| | import os |
| | import torch |
| | from transformers import AutoModelForCausalLM |
| | from janus.models import MultiModalityCausalLM, VLChatProcessor |
| | from janus.utils.io import load_pil_images |
| | from tqdm import tqdm |
| |
|
| | |
| |
|
| |
|
| | model_path = "deepseek-ai/Janus-Pro-7B" |
| | vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path, cache_dir=".") |
| | tokenizer = vl_chat_processor.tokenizer |
| | vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained( |
| | model_path, cache_dir=".", trust_remote_code=True |
| | ).to(torch.bfloat16).cuda().eval() |
| |
|
| |
|
| | question = ( |
| | ''' |
| | 1. Identify the type of fruit or crop shown in the image. |
| | 2. Determine its current growth stage. (Options: unripe, mature, pest-damaged, rotten) |
| | 3. Recommend the farmer’s next action. (Options: keep for further growth, try to recover it, discard it) |
| | 4. Evaluate the consumer’s willingness to consume this fruit, from 1 (very unlikely) to 100 (very likely). |
| | Please respond in the following format: |
| | Type: [Fruit/Crop Name] Growth Stage: [unripe / mature / pest-damaged / rotten] Recommendation: [keep for further growth / pick it / try to recover it / discard it] Consumer Score: [1-100] |
| | ''' |
| | ) |
| |
|
| | root_folder = "../data" |
| | output_root = "result" |
| | os.makedirs(output_root, exist_ok=True) |
| |
|
| |
|
| | for fruit in os.listdir(root_folder): |
| | fruit_path = os.path.join(root_folder, fruit) |
| | if not os.path.isdir(fruit_path): |
| | continue |
| |
|
| | for subfolder in os.listdir(fruit_path): |
| | subfolder_path = os.path.join(fruit_path, subfolder) |
| | if not os.path.isdir(subfolder_path): |
| | continue |
| |
|
| | image_files = [f for f in os.listdir(subfolder_path) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp'))] |
| | if not image_files: |
| | continue |
| |
|
| | output_file = os.path.join(output_root, f"{fruit}_{subfolder}.txt") |
| | |
| | with open(output_file, "w", encoding="utf-8") as fout: |
| | for image_file in tqdm(image_files, desc=f"{fruit}/{subfolder}"): |
| | try: |
| | image_path = os.path.join(subfolder_path, image_file) |
| |
|
| | |
| | conversation = [ |
| | { |
| | "role": "<|User|>", |
| | "content": f"<image_placeholder>\n{question}", |
| | "images": [image_path], |
| | }, |
| | {"role": "<|Assistant|>", "content": ""}, |
| | ] |
| |
|
| | pil_images = load_pil_images(conversation) |
| | prepare_inputs = vl_chat_processor( |
| | conversations=conversation, |
| | images=pil_images, |
| | force_batchify=True |
| | ).to(vl_gpt.device) |
| |
|
| | |
| | inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs) |
| |
|
| | outputs = vl_gpt.language_model.generate( |
| | inputs_embeds=inputs_embeds, |
| | attention_mask=prepare_inputs.attention_mask, |
| | pad_token_id=tokenizer.eos_token_id, |
| | bos_token_id=tokenizer.bos_token_id, |
| | eos_token_id=tokenizer.eos_token_id, |
| | max_new_tokens=512, |
| | do_sample=False, |
| | use_cache=True, |
| | ) |
| |
|
| | answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True) |
| | fout.write(f"{'='*25} IMAGE START {'='*25}\n") |
| | fout.write(f"🖼️ Image Name: {image_file}\n") |
| | fout.write(f"📝 Answer:\n{answer.strip()}\n") |
| | fout.write(f"{'='*25} IMAGE END {'='*25}\n\n") |
| |
|
| | except Exception as e: |
| | print(f"[ERROR] {fruit}/{subfolder}/{image_file}: {e}") |
| | fout.write(f"{'='*25} IMAGE START {'='*25}\n") |
| | fout.write(f"🖼️ Image Name: {image_file}\n") |
| | fout.write(f"❌ ERROR: {e}\n") |
| | fout.write(f"{'='*25} IMAGE END {'='*25}\n\n") |
| |
|
