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perturbation_based_attribution_visualization_examples.py
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perturbation_based_attribution_visualization_examples.py
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from PIL import Image
import matplotlib.pyplot as plt
import torch
import timm
from timm.data import resolve_model_data_config
from timm.data.transforms_factory import create_transform
import requests
from attribution import Occlusion
from attribution.utils import normalize_saliency, visualize_single_saliency
if __name__ == '__main__':
# Load imagenet labels
IMAGENET_1k_URL = 'https://storage.googleapis.com/bit_models/ilsvrc2012_wordnet_lemmas.txt'
IMAGENET_1k_LABELS = requests.get(IMAGENET_1k_URL).text.strip().split('\n')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load a pretrained model
model = timm.create_model('resnet50', pretrained=True)
model = model.to(device)
model.eval()
config = resolve_model_data_config(model, None)
transform = create_transform(**config)
# Load an image
dog = Image.open('examples/dog.png').convert('RGB')
dog_tensor = transform(dog).unsqueeze(0)
H, W = dog_tensor.shape[-2:]
# Predict the image
img = transform(dog).unsqueeze(0)
img = torch.cat([img, img])
img = img.to(device)
output = model(img)
target_index = torch.argmax(output, dim=1).cpu()
print('Predicted:', IMAGENET_1k_LABELS[target_index[0].item()])
# Occlusion
occlusion_net = Occlusion(model)
occlusion = normalize_saliency(occlusion_net.get_mask(img, target_index))
occlusion_2 = normalize_saliency(occlusion_net.get_mask(img, target_index, size=30))
# Visualize the results
plt.figure(figsize=(16, 5))
plt.subplot(1, 3, 1)
plt.title('Input')
plt.axis('off')
plt.imshow(dog)
plt.subplot(1, 3, 2)
plt.title('Occlusion window-15')
visualize_single_saliency(occlusion[0].unsqueeze(0))
plt.subplot(1, 3, 3)
plt.title('Occlusion window-30')
visualize_single_saliency(occlusion_2[0].unsqueeze(0))
plt.tight_layout()
plt.savefig('examples/perturbation_based_visualization.png', bbox_inches='tight', pad_inches=0.5)