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import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), "..", ".."))
import time
from abc import ABC, abstractmethod
import numpy as np
import torch
from utils import configs
from utils.functional import (
check_data_type_variable,
euclidean_distance_normalized,
get_device,
image_augmentations,
)
class BaseModelImageSimilarity(ABC):
def __init__(
self,
name_model: str,
freeze_model: bool,
pretrained_model: bool,
support_set_method: str,
):
self.name_model = name_model
self.freeze_model = freeze_model
self.pretrained_model = pretrained_model
self.support_set_method = support_set_method
self.model = None
self.device = get_device()
self.check_arguments()
def check_arguments(self):
check_data_type_variable(self.name_model, str)
check_data_type_variable(self.freeze_model, bool)
check_data_type_variable(self.pretrained_model, bool)
check_data_type_variable(self.support_set_method, str)
old_name_model = self.name_model
if self.name_model == configs.CLIP_NAME_MODEL:
old_name_model = self.name_model
self.name_model = "clip"
if self.name_model not in tuple(configs.NAME_MODELS.keys()):
raise ValueError(f"Model {self.name_model} not supported")
if self.support_set_method not in configs.SUPPORT_SET_METHODS:
raise ValueError(
f"Support set method {self.support_set_method} not supported"
)
self.name_model = old_name_model
@abstractmethod
def init_model(self):
pass
def get_similarity(self, image1: np.ndarray, image2: np.ndarray) -> dict:
image1_input = image_augmentations()(image=image1)["image"]
image2_input = image_augmentations()(image=image2)["image"]
image1_input = image1_input.unsqueeze(axis=0).to(self.device)
image2_input = image2_input.unsqueeze(axis=0).to(self.device)
with torch.no_grad():
start_time = time.perf_counter()
image1_input = self.model(image1_input)
image2_input = self.model(image2_input)
end_time = time.perf_counter() - start_time
image1_input = image1_input.detach().cpu().numpy()
image2_input = image2_input.detach().cpu().numpy()
similarity = euclidean_distance_normalized(image1_input, image2_input)
result_similarity = (
"same image"
if similarity
> configs.NAME_MODELS[self.name_model]["image_similarity_threshold"]
else "not same image"
)
return {
"similarity": similarity,
"result_similarity": result_similarity,
"inference_time": end_time,
}
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