explain.py

import random
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import matplotlib.pyplot as plt

from glob import glob
from PIL import Image
from model.load_model import get_model
from torchvision import transforms

from pytorch_grad_cam import GradCAM, GuidedBackpropReLUModel
from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
from pytorch_grad_cam.utils.image import show_cam_on_image, deprocess_image

from ultralytics import YOLO
# from rembg import remove
import uuid


# Static variables
model_path = "efficientnet-b0-best.pth"
model_name = "efficientnet_b0"
YOLO_MODEL_WEIGHTS = "yolo-v11-best.pt"
classes = ["Healthy", "Resistant", "Susceptible"]
resizing_transforms = transforms.Compose([transforms.CenterCrop(224)])


# Function definitions
def reproduce(seed=42):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    cudnn.deterministic = True
    cudnn.benchmark = False


def get_grad_cam_results(image, transformed_image, class_index=0):
    with GradCAM(model=model, target_layers=target_layers) as cam:
        targets = [ClassifierOutputTarget(class_index)]
        grayscale_cam = cam(
            input_tensor=transformed_image.unsqueeze(0), targets=targets
        )
        grayscale_cam = grayscale_cam[0, :]

        visualization = show_cam_on_image(
            np.array(image) / 255.0, grayscale_cam, use_rgb=True
        )
    return visualization, grayscale_cam


def get_backpropagation_results(transformed_image, class_index=0):
    transformed_image = transformed_image.unsqueeze(0)
    backpropagation = gbp_model(transformed_image, target_category=class_index)
    bp_deprocessed = deprocess_image(backpropagation)
    return backpropagation, bp_deprocessed


def get_guided_gradcam(image, cam_grayscale, bp):
    cam_mask = np.expand_dims(cam_grayscale, axis=-1)
    cam_mask = np.repeat(cam_mask, 3, axis=-1)
    img = show_cam_on_image(
        np.array(image) / 255.0, deprocess_image(cam_mask * bp), use_rgb=False
    )
    return img


def explain_results(image, class_index=0):
    transformed_image = image_transform(image)
    image = resizing_transforms(image)

    visualization, cam_mask = get_grad_cam_results(
        image, transformed_image, class_index
    )
    backpropagation, bp_deprocessed = get_backpropagation_results(
        transformed_image, class_index
    )
    guided_gradcam = get_guided_gradcam(image, cam_mask, backpropagation)

    return visualization, bp_deprocessed, guided_gradcam


def make_prediction_and_explain(image):
    transformed_image = image_transform(image)
    transformed_image = transformed_image.unsqueeze(0)
    model.eval()
    with torch.no_grad():
        output = model(transformed_image)
        output = torch.nn.functional.softmax(output, dim=1)

    predictions = [round(x, 4) * 100 for x in output[0].tolist()]
    results = {}

    for i, k in enumerate(classes):
        gradcam, bp_deprocessed, guided_gradcam = explain_results(image, class_index=i)

        results[k] = {
            "original_image": image,
            "prediction": f"{k} ({predictions[i]}%)",
            "gradcam": gradcam,
            "backpropagation": bp_deprocessed,
            "guided_gradcam": guided_gradcam,
        }

    return results


def save_explanation_results(res, path):
    fig, ax = plt.subplots(3, 4, figsize=(15, 15))
    for i, (k, v) in enumerate(res.items()):
        ax[i, 0].imshow(v["original_image"])
        ax[i, 0].set_title(f"Original Image (class: {v['prediction']}")
        ax[i, 0].axis("off")

        ax[i, 1].imshow(v["gradcam"])
        ax[i, 1].set_title("GradCAM")
        ax[i, 1].axis("off")

        ax[i, 2].imshow(v["backpropagation"])
        ax[i, 2].set_title("Backpropagation")
        ax[i, 2].axis("off")

        ax[i, 3].imshow(v["guided_gradcam"])
        ax[i, 3].set_title("Guided GradCAM")
        ax[i, 3].axis("off")

    plt.tight_layout()
    plt.savefig(path, bbox_inches="tight")
    plt.close(fig)


model, image_transform = get_model(model_name)
model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
model.train()
target_layers = [model.conv_head]
gbp_model = GuidedBackpropReLUModel(model=model, device="cpu")

yolo_model = YOLO(YOLO_MODEL_WEIGHTS)


def get_results(img_path=None, img_for_testing=None):
    if img_path is None and img_for_testing is None:
        raise ValueError("Either img_path or img_for_testing should be provided.")

    if img_path is not None:
        results = yolo_model(img_path)
        image = Image.open(img_path)

    if img_for_testing is not None:
        results = yolo_model(img_for_testing)
        image = Image.fromarray(img_for_testing)

    result_paths = []

    for i, result in enumerate(results):
        unique_id = uuid.uuid4().hex
        save_path = f"/tmp/with-white-bg-result-{unique_id}.png"
        bbox = result.boxes.xyxy[0].cpu().numpy().astype(int)
        bbox_image = image.crop((bbox[0], bbox[1], bbox[2], bbox[3]))

        # bbox_image = remove(bbox_image).convert("RGB")
        # bbox_image = Image.fromarray(
        #     np.where(
        #         np.array(bbox_image) == [0, 0, 0],
        #         [255, 255, 255],
        #         np.array(bbox_image),
        #     ).astype(np.uint8)
        # )

        res = make_prediction_and_explain(bbox_image)
        save_explanation_results(res, save_path)

        result_paths.append(save_path)

    return result_paths


if __name__ == "__main__":
    # Actual logic
    reproduce()

    model, image_transform = get_model(model_name)
    model.load_state_dict(torch.load(model_path))
    model.train()
    target_layers = [model.conv_head]
    gbp_model = GuidedBackpropReLUModel(model=model, device="cpu")

    yolo_model = YOLO(YOLO_MODEL_WEIGHTS)

    for IMAGE_PATH in glob("samples/*"):
        start = time.perf_counter()

        results = yolo_model(IMAGE_PATH)
        image = Image.open(IMAGE_PATH)

        for i, result in enumerate(results):
            save_path = IMAGE_PATH.replace(
                "samples/", f"sample-results/with-white-bg-result-{i:02d}-"
            )
            bbox = result.boxes.xyxy[0].cpu().numpy().astype(int)
            bbox_image = image.crop((bbox[0], bbox[1], bbox[2], bbox[3]))

            # bbox_image = remove(bbox_image).convert("RGB")
            # bbox_image = Image.fromarray(
            #     np.where(
            #         np.array(bbox_image) == [0, 0, 0],
            #         [255, 255, 255],
            #         np.array(bbox_image),
            #     ).astype(np.uint8)
            # )

            res = make_prediction_and_explain(bbox_image)
            save_explanation_results(res, save_path)

        end = time.perf_counter() - start
        print(f"Completed in {end}s")