CLIP_Explainability/rn_cam.py

import torch
import numpy as np

# from PIL import Image
# import matplotlib.pyplot as plt
import cv2
import re

from .image_utils import show_cam_on_image, show_overlapped_cam


def rn_relevance(
    image,
    target_features,
    img_encoder,
    method,
    device,
    neg_saliency=False,
    img_dim=224,
):
    target_layers = [img_encoder.layer4[-1]]

    cam = method(
        model=img_encoder,
        target_layers=target_layers,
        use_cuda=torch.cuda.is_available() and device != "cpu",
    )

    if neg_saliency:
        target_encoding = -target_features
    else:
        target_encoding = target_features

    image_relevance = cam(input_tensor=image, target_encoding=target_encoding)[
        0
    ].squeeze()
    image_relevance = torch.FloatTensor(image_relevance)

    resize_dim = int(list(image_relevance.shape)[0])

    image_relevance = image_relevance.reshape(1, 1, resize_dim, resize_dim)

    # image_relevance = image_relevance.reshape(1, 1, 7, 7)
    image_relevance = torch.nn.functional.interpolate(
        image_relevance, size=img_dim, mode="bilinear"
    )
    image_relevance = image_relevance.reshape(img_dim, img_dim).data.cpu().numpy()
    image_relevance = (image_relevance - image_relevance.min()) / (
        1e-7 + image_relevance.max() - image_relevance.min()
    )
    image = image[0].permute(1, 2, 0).data.cpu().numpy()
    image = (image - image.min()) / (image.max() - image.min())

    return image_relevance, image


def interpret_rn(
    image,
    target_features,
    img_encoder,
    method,
    device,
    neg_saliency=False,
    img_dim=224,
):
    image_relevance, image = rn_relevance(
        image,
        target_features,
        img_encoder,
        method,
        device,
        neg_saliency=neg_saliency,
        img_dim=img_dim,
    )
    vis = show_cam_on_image(image, image_relevance, neg_saliency=neg_saliency)
    vis = np.uint8(255 * vis)
    vis = cv2.cvtColor(np.array(vis), cv2.COLOR_RGB2BGR)

    return vis
    # plt.imshow(vis)


def interpret_rn_overlapped(
    image, target_features, img_encoder, method, device, img_dim=224
):
    pos_image_relevance, _ = rn_relevance(
        image,
        target_features,
        img_encoder,
        method,
        device,
        neg_saliency=False,
        img_dim=img_dim,
    )
    neg_image_relevance, image = rn_relevance(
        image,
        target_features,
        img_encoder,
        method,
        device,
        neg_saliency=True,
        img_dim=img_dim,
    )

    vis = show_overlapped_cam(image, neg_image_relevance, pos_image_relevance)
    vis = np.uint8(255 * vis)
    vis = cv2.cvtColor(np.array(vis), cv2.COLOR_RGB2BGR)

    return vis
    # plt.imshow(vis)


def rn_perword_relevance(
    image,
    text,
    clip_model,
    clip_tokenizer,
    method,
    device,
    masked_word="",
    data_only=False,
    img_dim=224,
):
    clip_model.eval()

    main_text = clip_tokenizer(text).to(device)
    # remove the word for which you want to visualize the saliency
    masked_text = re.sub(masked_word, "", text)
    masked_text = clip_tokenizer(masked_text).to(device)

    # image_features = clip_model.encode_image(image)
    main_text_features = clip_model.encode_text(main_text)
    masked_text_features = clip_model.encode_text(masked_text)

    # image_features_norm = image_features.norm(dim=-1, keepdim=True)
    # image_features_new = image_features / image_features_norm

    main_text_features_norm = main_text_features.norm(dim=-1, keepdim=True)
    main_text_features_new = main_text_features / main_text_features_norm

    masked_text_features_norm = masked_text_features.norm(dim=-1, keepdim=True)
    masked_text_features_new = masked_text_features / masked_text_features_norm

    target_encoding = main_text_features_new - masked_text_features_new

    target_layers = [clip_model.visual.layer4[-1]]

    cam = method(
        model=clip_model.visual,
        target_layers=target_layers,
        use_cuda=torch.cuda.is_available() and device != "cpu",
    )

    # image_features = clip_model.visual(image)

    image_relevance = cam(input_tensor=image, target_encoding=target_encoding)[
        0
    ].squeeze()
    image_relevance = torch.FloatTensor(image_relevance)

    resize_dim = int(list(image_relevance.shape)[0])

    image_relevance = image_relevance.reshape(1, 1, resize_dim, resize_dim)

    # image_relevance = image_relevance.reshape(1, 1, 7, 7)
    image_relevance = torch.nn.functional.interpolate(
        image_relevance, size=img_dim, mode="bilinear"
    )
    image_relevance = image_relevance.reshape(img_dim, img_dim).data.cpu().numpy()
    image_relevance = (image_relevance - image_relevance.min()) / (
        1e-7 + image_relevance.max() - image_relevance.min()
    )

    if data_only:
        return image_relevance

    image = image[0].permute(1, 2, 0).data.cpu().numpy()
    image = (image - image.min()) / (image.max() - image.min())

    return image_relevance


def interpret_perword_rn(
    image,
    text,
    clip_model,
    clip_tokenizer,
    method,
    device,
    masked_word="",
    data_only=False,
    img_dim=224,
):
    image_relevance = rn_perword_relevance(
        image,
        text,
        clip_model,
        clip_tokenizer,
        method,
        device,
        masked_word,
        data_only=data_only,
        img_dim=img_dim,
    )
    vis = show_cam_on_image(image, image_relevance)
    vis = np.uint8(255 * vis)
    vis = cv2.cvtColor(np.array(vis), cv2.COLOR_RGB2BGR)

    return vis
    # plt.imshow(vis)