|
|
| import os |
| from pathlib import Path |
| import yaml |
| from loguru import logger as eval_logger |
| from functools import partial |
| import numpy as np |
| import pandas as pd |
| from PIL import Image |
| import datasets |
|
|
| MCA_QUESTION_TYPES = [ |
| ] |
| NA_QUESTION_TYPES = [ |
| "object_width", |
| "object_height", |
| "direct_distance", |
| "horizontal_distance", |
| "vertical_distance", |
| ] |
|
|
| METRICS_FOR_MCA = { |
| "accuracy": "exact_match", |
| } |
|
|
| METRICS_FOR_NA = { |
| "MRA:.5:.95:.05": "partial(relative_accuracy, delta=2)", |
| } |
|
|
| def to_float(pred): |
| try: |
| pred = float(pred) |
| except BaseException as e: |
| pred = None |
| return pred |
|
|
| def relative_accuracy(pred, target, delta=2): |
|
|
| pred = to_float(pred) |
| target = to_float(target) |
|
|
| if pred is None: return 0. |
|
|
|
|
| if pred >= target/delta and pred <= target*delta: |
| return 1. |
| else: return 0. |
|
|
|
|
| |
| |
| from pathlib import Path |
| import yaml |
|
|
| yaml_path = Path(__file__).parent / "ViewSpatial.yaml" |
| with open(yaml_path, "r", encoding="utf-8") as f: |
| raw_data = f.readlines() |
|
|
| safe_data = [] |
| for i, line in enumerate(raw_data): |
| if "!function" not in line: |
| safe_data.append(line) |
|
|
| dataset_path = yaml.safe_load("".join(safe_data))["dataset_path"] |
|
|
|
|
| |
| cache_dir = dataset_path |
| |
| |
| |
|
|
| def ViewSpatial_doc_to_visual(doc): |
|
|
| images = [ |
| Image.open( |
| os.path.join(cache_dir, image_path).replace("evaluation/ViewSpatial/ViewSpatial-Bench", "media/ViewSpatial") |
| ).convert("RGB") for image_path in doc["image_path"] |
| ] |
|
|
| return [images] |
|
|
|
|
| def ViewSpatial_doc_to_text(doc, lmms_eval_specific_kwargs=None): |
|
|
| |
| |
|
|
| question = doc["question"] |
|
|
| pre_prompt = lmms_eval_specific_kwargs.get("pre_prompt", "") or "These are frames of a video." |
| |
| if doc['question_type'] in NA_QUESTION_TYPES: |
|
|
| post_prompt = "Please answer the question using a single word in " + doc['answer_unit'] + "." |
|
|
| return pre_prompt + "\n" + question + "\n" + post_prompt |
| else: |
| options = "Options:\n" + doc["choices"] |
| post_prompt = lmms_eval_specific_kwargs.get("mca_post_prompt", "") or "Answer with the option's letter from the given choices directly." |
| return "\n".join([pre_prompt, question, options, post_prompt]) |
|
|
| def fuzzy_matching(text: str) -> str: |
| |
| return (text or "").split(" ")[0].rstrip(".").strip().lower() |
| def exact_match(pred, target): |
| return 1. if pred.lower() == target.lower() else 0. |
|
|
| def ViewSpatial_process_results(doc, results): |
| doc["prediction"] = results[0] |
| for key, value in METRICS_FOR_MCA.items(): |
| doc[key] = eval(value)(fuzzy_matching(doc['prediction']), doc["answer"]) |
| |
| return {"ViewSpatial_score": doc} |
|
|
|
|
| def ViewSpatial_aggregate_results(results): |
|
|
| results = pd.DataFrame(results) |
| output = {} |
|
|
|
|
| for question_type, question_type_indexes in results.groupby('question_type').groups.items(): |
| per_question_type = results.iloc[question_type_indexes] |
| |
| for metric in METRICS_FOR_MCA.keys(): |
| output[f"{question_type}_{metric}"] = per_question_type[metric].mean() |
|
|
| output['overall'] = sum([_ for _ in output.values()]) / len(output) |
|
|
| eval_logger.info(f"Evaluation results: {output}") |
| return output['overall'] * 100. |
