str_exp_demo_huggingface.py

import settings
import captum
import numpy as np
import torch
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
from utils import get_args
from utils import CTCLabelConverter, AttnLabelConverter, Averager, TokenLabelConverter
import string
import time
import sys
from dataset import hierarchical_dataset, AlignCollate
import validators
from model import Model, STRScore
from PIL import Image
from lime.wrappers.scikit_image import SegmentationAlgorithm
from captum._utils.models.linear_model import SkLearnLinearModel, SkLearnRidge
import random
import os
from skimage.color import gray2rgb
import pickle
from train_shap_corr import getPredAndConf
import re
from captum_test import acquire_average_auc, saveAttrData
import copy
from skimage.color import gray2rgb
from matplotlib import pyplot as plt
from torchvision import transforms

device = torch.device('cpu')

from captum.attr import (
    GradientShap,
    DeepLift,
    DeepLiftShap,
    IntegratedGradients,
    LayerConductance,
    NeuronConductance,
    NoiseTunnel,
    Saliency,
    InputXGradient,
    GuidedBackprop,
    Deconvolution,
    GuidedGradCam,
    FeatureAblation,
    ShapleyValueSampling,
    Lime,
    KernelShap
)

from captum.metrics import (
    infidelity,
    sensitivity_max
)

from captum.attr._utils.visualization import visualize_image_attr

### Acquire pixelwise attributions and replace them with ranked numbers averaged
### across segmentation with the largest contribution having the largest number
### and the smallest set to 1, which is the minimum number.
### attr - original attribution
### segm - image segmentations
def rankedAttributionsBySegm(attr, segm):
    aveSegmentations, sortedDict = averageSegmentsOut(attr[0,0], segm)
    totalSegm = len(sortedDict.keys()) # total segmentations
    sortedKeys = [k for k, v in sorted(sortedDict.items(), key=lambda item: item[1])]
    sortedKeys = sortedKeys[::-1] ### A list that should contain largest to smallest score
    currentRank = totalSegm
    rankedSegmImg = torch.clone(attr)
    for totalSegToHide in range(0, len(sortedKeys)):
        currentSegmentToHide = sortedKeys[totalSegToHide]
        rankedSegmImg[0,0][segm == currentSegmentToHide] = currentRank
        currentRank -= 1
    return rankedSegmImg

### Returns the mean for each segmentation having shape as the same as the input
### This function can only one attribution image at a time
def averageSegmentsOut(attr, segments):
    averagedInput = torch.clone(attr)
    sortedDict = {}
    for x in np.unique(segments):
        segmentMean = torch.mean(attr[segments == x][:])
        sortedDict[x] = float(segmentMean.detach().cpu().numpy())
        averagedInput[segments == x] = segmentMean
    return averagedInput, sortedDict

### Output and save segmentations only for one dataset only
def outputSegmOnly(opt):
    ### targetDataset - one dataset only, SVTP-645, CUTE80-288images
    targetDataset = "CUTE80" # ['IIIT5k_3000', 'SVT', 'IC03_867', 'IC13_1015', 'IC15_2077', 'SVTP', 'CUTE80']
    segmRootDir = "/home/uclpc1/Documents/STR/datasets/segmentations/224X224/{}/".format(targetDataset)

    if not os.path.exists(segmRootDir):
        os.makedirs(segmRootDir)

    opt.eval = True
    ### Only IIIT5k_3000
    if opt.fast_acc:
    # # To easily compute the total accuracy of our paper.
        eval_data_list = [targetDataset]
    else:
        # The evaluation datasets, dataset order is same with Table 1 in our paper.
        eval_data_list = [targetDataset]

    ### Taken from LIME
    segmentation_fn = SegmentationAlgorithm('quickshift', kernel_size=4,
                                            max_dist=200, ratio=0.2,
                                            random_seed=random.randint(0, 1000))

    for eval_data in eval_data_list:
        eval_data_path = os.path.join(opt.eval_data, eval_data)
        AlignCollate_evaluation = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD, opt=opt)
        eval_data, eval_data_log = hierarchical_dataset(root=eval_data_path, opt=opt)
        evaluation_loader = torch.utils.data.DataLoader(
            eval_data, batch_size=1,
            shuffle=False,
            num_workers=int(opt.workers),
            collate_fn=AlignCollate_evaluation, pin_memory=True)
        for i, (image_tensors, labels) in enumerate(evaluation_loader):
            imgDataDict = {}
            img_numpy = image_tensors.cpu().detach().numpy()[0] ### Need to set batch size to 1 only
            if img_numpy.shape[0] == 1:
                img_numpy = gray2rgb(img_numpy[0])
            # print("img_numpy shape: ", img_numpy.shape) # (224,224,3)
            segmOutput = segmentation_fn(img_numpy)
            imgDataDict['segdata'] = segmOutput
            imgDataDict['label'] = labels[0]
            outputPickleFile = segmRootDir + "{}.pkl".format(i)
            with open(outputPickleFile, 'wb') as f:
                pickle.dump(imgDataDict, f)

def acquireSelectivityHit(origImg, attributions, segmentations, model, converter, labels, scoring):
    # print("segmentations unique len: ", np.unique(segmentations))
    aveSegmentations, sortedDict = averageSegmentsOut(attributions[0,0], segmentations)
    sortedKeys = [k for k, v in sorted(sortedDict.items(), key=lambda item: item[1])]
    sortedKeys = sortedKeys[::-1] ### A list that should contain largest to smallest score
    # print("sortedDict: ", sortedDict) # {0: -5.51e-06, 1: -1.469e-05, 2: -3.06e-05,...}
    # print("aveSegmentations unique len: ", np.unique(aveSegmentations))
    # print("aveSegmentations device: ", aveSegmentations.device) # cuda:0
    # print("aveSegmentations shape: ", aveSegmentations.shape) # (224,224)
    # print("aveSegmentations: ", aveSegmentations)

    n_correct = []
    confidenceList = [] # First index is one feature removed, second index two features removed, and so on...
    clonedImg = torch.clone(origImg)
    gt = str(labels)
    for totalSegToHide in range(0, len(sortedKeys)):
        ### Acquire LIME prediction result
        currentSegmentToHide = sortedKeys[totalSegToHide]
        clonedImg[0,0][segmentations == currentSegmentToHide] = 0.0
        pred, confScore = getPredAndConf(opt, model, scoring, clonedImg, converter, np.array([gt]))
        # To evaluate 'case sensitive model' with alphanumeric and case insensitve setting.
        if opt.sensitive and opt.data_filtering_off:
            pred = pred.lower()
            gt = gt.lower()
            alphanumeric_case_insensitve = '0123456789abcdefghijklmnopqrstuvwxyz'
            out_of_alphanumeric_case_insensitve = f"[^{alphanumeric_case_insensitve}]"
            pred = re.sub(out_of_alphanumeric_case_insensitve, '', pred)
            gt = re.sub(out_of_alphanumeric_case_insensitve, '', gt)
        if pred == gt:
            n_correct.append(1)
        else:
            n_correct.append(0)
        confScore = confScore[0][0]*100
        confidenceList.append(confScore)
    return n_correct, confidenceList

### Once you have the selectivity_eval_results.pkl file,
def acquire_selectivity_auc(opt, pkl_filename=None):
    if pkl_filename is None:
        pkl_filename = "/home/goo/str/str_vit_dataexplain_lambda/metrics_sensitivity_eval_results_CUTE80.pkl" # VITSTR
    accKeys = []

    with open(pkl_filename, 'rb') as f:
        selectivity_data = pickle.load(f)

    for resDictIdx, resDict in enumerate(selectivity_data):
        keylistAcc = []
        keylistConf = []
        metricsKeys = resDict.keys()
        for keyStr in resDict.keys():
            if "_acc" in keyStr: keylistAcc.append(keyStr)
            if "_conf" in keyStr: keylistConf.append(keyStr)
        # Need to check if network correctly predicted the image
        for metrics_accStr in keylistAcc:
            if 1 not in resDict[metrics_accStr]: print("resDictIdx")

# Single directory STRExp explanations output demo
def sampleDemo(opt, modelName):
    targetDataset = "SVTP"
    demoImgDir = "demo_image/"
    outputDir = "demo_image_output/"

    if not os.path.exists(outputDir):
        os.makedirs(outputDir)

    segmentation_fn = SegmentationAlgorithm('quickshift', kernel_size=4,
                                            max_dist=200, ratio=0.2,
                                            random_seed=random.randint(0, 1000))

    if modelName=="vitstr":
        if opt.Transformer:
            converter = TokenLabelConverter(opt)
        elif 'CTC' in opt.Prediction:
            converter = CTCLabelConverter(opt.character)
        else:
            converter = AttnLabelConverter(opt.character)
        opt.num_class = len(converter.character)
        if opt.rgb:
            opt.input_channel = 3
        model_obj = Model(opt)

        model = torch.nn.DataParallel(model_obj).to(device)
        modelCopy = copy.deepcopy(model)

        """ evaluation """
        scoring_singlechar = STRScore(opt=opt, converter=converter, device=device, enableSingleCharAttrAve=True)
        super_pixel_model_singlechar = torch.nn.Sequential(
            # super_pixler,
            # numpy2torch_converter,
            modelCopy,
            scoring_singlechar
        ).to(device)
        modelCopy.eval()
        scoring_singlechar.eval()
        super_pixel_model_singlechar.eval()

        # Single Char Attribution Averaging
        # enableSingleCharAttrAve - set to True
        scoring = STRScore(opt=opt, converter=converter, device=device)
        super_pixel_model = torch.nn.Sequential(
            # super_pixler,
            # numpy2torch_converter,
            model,
            scoring
        ).to(device)
        model.eval()
        scoring.eval()
        super_pixel_model.eval()

    elif modelName=="parseq":
        model = torch.hub.load('baudm/parseq', 'parseq', pretrained=True)
        # checkpoint = torch.hub.load_state_dict_from_url('https://github.com/baudm/parseq/releases/download/v1.0.0/parseq-bb5792a6.pt', map_location="cpu")
        # # state_dict = {key.replace("module.", ""): value for key, value in checkpoint["state_dict"].items()}
        # model.load_state_dict(checkpoint)
        model = model.to(device)
        model_obj = model
        converter = TokenLabelConverter(opt)
        modelCopy = copy.deepcopy(model)

        """ evaluation """
        scoring_singlechar = STRScore(opt=opt, converter=converter, device=device, enableSingleCharAttrAve=True, model=modelCopy)
        super_pixel_model_singlechar = torch.nn.Sequential(
            # super_pixler,
            # numpy2torch_converter,
            modelCopy,
            scoring_singlechar
        ).to(device)
        modelCopy.eval()
        scoring_singlechar.eval()
        super_pixel_model_singlechar.eval()

        # Single Char Attribution Averaging
        # enableSingleCharAttrAve - set to True
        scoring = STRScore(opt=opt, converter=converter, device=device, model=model)
        super_pixel_model = torch.nn.Sequential(
            # super_pixler,
            # numpy2torch_converter,
            model,
            scoring
        ).to(device)
        model.eval()
        scoring.eval()
        super_pixel_model.eval()


    if opt.blackbg:
        shapImgLs = np.zeros(shape=(1, 1, 224, 224)).astype(np.float32)
        trainList = np.array(shapImgLs)
        background = torch.from_numpy(trainList).to(device)

    opt.eval = True
    for path, subdirs, files in os.walk(demoImgDir):
        for name in files:
            nameNoExt = name.split('.')[0]
            labels = nameNoExt.split("_")[-1]
            fullfilename = os.path.join(demoImgDir, name) # Value
            pilImg = Image.open(fullfilename)

            pilImg = pilImg.resize((opt.imgW, opt.imgH))
            # fullfilename: /data/goo/strattr/attributionData/trba/CUTE80/66_featablt.pkl

            ### Single char averaging
            if modelName == 'vitstr':

                orig_img_tensors = transforms.ToTensor()(pilImg)
                orig_img_tensors = torch.mean(orig_img_tensors, dim=0).unsqueeze(0).unsqueeze(0)
                image_tensors = ((torch.clone(orig_img_tensors) + 1.0) / 2.0) * 255.0
                imgDataDict = {}
                img_numpy = image_tensors.cpu().detach().numpy()[0] ### Need to set batch size to 1 only
                if img_numpy.shape[0] == 1:
                    img_numpy = gray2rgb(img_numpy[0])
                # print("img_numpy shape: ", img_numpy.shape) # (32,100,3)
                segmOutput = segmentation_fn(img_numpy)
                # print("orig_img_tensors shape: ", orig_img_tensors.shape) # (3, 224, 224)
                # print("orig_img_tensors max: ", orig_img_tensors.max()) # 0.6824 (1)
                # print("orig_img_tensors min: ", orig_img_tensors.min()) # 0.0235 (0)
                # sys.exit()

                results_dict = {}
                aveAttr = []
                aveAttr_charContrib = []
                # segmData, labels = segAndLabels[0]
                target = converter.encode([labels])

                # labels: RONALDO
                segmDataNP = segmOutput
                segmTensor = torch.from_numpy(segmDataNP).unsqueeze(0).unsqueeze(0)
                # print("segmTensor min: ", segmTensor.min()) # 0 starting segmentation
                segmTensor = segmTensor.to(device)
                # print("segmTensor shape: ", segmTensor.shape)
                # img1 = np.asarray(imgPIL.convert('L'))
                # sys.exit()
                # img1 = img1 / 255.0
                # img1 = torch.from_numpy(img1).unsqueeze(0).unsqueeze(0).type(torch.FloatTensor).to(device)
                img1 = orig_img_tensors.to(device)
                img1.requires_grad = True
                bgImg = torch.zeros(img1.shape).to(device)
                input = img1
                origImgNP = torch.clone(orig_img_tensors).detach().cpu().numpy()[0][0] # (1, 1, 224, 224)
                origImgNP = gray2rgb(origImgNP)
                charOffset = 1
                # preds = model(img1, seqlen=converter.batch_max_length)

                ### Local explanations only
                collectedAttributions = []
                for charIdx in range(0, len(labels)):
                    scoring_singlechar.setSingleCharOutput(charIdx + charOffset)
                    gtClassNum = target[0][charIdx + charOffset]

                    ### Shapley Value Sampling
                    svs = ShapleyValueSampling(super_pixel_model_singlechar)
                    # attr = svs.attribute(input, target=0, n_samples=200) ### Individual pixels, too long to calculate
                    attributions = svs.attribute(input, target=gtClassNum, feature_mask=segmTensor)
                    collectedAttributions.append(attributions)
                aveAttributions = torch.mean(torch.cat(collectedAttributions,dim=0), dim=0).unsqueeze(0)
                if not torch.isnan(aveAttributions).any():
                    rankedAttr = rankedAttributionsBySegm(aveAttributions, segmDataNP)
                    rankedAttr = rankedAttr.detach().cpu().numpy()[0][0]
                    rankedAttr = gray2rgb(rankedAttr)
                    mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map', cmap='RdYlGn')
                    mplotfig.savefig(outputDir + '{}_shapley_l.png'.format(nameNoExt))
                    mplotfig.clear()
                    plt.close(mplotfig)

                ### Shapley Value Sampling
                svs = ShapleyValueSampling(super_pixel_model)
                # attr = svs.attribute(input, target=0, n_samples=200) ### Individual pixels, too long to calculate
                attributions = svs.attribute(input, target=0, feature_mask=segmTensor)
                if not torch.isnan(attributions).any():
                    collectedAttributions.append(attributions)
                    rankedAttr = rankedAttributionsBySegm(attributions, segmDataNP)
                    rankedAttr = rankedAttr.detach().cpu().numpy()[0][0]
                    rankedAttr = gray2rgb(rankedAttr)
                    mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map', cmap='RdYlGn')
                    mplotfig.savefig(outputDir + '{}_shapley.png'.format(nameNoExt))
                    mplotfig.clear()
                    plt.close(mplotfig)

                ### Global + Local context
                aveAttributions = torch.mean(torch.cat(collectedAttributions,dim=0), dim=0).unsqueeze(0)
                if not torch.isnan(aveAttributions).any():
                    rankedAttr = rankedAttributionsBySegm(aveAttributions, segmDataNP)
                    rankedAttr = rankedAttr.detach().cpu().numpy()[0][0]
                    rankedAttr = gray2rgb(rankedAttr)
                    mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map', cmap='RdYlGn')
                    mplotfig.savefig(outputDir + '{}_shapley_gl.png'.format(nameNoExt))
                    mplotfig.clear()
                    plt.close(mplotfig)

                return

            elif modelName == 'parseq':
                orig_img_tensors = transforms.ToTensor()(pilImg).unsqueeze(0)
                img1 = orig_img_tensors.to(device)
                # image_tensors = ((torch.clone(orig_img_tensors) + 1.0) / 2.0) * 255.0
                image_tensors = torch.mean(orig_img_tensors, dim=1).unsqueeze(0).unsqueeze(0)
                imgDataDict = {}
                img_numpy = image_tensors.cpu().detach().numpy()[0] ### Need to set batch size to 1 only
                if img_numpy.shape[0] == 1:
                    img_numpy = gray2rgb(img_numpy[0])
                # print("img_numpy shape: ", img_numpy.shape) # (1, 32, 128, 3)
                segmOutput = segmentation_fn(img_numpy[0])

                results_dict = {}
                aveAttr = []
                aveAttr_charContrib = []
                target = converter.encode([labels])

                # labels: RONALDO
                segmDataNP = segmOutput
                img1.requires_grad = True
                bgImg = torch.zeros(img1.shape).to(device)

                # preds = model(img1, seqlen=converter.batch_max_length)
                input = img1
                origImgNP = torch.clone(orig_img_tensors).detach().cpu().numpy()[0][0] # (1, 1, 224, 224)
                origImgNP = gray2rgb(origImgNP)
                charOffset = 0
                img1 = transforms.Normalize(0.5, 0.5)(img1) # Between -1 to 1
                target = converter.encode([labels])

                ### Local explanations only
                collectedAttributions = []
                for charIdx in range(0, len(labels)):
                    scoring_singlechar.setSingleCharOutput(charIdx + charOffset)
                    gtClassNum = target[0][charIdx + charOffset]

                    gs = GradientShap(super_pixel_model_singlechar)
                    baseline_dist = torch.zeros((1, 3, opt.imgH, opt.imgW))
                    baseline_dist = baseline_dist.to(device)
                    attributions = gs.attribute(input, baselines=baseline_dist, target=0)
                    collectedAttributions.append(attributions)
                aveAttributions = torch.mean(torch.cat(collectedAttributions,dim=0), dim=0).unsqueeze(0)
                if not torch.isnan(aveAttributions).any():
                    rankedAttr = rankedAttributionsBySegm(aveAttributions, segmDataNP)
                    rankedAttr = rankedAttr.detach().cpu().numpy()[0][0]
                    rankedAttr = gray2rgb(rankedAttr)
                    mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map', cmap='RdYlGn')
                    mplotfig.savefig(outputDir + '{}_shapley_l.png'.format(nameNoExt))
                    mplotfig.clear()
                    plt.close(mplotfig)

                ### Local Sampling
                gs = GradientShap(super_pixel_model)
                baseline_dist = torch.zeros((1, 3, opt.imgH, opt.imgW))
                baseline_dist = baseline_dist.to(device)
                attributions = gs.attribute(input, baselines=baseline_dist, target=0)
                if not torch.isnan(attributions).any():
                    collectedAttributions.append(attributions)
                    rankedAttr = rankedAttributionsBySegm(attributions, segmDataNP)
                    rankedAttr = rankedAttr.detach().cpu().numpy()[0][0]
                    rankedAttr = gray2rgb(rankedAttr)
                    mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map', cmap='RdYlGn')
                    mplotfig.savefig(outputDir + '{}_shapley.png'.format(nameNoExt))
                    mplotfig.clear()
                    plt.close(mplotfig)

                ### Global + Local context
                aveAttributions = torch.mean(torch.cat(collectedAttributions,dim=0), dim=0).unsqueeze(0)
                if not torch.isnan(aveAttributions).any():
                    rankedAttr = rankedAttributionsBySegm(aveAttributions, segmDataNP)
                    rankedAttr = rankedAttr.detach().cpu().numpy()[0][0]
                    rankedAttr = gray2rgb(rankedAttr)
                    mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map', cmap='RdYlGn')
                    mplotfig.savefig(outputDir + '{}_shapley_gl.png'.format(nameNoExt))
                    mplotfig.clear()
                    plt.close(mplotfig)

                continue

if __name__ == '__main__':
    # deleteInf()
    opt = get_args(is_train=False)

    """ vocab / character number configuration """
    if opt.sensitive:
        opt.character = string.printable[:-6]  # same with ASTER setting (use 94 char).

    cudnn.benchmark = True
    cudnn.deterministic = True
    # opt.num_gpu = torch.cuda.device_count()

    # combineBestDataXAI(opt)
    # acquire_average_auc(opt)
    # acquireSingleCharAttrAve(opt)
    modelName = "parseq"
    opt.modelName = modelName
    opt.eval_data = "datasets/data_lmdb_release/evaluation"

    if modelName=="vitstr":
        opt.benchmark_all_eval = True
        opt.Transformation = "None"
        opt.FeatureExtraction = "None"
        opt.SequenceModeling = "None"
        opt.Prediction = "None"
        opt.Transformer = True
        opt.sensitive = True
        opt.imgH = 224
        opt.imgW = 224
        opt.data_filtering_off = True
        opt.TransformerModel= "vitstr_base_patch16_224"
        opt.saved_model = "pretrained/vitstr_base_patch16_224_aug.pth"
        opt.batch_size = 1
        opt.workers = 0
        opt.scorer = "mean"
        opt.blackbg = True
    elif modelName=="parseq":
        opt.benchmark_all_eval = True
        opt.Transformation = "None"
        opt.FeatureExtraction = "None"
        opt.SequenceModeling = "None"
        opt.Prediction = "None"
        opt.Transformer = True
        opt.sensitive = True
        opt.imgH = 32
        opt.imgW = 128
        opt.data_filtering_off = True
        opt.batch_size = 1
        opt.workers = 0
        opt.scorer = "mean"
        opt.blackbg = True
    sampleDemo(opt, modelName)