utils.py

from typing import Dict

import numpy as np
import tensorflow as tf
from PIL import Image
from tensorflow import keras

RESOLUTION = 224
PATCH_SIZE = 16


crop_layer = keras.layers.CenterCrop(RESOLUTION, RESOLUTION)
norm_layer = keras.layers.Normalization(
    mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
    variance=[(0.229 * 255) ** 2, (0.224 * 255) ** 2, (0.225 * 255) ** 2],
)


def preprocess_image(orig_image: Image, size: int):
    """Image preprocessing utility."""
    image = np.array(orig_image)
    image_resized = tf.expand_dims(image, 0)
    resize_size = int((256 / 224) * size)
    image_resized = tf.image.resize(
        image_resized, (resize_size, resize_size), method="bicubic"
    )
    image_resized = crop_layer(image_resized)
    return image_resized.numpy().squeeze(), norm_layer(image_resized).numpy()


# Reference:
# https://github.com/facebookresearch/dino/blob/main/visualize_attention.py


def get_cls_attention_map(
    preprocessed_image: np.ndarray,
    attn_score_dict: Dict[str, np.ndarray],
    block_key="ca_ffn_block_0_att",
):
    """Utility to generate class saliency map modeling spatial-class relationships."""
    w_featmap = preprocessed_image.shape[2] // PATCH_SIZE
    h_featmap = preprocessed_image.shape[1] // PATCH_SIZE

    attention_scores = attn_score_dict[block_key]
    nh = attention_scores.shape[1]  # Number of attention heads.

    # Taking the representations from CLS token.
    attentions = attention_scores[0, :, 0, 1:].reshape(nh, -1)

    # Reshape the attention scores to resemble mini patches.
    attentions = attentions.reshape(nh, w_featmap, h_featmap)

    attentions = np.mean(attentions, axis=0)
    attentions = (attentions - attentions.min()) / (
        attentions.max() - attentions.min()
    )
    attentions = np.expand_dims(attentions, -1)

    # Resize the attention patches to 224x224 (224: 14x16)
    attentions = tf.image.resize(
        attentions,
        size=(h_featmap * PATCH_SIZE, w_featmap * PATCH_SIZE),
        method="bicubic",
    )

    return attentions.numpy()