xai-inference-engine 0.1.1

A pip package for XAI Inferencing

XAI Inference Engine

Todo Description...

Installation

Execute the following command in your terminal to install the package.

pip install xai-inference-engine

Usage

Follow the example below to use the package. Copy and paste the code into a python script and run it.

print("[INFO]: Testing XAIInferenceEngine...")

print("[INFO]: Importing Libraries...")
from xai_inference_engine import XAIInferenceEngine
from torchvision.models import resnet50, ResNet50_Weights

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("[INFO]: Device: {}".format(device))

print("[INFO]: Loading Model...")
# Model
model = resnet50(weights=ResNet50_Weights.IMAGENET1K_V2).to(device)
weights = ResNet50_Weights.DEFAULT
preprocess = weights.transforms()

# Model config
# Set model to eval mode
model.eval()
last_conv_layer = model.layer4[2].conv3
class_count = 5
class_list = weights.meta["categories"]
img_h = 224

print("[INFO]: Image Preprocessing...")
# Image Preprocessing
url = "https://raw.githubusercontent.com/utkuozbulak/pytorch-cnn-visualizations/master/input_images/cat_dog.png"
r = requests.get(url, allow_redirects=True)
open("dog-and-cat-cover.jpg", "wb").write(r.content)
img = Image.open("dog-and-cat-cover.jpg")
img = img.resize((img_h, img_h), resample=Image.BICUBIC)
img_tensor = preprocess(img).to(device)


print("[INFO]: Creating XAIInferenceEngine...")
xai_inferencer = XAIInferenceEngine(
    model=model,
    last_conv_layer=last_conv_layer,
    device=device,
)

print("[INFO]: Running XAIInferenceEngine.predict()...")
preds, sorted_pred_indices, super_imp_img, heatmaps = xai_inferencer.predict(
    img=img,
    img_tensor=img_tensor,
)

print("[INFO]: Saving Results to the root folder...")
super_imp_img.save("super_imp_img.jpg")
heatmaps.save("heatmaps.jpg")

print("[INFO]: Displaying Results...")
print("        Predictions: {}".format(preds.shape))
print("        Sorted Prediction Indices: {}".format(sorted_pred_indices.cpu().numpy()[:10]))
print("        Heatmaps shape: {}".format(heatmaps))
print("        Super Imposed Image: {}".format(super_imp_img))