training/detectors/clip_image_detector.py

import os
import logging
import numpy as np
from typing import List

import torch
import torch.nn as nn
import torch.nn.functional as F
import clip

from metrics.base_metrics_class import calculate_acc_for_train
from .base_detector import AbstractDetector
from detectors import DETECTOR
from loss import LOSSFUNC

logger = logging.getLogger(__name__)

# Linear classifier module (adapted to the 512-dimensional CLIP image embedding)
class LinearClassifier(nn.Module):
    def __init__(self, input_size: int, hidden_size_list: List[int], num_classes: int):
        super(LinearClassifier, self).__init__()
        self.dropout = nn.Dropout(0.5)
        self.fc1 = nn.Linear(input_size, hidden_size_list[0])
        self.fc2 = nn.Linear(hidden_size_list[0], hidden_size_list[1])
        self.fc3 = nn.Linear(hidden_size_list[1], num_classes)

    def forward(self, x: torch.tensor) -> torch.tensor:
        out = self.fc1(x)
        out = F.relu(out)
        out = self.dropout(out)
        out = self.fc2(out)
        out = F.relu(out)
        out = self.fc3(out)
        return out

@DETECTOR.register_module(module_name='clip_image')
class CLIPImageDetector(AbstractDetector):
    def __init__(self, config):
        super().__init__(config)  # keep the parent `__init__` signature aligned
        self.config = config
        self.device = torch.device("cuda" if config['cuda'] else "cpu")
        self.backbone = self.build_backbone(config)
        self.classifier_module = self.build_classifier(config)  # avoid naming conflicts with the parent method
        self.loss_func = self.build_loss(config)

    def build_backbone(self, config) -> clip.model.CLIP:
        # Load only the CLIP model and use its image encoder.
        clip_model, _ = clip.load(config['clip_model_name'], device=self.device)
        logger.info(f"Loaded CLIP image encoder: {config['clip_model_name']}")
        return clip_model

    def build_loss(self, config) -> nn.CrossEntropyLoss:
        # Implement the abstract parent method to build the multi-classification loss.
        loss_class = LOSSFUNC[config['loss_func']]
        loss_func = loss_class()
        return loss_func.to(self.device)

    def features(self, data_dict: dict) -> torch.tensor:
        # Implement the abstract parent method to extract image features only, without text logic.
        images = data_dict['image'].to(self.device)
        # Extract the 512-dimensional CLIP image embedding.
        with torch.no_grad():
            image_emb = self.backbone.encode_image(images)
        return image_emb.float()

    def classifier(self, features: torch.tensor) -> torch.tensor:
        # Implement the abstract parent method for feature classification.
        return self.classifier_module(features)

    def get_losses(self, data_dict: dict, pred_dict: dict) -> dict:
        # Implement the abstract parent method to compute the loss.
        labels = data_dict['label'].to(self.device)
        preds = pred_dict['cls']
        loss = self.loss_func(preds, labels)
        return {'overall': loss}

    def get_train_metrics(self, data_dict: dict, pred_dict: dict) -> dict:
        # Implement the abstract parent method to compute training metrics for multi-class classification.
        labels = data_dict['label'].detach().cpu()
        preds = pred_dict['cls'].detach().cpu()
        num_classes = self.config['classifier_config']['num_classes']
        acc, mAP = calculate_acc_for_train(labels, preds, num_classes)
        return {'acc': acc, 'mAP': mAP}

    def forward(self, data_dict: dict, inference=False) -> dict:
        # Implement the abstract parent method for forward propagation.
        features = self.features(data_dict)
        cls_pred = self.classifier(features)
        prob = torch.softmax(cls_pred, dim=1)
        return {'cls': cls_pred, 'prob': prob, 'feat': features}

    def build_classifier(self, config) -> LinearClassifier:
        # Helper method to build the linear classifier (input dimension = 512-dimensional CLIP image embedding).
        input_size = 512  # the CLIP ViT-B/32 image embedding dimension is fixed at 512
        hidden_size_list = config['classifier_config']['hidden_size_list']
        num_classes = config['classifier_config']['num_classes']
        classifier = LinearClassifier(input_size, hidden_size_list, num_classes)
        return classifier.to(self.device)