visualize_detector_attention.py

import os
import sys
import argparse
import random
from tqdm import tqdm
import torch
import torchvision
import matplotlib.pyplot as plt

from utils.torch_to_pil import tensor_to_pil_image
from vit.src.model import VisionTransformer as ViT
from utils.ood_detection.ood_detector import MiniNet, CNN_IBP
from utils.ood_detection.PGD_attack import MonotonePGD, MaxConf
from utils.store_model import load_classifier, load_detector
from train_detector import get_noise_from_args
import utils.ood_detection.data_loaders as DataLoader # KEEP THIS, because of eval
from utils.image_modification_path import print_single_image_modification_path

from utils.cifar10_labels import cifar10_labels
from utils.cifar100_labels import cifar100_labels


def get_model_from_args(args, model_name, num_classes):
    """
    get_model_from_args loads a classifier model from the specified arguments dotdict

    :args: dotdict containing all the arguments
    :model_name: string stating what kind of model should be loaded as a classifier
    :num_classes: integer specifying how many classes the classifier should be able to detect
    :return: loaded classifier model
    """
    if model_name.lower() == "resnet":
        model = torchvision.models.resnet18(pretrained=False, num_classes=num_classes).to(device=args.device) # cuda()
    elif model_name.lower() == "vit":
        model = ViT(image_size=(args.img_size, args.img_size),  # 224,224
                    num_heads=args.num_heads, #12 #also a very small amount of heads to speed up training
                    num_layers=args.num_layers,  # 12 # 5 is a very small vit model
                    num_classes=num_classes,  # 2 for OOD detection, 10 or more for classification
                    contrastive=False,
                    timm=True).to(device=args.device) # cuda()
    elif model_name.lower() == "cnn_ibp":
        #TODO currently not working, throwing error
        #"RuntimeError: mat1 dim 1 must match mat2 dim 0"
        model = CNN_IBP().to(device=args.device)
    elif model_name.lower() == "mininet":
        model = MiniNet().to(device=args.device)
    else:
        raise ValueError("Error - wrong model specified in 'args'")
    return model



def visualize_attn_embeddings(model, img, img_label, ood=False, pert=False, save_attention_maps=True): # img [3, 224, 224]
    """
    visualize_attn_embeddings visualizes attention weights of every encoder layer. This corresponds to visualizing,
    for each detected object, which part of the image the model was looked at to predict this specific bounding box
    and class. It uses hooks to extract attention weights (averaged over all heads) from the transformer.
    The attention maps for this sample are plotted and saved in the 'figures/attention/' directory.

    :model: detector model
    :img: one image in tensor representation [channel_size, pixel_width, pixel_height] with values [0;1]
    :img_label: string representation of the image label
    :ood: boolean flag if it is an ood sample
    :pert: boolean flag if it is a perturbed sample
    """
    # # Detection - Visualize encoder-decoder multi-head attention weights
    # use lists to store the outputs via up-values
    conv_features, enc_attn_weights, cls_token_attn = [], [], []
    hooks = [
        model.transformer.encoder_layers[-2].attn.register_forward_hook(
            lambda self, input, output: conv_features.append(output)
        ), ]

    # CHANGE not needed here
    # img = transform(img)

    # propagate through the model
    _ = model(img.unsqueeze(0)) # not_contrastive_acc=True

    for hook in hooks:
        hook.remove()

    # enc_attn_weights.append(model.backbone.transformer.blocks[-1].attn.scores)

    # don't need the list anymore
    conv_features = conv_features[0]

    for i in range(len(model.transformer.encoder_layers)):
        cls_token_attn.append(torch.squeeze(model.transformer.encoder_layers[i].attn.score)[:, 0, 1:])
        enc_attn_weights.append(torch.squeeze(model.transformer.encoder_layers[i].attn.score)[:, 1:, 1:])

    im = tensor_to_pil_image(img)

    f_map = (14, 14)
    head_to_show = 2  # change head number based on which attention u want to see

    print_attention_layers = [x for x in range(len(model.transformer.encoder_layers))]
    for i in range(len(model.transformer.encoder_layers)):
        if i in print_attention_layers:  # show only 4th and 11th or last layer
            # get the HxW shape of the feature maps of the ViT
            shape = f_map
            # and reshape the self-attention to a more interpretable shape
            cls_attn = cls_token_attn[i][head_to_show].reshape(shape)
            # print(np.around(cls_attn,2))
            # print('Shape of cls attn : ',cls_attn.shape)
            sattn = enc_attn_weights[i][head_to_show].reshape(shape + shape)
            # print("Reshaped self-attention:", sattn.shape)
            # print('Showing layer {} and and head {}'.format(i,head_to_show))

            fact = 16  # as image size was 160 and number of attn block 160/16=10

            # let's select 3 reference points AND CLASIFICATION TOKEN for visualization in transformed image space
            idxs = [(0, 0), (48, 48), (96, 96), (144, 144), ]

            # here we create the canvas
            fig = plt.figure(constrained_layout=True, figsize=(25 * 0.9, 12 * 0.9))
            # and we add one plot per reference point
            gs = fig.add_gridspec(2, 4)
            axs = [
                fig.add_subplot(gs[0, 0]),
                fig.add_subplot(gs[1, 0]),
                fig.add_subplot(gs[0, -1]),
                fig.add_subplot(gs[1, -1]),
            ]

            # CHANGE casting to cpu device to be able to cast to numpy
            cls_attn = cls_attn.to(device="cpu")
            sattn = sattn.to(device="cpu")

            # for each one of the reference points, let's plot the self-attention
            # for that point
            for idx_o, ax in zip(idxs, axs):
                if idx_o == (0, 0):
                    idx = (idx_o[0] // fact, idx_o[1] // fact)
                    ax.imshow(cls_attn, cmap='cividis', interpolation='nearest')
                    ax.axis('off')
                    # ax.set_title(f'cls attn at layer: {i}')
                    ax.set_title('cls token attention', fontsize=22)
                else:
                    idx = (idx_o[0] // fact, idx_o[1] // fact)
                    ax.imshow(sattn[..., idx[0], idx[1]], cmap='cividis', interpolation='nearest')
                    ax.axis('off')
                    # ax.set_title(f'self-attn{idx_o} at layer: {i}')
                    ax.set_title(f'self-attention at{idx_o}', fontsize=22)

            # and now let's add the central image, with the reference points as red circles
            fcenter_ax = fig.add_subplot(gs[:, 1:-1])
            # CHANGE removed resize_transform
            #fcenter_ax.imshow(resize_transform(im))  # cls_attn
            fcenter_ax.imshow(im)  # cls_attn
            for (y, x) in idxs:
                if not (x == 0 and y == 0):
                    x = ((x // fact) + 0.5) * fact
                    y = ((y // fact) + 0.5) * fact
                    fcenter_ax.add_patch(plt.Circle((x, y), fact // 3, color='r'))
                    fcenter_ax.axis('off')

            if ood:
                if pert:
                    plt.savefig(f'figures/attention/perturbed/ood_pert_{img_label}_att-layer{i}.png')
                else:
                    plt.savefig(f'figures/attention/clean/ood_clean_{img_label}_att-layer{i}.png')
            else:
                if pert:
                    plt.savefig(f'figures/attention/perturbed/id_pert_{img_label}_att-layer{i}.png')
                else:
                    plt.savefig(f'figures/attention/clean/id_clean_{img_label}_att-layer{i}.png')

            plt.close('all')

    id_ood_string = "OOD" if ood else "ID"
    pert_string = "perturbed" if pert else "clean"
    print("\nSaved", id_ood_string, pert_string, img_label)

    return cls_attn


def visualize_detector_attention(args):
    """
    visualize_detector_attention runs one epoch of all test samples and breaks after a specified amount of batches.
    The number of batches can be specified in the args. For every batch one sample is randomly chosen and its attention
    maps are plotted and saved in the 'figures/attention/' directory.
    The plotting and storing is done for a clean and for a perturbed sample.

    :args: dotdict containing all the arguments
    """
    classifier = load_classifier(args)
    detector = load_detector(args)
    classifier, detector = classifier.to(args.device), detector.to(args.device)

    # writes the datadirs directly into the args
    set_id_ood_datadirs(args)

    # get an ID and OOD dataloader to visualize one random image per batch
    # SVHNDataLoader has svhn written in capital letters
    if args.dataset == 'svhn': args.dataset = 'SVHN'
    if args.ood_dataset == 'svhn': args.ood_dataset = 'SVHN'

    id_dataloader = eval("DataLoader.{}DataLoader".format(args.dataset))(
        data_dir=args.data_dir,  # os.path.join(config.data_dir, config.dataset),
        image_size=args.image_size,
        batch_size=args.batch_size,
        num_workers=args.num_workers,
        split='val',
        net=args.model)

    ood_dataloader = eval("DataLoader.{}DataLoader".format(args.ood_dataset))(
        data_dir=args.ood_data_dir,  # os.path.join(config.data_dir, config.dataset),
        image_size=args.image_size,
        batch_size=args.batch_size,
        num_workers=args.num_workers,
        split='val',
        net=args.model)

    # uncapitalize svhn back again
    if args.dataset == 'SVHN': args.dataset = 'svhn'
    if args.ood_dataset == 'SVHN': args.ood_dataset = 'svhn'

    # attack instance
    if args.attack:
        noise = get_noise_from_args(args.noise, args.eps)
        attack = MonotonePGD(args.eps, args.iterations, args.stepsize, num_classes=2, momentum=0.9,
                             norm=args.norm, loss=MaxConf(True), normalize_grad=False, early_stopping=0,
                             restarts=args.restarts, init_noise_generator=noise, model=classifier, save_trajectory=False)
    else:
        attack = None

    with torch.no_grad():
        classifier.eval()
        detector.eval()

        if args.print_attention_values:
            id_max_cls_attn_diff_list, ood_max_cls_attn_diff_list = [], []
            id_min_cls_attn_diff_list, ood_min_cls_attn_diff_list = [], []
            id_avg_cls_attn_diff_list, ood_avg_cls_attn_diff_list = [], []
            id_avg_cls_attn_list, p_id_avg_cls_attn_list = [], []
            ood_avg_cls_attn_list, p_ood_avg_cls_attn_list = [], []
            id_sum_cls_attn_list, p_id_sum_cls_attn_list = [], []
            ood_sum_cls_attn_list, p_ood_sum_cls_attn_list = [], []
            id_max_max_cls_attn_list, p_id_max_max_cls_attn_list = [], []
            ood_max_max_cls_attn_list, p_ood_max_max_cls_attn_list = [], []
            id_min_min_cls_attn_list, p_id_min_min_cls_attn_list = [], []
            ood_min_min_cls_attn_list, p_ood_min_min_cls_attn_list = [], []
            absolute_max_att_value, absolute_min_att_value = 0, 99999


        dataloader_iterator = iter(id_dataloader)

        for index, [ood_inputs, ood_labels] in enumerate(tqdm(ood_dataloader, disable=args.print_perturbation_path)):
            try:
                [id_inputs, id_labels] = next(dataloader_iterator)
            except StopIteration:
                dataloader_iterator = iter(id_dataloader)
                [ood_inputs, ood_labels] = next(dataloader_iterator)

            id_inputs, id_labels = id_inputs.to(device=args.device), id_labels.to(device=args.device)
            ood_inputs, ood_labels = ood_inputs.to(device=args.device), ood_labels.to(device=args.device)

            # select a random sample from the ID and the OOD batch (same index)
            random_selection = random.randint(0, args.batch_size-1)

            if args.print_specific_id_label:
                specific_label = "possum"
                if args.dataset == "cifar10":
                    label_index = cifar10_labels.index(specific_label)
                elif args.dataset == "cifar100":
                    label_index = cifar100_labels.index(specific_label)
                elif args.dataset == "svhn":
                    label_index = int(specific_label)
                else:
                    raise ValueError("ID dataset not existing")

                selected_batch_index = (id_labels == label_index).nonzero(as_tuple=True)[0]
                if selected_batch_index.nelement() != 0:
                    if isinstance(selected_batch_index.item(), list):
                        random_selection = selected_batch_index.item()[0]
                    else:
                        random_selection = selected_batch_index.item()
                else:
                    continue


            id_input, ood_input = id_inputs[random_selection], ood_inputs[random_selection]
            id_label, ood_label = id_labels[random_selection], ood_labels[random_selection]

            id_label_string = get_string_label_for_sample(id_label, args.dataset)
            ood_label_string = get_string_label_for_sample(ood_label, args.ood_dataset)

            if not args.print_specific_id_label and not args.print_perturbation_path:
                id_last_layer_cls_attn = visualize_attn_embeddings(detector, id_input, id_label_string, ood=False, pert=False, save_attention_maps=not args.print_attention_values)
                ood_last_layer_cls_attn = visualize_attn_embeddings(detector, ood_input, ood_label_string, ood=True, pert=False, save_attention_maps=not args.print_attention_values)

            if args.print_specific_id_label:
                specific_label = args.specific_label
                if id_label_string == specific_label:
                    visualize_attn_embeddings(detector, id_input, id_label_string, ood=False, pert=False, save_attention_maps=not args.print_attention_values)
                    print("Found a sample with the label:", specific_label)
                    return

            if attack:
                perturbed_id_inputs, _, _ = attack(id_inputs, id_labels)
                perturbed_ood_inputs, p_best_softmax_list, p_best_idx = attack(ood_inputs, ood_labels)
                perturbed_id_input, perturbed_ood_input = perturbed_id_inputs[random_selection], perturbed_ood_inputs[random_selection]

                if not args.print_specific_id_label and not args.print_perturbation_path:
                    p_id_last_layer_cls_attn = visualize_attn_embeddings(detector, perturbed_id_input, id_label_string, ood=False, pert=True, save_attention_maps=not args.print_attention_values)
                    p_ood_last_layer_cls_attn = visualize_attn_embeddings(detector, perturbed_ood_input, ood_label_string, ood=True, pert=True, save_attention_maps=not args.print_attention_values)

                    if args.print_attention_values:
                        store_class_attention_values(absolute_max_att_value, absolute_min_att_value,
                                                     id_avg_cls_attn_diff_list, id_avg_cls_attn_list,
                                                     id_last_layer_cls_attn, id_max_cls_attn_diff_list,
                                                     id_max_max_cls_attn_list, id_min_cls_attn_diff_list,
                                                     id_min_min_cls_attn_list, id_sum_cls_attn_list,
                                                     ood_avg_cls_attn_diff_list, ood_avg_cls_attn_list,
                                                     ood_last_layer_cls_attn, ood_max_cls_attn_diff_list,
                                                     ood_max_max_cls_attn_list, ood_min_cls_attn_diff_list,
                                                     ood_min_min_cls_attn_list, ood_sum_cls_attn_list,
                                                     p_id_avg_cls_attn_list, p_id_last_layer_cls_attn,
                                                     p_id_max_max_cls_attn_list, p_id_min_min_cls_attn_list,
                                                     p_id_sum_cls_attn_list, p_ood_avg_cls_attn_list,
                                                     p_ood_last_layer_cls_attn, p_ood_max_max_cls_attn_list,
                                                     p_ood_min_min_cls_attn_list, p_ood_sum_cls_attn_list)

                if args.print_perturbation_path:
                    if args.dataset == "cifar10":
                        id_class_list = cifar10_labels
                    elif args.dataset == "cifar100":
                        id_class_list = cifar100_labels
                    elif args.dataset == "svhn":
                        id_class_list = [str(x) for x in range(0,10)]
                    else:
                        raise ValueError("ID dataset not existing")

                    print_single_image_modification_path(p_best_softmax_list[p_best_idx[random_selection].item()], len(id_dataloader.dataset.classes), id_class_list, ood_label_string)

            if index == args.visualize: break

        if args.print_attention_values:
            print_class_attention_info(id_max_max_cls_attn_list, id_min_min_cls_attn_list, ood_max_max_cls_attn_list,
                                       ood_min_min_cls_attn_list, p_id_max_max_cls_attn_list,
                                       p_id_min_min_cls_attn_list, p_ood_max_max_cls_attn_list,
                                       p_ood_min_min_cls_attn_list)

    print("Finished visualizing the detector attention")


def set_id_ood_datadirs(args):
    """
    set_id_ood_datadirs modifies the arguments dotdict to contain the right paths where to load the datasets from.

    :args: dotdict containing all arguments (including the paths of the id and ood datasets)
    """
    if args.dataset.lower() == "cifar10":
        args.data_dir = "data/cifar10/"
    elif args.dataset.lower() == "cifar100":
        args.data_dir = "data/cifar100/"
    elif args.dataset.lower() == "svhn":
        args.data_dir = "data/"
    else:
        raise ValueError("Unknown ID dataset specified, no path to the dataset available")
    if args.ood_dataset.lower() == "cifar10":
        args.ood_data_dir = "data/cifar10/"
    elif args.ood_dataset.lower() == "cifar100":
        args.ood_data_dir = "data/cifar100/"
    elif args.ood_dataset.lower() == "svhn":
        args.ood_data_dir = "data/"
    else:
        raise ValueError("Unknown OOD dataset specified, no path to the dataset available")


def get_string_label_for_sample(label, dataset):
    """
    get_string_label_for_sample returns the string representation from the numerical representaion of a label.

    :label: integer for the numerical representation of a label
    :dataset: string indicating which dataset the label is from
    :return: string representation of the label
    """
    if dataset == "cifar10":
        return cifar10_labels[label]
    elif dataset == "cifar100":
        return cifar100_labels[label]
    elif dataset == "svhn":
        return str(label.item())
    else:
        raise ValueError("dataset not supported, labels could not get fetched")


def store_class_attention_values(absolute_max_att_value, absolute_min_att_value, id_avg_cls_attn_diff_list,
                                 id_avg_cls_attn_list, id_last_layer_cls_attn, id_max_cls_attn_diff_list,
                                 id_max_max_cls_attn_list, id_min_cls_attn_diff_list, id_min_min_cls_attn_list,
                                 id_sum_cls_attn_list, ood_avg_cls_attn_diff_list, ood_avg_cls_attn_list,
                                 ood_last_layer_cls_attn, ood_max_cls_attn_diff_list, ood_max_max_cls_attn_list,
                                 ood_min_cls_attn_diff_list, ood_min_min_cls_attn_list, ood_sum_cls_attn_list,
                                 p_id_avg_cls_attn_list, p_id_last_layer_cls_attn, p_id_max_max_cls_attn_list,
                                 p_id_min_min_cls_attn_list, p_id_sum_cls_attn_list, p_ood_avg_cls_attn_list,
                                 p_ood_last_layer_cls_attn, p_ood_max_max_cls_attn_list, p_ood_min_min_cls_attn_list,
                                 p_ood_sum_cls_attn_list):

    id_min = torch.min(id_last_layer_cls_attn).item()
    p_id_min = torch.min(p_id_last_layer_cls_attn).item()
    ood_min = torch.min(ood_last_layer_cls_attn).item()
    p_ood_min = torch.min(p_ood_last_layer_cls_attn).item()
    id_max = torch.max(id_last_layer_cls_attn).item()
    p_id_max = torch.max(p_id_last_layer_cls_attn).item()
    ood_max = torch.max(ood_last_layer_cls_attn).item()
    p_ood_max = torch.max(p_ood_last_layer_cls_attn).item()

    id_min_min_cls_attn_list.append(id_min)
    p_id_min_min_cls_attn_list.append(p_id_min)
    ood_min_min_cls_attn_list.append(ood_min)
    p_ood_min_min_cls_attn_list.append(p_ood_min)
    id_max_max_cls_attn_list.append(id_max)
    p_id_max_max_cls_attn_list.append(p_id_max)
    ood_max_max_cls_attn_list.append(ood_max)
    p_ood_max_max_cls_attn_list.append(p_ood_max)

    id_diff_cls_attn = p_id_last_layer_cls_attn - id_last_layer_cls_attn
    ood_diff_cls_attn = p_ood_last_layer_cls_attn - ood_last_layer_cls_attn

    id_max_cls_attn_diff = torch.max(id_diff_cls_attn).item()
    ood_max_cls_attn_diff = torch.max(ood_diff_cls_attn).item()
    id_min_cls_attn_diff = torch.min(id_diff_cls_attn).item()
    ood_min_cls_attn_diff = torch.min(ood_diff_cls_attn).item()
    id_avg_cls_attn_diff = torch.mean(id_diff_cls_attn).item()
    ood_avg_cls_attn_diff = torch.mean(ood_diff_cls_attn).item()

    if id_max > absolute_max_att_value:
        absolute_max_att_value = id_max
    if p_id_max > absolute_max_att_value:
        absolute_max_att_value = p_id_max
    if ood_max > absolute_max_att_value:
        absolute_max_att_value = ood_max
    if p_ood_max > absolute_max_att_value:
        absolute_max_att_value = p_ood_max

    if id_min < absolute_min_att_value:
        absolute_min_att_value = id_min
    if p_id_min < absolute_min_att_value:
        absolute_min_att_value = p_id_min
    if ood_min < absolute_min_att_value:
        absolute_min_att_value = ood_min
    if p_ood_min < absolute_min_att_value:
        absolute_min_att_value = p_ood_min

    id_max_cls_attn_diff_list.append(id_max_cls_attn_diff)
    ood_max_cls_attn_diff_list.append(ood_max_cls_attn_diff)
    id_min_cls_attn_diff_list.append(id_min_cls_attn_diff)
    ood_min_cls_attn_diff_list.append(ood_min_cls_attn_diff)
    id_avg_cls_attn_diff_list.append(id_avg_cls_attn_diff)
    ood_avg_cls_attn_diff_list.append(ood_avg_cls_attn_diff)

    id_avg_cls_attn_list.append(torch.mean(id_last_layer_cls_attn).item())
    p_id_avg_cls_attn_list.append(torch.mean(p_id_last_layer_cls_attn).item())
    ood_avg_cls_attn_list.append(torch.mean(ood_last_layer_cls_attn).item())
    p_ood_avg_cls_attn_list.append(torch.mean(p_ood_last_layer_cls_attn).item())
    id_sum_cls_attn_list.append(torch.sum(id_last_layer_cls_attn).item())
    p_id_sum_cls_attn_list.append(torch.sum(p_id_last_layer_cls_attn).item())
    ood_sum_cls_attn_list.append(torch.sum(ood_last_layer_cls_attn).item())
    p_ood_sum_cls_attn_list.append(torch.sum(p_ood_last_layer_cls_attn).item())


def print_class_attention_info(id_max_max_cls_attn_list, id_min_min_cls_attn_list, ood_max_max_cls_attn_list,
                               ood_min_min_cls_attn_list, p_id_max_max_cls_attn_list, p_id_min_min_cls_attn_list,
                               p_ood_max_max_cls_attn_list, p_ood_min_min_cls_attn_list):
    print("ID:", args.dataset, " ---  OOD:", args.ood_dataset)
    """
            print("Max List ID Attention difference: ", max(id_max_cls_attn_diff_list))
            print("Max List OOD Attention difference:", max(ood_max_cls_attn_diff_list))
            print("Min List ID Attention difference: ", abs(min(id_min_cls_attn_diff_list)))
            print("Min List OOD Attention difference:", abs(min(ood_min_cls_attn_diff_list)))
            print("Avg List ID Attention difference: ", sum(id_avg_cls_attn_diff_list)/len(id_avg_cls_attn_diff_list))
            print("Avg List OOD Attention difference:", sum(ood_avg_cls_attn_diff_list)/len(ood_avg_cls_attn_diff_list))

            print("Avg ID unperturbed: ", sum(id_avg_cls_attn_list)/len(id_avg_cls_attn_list))
            print("Avg ID perturbed:   ", sum(p_id_avg_cls_attn_list)/len(p_id_avg_cls_attn_list))
            print("Avg OOD unperturbed:", sum(ood_avg_cls_attn_list)/len(ood_avg_cls_attn_list))
            print("Avg OOD perturbed:  ", sum(p_ood_avg_cls_attn_list)/len(p_ood_avg_cls_attn_list))

            print("Avg ID sum unperturbed: ", sum(id_sum_cls_attn_list) / len(id_sum_cls_attn_list))
            print("Avg ID sum perturbed:   ", sum(p_id_sum_cls_attn_list) / len(p_id_sum_cls_attn_list))
            print("Avg OOD sum unperturbed:", sum(ood_sum_cls_attn_list) / len(ood_sum_cls_attn_list))
            print("Avg OOD sum perturbed:  ", sum(p_ood_sum_cls_attn_list) / len(p_ood_sum_cls_attn_list))

            print("Min ID sum unperturbed: ", min(id_sum_cls_attn_list))
            print("Min ID sum perturbed:   ", min(p_id_sum_cls_attn_list))
            print("Max ID sum unperturbed: ", max(id_sum_cls_attn_list))
            print("Max ID sum perturbed:   ", max(p_id_sum_cls_attn_list))
            print("Min OOD sum unperturbed:", min(ood_sum_cls_attn_list))
            print("Min OOD sum perturbed:  ", min(p_ood_sum_cls_attn_list))
            print("Max OOD sum unperturbed:", max(ood_sum_cls_attn_list))
            print("Max OOD sum perturbed:  ", max(p_ood_sum_cls_attn_list))

            print("Absolute Minimum:", absolute_min_att_value)
            print("Absolute Maximum:", absolute_max_att_value)
            """
    patch_attention_factor = 14 ** 2
    print("ID avg min min:   ", sum(id_min_min_cls_attn_list) / len(id_min_min_cls_attn_list) * patch_attention_factor)
    print("p_ID avg min min: ",
          sum(p_id_min_min_cls_attn_list) / len(p_id_min_min_cls_attn_list) * patch_attention_factor)
    print("OOD avg min min:  ",
          sum(ood_min_min_cls_attn_list) / len(ood_min_min_cls_attn_list) * patch_attention_factor)
    print("p_OOD avg min min:",
          sum(p_ood_min_min_cls_attn_list) / len(p_ood_min_min_cls_attn_list) * patch_attention_factor)
    print("ID avg max max:   ", sum(id_max_max_cls_attn_list) / len(id_max_max_cls_attn_list) * patch_attention_factor)
    print("p_ID avg max max: ",
          sum(p_id_max_max_cls_attn_list) / len(p_id_max_max_cls_attn_list) * patch_attention_factor)
    print("OOD avg max max:  ",
          sum(ood_max_max_cls_attn_list) / len(ood_max_max_cls_attn_list) * patch_attention_factor)
    print("p_OOD avg max max:",
          sum(p_ood_max_max_cls_attn_list) / len(p_ood_max_max_cls_attn_list) * patch_attention_factor)


def parse_args():
    """
    parse_args retrieves the arguments from the command line and parses them into the arguments dotdict.

    :return: dotdict with all the arguments
    """
    parser = argparse.ArgumentParser(description='Run the monotone PGD attack on a batch of images, default is with ViT and the MPGD of Alex, where cifar10 is ID and cifar100 is OOD')

    parser.add_argument('--model', type=str, default="vit", help='str - what model should be used to classify input samples "vit", "resnet", "mininet" or "cnn_ibp"')
    parser.add_argument("--classification-ckpt", type=str, default=None, help="model checkpoint to load weights")
    parser.add_argument("--detector-ckpt-path", type=str, default=None, help="model checkpoint to load weights")

    parser.add_argument('--device', type=str, default="cuda", help='str - cpu or cuda to calculate the tensors on')
    parser.add_argument("--num-workers", type=int, default=8, help="number of workers")
    parser.add_argument("--select-gpu", type=int, default=0, help="select gpu to use, no parallelization possible")
    parser.add_argument('--attack', action='store_true', help='toggels the MonotonePGD attack')
    parser.add_argument("--visualize", type=int, default=3, help="amount of id and ood images to visualize")
    parser.add_argument('--print-perturbation-path', action='store_true', help='toggels printing the ood perturbation path')
    parser.add_argument('--print-specific-id-label', action='store_true', help='toggels printing specific id label mode')
    parser.add_argument('--specific-label', type=str, help='str - string representation of a label that should be found in the ID dataset and its attention be printed')
    parser.add_argument('--print-attention-values', action='store_true', help='toggels printing attention value statistics')

    parser.add_argument('--albumentation', action='store_true', help='use albumentation as data aug')
    parser.add_argument('--contrastive', action='store_true', help='using distributed loss calculations across multiple GPUs')

    return parser.parse_args()

if __name__ == '__main__':
    torch.set_grad_enabled(False)
    args = parse_args()
    device_id_list = list(range(torch.cuda.device_count()))
    # it is NOT possible to Parallelize the attack, so cuda is set to one GPU and device_ids are set to None
    if args.device == "cuda":
        if args.select_gpu in device_id_list:
            # allows to specify a certain cuda core, because parallelization is not possible
            os.environ["CUDA_VISIBLE_DEVICES"] = str(args.select_gpu)
        else:
            print("Specified selected GPU is not available, not that many GPUs available --> Defaulted to cuda:0")
            os.environ["CUDA_VISIBLE_DEVICES"] = "0"

    gettrace = getattr(sys, 'gettrace', None)
    if gettrace():
        print("num_workers is set to 0 in debugging mode, otherwise debugging issues occur")
        args.num_workers = 0

    visualize_detector_attention(args)
    print("finished all executions")