LIME for image classification |
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LIME for image classification
This is an example of the LIME explainer for image data. This explainer only supports image classification tasks. If using this explainer, please cite the original work: https://github.com/marcotcr/lime. [1]: # This default renderer is used for sphinx docs only. Please delete this cell in IPython. import plotly.io as pio pio.renderers.default = "png" [2]: import json import unittest import torch from torchvision import models, transforms from PIL import Image as PilImage from omnixai.data.image import Image from omnixai.explainers.vision import LimeImageWe recommend using Image to represent a batch of images. Image can be constructed from a numpy array or a Pillow image. The following code loads one test image and the class names on ImageNet. [3]: # Load the test image img = Image(PilImage.open('../data/images/dog_cat.png').convert('RGB')) # Load the class names with open('../data/images/imagenet_class_index.json', 'r') as read_file: class_idx = json.load(read_file) idx2label = [class_idx[str(k)][1] for k in range(len(class_idx))]The model applied here is a Inception model pretrained on ImageNet. [4]: model = models.inception_v3(pretrained=True) transform = transforms.Compose([ transforms.Resize((256, 256)), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ])Let’s check the predicted labels of the test image. [5]: model.eval() input_img = transform(img.to_pil()).unsqueeze(dim=0) probs_top_5 = torch.nn.functional.softmax(model(input_img), dim=1).topk(5) r = tuple((p, c, idx2label[c]) for p, c in zip(probs_top_5[0][0].detach().numpy(), probs_top_5[1][0].detach().numpy())) print(r) ((0.93592954, 239, 'Bernese_mountain_dog'), (0.038448237, 241, 'EntleBucher'), (0.023756476, 240, 'Appenzeller'), (0.0018181928, 238, 'Greater_Swiss_Mountain_dog'), (9.113302e-06, 214, 'Gordon_setter'))To initialize LimeImage, we need to set the following parameters: predict_function: The prediction function corresponding to the machine learning model to explain. For classification, the outputs of the predict_function are the class probabilities. [7]: def batch_predict(images): device = "cuda" if torch.cuda.is_available() else "cpu" model.eval() model.to(device) batch = torch.stack([transform(img.to_pil()) for img in images]) batch = batch.to(device) logits = model(batch) probs = torch.nn.functional.softmax(logits, dim=1) return probs.detach().cpu().numpy() explainer = LimeImage(predict_function=batch_predict) # Explain the top labels explanations = explainer.explain(img, hide_color=0, num_samples=1000) explanations.ipython_plot(index=0, class_names=idx2label) |
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