Delete clip_model.py

clip_model.py

@@ -1,94 +0,0 @@
-import os
-import torch
-from PIL import Image
-from transformers import ChineseCLIPProcessor, ChineseCLIPModel
-import psutil
-
-def check_memory_usage():
-    # Get memory details
-    memory_info = psutil.virtual_memory()
-    
-    total_memory = memory_info.total / (1024 * 1024)  # Convert bytes to MB
-    available_memory = memory_info.available / (1024 * 1024)
-    used_memory = memory_info.used / (1024 * 1024)
-    memory_usage_percent = memory_info.percent
-    
-    print(f"^^^^^^ Total Memory: {total_memory:.2f} MB ^^^^^^")
-    print(f"^^^^^^ Available Memory: {available_memory:.2f} MB ^^^^^^")
-    print(f"^^^^^^ Used Memory: {used_memory:.2f} MB ^^^^^^")
-    print(f"^^^^^^ Memory Usage (%): {memory_usage_percent}% ^^^^^^")
-
-class ClipModel:
-    def __init__(self, model_name="OFA-Sys/chinese-clip-vit-base-patch16", model_path=None, vocab_path='./chiikawa/word_list.txt'):
-        # Set device
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-
-        # Load model and processor
-        self.model = ChineseCLIPModel.from_pretrained(model_name)
-        if model_path is None:
-            script_dir = os.path.dirname(os.path.abspath(__file__))
-            model_path = os.path.join(script_dir, 'artifacts/models', 'best_clip_model.pth')
-        self.model.load_state_dict(torch.load(model_path, map_location=self.device))
-        self.model = self.model.to(self.device)
-        self.model.eval()
-
-        self.processor = ChineseCLIPProcessor.from_pretrained(model_name)
-
-        print("***** Clip Model LOAD DONE *****")
-        check_memory_usage()
-
-        # Load Chinese vocabulary
-        with open(vocab_path, 'r', encoding='utf-8') as f:
-            self.vocab = [line.strip() for line in f.readlines()]
-
-    def clip_result(self, image_path, top_k=3):
-        """
-        給定圖片路徑，返回最接近的 top_k 詞彙
-        """
-        # Load image
-        image = Image.open(image_path)
-
-        print(f"===== Clip Model_clip_result : {image_path} ===== ")
-        # Run the function
-        check_memory_usage()
-
-        # Process images and texts
-        batch_size = 16  # Process 16 vocab at a time
-        similarities = []
-
-        # # Check memory usage before calling empty_cache
-        # reserved_memory = torch.cuda.memory_reserved()
-        # allocated_memory = torch.cuda.memory_allocated()
-        
-        # # Only call empty_cache if reserved memory exceeds a threshold
-        # if reserved_memory > 0.8 * torch.cuda.get_device_properties(0).total_memory:
-        #     # Release unused memory
-        torch.cuda.empty_cache()
-
-        with torch.no_grad():
-            for i in range(0, len(self.vocab), batch_size):
-                batch_vocab = self.vocab[i:i + batch_size]
-                inputs = self.processor(
-                    text=batch_vocab,
-                    images=image,
-                    return_tensors="pt",
-                    padding=True
-                ).to(self.device)
-
-                # Inference and similarity calculation
-                outputs = self.model(**inputs)
-                image_embeds = outputs.image_embeds / outputs.image_embeds.norm(dim=-1, keepdim=True)
-                text_embeds = outputs.text_embeds / outputs.text_embeds.norm(dim=-1, keepdim=True)
-                similarity = torch.matmul(image_embeds, text_embeds.T).squeeze(0)
-                similarities.append(similarity)
-
-        # Merge all similarities
-        similarity = torch.cat(similarities, dim=0)
-
-        # Find top-3 similarities
-        top_k_indices = torch.topk(similarity, top_k).indices.tolist()
-        top_k_words = [self.vocab[idx] for idx in top_k_indices]
-        
-        # 6. 輸出最接近的前3名中文詞彙
-        return top_k_words
-