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| import xplique | |
| import tensorflow as tf | |
| from xplique.attributions import (Saliency, GradientInput, IntegratedGradients, SmoothGrad, VarGrad, | |
| SquareGrad, GradCAM, Occlusion, Rise, GuidedBackprop, | |
| GradCAMPP, Lime, KernelShap,SobolAttributionMethod,HsicAttributionMethod) | |
| from xplique.attributions.global_sensitivity_analysis import LatinHypercube | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from inference_resnet import inference_resnet_finer, preprocess, _clever_crop | |
| BATCH_SIZE = 1 | |
| def show(img, p=False, **kwargs): | |
| img = np.array(img, dtype=np.float32) | |
| # check if channel first | |
| if img.shape[0] == 1: | |
| img = img[0] | |
| # check if cmap | |
| if img.shape[-1] == 1: | |
| img = img[:,:,0] | |
| elif img.shape[-1] == 3: | |
| img = img[:,:,::-1] | |
| # normalize | |
| if img.max() > 1 or img.min() < 0: | |
| img -= img.min(); img/=img.max() | |
| # check if clip percentile | |
| if p is not False: | |
| img = np.clip(img, np.percentile(img, p), np.percentile(img, 100-p)) | |
| plt.imshow(img, **kwargs) | |
| plt.axis('off') | |
| #return img | |
| def explain(model, input_image,size=600, n_classes=171) : | |
| """ | |
| Generate explanations for a given model and dataset. | |
| :param model: The model to explain. | |
| :param X: The dataset. | |
| :param Y: The labels. | |
| :param explainer: The explainer to use. | |
| :param batch_size: The batch size to use. | |
| :return: The explanations. | |
| """ | |
| # we only need the classification part of the model | |
| class_model = tf.keras.Model(model.input, model.output[1]) | |
| explainers = [ | |
| #Sobol, RISE, HSIC, Saliency | |
| #IntegratedGradients(class_model, steps=50, batch_size=BATCH_SIZE), | |
| #SmoothGrad(class_model, nb_samples=50, batch_size=BATCH_SIZE), | |
| #GradCAM(class_model), | |
| SobolAttributionMethod(class_model, grid_size=8, nb_design=32), | |
| Rise(class_model,nb_samples = 5000, batch_size = BATCH_SIZE,grid_size=15, | |
| preservation_probability=0.5), | |
| HsicAttributionMethod(class_model, | |
| grid_size=7, nb_design=1500, | |
| sampler = LatinHypercube(binary=True)), | |
| Saliency(class_model), | |
| # | |
| ] | |
| cropped,repetitions = _clever_crop(input_image,(size,size)) | |
| size_repetitions = int(size//(repetitions.numpy()+1)) | |
| X = preprocess(cropped,size=size) | |
| predictions = class_model.predict(np.array([X])) | |
| #Y = np.argmax(predictions) | |
| top_5_indices = np.argsort(predictions[0])[-5:][::-1] | |
| #print(top_5_indices) | |
| X = np.expand_dims(X, 0) | |
| explanations = [] | |
| for e,explainer in enumerate(explainers): | |
| print(f'{e}/{len(explainers)}') | |
| for i,Y in enumerate(top_5_indices): | |
| Y = tf.one_hot([Y], n_classes) | |
| print(f'{i}/{len(top_5_indices)}') | |
| phi = np.abs(explainer(X, Y))[0] | |
| if len(phi.shape) == 3: | |
| phi = np.mean(phi, -1) | |
| show(X[0][:,size_repetitions:2*size_repetitions,:]) | |
| show(phi[:,size_repetitions:2*size_repetitions], p=1, alpha=0.4) | |
| plt.savefig(f'phi_{e}{i}.png') | |
| explanations.append(f'phi_{e}{i}.png') | |
| # avg=[] | |
| # for i,Y in enumerate(top_5_indices): | |
| # Y = tf.one_hot([Y], n_classes) | |
| # print(f'{i}/{len(top_5_indices)}') | |
| # phi = np.abs(explainer(X, Y))[0] | |
| # if len(phi.shape) == 3: | |
| # phi = np.mean(phi, -1) | |
| # show(X[0][:,size_repetitions:2*size_repetitions,:]) | |
| # show(phi[:,size_repetitions:2*size_repetitions], p=1, alpha=0.4) | |
| # plt.savefig(f'phi_6.png') | |
| # avg.append(f'phi_6.png') | |
| print('Done') | |
| if len(explanations)==1: | |
| explanations = explanations[0] | |
| # return explanations,avg | |
| return explanations | |