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import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), "..", ".."))
import numpy as np
import torch.nn as nn
from PIL import Image
from models.base_model import BaseModelImageEmbeddings
from utils import configs
from .lightning_module import ImageClassificationLightningModule
class ImageEmbeddings(BaseModelImageEmbeddings):
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()
def init_model(self):
self.model = ImageClassificationLightningModule.load_from_checkpoint(
os.path.join(
configs.WEIGHTS_PATH,
self.name_model,
self.support_set_method,
"best.ckpt",
),
name_model=self.name_model,
freeze_model=self.freeze_model,
pretrained_model=self.pretrained_model,
)
self.model = self.model.model
self.model.classifier = nn.Identity()
for layer in self.model.children():
if hasattr(layer, "reset_parameters") and not self.pretrained_model:
layer.reset_parameters()
for param in self.model.parameters():
param.required_grad = False if not self.freeze_model else True
self.model.to(self.device)
self.model.eval()
if __name__ == "__main__":
model = ImageEmbeddings("resnet50", True, True, "1_shot")
image = np.array(
Image.open(
"../../assets/example_images/gon/306e5d35-b301-4299-8022-0c89dc0b7690.png"
).convert("RGB")
)
result = model.get_embeddings(image)
print(result)
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