoang</a>. Based on the following Keras example <a href=\"https://keras.io/examples/vision/attention_mil_classification\"> Classification using Attention-based Deep Multiple Instance Learning (MIL)</a> by <a href=\"https://www.linkedin.com/in/mohamadjaber1\">Mohamad Jaber.</a> <br> Check out the model <a href=\"https://huggingface.co/keras-io/attention_mil\">here</a>" |
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gr_interface = gr.Interface( |
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infer, inputs=[input1, input2, input3], outputs=output, allow_flagging='never', |
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analytics_enabled=False, title=title, description=description, article=article, |
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examples = [['samples/0.png', 'samples/6.png', 'samples/2.png'], ['samples/1.png','samples/2.png', 'samples/3.png'], |
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['samples/4.png', 'samples/8.png', 'samples/7.png'], ['samples/8.png', 'samples/0.png', 'samples/9.png'], |
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['samples/5.png', 'samples/6.png', 'samples/3.png'], ['samples/7.png', 'samples/8.png', 'samples/9.png']] |
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) |
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gr_interface.launch(enable_queue=True, debug=False) |
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