fix ocrbench v2 scoring regressions

lmms_eval/tasks/ocrbench_v2/spotting_metric.py

@@ -1,11 +1,9 @@
 import ast
 import os
 import re
-import shutil
+import tempfile
 import zipfile
 
-import ipdb
-
 import lmms_eval.tasks.ocrbench_v2.spotting_eval.rrc_evaluation_funcs_1_1 as rrc_evaluation_funcs
 from lmms_eval.tasks.ocrbench_v2.spotting_eval.script import (
     default_evaluation_params,
@@ -127,58 +125,53 @@ def zip_folder(source_folder, destination_zip):
 def spotting_evaluation(prediction_list, img_metas):
     score = 0
 
-    submit_path = "./lmms_eval/tasks/ocrbench_v2/spotting_eval/submit"
-    gt_path = "./lmms_eval/tasks/ocrbench_v2/spotting_eval/gt"
-    submit_zip_path = "./lmms_eval/tasks/ocrbench_v2/spotting_eval/submit.zip"
-    gt_zip_path = "./lmms_eval/tasks/ocrbench_v2/spotting_eval/gt.zip"
-    for file_path in [submit_path, gt_path, submit_zip_path, gt_zip_path]:
-        if "zip" in file_path:
-            if os.path.exists(file_path):
-                os.remove(file_path)
-        else:
-            if os.path.exists(file_path):
-                shutil.rmtree(file_path)
-            os.makedirs(file_path)
-
-    res_submit_list = []
-    for item in prediction_list:
-        if len(item) != 5:
-            ipdb.set_trace()
-        x1, y1, x2, y2, rec = item
-        if x1 >= x2 or y1 >= y2:
-            continue
-
-        res_submit_list.append(",".join([str(x1), str(y1), str(x2), str(y1), str(x2), str(y2), str(x1), str(y2), rec]))
-
-    res_gt_list = []
-    for bbox, rec in zip(img_metas["bbox_list"], img_metas["content"]):
-        x_coords = bbox[0::2]
-        y_coords = bbox[1::2]
-
-        x1, y1 = min(x_coords), min(y_coords)
-        x2, y2 = max(x_coords), max(y_coords)
-
-        res_gt_list.append(",".join([str(x1), str(y1), str(x2), str(y1), str(x2), str(y2), str(x1), str(y2), rec]))
-
-    if len(res_submit_list) == 0 or len(res_gt_list) == 0:
-        return 0
-
-    with open(os.path.join(submit_path, "res_img_0.txt"), "w") as f:
-        for item in res_submit_list[:-1]:
-            f.write(item + "\n")
-        f.write(res_submit_list[-1])
-
-    with open(os.path.join(gt_path, "gt_img_0.txt"), "w") as f:
-        for item in res_gt_list[:-1]:
-            f.write(item + "\n")
-        f.write(res_gt_list[-1])
-
-    zip_folder(submit_path, submit_zip_path)
-    zip_folder(gt_path, gt_zip_path)
-
-    command = {"g": gt_zip_path, "s": submit_zip_path, "o": "./", "p": '{"IOU_CONSTRAINT":0.5}'}
-
-    # run rrc_evaluation_funcs
-    result = rrc_evaluation_funcs.main_evaluation(command, default_evaluation_params, validate_data, evaluate_method)
-    score = result["method"]["hmean"]
+    with tempfile.TemporaryDirectory(prefix="ocrbench-v2-spotting-") as temp_dir:
+        submit_path = os.path.join(temp_dir, "submit")
+        gt_path = os.path.join(temp_dir, "gt")
+        submit_zip_path = os.path.join(temp_dir, "submit.zip")
+        gt_zip_path = os.path.join(temp_dir, "gt.zip")
+        os.makedirs(submit_path, exist_ok=True)
+        os.makedirs(gt_path, exist_ok=True)
+        os.makedirs(os.path.join(temp_dir, "lmms_eval", "tasks", "ocrbench_v2", "spotting_eval"), exist_ok=True)
+
+        res_submit_list = []
+        for item in prediction_list:
+            if len(item) != 5:
+                continue
+            x1, y1, x2, y2, rec = item
+            if x1 >= x2 or y1 >= y2:
+                continue
+
+            res_submit_list.append(",".join([str(x1), str(y1), str(x2), str(y1), str(x2), str(y2), str(x1), str(y2), rec]))
+
+        res_gt_list = []
+        for bbox, rec in zip(img_metas["bbox_list"], img_metas["content"]):
+            x_coords = bbox[0::2]
+            y_coords = bbox[1::2]
+
+            x1, y1 = min(x_coords), min(y_coords)
+            x2, y2 = max(x_coords), max(y_coords)
+
+            res_gt_list.append(",".join([str(x1), str(y1), str(x2), str(y1), str(x2), str(y2), str(x1), str(y2), rec]))
+
+        if len(res_submit_list) == 0 or len(res_gt_list) == 0:
+            return 0
+
+        with open(os.path.join(submit_path, "res_img_0.txt"), "w") as f:
+            for item in res_submit_list[:-1]:
+                f.write(item + "\n")
+            f.write(res_submit_list[-1])
+
+        with open(os.path.join(gt_path, "gt_img_0.txt"), "w") as f:
+            for item in res_gt_list[:-1]:
+                f.write(item + "\n")
+            f.write(res_gt_list[-1])
+
+        zip_folder(submit_path, submit_zip_path)
+        zip_folder(gt_path, gt_zip_path)
+
+        command = {"g": gt_zip_path, "s": submit_zip_path, "o": temp_dir, "p": '{"IOU_CONSTRAINT":0.5}'}
+
+        result = rrc_evaluation_funcs.main_evaluation(command, default_evaluation_params, validate_data, evaluate_method)
+        score = result["method"]["hmean"]
     return score

lmms_eval/tasks/ocrbench_v2/utils.py

@@ -34,22 +34,23 @@
     vqa_evaluation_case_sensitive,
 )
 
-# Add the following functions to your existing utils.py file
-OCRBench_v2_score = {
-    "text_recognition_en": [],
-    "text_detection_en": [],
-    "text_spotting_en": [],
-    "relationship_extraction_en": [],
-    "element_parsing_en": [],
-    "mathematical_calculation_en": [],
-    "visual_text_understanding_en": [],
-    "knowledge_reasoning_en": [],
-    "text_recognition_cn": [],
-    "relationship_extraction_cn": [],
-    "element_parsing_cn": [],
-    "visual_text_understanding_cn": [],
-    "knowledge_reasoning_cn": [],
-}
+
+def _make_score_buckets():
+    return {
+        "text_recognition_en": [],
+        "text_detection_en": [],
+        "text_spotting_en": [],
+        "relationship_extraction_en": [],
+        "element_parsing_en": [],
+        "mathematical_calculation_en": [],
+        "visual_text_understanding_en": [],
+        "knowledge_reasoning_en": [],
+        "text_recognition_cn": [],
+        "relationship_extraction_cn": [],
+        "element_parsing_cn": [],
+        "visual_text_understanding_cn": [],
+        "knowledge_reasoning_cn": [],
+    }
 
 
 teds = TEDS(n_jobs=32)
@@ -253,7 +254,7 @@ def ocrbench_v2_process_results(doc, results):
             else:
                 pred_chart_html = dict_to_html(pred_chart_dict)
                 if isinstance(answer, str):
-                    answer = convert_str_to_multi_dict(pred)
+                    answer = convert_str_to_multi_dict(answer)
                 gt_chart_html = dict_to_html(answer)
                 score = teds.evaluate(pred_chart_html, gt_chart_html)
         else:
@@ -332,7 +333,7 @@ def ocrbench_v2_process_results(doc, results):
             score = (get_value_or_zero(ocr_metric["bleu"]) + get_value_or_zero(ocr_metric["meteor"]) + get_value_or_zero(ocr_metric["f_measure"]) + (1 - get_value_or_zero(ocr_metric["edit_dist"]))) / 4
     elif data_type == "full-page OCR en":
         if not pred:
-            score == 0
+            score = 0
         else:
             ocr_metric = cal_per_metrics(pred, gt_ans[0])
             score = (get_value_or_zero(ocr_metric["bleu"]) + get_value_or_zero(ocr_metric["meteor"]) + get_value_or_zero(ocr_metric["f_measure"]) + (1 - get_value_or_zero(ocr_metric["edit_dist"]))) / 4
@@ -372,12 +373,13 @@ def ocrbench_v2_process_results(doc, results):
     }
 
 
-def calculate_average_score(categories):
-    return sum(sum(OCRBench_v2_score[cat]) / len(OCRBench_v2_score[cat]) if len(OCRBench_v2_score[cat]) > 0 else 0 for cat in categories) / len(categories)
+def calculate_average_score(categories, score_buckets):
+    return sum(sum(score_buckets[cat]) / len(score_buckets[cat]) if len(score_buckets[cat]) > 0 else 0 for cat in categories) / len(categories)
 
 
 def ocrbench_v2_aggregate_accuracy(results, args):
     question_type_scores = {}
+    score_buckets = _make_score_buckets()
 
     for result in results:
         if "ignore" in result.keys() and result["ignore"] == "True":
@@ -391,43 +393,43 @@ def ocrbench_v2_aggregate_accuracy(results, args):
         question_type_scores[question_type].append(score)
 
         if question_type in ["text recognition en", "fine-grained text recognition en", "full-page OCR en"]:
-            OCRBench_v2_score["text_recognition_en"].append(score)
+            score_buckets["text_recognition_en"].append(score)
 
         elif question_type in ["text grounding en", "VQA with position en"]:
-            OCRBench_v2_score["text_detection_en"].append(score)
+            score_buckets["text_detection_en"].append(score)
 
         elif question_type == "text spotting en":
-            OCRBench_v2_score["text_spotting_en"].append(score)
+            score_buckets["text_spotting_en"].append(score)
 
         elif question_type in ["key information extraction en", "key information mapping en"]:
-            OCRBench_v2_score["relationship_extraction_en"].append(score)
+            score_buckets["relationship_extraction_en"].append(score)
 
         elif question_type in ["document parsing en", "chart parsing en", "table parsing en", "formula recognition en"]:
-            OCRBench_v2_score["element_parsing_en"].append(score)
+            score_buckets["element_parsing_en"].append(score)
 
         elif question_type in ["math QA en", "text counting en"]:
-            OCRBench_v2_score["mathematical_calculation_en"].append(score)
+            score_buckets["mathematical_calculation_en"].append(score)
 
         elif question_type in ["document classification en", "cognition VQA en", "diagram QA en"]:
-            OCRBench_v2_score["visual_text_understanding_en"].append(score)
+            score_buckets["visual_text_understanding_en"].append(score)
 
         elif question_type in ["reasoning VQA en", "science QA en", "APP agent en", "ASCII art classification en"]:
-            OCRBench_v2_score["knowledge_reasoning_en"].append(score)
+            score_buckets["knowledge_reasoning_en"].append(score)
 
         elif question_type == "full-page OCR cn":
-            OCRBench_v2_score["text_recognition_cn"].append(score)
+            score_buckets["text_recognition_cn"].append(score)
 
         elif question_type in ["key information extraction cn", "handwritten answer extraction cn"]:
-            OCRBench_v2_score["relationship_extraction_cn"].append(score)
+            score_buckets["relationship_extraction_cn"].append(score)
 
         elif question_type in ["document parsing cn", "table parsing cn", "formula recognition cn"]:
-            OCRBench_v2_score["element_parsing_cn"].append(score)
+            score_buckets["element_parsing_cn"].append(score)
 
         elif question_type == "cognition VQA cn":
-            OCRBench_v2_score["visual_text_understanding_cn"].append(score)
+            score_buckets["visual_text_understanding_cn"].append(score)
 
         elif question_type in ["reasoning VQA cn", "text translation cn"]:
-            OCRBench_v2_score["knowledge_reasoning_cn"].append(score)
+            score_buckets["knowledge_reasoning_cn"].append(score)
 
         else:
             print("No such task!")
@@ -437,8 +439,8 @@ def ocrbench_v2_aggregate_accuracy(results, args):
 
     chinese_tasks = ["text_recognition_cn", "relationship_extraction_cn", "element_parsing_cn", "visual_text_understanding_cn", "knowledge_reasoning_cn"]
 
-    OCRBench_v2_English_subset_score = calculate_average_score(english_tasks)
-    OCRBench_v2_Chinese_subset_score = calculate_average_score(chinese_tasks)
+    OCRBench_v2_English_subset_score = calculate_average_score(english_tasks, score_buckets)
+    OCRBench_v2_Chinese_subset_score = calculate_average_score(chinese_tasks, score_buckets)
 
     Final_score = (OCRBench_v2_English_subset_score + OCRBench_v2_Chinese_subset_score) / 2
     file_name = generate_submission_file("ocrbench_v2_results.txt", args, subpath="results")
@@ -450,14 +452,14 @@ def ocrbench_v2_aggregate_accuracy(results, args):
             print(f"{q_type} (sample number: {len(scores)}): {avg_score:.2f}", file=f)
         print("######################### English Subsets ######################", file=f)
         for task in english_tasks:
-            num_samples = len(OCRBench_v2_score[task])
-            avg_score = sum(OCRBench_v2_score[task]) / num_samples if num_samples > 0 else 0
+            num_samples = len(score_buckets[task])
+            avg_score = sum(score_buckets[task]) / num_samples if num_samples > 0 else 0
             print(f"{task.replace('_', ' ').title()} (Total {num_samples}): {avg_score:.2f}", file=f)
         print(f"Overall English Score: {OCRBench_v2_English_subset_score:.2f}", file=f)
         print("######################### Chinese Subsets ######################", file=f)
         for task in chinese_tasks:
-            num_samples = len(OCRBench_v2_score[task])
-            avg_score = sum(OCRBench_v2_score[task]) / num_samples if num_samples > 0 else 0
+            num_samples = len(score_buckets[task])
+            avg_score = sum(score_buckets[task]) / num_samples if num_samples > 0 else 0
             print(f"{task.replace('_', ' ').title()} (Total {num_samples}): {avg_score:.2f}", file=f)
         print(f"Overall Chinese Score: {OCRBench_v2_Chinese_subset_score:.2f}", file=f)
         print("######################### Final Score ##########################", file=f)

lmms_eval/tasks/ocrbench_v2/vqa_metric.py

@@ -1,8 +1,6 @@
 import math
 import re
 
-import ipdb
-
 
 def levenshtein_distance(s1, s2):
     if len(s1) > len(s2):
@@ -26,10 +24,7 @@ def vqa_evaluation(predict, answers):
         for j in range(len(answers)):
             if isinstance(answers[j], (int, float)):
                 answers[j] = str(answers[j])
-            try:
-                answer = answers[j].lower().strip().replace("\n", " ")
-            except:
-                ipdb.set_trace()
+            answer = str(answers[j]).lower().strip().replace("\n", " ")
             if isinstance(predict, (int, float)):
                 predict = str(predict)
             predict = predict.lower().strip().replace("\n", " ")
@@ -69,10 +64,7 @@ def cn_vqa_evaluation(predict, answers):
         for j in range(len(answers)):
             if isinstance(answers[j], (int, float)):
                 answers[j] = str(answers[j])
-            try:
-                answer = answers[j].lower().strip().replace("\n", " ").replace(" ", "")
-            except:
-                ipdb.set_trace()
+            answer = str(answers[j]).lower().strip().replace("\n", " ").replace(" ", "")
             if isinstance(predict, (int, float)):
                 predict = str(predict)
             predict = predict.lower().strip().replace("\n", " ").replace(" ", "")
@@ -91,7 +83,7 @@ def cn_vqa_evaluation(predict, answers):
     else:
         answers = answers.lower().strip().replace("\n", " ").replace(" ", "")
         predict = predict.lower().strip().replace("\n", " ").replace(" ", "")
-        if len(answer.split(",")) < 4:
+        if len(answers.split(",")) < 4:
             if answers in predict:
                 score = 1
             else:
@@ -112,10 +104,7 @@ def vqa_evaluation_case_sensitive(predict, answers):
         for j in range(len(answers)):
             if isinstance(answers[j], (int, float)):
                 answers[j] = str(answers[j])
-            try:
-                answer = answers[j].strip().replace("\n", " ")
-            except:
-                ipdb.set_trace()
+            answer = str(answers[j]).strip().replace("\n", " ")
             predict = predict.strip().replace("\n", " ")
             if len(answer.split()) < 5:
                 if answer in predict:
@@ -195,16 +184,16 @@ def counting_evaluation(predict, answers, eval_method):
 
     else:
         answers = answers.lower().strip().replace("\n", " ")
-        predict = predict.lower().strip().replace("\n", " ")
+        predict = predict_processed
         if eval_method == "exact match":
-            if answer in predict:
+            if answers in predict:
                 score = 1
             else:
                 score = 0
         elif eval_method == "regression":
             predict = extract_first_number(predict)
             if predict:
-                answer = int(answer)
+                answer = int(answers)
                 if predict <= 0 or predict >= 2 * answer:
                     score = 0
                 else: