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import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), "..", ".."))
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from models.base_model import BaseModelGradCAM
from utils import configs
from .backbone_model import CLIPModel, TorchModel
class PrototypicalNetworksGradCAM(BaseModelGradCAM):
def __init__(
self,
name_model: str,
freeze_model: bool,
pretrained_model: bool,
support_set_method: str,
):
super().__init__(name_model, freeze_model, pretrained_model, support_set_method)
self.init_model()
self.set_grad_cam()
def init_model(self):
if self.name_model == "clip":
self.model = CLIPModel(
configs.CLIP_NAME_MODEL, self.freeze_model, self.pretrained_model
)
else:
self.model = TorchModel(
self.name_model, self.freeze_model, self.pretrained_model
)
self.model.to(self.device)
self.model.eval()
if __name__ == "__main__":
model = PrototypicalNetworksGradCAM("resnet50", False, True, "5_shot")
image = np.array(
Image.open(
"../../assets/example_images/gon/306e5d35-b301-4299-8022-0c89dc0b7690.png"
).convert("RGB")
)
gradcam = model.get_grad_cam(image)
plt.imshow(gradcam)
plt.show()
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