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import os |
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import torch |
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from PIL import Image |
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from modelscope import AutoModelForCausalLM, AutoTokenizer |
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MODEL_PATH = "./cogvlm2-llama3-chat-19B-int4" |
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DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' |
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TORCH_TYPE = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.get_device_capability()[0] >= 8 else torch.float16 |
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tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained( |
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MODEL_PATH, |
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torch_dtype=TORCH_TYPE, |
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trust_remote_code=True, |
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low_cpu_mem_usage=True, |
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).eval() |
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question = ( |
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''' "1. Identify the type of fruit or crop shown in the image. " |
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"2. Determine its current growth stage. (Options: unripe, mature, pest-damaged, rotten) " |
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"3. Recommend the farmer’s next action. (Options: keep for further growth, try to recover it, picking it, discard it) " |
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"4. Evaluate the consumer’s willingness to consume this fruit, from 1 (very unlikely) to 100 (very likely). " |
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"Please respond in the following format:\n" |
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"Type: [Fruit/Crop Name] Growth Stage: [unripe / mature / pest-damaged / rotten] " |
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"Recommendation: [keep for further growth / try to recover it / picking it / discard it] Consumer Score: [1-100]''' |
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) |
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root_folder = "../data" |
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output_root = "result" |
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os.makedirs(output_root, exist_ok=True) |
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for fruit in os.listdir(root_folder): |
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fruit_path = os.path.join(root_folder, fruit) |
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if not os.path.isdir(fruit_path): |
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continue |
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for subfolder in os.listdir(fruit_path): |
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subfolder_path = os.path.join(fruit_path, subfolder) |
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if not os.path.isdir(subfolder_path): |
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continue |
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image_files = [f for f in os.listdir(subfolder_path) if f.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp', '.webp'))] |
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if not image_files: |
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continue |
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output_file = os.path.join(output_root, f"{fruit}_{subfolder}.txt") |
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with open(output_file, "w", encoding="utf-8") as out_file: |
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for filename in image_files: |
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image_path = os.path.join(subfolder_path, filename) |
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try: |
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print(f"🖼️ Processing {filename}...") |
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image = Image.open(image_path).convert('RGB') |
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history = [] |
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input_by_model = model.build_conversation_input_ids( |
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tokenizer, |
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query=question, |
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history=history, |
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images=[image], |
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template_version='chat' |
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) |
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inputs = { |
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'input_ids': input_by_model['input_ids'].unsqueeze(0).to(DEVICE), |
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'token_type_ids': input_by_model['token_type_ids'].unsqueeze(0).to(DEVICE), |
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'attention_mask': input_by_model['attention_mask'].unsqueeze(0).to(DEVICE), |
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'images': [[input_by_model['images'][0].to(DEVICE).to(TORCH_TYPE)]], |
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} |
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gen_kwargs = { |
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"max_new_tokens": 2048, |
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"pad_token_id": 128002, |
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} |
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with torch.no_grad(): |
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outputs = model.generate(**inputs, **gen_kwargs) |
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outputs = outputs[:, inputs['input_ids'].shape[1]:] |
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response = tokenizer.decode(outputs[0]) |
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response = response.split("<|end_of_text|>")[0].strip() |
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print(f"✅ Response: {response}\n") |
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out_file.write(f"{'='*25} IMAGE START {'='*25}\n") |
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out_file.write(f"🖼️ Image Name: {filename}\n") |
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out_file.write(f"📝 Answer:\n{response}\n") |
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out_file.write(f"{'='*25} IMAGE END {'='*25}\n\n") |
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except Exception as e: |
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print(f"[ERROR] {fruit}/{subfolder}/{filename}: {e}") |
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out_file.write(f"{'='*25} IMAGE START {'='*25}\n") |
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out_file.write(f"🖼️ Image Name: {filename}\n") |
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out_file.write(f"❌ ERROR: {e}\n") |
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out_file.write(f"{'='*25} IMAGE END {'='*25}\n\n") |
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