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import argparse
import torch
import os
import json
from tqdm import tqdm
import shortuuid
from ChatUniVi.constants import *
from ChatUniVi.conversation import conv_templates, SeparatorStyle
from ChatUniVi.model.builder import load_pretrained_model
from ChatUniVi.utils import disable_torch_init
from ChatUniVi.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
from PIL import Image
import math
from abc import ABC
import numpy as np
import jsonlines
def get_acc(file):
acc, num = 0, 0
yes, no, fail = 0, 0, 0
tp, fp, fn, tn = 0, 0, 0, 0
with open(file, "r", encoding="utf8") as f:
for item in jsonlines.Reader(f):
num += 1
if "Yes" in item["text"] or "yes" in item["text"]:
yes += 1
if "Yes" in item["label"] or "yes" in item["label"]:
acc += 1
tp += 1
else:
fp += 1
elif "No" in item["text"] or "no" in item["text"]:
no += 1
if "No" in item["label"] or "no" in item["label"]:
acc += 1
tn += 1
else:
fn += 1
else:
fail += 1
result = {
"acc": acc / num,
"yes": yes / num,
"no": no / num,
"fail": fail / num,
"precision": tp / (tp + fp),
"recall": tp / (tp + fn),
}
result["F1-score"] = 2 * result["precision"] * result["recall"] / (result["precision"] + result["recall"])
print("\n========================================================================")
print(file)
print(result)
print("========================================================================\n")
return result
def split_list(lst, n):
"""Split a list into n (roughly) equal-sized chunks"""
chunk_size = math.ceil(len(lst) / n) # integer division
return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
def get_chunk(lst, n, k):
chunks = split_list(lst, n)
return chunks[k]
class LogitsProcessor(ABC):
"""Abstract base class for all logit processors that can be applied during generation."""
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
"""Torch method for processing logits."""
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."
)
def eval_model(args):
# Model
disable_torch_init()
model_path = os.path.expanduser(args.model_path)
model_name = "ChatUniVi"
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)
mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True)
if mm_use_im_patch_token:
tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
if mm_use_im_start_end:
tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
model.resize_token_embeddings(len(tokenizer))
vision_tower = model.get_vision_tower()
if not vision_tower.is_loaded:
vision_tower.load_model()
image_processor = vision_tower.image_processor
questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")]
questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
answers_file = os.path.expanduser(args.answers_file)
os.makedirs(os.path.dirname(answers_file), exist_ok=True)
ans_file = open(answers_file, "w")
for line in tqdm(questions):
try:
idx = line["question_id"]
image_file = line["image"]
qs = line["text"]
label = line["label"]
cur_prompt = qs
if model.config.mm_use_im_start_end:
qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
else:
qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
conv = conv_templates[args.conv_mode].copy()
conv.append_message(conv.roles[0], qs)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
image = Image.open(os.path.join(args.image_folder, image_file))
image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
if args.answer_prompter:
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor.unsqueeze(0).half().cuda(),
do_sample=True,
temperature=args.temperature,
top_p=args.top_p,
num_beams=args.num_beams,
max_new_tokens=1024,
use_cache=True,
stopping_criteria=[stopping_criteria]
)
input_token_len = input_ids.shape[1]
n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
if n_diff_input_output > 0:
print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
outputs = outputs.strip()
if outputs.endswith(stop_str):
outputs = outputs[:-len(stop_str)]
outputs = outputs.strip()
outputs_reasoning = outputs
input_ids = tokenizer_image_token(prompt + outputs_reasoning + ' The answer is ', tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
else:
outputs_reasoning = ""
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor.unsqueeze(0).half().cuda(),
do_sample=True,
temperature=args.temperature,
top_p=args.top_p,
num_beams=args.num_beams,
output_scores=True,
return_dict_in_generate=True,
max_new_tokens=1024,
use_cache=True,
stopping_criteria=[stopping_criteria]
)
scores = output_ids.scores[0][0].to(torch.float32)
label_score = []
candidates = ["yes", "Yes", "no", "No"]
for can in candidates:
can_id = tokenizer.encode(can)[-1]
label_score.append(scores[can_id].item())
outputs = candidates[np.argmax(label_score)]
ans_id = shortuuid.uuid()
ans_file.write(json.dumps({"question_id": idx,
"prompt": cur_prompt,
"outputs_reasoning": outputs_reasoning + ' The answer is ' + outputs,
"text": outputs,
"label": label,
"answer_id": ans_id,
"model_id": model_name,
"metadata": {}}) + "\n")
ans_file.flush()
except Exception as e:
print(f"Error processing image file '{image_file}': {e}")
ans_file.close()
get_acc(answers_file)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--image-folder", type=str, default="")
parser.add_argument("--question-file", type=str, default="tables/question.jsonl")
parser.add_argument("--answers-file", type=str, default="answer.jsonl")
parser.add_argument("--conv-mode", type=str, default="simpleqa")
parser.add_argument("--num-chunks", type=int, default=1)
parser.add_argument("--chunk-idx", type=int, default=0)
parser.add_argument("--temperature", type=float, default=0.2)
parser.add_argument("--top_p", type=float, default=None)
parser.add_argument("--num_beams", type=int, default=1)
parser.add_argument("--model_use", type=str, default="BASE")
parser.add_argument("--answer-prompter", action="store_true")
args = parser.parse_args()
eval_model(args)
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