File size: 8,171 Bytes
b0c0df0 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 |
import datetime
import json
import os
import random
import sys
from pathlib import Path
import numpy as np
import yaml
from decord import VideoReader, cpu
import lmms_eval.tasks._task_utils.file_utils as file_utils
with open(Path(__file__).parent / "_default_template_yaml", "r") as f:
raw_data = f.readlines()
safe_data = []
for i, line in enumerate(raw_data):
# remove function definition since yaml load cannot handle it
if "!function" not in line:
safe_data.append(line)
config = yaml.safe_load("".join(safe_data))
# We will unzip all the zip files
# To HF HOME cache dir
# And load it here
HF_HOME = os.environ["HF_HOME"] if "HF_HOME" in os.environ else os.path.expanduser("~/.cache/huggingface/hub")
cache_dir = config["dataset_kwargs"]["cache_dir"]
cache_dir = os.path.join(HF_HOME, cache_dir)
cache_dir = os.path.join(cache_dir, "videos")
from loguru import logger as eval_logger
# Pass in video path here
# Can only work correctly with video llm
def egoschema_doc_to_visual(doc):
video_path = doc["video_idx"] + ".mp4"
video_path = os.path.join(cache_dir, video_path)
if os.path.exists(video_path):
video_path = video_path
elif os.path.exists(video_path.replace("mp4", "MP4")):
video_path = video_path.replace("mp4", "MP4")
else:
sys.exit(f"video path:{video_path} does not exist, please check")
return [video_path]
# This is the place where you format your question
def egoschema_doc_to_text(doc, lmms_eval_specific_kwargs=None):
if lmms_eval_specific_kwargs is None:
lmms_eval_specific_kwargs = {}
pre_prompt = ""
post_prompt = ""
if "pre_prompt" in lmms_eval_specific_kwargs:
pre_prompt = lmms_eval_specific_kwargs["pre_prompt"]
if "post_prompt" in lmms_eval_specific_kwargs:
post_prompt = lmms_eval_specific_kwargs["post_prompt"]
question = doc["question"]
if "option" in doc:
for op in doc["option"]:
question += "\n" + op
post_prompt = "\nAnswer with the option's letter from the given choices directly."
return f"{pre_prompt}{question}{post_prompt}"
def egoschema_doc_to_answer(doc):
return doc["answer"]
# Process result for mc_ppl
def egoschema_process_results(doc, result):
# Initialize minimum value and index
min_value = float("inf")
min_index = -1
# Iterate through the results to find the index of the lowest value
for i, (value, _) in enumerate(result):
if value < min_value:
min_value = value
min_index = i
# Return the result with the index of the lowest value
return {"submission": {doc["video_idx"]: min_index}, "score": {"pred": min_index, "ground_truth": doc["answer"]}}
def get_multi_choice_info(doc):
all_choices = []
index2ans = {}
OPTIONS = ["A", "B", "C", "D", "E"]
for i in range(5):
# import pdb;pdb.set_trace()
index2ans[OPTIONS[i]] = doc["option"][i].strip()
all_choices.append(OPTIONS[i])
return index2ans, all_choices
def parse_multi_choice_response(response, all_choices, index2ans):
"""
Parse the prediction from the generated response.
Return the predicted index e.g., A, B, C, D.
https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L10
"""
for char in [",", ".", "!", "?", ";", ":", "'"]:
response = response.strip(char)
response = " " + response + " " # add space to avoid partial match
index_ans = True
ans_with_brack = False
ans_with_space = False
ans_with_dot = False
candidates = []
# import pdb; pdb.set_trace()
for choice in all_choices: # e.g., (A) (B) (C) (D)
if f"({choice})" in response:
candidates.append(f"({choice})")
ans_with_brack = True
# if len(candidates) == 0:
for choice in all_choices: # e.g., A B C D
if f"{choice} " in response:
candidates.append(f"{choice} ")
ans_with_space = True
# if len(candidates) == 0:
for choice in all_choices: # e.g., A. B. C. D.
if f"{choice}." in response:
candidates.append(f"{choice}.")
ans_with_dot = True
# if all above doesn't get candidates, check if the content is larger than 5 tokens and try to parse the example
if len(candidates) == 0 and len(response.split()) > 5:
for index, ans in index2ans.items():
if ans.lower() in response.lower():
candidates.append(index)
index_ans = False # it's content ans.
if len(candidates) == 0: # still not get answer, randomly choose one.
pred_index = random.choice(all_choices)
elif len(candidates) > 1:
# candidates = list(set(candidates))
start_indexes = []
if index_ans:
# if ans_with_brack:
for can in candidates:
index = response.rfind(can)
start_indexes.append(index) # -1 will be ignored anyway
# start_indexes = [generated_response.index(f'({can})') for can in candidates]
# if ans_with_space:
# for can in candidates:
# index = response.rfind(f"{can} ")
# start_indexes.append(index)
# if ans_with_dot:
# for can in candidates:
# index = response.rfind(f"{can}.")
# start_indexes.append(index)
# if not ans_with_brack and not ans_with_space and not ans_with_dot:
# for can in candidates:
# index = response.rfind(f" {can} ")
# start_indexes.append(index)
else:
for can in candidates:
index = response.lower().rfind(index2ans[can].lower())
start_indexes.append(index)
# get the first one
pred_index = candidates[np.argmin(start_indexes)]
pred_index = pred_index.replace("(", "").replace(")", "").replace(".", "").strip()
else: # if only one candidate, use it.
pred_index = candidates[0]
pred_index = pred_index.replace("(", "").replace(")", "").replace(".", "").strip()
return pred_index, len(candidates) > 0
# Process result for mcq answer generation
def egoschema_process_results_generation(doc, result):
# import pdb;pdb.set_trace()
pred = result[0]
index2ans, all_choices = get_multi_choice_info(doc)
parsed_pred, matched_tag = parse_multi_choice_response(pred, all_choices, index2ans)
pred_to_index = {"A": 0, "B": 1, "C": 2, "D": 3, "E": 4}
index = pred_to_index.get(parsed_pred, -1) # Default to -1 if the prediction is not found
return {"submission": {doc["video_idx"]: index}, "score": {"pred": index, "ground_truth": doc["answer"]}}
def egoschema_aggregate_submissions(results, args, task):
now_date_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
submission_file_name = f"inference_results_egoschema_{task}_{now_date_time}.json"
path = file_utils.generate_submission_file(submission_file_name, args)
# results is a list of 5031 dict,
# need to convert results into a single dict with 5031 key-value pairs
combined_submission = {}
for submission_dict in results:
combined_submission.update(submission_dict)
with open(path, "w") as f:
json.dump(combined_submission, f, indent=4)
eval_logger.info(f"Submission file saved to {path}")
# Factory into different aggregate
def egoschema_aggregate_mc(results, args):
egoschema_aggregate_submissions(results, args, "MC")
def egoschema_aggregate_mc_ppl(results, args):
egoschema_aggregate_submissions(results, args, "MC_PPL")
def egoschema_aggregate_score(results, args):
yes_count = 0
# results is a list of dict
for answer_dict in results:
if str(answer_dict["ground_truth"]) == str(answer_dict["pred"]):
yes_count = yes_count + 1
accuracy = yes_count / len(results)
return accuracy
def egoschema_doc_to_choice(doc):
return [op.split(".")[1].strip() for op in doc["option"]]
|