Transformer-Explainability/baselines/ViT/misc_functions.py

#
# Copyright (c) 2019 Idiap Research Institute, http://www.idiap.ch/
# Written by Suraj Srinivas <suraj.srinivas@idiap.ch>
#

""" Misc helper functions """

import subprocess

import cv2
import numpy as np
import torch
import torchvision.transforms as transforms


class NormalizeInverse(transforms.Normalize):
    # Undo normalization on images

    def __init__(self, mean, std):
        mean = torch.as_tensor(mean)
        std = torch.as_tensor(std)
        std_inv = 1 / (std + 1e-7)
        mean_inv = -mean * std_inv
        super(NormalizeInverse, self).__init__(mean=mean_inv, std=std_inv)

    def __call__(self, tensor):
        return super(NormalizeInverse, self).__call__(tensor.clone())


def create_folder(folder_name):
    try:
        subprocess.call(["mkdir", "-p", folder_name])
    except OSError:
        None


def save_saliency_map(image, saliency_map, filename):
    """
    Save saliency map on image.

    Args:
        image: Tensor of size (3,H,W)
        saliency_map: Tensor of size (1,H,W)
        filename: string with complete path and file extension

    """

    image = image.data.cpu().numpy()
    saliency_map = saliency_map.data.cpu().numpy()

    saliency_map = saliency_map - saliency_map.min()
    saliency_map = saliency_map / saliency_map.max()
    saliency_map = saliency_map.clip(0, 1)

    saliency_map = np.uint8(saliency_map * 255).transpose(1, 2, 0)
    saliency_map = cv2.resize(saliency_map, (224, 224))

    image = np.uint8(image * 255).transpose(1, 2, 0)
    image = cv2.resize(image, (224, 224))

    # Apply JET colormap
    color_heatmap = cv2.applyColorMap(saliency_map, cv2.COLORMAP_JET)

    # Combine image with heatmap
    img_with_heatmap = np.float32(color_heatmap) + np.float32(image)
    img_with_heatmap = img_with_heatmap / np.max(img_with_heatmap)

    cv2.imwrite(filename, np.uint8(255 * img_with_heatmap))