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Model Interpretability using Captum¶
Captum helps you understand how the data features impact your model predictions or neuron activations, shedding light on how your model operates.
Using Captum, you can apply a wide range of state-of-the-art feature
attribution algorithms such as
Guided GradCam and
Integrated Gradients in a unified way.
In this recipe you will learn how to use Captum to:
Attribute the predictions of an image classifier to their corresponding image features.
Visualize the attribution results.
Before you begin¶
Make sure Captum is installed in your active Python environment. Captum
is available both on GitHub, as a
pip package, or as a
package. For detailed instructions, consult the installation guide at
For a model, we use a built-in image classifier in PyTorch. Captum can reveal which parts of a sample image support certain predictions made by the model.
import torchvision from torchvision import transforms from PIL import Image import requests from io import BytesIO model = torchvision.models.resnet18(pretrained=True).eval() response = requests.get("https://image.freepik.com/free-photo/two-beautiful-puppies-cat-dog_58409-6024.jpg") img = Image.open(BytesIO(response.content)) center_crop = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), ]) normalize = transforms.Compose([ transforms.ToTensor(), # converts the image to a tensor with values between 0 and 1 transforms.Normalize( # normalize to follow 0-centered imagenet pixel RGB distribution mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] ) ]) input_img = normalize(center_crop(img)).unsqueeze(0)
Among the top-3 predictions of the models are classes 208 and 283 which correspond to dog and cat.
Let us attribute each of these predictions to the corresponding part of
the input, using Captum’s
from captum.attr import Occlusion occlusion = Occlusion(model) strides = (3, 9, 9) # smaller = more fine-grained attribution but slower target=208, # Labrador index in ImageNet sliding_window_shapes=(3,45, 45) # choose size enough to change object appearance baselines = 0 # values to occlude the image with. 0 corresponds to gray attribution_dog = occlusion.attribute(input_img, strides = strides, target=target, sliding_window_shapes=sliding_window_shapes, baselines=baselines) target=283, # Persian cat index in ImageNet attribution_cat = occlusion.attribute(input_img, strides = strides, target=target, sliding_window_shapes=sliding_window_shapes, baselines=0)
Occlusion, Captum features many algorithms such as
GradientShap. All of these algorithms are subclasses of
Attribution which expects your model as a callable
upon initialization and has an
attribute(...) method which returns
the attribution result in a unified format.
Let us visualize the computed attribution results in case of images.
Visualizing the Results¶
visualization utility provides out-of-the-box methods
to visualize attribution results both for pictorial and for textual
import numpy as np from captum.attr import visualization as viz # Convert the compute attribution tensor into an image-like numpy array attribution_dog = np.transpose(attribution_dog.squeeze().cpu().detach().numpy(), (1,2,0)) vis_types = ["heat_map", "original_image"] vis_signs = ["all", "all"] # "positive", "negative", or "all" to show both # positive attribution indicates that the presence of the area increases the prediction score # negative attribution indicates distractor areas whose absence increases the score _ = viz.visualize_image_attr_multiple(attribution_dog, np.array(center_crop(img)), vis_types, vis_signs, ["attribution for dog", "image"], show_colorbar = True ) attribution_cat = np.transpose(attribution_cat.squeeze().cpu().detach().numpy(), (1,2,0)) _ = viz.visualize_image_attr_multiple(attribution_cat, np.array(center_crop(img)), ["heat_map", "original_image"], ["all", "all"], # positive/negative attribution or all ["attribution for cat", "image"], show_colorbar = True )
If your data is textual,
visualization.visualize_text() offers a
dedicated view to explore attribution on top of the input text. Find out
more at http://captum.ai/tutorials/IMDB_TorchText_Interpret
Captum can handle most model types in PyTorch across modalities including vision, text, and more. With Captum you can: * Attribute a specific output to the model input as illustrated above. * Attribute a specific output to a hidden-layer neuron (see Captum API reference). * Attribute a hidden-layer neuron response to the model input (see Captum API reference).
For complete API of the supported methods and a list of tutorials, consult our website http://captum.ai
Another useful post by Gilbert Tanner: https://gilberttanner.com/blog/interpreting-pytorch-models-with-captum
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