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Running
on
Zero
File size: 8,294 Bytes
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from transformers import AutoProcessor, AutoModelForVision2Seq, AutoTokenizer
from qwen_vl_utils import process_vision_info
import torch
import numpy as np
import cv2, os, re
def _get_video_fps(url_or_p:str):
cap = cv2.VideoCapture(url_or_p)
if not cap.isOpened():
raise ValueError(f"Cannot open video: {url_or_p}")
fps = cap.get(cv2.CAP_PROP_FPS)
cap.release()
return fps
class eval_VideoScore2_float:
def __init__(self, model_name: str):
self.model, self.processor = self.load_model_processor(model_name)
self.tokenizer = getattr(self.processor, "tokenizer", None)
if self.tokenizer is None:
self.tokenizer = AutoTokenizer.from_pretrained(
model_name,
trust_remote_code=True,
use_fast=False,
)
def load_model_processor(self, model_name):
model = AutoModelForVision2Seq.from_pretrained(
model_name,
trust_remote_code=True,
).to("cuda")
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
return model, processor
def evaluate_video(self,
user_prompt: str,
video_path: str,
kwargs: dict
) -> str | None:
if not os.path.exists(video_path):
raise ValueError(f"not exist: {video_path}")
max_tokens=kwargs.get("max_tokens",4096)
infer_fps=kwargs.get("infer_fps",2.0)
temperature=kwargs.get("temperature",0.7)
if infer_fps == "raw":
infer_fps=_get_video_fps(video_path)
messages = [
{
"role": "user",
"content": [
{
"type": "video",
"video": video_path,
"fps":infer_fps
},
{
"type": "text",
"text": user_prompt,
},
],
}
]
text = self.processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
try:
image_inputs, video_inputs = process_vision_info(messages)
except Exception as e:
raise ValueError(f"error when reading: {video_path}")
inputs = self.processor(
text=[text],
images=image_inputs,
videos=video_inputs,
fps=infer_fps,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
gen_out = self.model.generate(
**inputs,
max_new_tokens=max_tokens,
output_scores=True,
return_dict_in_generate=True,
do_sample=True,
temperature=temperature,
)
sequences = gen_out.sequences
scores = gen_out.scores
input_len = inputs["input_ids"].shape[1]
gen_token_ids = sequences[0, input_len:].tolist()
output_text = self.processor.batch_decode(
sequences[:, input_len:], skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0]
pattern = r"visual quality:\s*(\d+).*?text-to-video alignment:\s*(\d+).*?physical/common-sense consistency:\s*(\d+)"
match = re.search(pattern, output_text, re.DOTALL | re.IGNORECASE)
if match:
v_score_model = int(match.group(1))
t_score_model = int(match.group(2))
p_score_model = int(match.group(3))
else:
v_score_model = t_score_model = p_score_model = None
# def find_score_token_index_by_prompt(prompt_text: str) -> int:
# prompt_tokens = self.tokenizer.encode(prompt_text, add_special_tokens=False)
# gen_ids = gen_token_ids
# print("Prompt tokens:", prompt_tokens, self.tokenizer.decode(prompt_tokens))
# print("Generated tokens snippet:", gen_ids[:50], self.tokenizer.decode(gen_ids[:50]))
# for i in range(len(gen_ids) - len(prompt_tokens)):
# if gen_ids[i:i+len(prompt_tokens)] == prompt_tokens:
# j = i + len(prompt_tokens)
# while j < len(gen_ids):
# token_str = self.tokenizer.decode([gen_ids[j]]).strip()
# if token_str.isdigit():
# return j
# j += 1
# return -1
def find_score_token_index_by_prompt_v0(prompt_text: str) -> int:
prompt_tokens = self.tokenizer.encode(prompt_text, add_special_tokens=False)
gen_ids = gen_token_ids
for i in range(len(gen_ids) - len(prompt_tokens)):
if gen_ids[i:i+len(prompt_tokens)] == prompt_tokens:
j = i + len(prompt_tokens)
while j < len(gen_ids):
token_str = self.tokenizer.decode([gen_ids[j]], skip_special_tokens=True).strip()
if token_str.isdigit():
return j
j += 1
return -1
def find_score_token_index_by_prompt(prompt_text: str):
import re
gen_ids = gen_token_ids
gen_str = self.tokenizer.decode(gen_ids, skip_special_tokens=False)
pattern = r"(?:\(\d+\)\s*|\n\s*)?" + re.escape(prompt_text)
match = re.search(pattern, gen_str, flags=re.IGNORECASE)
if not match:
return -1
after_text = gen_str[match.end():]
num_match = re.search(r"\d", after_text)
if not num_match:
return -1
target_substr = gen_str[:match.end() + num_match.start() + 1]
for i in range(len(gen_ids)):
partial = self.tokenizer.decode(gen_ids[:i+1], skip_special_tokens=False)
if partial == target_substr:
return i
return -1
idx_v = find_score_token_index_by_prompt("visual quality:")
idx_t = find_score_token_index_by_prompt("text-to-video alignment:")
idx_p = find_score_token_index_by_prompt("physical/common-sense consistency:")
def ll_based_soft_score_normed(hard_val, token_idx) -> float:
if hard_val is None or token_idx < 0:
return None
logits = scores[token_idx][0] # [vocab]
score_range = list(range(1, 6))
score_probs = [] # [(score, prob)]
for s in score_range:
ids = self.tokenizer.encode(str(s), add_special_tokens=False)
if len(ids) == 1:
tid = ids[0]
logp = torch.log_softmax(logits, dim=-1)[tid].item()
prob = float(np.exp(logp))
score_probs.append((s, prob))
else:
print(f"[warn] score {s} maps to multi-token: {ids}, skipping.")
if not score_probs:
print("[warn] No valid score token found (1–5 all multi-token?)")
return None
scores_list, probs_list = zip(*score_probs)
total_prob = sum(probs_list)
max_prob = max(probs_list)
max_idx = probs_list.index(max_prob)
best_score = scores_list[max_idx]
normalized_prob = max_prob / total_prob if total_prob > 0 else 0
soft_score = best_score * normalized_prob
print(f"hard score={hard_val}, token_idx={token_idx}")
for s, p in score_probs:
print(f" score {s}: prob={p:.4f}")
print(f" max prob={max_prob:.4f} at score={best_score}, total prob={total_prob:.4f}")
print(f" normalized prob={normalized_prob:.4f}, soft score={soft_score:.4f}")
return round(soft_score,4)
v_soft = ll_based_soft_score_normed(v_score_model, idx_v)
t_soft = ll_based_soft_score_normed(t_score_model, idx_t)
p_soft = ll_based_soft_score_normed(p_score_model, idx_p)
return v_soft, t_soft, p_soft, output_text
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