#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. # #Licensed under the Apache License, Version 2.0 (the "License"); #you may not use this file except in compliance with the License. #You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #Unless required by applicable law or agreed to in writing, software #distributed under the License is distributed on an "AS IS" BASIS, #WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #See the License for the specific language governing permissions and #limitations under the License. import os import cv2 import copy import os.path as osp import numpy as np import paddlex as pdx from .interpretation_predict import interpretation_predict from .core.interpretation import Interpretation from .core.normlime_base import precompute_normlime_weights from .core._session_preparation import gen_user_home def lime(img_file, model, num_samples=3000, batch_size=50, save_dir='./'): """使用LIME算法将模型预测结果的可解释性可视化。 LIME表示与模型无关的局部可解释性，可以解释任何模型。LIME的思想是以输入样本为中心， 在其附近的空间中进行随机采样，每个采样通过原模型得到新的输出，这样得到一系列的输入 和对应的输出，LIME用一个简单的、可解释的模型（比如线性回归模型）来拟合这个映射关系， 得到每个输入维度的权重，以此来解释模型。 注意：LIME可解释性结果可视化目前只支持分类模型。 Args: img_file (str): 预测图像路径。 model (paddlex.cv.models): paddlex中的模型。 num_samples (int): LIME用于学习线性模型的采样数，默认为3000。 batch_size (int): 预测数据batch大小，默认为50。 save_dir (str): 可解释性可视化结果（保存为png格式文件）和中间文件存储路径。 """ assert model.model_type == 'classifier', \ 'Now the interpretation visualize only be supported in classifier!' if model.status != 'Normal': raise Exception('The interpretation only can deal with the Normal model') if not osp.exists(save_dir): os.makedirs(save_dir) model.arrange_transforms( transforms=model.test_transforms, mode='test') tmp_transforms = copy.deepcopy(model.test_transforms) tmp_transforms.transforms = tmp_transforms.transforms[:-2] img = tmp_transforms(img_file)[0] img = np.around(img).astype('uint8') img = np.expand_dims(img, axis=0) interpreter = None interpreter = get_lime_interpreter(img, model, num_samples=num_samples, batch_size=batch_size) img_name = osp.splitext(osp.split(img_file)[-1])[0] interpreter.interpret(img, save_dir=save_dir) def normlime(img_file, model, dataset=None, num_samples=3000, batch_size=50, save_dir='./'): """使用NormLIME算法将模型预测结果的可解释性可视化。 NormLIME是利用一定数量的样本来出一个全局的解释。NormLIME会提前计算一定数量的测 试样本的LIME结果，然后对相同的特征进行权重的归一化，这样来得到一个全局的输入和输出的关系。 注意1：dataset读取的是一个数据集，该数据集不宜过大，否则计算时间会较长，但应包含所有类别的数据。 注意2：NormLIME可解释性结果可视化目前只支持分类模型。 Args: img_file (str): 预测图像路径。 model (paddlex.cv.models): paddlex中的模型。 dataset (paddlex.datasets): 数据集读取器，默认为None。 num_samples (int): LIME用于学习线性模型的采样数，默认为3000。 batch_size (int): 预测数据batch大小，默认为50。 save_dir (str): 可解释性可视化结果（保存为png格式文件）和中间文件存储路径。 """ assert model.model_type == 'classifier', \ 'Now the interpretation visualize only be supported in classifier!' if model.status != 'Normal': raise Exception('The interpretation only can deal with the Normal model') if not osp.exists(save_dir): os.makedirs(save_dir) model.arrange_transforms( transforms=model.test_transforms, mode='test') tmp_transforms = copy.deepcopy(model.test_transforms) tmp_transforms.transforms = tmp_transforms.transforms[:-2] img = tmp_transforms(img_file)[0] img = np.around(img).astype('uint8') img = np.expand_dims(img, axis=0) interpreter = None if dataset is None: raise Exception('The dataset is None. Cannot implement this kind of interpretation') interpreter = get_normlime_interpreter(img, model, dataset, num_samples=num_samples, batch_size=batch_size, save_dir=save_dir) img_name = osp.splitext(osp.split(img_file)[-1])[0] interpreter.interpret(img, save_dir=save_dir) def get_lime_interpreter(img, model, num_samples=3000, batch_size=50): def predict_func(image): image = image.astype('float32') for i in range(image.shape[0]): image[i] = cv2.cvtColor(image[i], cv2.COLOR_RGB2BGR) tmp_transforms = copy.deepcopy(model.test_transforms.transforms) model.test_transforms.transforms = model.test_transforms.transforms[-2:] out = interpretation_predict(model, image) model.test_transforms.transforms = tmp_transforms return out[0] labels_name = None if hasattr(model, 'labels'): labels_name = model.labels interpreter = Interpretation('lime', predict_func, labels_name, num_samples=num_samples, batch_size=batch_size) return interpreter def get_normlime_interpreter(img, model, dataset, num_samples=3000, batch_size=50, save_dir='./'): def precompute_predict_func(image): image = image.astype('float32') tmp_transforms = copy.deepcopy(model.test_transforms.transforms) model.test_transforms.transforms = model.test_transforms.transforms[-2:] out = interpretation_predict(model, image) model.test_transforms.transforms = tmp_transforms return out[0] def predict_func(image): image = image.astype('float32') for i in range(image.shape[0]): image[i] = cv2.cvtColor(image[i], cv2.COLOR_RGB2BGR) tmp_transforms = copy.deepcopy(model.test_transforms.transforms) model.test_transforms.transforms = model.test_transforms.transforms[-2:] out = interpretation_predict(model, image) model.test_transforms.transforms = tmp_transforms return out[0] labels_name = None if dataset is not None: labels_name = dataset.labels root_path = gen_user_home() root_path = osp.join(root_path, '.paddlex') pre_models_path = osp.join(root_path, "pre_models") if not osp.exists(pre_models_path): if not osp.exists(root_path): os.makedirs(root_path) url = "https://bj.bcebos.com/paddlex/interpret/pre_models.tar.gz" pdx.utils.download_and_decompress(url, path=root_path) npy_dir = precompute_for_normlime(precompute_predict_func, dataset, num_samples=num_samples, batch_size=batch_size, save_dir=save_dir) interpreter = Interpretation('normlime', predict_func, labels_name, num_samples=num_samples, batch_size=batch_size, normlime_weights=npy_dir) return interpreter def precompute_for_normlime(predict_func, dataset, num_samples=3000, batch_size=50, save_dir='./'): image_list = [] for item in dataset.file_list: image_list.append(item[0]) return precompute_normlime_weights( image_list, predict_func, num_samples=num_samples, batch_size=batch_size, save_dir=save_dir)