Datasets:

TJIET
/

FruitBench

@@ -1,136 +0,0 @@
-# -*- coding: utf-8 -*-
-import os, gc, torch
-from tqdm import tqdm
-from transformers import AutoModelForCausalLM
-from deepseek_vl.models import VLChatProcessor, MultiModalityCausalLM
-from deepseek_vl.utils.io import load_pil_images
-chinese_to_english = {
-    'strawberry', 'tomato', 'guava', 'dragon fruit',
-    'orange',   'pear',     'lychee',   'mango',
-    'kiwi',    'papaya', 'apple',    'grape',
-    'pomegranate','peach', 'banana',   'pomelo'
-}
-stage_to_english = {
-    'unripe', 'mature', 'pest-damaged', 'rotten'
-}
-recommendation_map = {
-    'unripe': 'keep for further growth',
-    'mature': 'picking it',
-    'pest-damaged': 'try to recover it',
-    'rotten': 'discard it'
-}
-score_map = {
-    'unripe': 30, 'mature': 85, 'pest-damaged': 20, 'rotten': 5
-}
-model_path  = "deepseek-ai/deepseek-vl-7b-chat"
-root_folder = "../data/"                       #
-output_root = "result_1shot"
-os.makedirs(output_root, exist_ok=True)
-print("🚀 Loading DeepSeek‑VL‑7B ...")
-vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path)
-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.  \n"
-    "2. Determine its current growth stage. (Options: unripe, mature, pest-damaged, rotten)  \n"
-    "3. Recommend the farmer’s next action. (Options: keep for further growth, try to recover it, discard it)  \n"
-    "4. Evaluate the consumer’s willingness to consume this fruit, from 1 (very unlikely) to 100 (very likely).\n"
-    "Please respond in the following format:\n"
-    "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]"
-)
-def build_stage_example(fruit_dir: str, fruit_cn: str, stage_cn: str):
-    stage_path = os.path.join(fruit_dir, stage_cn)
-    imgs = sorted([f for f in os.listdir(stage_path)
-                   if f.lower().endswith((".png", ".jpg", ".jpeg", ".bmp"))])
-    if not imgs:
-        return []
-    img_path = os.path.join(stage_path, imgs[0])   #
-    fruit_en = chinese_to_english.get(fruit_cn, fruit_cn)
-    stage_en = stage_to_english.get(stage_cn, "mature")
-    assistant_reply = (
-        f"Type: {fruit_en}  Growth Stage: {stage_en}  "
-        f"Recommendation: {recommendation_map[stage_en]}  "
-        f"Consumer Score: {score_map[stage_en]}"
-    )
-    return [
-        {"role": "User", "content": "<image_placeholder>\n" + question, "images": [img_path]},
-        {"role": "Assistant", "content": assistant_reply},
-    ]
-for fruit_cn in os.listdir(root_folder):
-    fruit_path = os.path.join(root_folder, fruit_cn)
-    if not os.path.isdir(fruit_path):
-        continue
-    for stage_cn in os.listdir(fruit_path):
-        stage_path = os.path.join(fruit_path, stage_cn)
-        if not os.path.isdir(stage_path):
-            continue
-        img_files = sorted([f for f in os.listdir(stage_path)
-                            if f.lower().endswith((".png", ".jpg", ".jpeg", ".bmp"))])
-        example_msgs = build_stage_example(fruit_path, fruit_cn, stage_cn)
-        if not example_msgs:
-            continue
-        output_file = os.path.join(output_root, f"{fruit_cn}_{stage_cn}.txt")
-        with open(output_file, "w", encoding="utf-8") as fout:
-            for img_name in tqdm(img_files, desc=f"{fruit_cn}/{stage_cn}"):
-                img_path = os.path.join(stage_path, img_name)
-                if img_path in example_msgs[0]["images"]:
-                    continue
-                conversation = example_msgs + [
-                    {"role": "User", "content": "<image_placeholder>\n" + question, "images": [img_path]},
-                    {"role": "Assistant", "content": ""},
-                ]
-                try:
-                    with torch.no_grad():
-                        pil_imgs = load_pil_images(conversation)
-                        prep_in  = vl_chat_processor(conversations=conversation, images=pil_imgs, force_batchify=True).to(vl_gpt.device)
-                        embeds   = vl_gpt.prepare_inputs_embeds(**prep_in)
-                        out_ids  = vl_gpt.language_model.generate(inputs_embeds=embeds, attention_mask=prep_in.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=256, do_sample=False, use_cache=True)
-                        answer   = tokenizer.decode(out_ids[0], skip_special_tokens=True).strip()
-                    print(f"✅ {img_name} → {answer.splitlines()[0]}")
-                    fout.write("="*25 + " IMAGE START " + "="*25 + "\n")
-                    fout.write(f"🖼️ Image Name: {img_name}\n")
-                    fout.write(f"📝 Answer:\n{answer}\n")
-                    fout.write("="*25 + " IMAGE END " + "="*25 + "\n\n")
-                except Exception as e:
-                    print(f"[ERROR] {fruit_cn}/{stage_cn}/{img_name}: {e}")
-                finally:
-                    torch.cuda.empty_cache()
-                    gc.collect()