import settings import captum import numpy as np import argparse import torch import torch.nn.functional as F import torch.backends.cudnn as cudnn from torchvision import transforms from utils import get_args from utils import CTCLabelConverter, AttnLabelConverter, Averager, TokenLabelConverter, SRNConverter import string import time import sys from dataset import hierarchical_dataset, AlignCollate import validators from model_srn 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, acquireListOfAveAUC, saveAttrData import copy from model_srn import Model from captum_improve_vitstr import rankedAttributionsBySegm from matplotlib import pyplot as plt from captum.attr._utils.visualization import visualize_image_attr device = torch.device('cuda' if torch.cuda.is_available() else '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 ) ### 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") ### This acquires the attributes of the STR network on individual character levels, ### then averages them. def acquireSingleCharAttrAve(opt): ### targetDataset - one dataset only, CUTE80 has 288 samples # 'IIIT5k_3000', 'SVT', 'IC03_860', 'IC03_867', 'IC13_857', 'IC13_1015', 'IC15_1811', 'IC15_2077', 'SVTP', 'CUTE80' targetDataset = settings.TARGET_DATASET segmRootDir = "{}/{}X{}/{}/".format(settings.SEGM_DIR, opt.imgH, opt.imgW, targetDataset) outputSelectivityPkl = "strexp_ave_{}_{}.pkl".format(settings.MODEL, targetDataset) outputDir = "./attributionImgs/{}/{}/".format(settings.MODEL, targetDataset) attrOutputDir = "./attributionData/{}/{}/".format(settings.MODEL, targetDataset) ### Set only one below to True to have enough GPU acquireSelectivity = True acquireInfidelity = False acquireSensitivity = False ### GPU error if not os.path.exists(outputDir): os.makedirs(outputDir) if not os.path.exists(attrOutputDir): os.makedirs(attrOutputDir) converter = SRNConverter(opt.character, opt.SRN_PAD) opt.num_class = len(converter.character) length_for_pred = torch.cuda.IntTensor([opt.batch_max_length] * opt.batch_size) model = Model(opt) print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel, opt.hidden_size, opt.num_class, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction, opt.SequenceModeling, opt.Prediction) model = torch.nn.DataParallel(model).cuda() # load model print('loading pretrained model from %s' % opt.saved_model) model.load_state_dict(torch.load(opt.saved_model)) model = model.to(device) model_obj = model 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() # scoring_charContrib = STRScore(opt=opt, converter=converter, device=device, hasCharContrib=True) # super_pixel_model_charContrib = torch.nn.Sequential( # # super_pixler, # # numpy2torch_converter, # model, # scoring_charContrib # ).to(device) # model.eval() # scoring_charContrib.eval() # super_pixel_model_charContrib.eval() 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 # if opt.fast_acc: # # # To easily compute the total accuracy of our paper. # eval_data_list = ['IIIT5k_3000', 'SVT', 'IC03_867', 'IC13_1015', 'IC15_2077', 'SVTP', 'CUTE80'] # else: # # The evaluation datasets, dataset order is same with Table 1 in our paper. # eval_data_list = ['IIIT5k_3000', 'SVT', 'IC03_860', 'IC03_867', 'IC13_857', # 'IC13_1015', 'IC15_1811', 'IC15_2077', 'SVTP', 'CUTE80'] # # To easily compute the total accuracy of our paper. eval_data_list = [targetDataset] ### One dataset only evaluation_batch_size = opt.batch_size selectivity_eval_results = [] testImgCount = 0 list_accuracy = [] total_forward_time = 0 total_evaluation_data_number = 0 total_correct_number = 0 # log = open(f'./result/{opt.exp_name}/log_all_evaluation.txt', 'a') # dashed_line = '-' * 80 # print(dashed_line) # log.write(dashed_line + '\n') 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, segmRootDir=segmRootDir) 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) testImgCount = 0 for i, (orig_img_tensors, segAndLabels) in enumerate(evaluation_loader): # img_rgb *= 255.0 # img_rgb = img_rgb.astype('int') # print("img_rgb max: ", img_rgb.max()) ### 255 # img_rgb = np.asarray(orig_img_tensors) # segmentations = segmentation_fn(img_rgb) # print("segmentations shape: ", segmentations.shape) # (224, 224) # print("segmentations min: ", segmentations.min()) 0 # print("Unique: ", len(np.unique(segmentations))) # (70) results_dict = {} aveAttr = [] aveAttr_charContrib = [] segmData, labels = segAndLabels[0] target = converter.encode([labels]) # labels: RONALDO segmDataNP = segmData["segdata"] 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) ### Single char averaging if settings.MODEL == 'vitstr': charOffset = 1 # img1 = transforms.Normalize(0.5, 0.5)(img1) # Between -1 to 1 elif settings.MODEL == 'srn': charOffset = 0 # SRN has no 'GO' token elif settings.MODEL == 'parseq': target = target[:, 1:] # First position [GO] not used in parseq too. # 0 index is [GO] char, not used in parseq, only the [EOS] which is in 1 index target[target > 0] -= 1 charOffset = 0 img1 = transforms.Normalize(0.5, 0.5)(img1) # Between -1 to 1 # 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) ### Captum test collectedAttributions = [] for charIdx in range(0, len(labels)): scoring_singlechar.setSingleCharOutput(charIdx + charOffset) # print("charIdx + charOffset: ", charIdx + charOffset) # print("target[0]: ", target[0]) gtClassNum = target[0][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) rankedAttr = rankedAttributionsBySegm(aveAttributions, segmDataNP) rankedAttr = rankedAttr.detach().cpu().numpy()[0][0] rankedAttr = gray2rgb(rankedAttr) mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map') mplotfig.savefig(outputDir + '{}_shapley_l.png'.format(i)) mplotfig.clear() plt.close(mplotfig) saveAttrData(attrOutputDir + f'{i}_shapley_l.pkl', aveAttributions, segmDataNP, origImgNP) if acquireSelectivity: n_correct, confidenceList = acquireSelectivityHit(img1, aveAttributions, segmDataNP, modelCopy, converter, labels, scoring_singlechar) results_dict["shapley_local_acc"] = n_correct results_dict["shapley_local_conf"] = confidenceList if acquireInfidelity: infid = float(infidelity(super_pixel_model_singlechar, perturb_fn, img1, aveAttributions).detach().cpu().numpy()) results_dict["shapley_local_infid"] = infid if acquireSensitivity: sens = float(sensitivity_max(svs.attribute, img1, target=0).detach().cpu().numpy()) results_dict["shapley_local_sens"] = sens ### 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) 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') mplotfig.savefig(outputDir + '{}_shapley.png'.format(i)) mplotfig.clear() plt.close(mplotfig) saveAttrData(attrOutputDir + f'{i}_shapley.pkl', attributions, segmDataNP, origImgNP) if acquireSelectivity: n_correct, confidenceList = acquireSelectivityHit(img1, attributions, segmDataNP, model, converter, labels, scoring) results_dict["shapley_acc"] = n_correct results_dict["shapley_conf"] = confidenceList if acquireInfidelity: infid = float(infidelity(super_pixel_model, perturb_fn, img1, attributions).detach().cpu().numpy()) results_dict["shapley_infid"] = infid if acquireSensitivity: sens = float(sensitivity_max(svs.attribute, img1, target=0).detach().cpu().numpy()) results_dict["shapley_sens"] = sens ### Global + Local context aveAttributions = torch.mean(torch.cat(collectedAttributions,dim=0), dim=0).unsqueeze(0) rankedAttr = rankedAttributionsBySegm(aveAttributions, segmDataNP) rankedAttr = rankedAttr.detach().cpu().numpy()[0][0] rankedAttr = gray2rgb(rankedAttr) mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map') mplotfig.savefig(outputDir + '{}_shapley_gl.png'.format(i)) mplotfig.clear() plt.close(mplotfig) saveAttrData(attrOutputDir + f'{i}_shapley_gl.pkl', aveAttributions, segmDataNP, origImgNP) if acquireSelectivity: n_correct, confidenceList = acquireSelectivityHit(img1, aveAttributions, segmDataNP, modelCopy, converter, labels, scoring_singlechar) results_dict["shapley_global_local_acc"] = n_correct results_dict["shapley_global_local_conf"] = confidenceList if acquireInfidelity: infid = float(infidelity(super_pixel_model_singlechar, perturb_fn, img1, aveAttributions).detach().cpu().numpy()) results_dict["shapley_global_local_infid"] = infid if acquireSensitivity: sens = float(sensitivity_max(svs.attribute, img1, target=0).detach().cpu().numpy()) results_dict["shapley_global_local_sens"] = sens ### BASELINE Evaluations ### Integrated Gradients ig = IntegratedGradients(super_pixel_model) attributions = ig.attribute(input, target=0) rankedAttr = rankedAttributionsBySegm(attributions, segmDataNP) rankedAttr = rankedAttr.detach().cpu().numpy()[0][0] rankedAttr = gray2rgb(rankedAttr) mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map') mplotfig.savefig(outputDir + '{}_intgrad.png'.format(i)) mplotfig.clear() plt.close(mplotfig) saveAttrData(attrOutputDir + f'{i}_intgrad.pkl', attributions, segmDataNP, origImgNP) if acquireSelectivity: n_correct, confidenceList = acquireSelectivityHit(img1, attributions, segmDataNP, model, converter, labels, scoring) results_dict["intgrad_acc"] = n_correct results_dict["intgrad_conf"] = confidenceList if acquireInfidelity: infid = float(infidelity(super_pixel_model, perturb_fn, img1, attributions).detach().cpu().numpy()) results_dict["intgrad_infid"] = infid if acquireSensitivity: sens = float(sensitivity_max(ig.attribute, img1, target=0).detach().cpu().numpy()) results_dict["intgrad_sens"] = sens ### Gradient SHAP using zero-background gs = GradientShap(super_pixel_model) # We define a distribution of baselines and draw `n_samples` from that # distribution in order to estimate the expectations of gradients across all baselines channelDim = 3 if opt.rgb else 1 baseline_dist = torch.zeros((1, channelDim, opt.imgH, opt.imgW)) baseline_dist = baseline_dist.to(device) attributions = gs.attribute(input, baselines=baseline_dist, target=0) rankedAttr = rankedAttributionsBySegm(attributions, segmDataNP) rankedAttr = rankedAttr.detach().cpu().numpy()[0][0] rankedAttr = gray2rgb(rankedAttr) mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map') mplotfig.savefig(outputDir + '{}_gradshap.png'.format(i)) mplotfig.clear() plt.close(mplotfig) saveAttrData(attrOutputDir + f'{i}_gradshap.pkl', attributions, segmDataNP, origImgNP) if acquireSelectivity: n_correct, confidenceList = acquireSelectivityHit(img1, attributions, segmDataNP, model, converter, labels, scoring) results_dict["gradshap_acc"] = n_correct results_dict["gradshap_conf"] = confidenceList if acquireInfidelity: infid = float(infidelity(super_pixel_model, perturb_fn, img1, attributions).detach().cpu().numpy()) results_dict["gradshap_infid"] = infid if acquireSensitivity: sens = float(sensitivity_max(gs.attribute, img1, target=0).detach().cpu().numpy()) results_dict["gradshap_sens"] = sens ### DeepLift using zero-background dl = DeepLift(super_pixel_model) attributions = dl.attribute(input, target=0) rankedAttr = rankedAttributionsBySegm(attributions, segmDataNP) rankedAttr = rankedAttr.detach().cpu().numpy()[0][0] rankedAttr = gray2rgb(rankedAttr) mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map') mplotfig.savefig(outputDir + '{}_deeplift.png'.format(i)) mplotfig.clear() plt.close(mplotfig) saveAttrData(attrOutputDir + f'{i}_deeplift.pkl', attributions, segmDataNP, origImgNP) if acquireSelectivity: n_correct, confidenceList = acquireSelectivityHit(img1, attributions, segmDataNP, model, converter, labels, scoring) results_dict["deeplift_acc"] = n_correct results_dict["deeplift_conf"] = confidenceList if acquireInfidelity: infid = float(infidelity(super_pixel_model, perturb_fn, img1, attributions).detach().cpu().numpy()) results_dict["deeplift_infid"] = infid if acquireSensitivity: sens = float(sensitivity_max(dl.attribute, img1, target=0).detach().cpu().numpy()) results_dict["deeplift_sens"] = sens ### Saliency saliency = Saliency(super_pixel_model) attributions = saliency.attribute(input, target=0) ### target=class0 rankedAttr = rankedAttributionsBySegm(attributions, segmDataNP) rankedAttr = rankedAttr.detach().cpu().numpy()[0][0] rankedAttr = gray2rgb(rankedAttr) mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map') mplotfig.savefig(outputDir + '{}_saliency.png'.format(i)) mplotfig.clear() plt.close(mplotfig) saveAttrData(attrOutputDir + f'{i}_saliency.pkl', attributions, segmDataNP, origImgNP) if acquireSelectivity: n_correct, confidenceList = acquireSelectivityHit(img1, attributions, segmDataNP, model, converter, labels, scoring) results_dict["saliency_acc"] = n_correct results_dict["saliency_conf"] = confidenceList if acquireInfidelity: infid = float(infidelity(super_pixel_model, perturb_fn, img1, attributions).detach().cpu().numpy()) results_dict["saliency_infid"] = infid if acquireSensitivity: sens = float(sensitivity_max(saliency.attribute, img1, target=0).detach().cpu().numpy()) results_dict["saliency_sens"] = sens ### InputXGradient input_x_gradient = InputXGradient(super_pixel_model) attributions = input_x_gradient.attribute(input, target=0) rankedAttr = rankedAttributionsBySegm(attributions, segmDataNP) rankedAttr = rankedAttr.detach().cpu().numpy()[0][0] rankedAttr = gray2rgb(rankedAttr) mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map') mplotfig.savefig(outputDir + '{}_inpxgrad.png'.format(i)) mplotfig.clear() plt.close(mplotfig) saveAttrData(attrOutputDir + f'{i}_inpxgrad.pkl', attributions, segmDataNP, origImgNP) if acquireSelectivity: n_correct, confidenceList = acquireSelectivityHit(img1, attributions, segmDataNP, model, converter, labels, scoring) results_dict["inpxgrad_acc"] = n_correct results_dict["inpxgrad_conf"] = confidenceList if acquireInfidelity: infid = float(infidelity(super_pixel_model, perturb_fn, img1, attributions).detach().cpu().numpy()) results_dict["inpxgrad_infid"] = infid if acquireSensitivity: sens = float(sensitivity_max(input_x_gradient.attribute, img1, target=0).detach().cpu().numpy()) results_dict["inpxgrad_sens"] = sens ### GuidedBackprop gbp = GuidedBackprop(super_pixel_model) attributions = gbp.attribute(input, target=0) rankedAttr = rankedAttributionsBySegm(attributions, segmDataNP) rankedAttr = rankedAttr.detach().cpu().numpy()[0][0] rankedAttr = gray2rgb(rankedAttr) mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map') mplotfig.savefig(outputDir + '{}_guidedbp.png'.format(i)) mplotfig.clear() plt.close(mplotfig) saveAttrData(attrOutputDir + f'{i}_guidedbp.pkl', attributions, segmDataNP, origImgNP) if acquireSelectivity: n_correct, confidenceList = acquireSelectivityHit(img1, attributions, segmDataNP, model, converter, labels, scoring) results_dict["guidedbp_acc"] = n_correct results_dict["guidedbp_conf"] = confidenceList if acquireInfidelity: infid = float(infidelity(super_pixel_model, perturb_fn, img1, attributions).detach().cpu().numpy()) results_dict["guidedbp_infid"] = infid if acquireSensitivity: sens = float(sensitivity_max(gbp.attribute, img1, target=0).detach().cpu().numpy()) results_dict["guidedbp_sens"] = sens ### Deconvolution deconv = Deconvolution(super_pixel_model) attributions = deconv.attribute(input, target=0) rankedAttr = rankedAttributionsBySegm(attributions, segmDataNP) rankedAttr = rankedAttr.detach().cpu().numpy()[0][0] rankedAttr = gray2rgb(rankedAttr) mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map') mplotfig.savefig(outputDir + '{}_deconv.png'.format(i)) mplotfig.clear() plt.close(mplotfig) saveAttrData(attrOutputDir + f'{i}_deconv.pkl', attributions, segmDataNP, origImgNP) if acquireSelectivity: n_correct, confidenceList = acquireSelectivityHit(img1, attributions, segmDataNP, model, converter, labels, scoring) results_dict["deconv_acc"] = n_correct results_dict["deconv_conf"] = confidenceList if acquireInfidelity: infid = float(infidelity(super_pixel_model, perturb_fn, img1, attributions).detach().cpu().numpy()) results_dict["deconv_infid"] = infid if acquireSensitivity: sens = float(sensitivity_max(deconv.attribute, img1, target=0).detach().cpu().numpy()) results_dict["deconv_sens"] = sens ### Feature ablator ablator = FeatureAblation(super_pixel_model) attributions = ablator.attribute(input, target=0, feature_mask=segmTensor) rankedAttr = rankedAttributionsBySegm(attributions, segmDataNP) rankedAttr = rankedAttr.detach().cpu().numpy()[0][0] rankedAttr = gray2rgb(rankedAttr) mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map') mplotfig.savefig(outputDir + '{}_featablt.png'.format(i)) mplotfig.clear() plt.close(mplotfig) saveAttrData(attrOutputDir + f'{i}_featablt.pkl', attributions, segmDataNP, origImgNP) if acquireSelectivity: n_correct, confidenceList = acquireSelectivityHit(img1, attributions, segmDataNP, model, converter, labels, scoring) results_dict["featablt_acc"] = n_correct results_dict["featablt_conf"] = confidenceList if acquireInfidelity: infid = float(infidelity(super_pixel_model, perturb_fn, img1, attributions).detach().cpu().numpy()) results_dict["featablt_infid"] = infid if acquireSensitivity: sens = float(sensitivity_max(ablator.attribute, img1, target=0).detach().cpu().numpy()) results_dict["featablt_sens"] = sens ## LIME interpretable_model = SkLearnRidge(alpha=1, fit_intercept=True) ### This is the default used by LIME lime = Lime(super_pixel_model, interpretable_model=interpretable_model) attributions = lime.attribute(input, target=0, feature_mask=segmTensor) rankedAttr = rankedAttributionsBySegm(attributions, segmDataNP) rankedAttr = rankedAttr.detach().cpu().numpy()[0][0] rankedAttr = gray2rgb(rankedAttr) mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map') mplotfig.savefig(outputDir + '{}_lime.png'.format(i)) mplotfig.clear() plt.close(mplotfig) saveAttrData(attrOutputDir + f'{i}_lime.pkl', attributions, segmDataNP, origImgNP) if acquireSelectivity: n_correct, confidenceList = acquireSelectivityHit(img1, attributions, segmDataNP, model, converter, labels, scoring) results_dict["lime_acc"] = n_correct results_dict["lime_conf"] = confidenceList if acquireInfidelity: infid = float(infidelity(super_pixel_model, perturb_fn, img1, attributions).detach().cpu().numpy()) results_dict["lime_infid"] = infid if acquireSensitivity: sens = float(sensitivity_max(lime.attribute, img1, target=0).detach().cpu().numpy()) results_dict["lime_sens"] = sens ### KernelSHAP ks = KernelShap(super_pixel_model) attributions = ks.attribute(input, target=0, feature_mask=segmTensor) rankedAttr = rankedAttributionsBySegm(attributions, segmDataNP) rankedAttr = rankedAttr.detach().cpu().numpy()[0][0] rankedAttr = gray2rgb(rankedAttr) mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map') mplotfig.savefig(outputDir + '{}_kernelshap.png'.format(i)) mplotfig.clear() plt.close(mplotfig) saveAttrData(attrOutputDir + f'{i}_kernelshap.pkl', attributions, segmDataNP, origImgNP) if acquireSelectivity: n_correct, confidenceList = acquireSelectivityHit(img1, attributions, segmDataNP, model, converter, labels, scoring) results_dict["kernelshap_acc"] = n_correct results_dict["kernelshap_conf"] = confidenceList if acquireInfidelity: infid = float(infidelity(super_pixel_model, perturb_fn, img1, attributions).detach().cpu().numpy()) results_dict["kernelshap_infid"] = infid if acquireSensitivity: sens = float(sensitivity_max(ks.attribute, img1, target=0).detach().cpu().numpy()) results_dict["kernelshap_sens"] = sens selectivity_eval_results.append(results_dict) with open(outputSelectivityPkl, 'wb') as f: pickle.dump(selectivity_eval_results, f) testImgCount += 1 print("testImgCount: ", testImgCount) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--eval_data', default='/home/deepblue/deepbluetwo/chenjun/1_OCR/data/data_lmdb_release/evaluation', help='path to evaluation dataset') parser.add_argument('--benchmark_all_eval', default=True, help='evaluate 10 benchmark evaluation datasets') parser.add_argument('--workers', type=int, help='number of data loading workers', default=4) parser.add_argument('--batch_size', type=int, default=64, help='input batch size') parser.add_argument('--saved_model', default='./saved_models/None-ResNet-SRN-SRN-Seed666/iter_65000.pth', help="path to saved_model to evaluation") """ Data processing """ parser.add_argument('--scorer', type=str, default='mean', help='See STRScore: cumprod | mean') parser.add_argument('--Transformer', action='store_true', help='Use end-to-end transformer') parser.add_argument('--selective_sample_str', type=str, default='', help='If =='', only sample images with string matching this (see --sensitive for case sensitivity)') parser.add_argument('--max_selective_list', type=int, default=-1, help='if selective sample list has elements greater than this, autoclear list for batch selection') parser.add_argument('--batch_max_length', type=int, default=25, help='maximum-label-length') parser.add_argument('--imgH', type=int, default=32, help='the height of the input image') parser.add_argument('--imgW', type=int, default=100, help='the width of the input image') parser.add_argument('--rgb', action='store_true', help='use rgb input') parser.add_argument('--character', type=str, default='0123456789abcdefghijklmnopqrstuvwxyz$#', help='character label') parser.add_argument('--sensitive', action='store_true', help='for sensitive character mode') parser.add_argument('--PAD', action='store_true', help='whether to keep ratio then pad for image resize') parser.add_argument('--data_filtering_off', action='store_true', help='for data_filtering_off mode') """ Model Architecture """ parser.add_argument('--confidence_mode', type=int, default=0, help='0-sum of argmax; 1-edit distance') parser.add_argument('--Transformation', type=str, default='None', help='Transformation stage. None|TPS') parser.add_argument('--FeatureExtraction', type=str, default='ResNet', help='FeatureExtraction stage. VGG|RCNN|ResNet|AsterRes') parser.add_argument('--SequenceModeling', type=str, default='SRN', help='SequenceModeling stage. None|BiLSTM|Bert') parser.add_argument('--Prediction', type=str, default='SRN', help='Prediction stage. CTC|Attn|Bert_pred') parser.add_argument('--num_fiducial', type=int, default=20, help='number of fiducial points of TPS-STN') parser.add_argument('--input_channel', type=int, default=1, help='the number of input channel of Feature extractor') parser.add_argument('--output_channel', type=int, default=512, help='the number of output channel of Feature extractor') parser.add_argument('--hidden_size', type=int, default=256, help='the size of the LSTM hidden state') parser.add_argument('--position_dim', type=int, default=26, help='the length sequence out from cnn encoder,resnet:65;resnetfpn:256') parser.add_argument('--SRN_PAD', type=int, default=36, help='the pad character for srn') parser.add_argument('--batch_max_character', type=int, default=25, help='the max sequence length') opt = parser.parse_args() """ vocab / character number configuration """ if opt.sensitive: opt.character = string.printable[:-6] # same with ASTER setting (use 94 char). opt.alphabet_size = len(opt.character) # opt.SRN_PAD = len(opt.character)-1 cudnn.benchmark = True cudnn.deterministic = True opt.num_gpu = torch.cuda.device_count() # combineBestDataXAI(opt) # acquire_average_auc(opt) # acquireListOfAveAUC(opt) acquireSingleCharAttrAve(opt)