Datasets:
File size: 4,219 Bytes
3baa3fd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | import os
import torch
from PIL import Image
from modelscope import AutoModelForCausalLM, AutoTokenizer
MODEL_PATH = "./cogvlm2-llama3-chat-19B-int4"
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
TORCH_TYPE = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.get_device_capability()[0] >= 8 else torch.float16
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
MODEL_PATH,
torch_dtype=TORCH_TYPE,
trust_remote_code=True,
low_cpu_mem_usage=True,
).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, picking 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:\n"
"Type: [Fruit/Crop Name] Growth Stage: [unripe / mature / pest-damaged / rotten] "
"Recommendation: [keep for further growth / try to recover it / picking 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(('.jpg', '.jpeg', '.png', '.bmp', '.webp'))]
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 out_file:
for filename in image_files:
image_path = os.path.join(subfolder_path, filename)
try:
print(f"🖼️ Processing {filename}...")
image = Image.open(image_path).convert('RGB')
history = []
input_by_model = model.build_conversation_input_ids(
tokenizer,
query=question,
history=history,
images=[image],
template_version='chat'
)
inputs = {
'input_ids': input_by_model['input_ids'].unsqueeze(0).to(DEVICE),
'token_type_ids': input_by_model['token_type_ids'].unsqueeze(0).to(DEVICE),
'attention_mask': input_by_model['attention_mask'].unsqueeze(0).to(DEVICE),
'images': [[input_by_model['images'][0].to(DEVICE).to(TORCH_TYPE)]],
}
gen_kwargs = {
"max_new_tokens": 2048,
"pad_token_id": 128002,
}
with torch.no_grad():
outputs = model.generate(**inputs, **gen_kwargs)
outputs = outputs[:, inputs['input_ids'].shape[1]:]
response = tokenizer.decode(outputs[0])
response = response.split("<|end_of_text|>")[0].strip()
print(f"✅ Response: {response}\n")
out_file.write(f"{'='*25} IMAGE START {'='*25}\n")
out_file.write(f"🖼️ Image Name: {filename}\n")
out_file.write(f"📝 Answer:\n{response}\n")
out_file.write(f"{'='*25} IMAGE END {'='*25}\n\n")
except Exception as e:
print(f"[ERROR] {fruit}/{subfolder}/{filename}: {e}")
out_file.write(f"{'='*25} IMAGE START {'='*25}\n")
out_file.write(f"🖼️ Image Name: {filename}\n")
out_file.write(f"❌ ERROR: {e}\n")
out_file.write(f"{'='*25} IMAGE END {'='*25}\n\n")
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