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| import gradio as gr | |
| import numpy as np | |
| import tensorflow as tf | |
| import matplotlib.pyplot as plt | |
| from huggingface_hub.keras_mixin import from_pretrained_keras | |
| from PIL import Image | |
| import utils | |
| _RESOLUTION = 224 | |
| def get_model() -> tf.keras.Model: | |
| """Initiates a tf.keras.Model from HF Hub.""" | |
| inputs = tf.keras.Input((_RESOLUTION, _RESOLUTION, 3)) | |
| hub_module = from_pretrained_keras("probing-vits/cait_xxs24_224_classification") | |
| logits, sa_atn_score_dict, ca_atn_score_dict = hub_module(inputs, training=False) | |
| return tf.keras.Model( | |
| inputs, [logits, sa_atn_score_dict, ca_atn_score_dict] | |
| ) | |
| _MODEL = get_model() | |
| def show_plot(image): | |
| """Function to be called when user hits submit on the UI.""" | |
| original_image, preprocessed_image = utils.preprocess_image( | |
| image, _RESOLUTION | |
| ) | |
| _, _, ca_atn_score_dict = _MODEL.predict(preprocessed_image) | |
| # Compute the saliency map and superimpose. | |
| saliency_attention = utils.get_cls_attention_map( | |
| preprocessed_image, ca_atn_score_dict, block_key="ca_ffn_block_0_att" | |
| ) | |
| fig = plt.figure() | |
| plt.imshow(original_image.astype("int32")) | |
| plt.imshow(saliency_attention.squeeze(), cmap="cividis", alpha=0.9) | |
| plt.axis("off") | |
| return fig | |
| title = "Generate Class Saliency Plots" | |
| article = "Class saliency maps as investigated in [Going deeper with Image Transformers](https://arxiv.org/abs/2103.17239) (Touvron et al.)." | |
| iface = gr.Interface( | |
| show_plot, | |
| inputs=gr.inputs.Image(type="pil", label="Input Image"), | |
| outputs=gr.outputs.Plot(type="auto"), | |
| title=title, | |
| article=article, | |
| allow_flagging="never", | |
| examples=[["./butterfly.jpg"]], | |
| ) | |
| iface.launch(debug=True) | |