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scripts/.ipynb_checkpoints/zero3-checkpoint.json

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+{
+    "fp16": {
+        "enabled": "auto",
+        "loss_scale": 0,
+        "loss_scale_window": 1000,
+        "initial_scale_power": 16,
+        "hysteresis": 2,
+        "min_loss_scale": 1
+    },
+    "bf16": {
+        "enabled": "auto"
+    },
+    "train_micro_batch_size_per_gpu": "auto",
+    "train_batch_size": "auto",
+    "gradient_accumulation_steps": "auto",
+    "zero_optimization": {
+        "stage": 3,
+        "overlap_comm": true,
+        "contiguous_gradients": true,
+        "sub_group_size": 1e9,
+        "reduce_bucket_size": "auto",
+        "stage3_prefetch_bucket_size": "auto",
+        "stage3_param_persistence_threshold": "auto",
+        "stage3_max_live_parameters": 1e9,
+        "stage3_max_reuse_distance": 1e9,
+        "stage3_gather_16bit_weights_on_model_save": true
+    }
+}

scripts/convert_gqa_for_eval.py

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+import os
+import json
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--src", type=str)
+parser.add_argument("--dst", type=str)
+args = parser.parse_args()
+
+all_answers = []
+for line_idx, line in enumerate(open(args.src)):
+    res = json.loads(line)
+    question_id = res['question_id']
+    text = res['text'].rstrip('.').lower()
+    all_answers.append({"questionId": question_id, "prediction": text})
+
+with open(args.dst, 'w') as f:
+    json.dump(all_answers, f)

scripts/convert_mmbench_for_submission.py

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+import os
+import json
+import argparse
+import pandas as pd
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--annotation-file", type=str, required=True)
+    parser.add_argument("--result-dir", type=str, required=True)
+    parser.add_argument("--upload-dir", type=str, required=True)
+    parser.add_argument("--experiment", type=str, required=True)
+
+    return parser.parse_args()
+
+if __name__ == "__main__":
+    args = get_args()
+
+    df = pd.read_table(args.annotation_file)
+
+    cur_df = df.copy()
+    cur_df = cur_df.drop(columns=['hint', 'category', 'source', 'image', 'comment', 'l2-category'])
+    cur_df.insert(6, 'prediction', None)
+    for pred in open(os.path.join(args.result_dir, f"{args.experiment}.jsonl")):
+        pred = json.loads(pred)
+        cur_df.loc[df['index'] == pred['question_id'], 'prediction'] = pred['text']
+
+    cur_df.to_excel(os.path.join(args.upload_dir, f"{args.experiment}.xlsx"), index=False, engine='openpyxl')

scripts/convert_mmvet_for_eval.py

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+import os
+import json
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--src", type=str)
+parser.add_argument("--dst", type=str)
+args = parser.parse_args()
+
+cur_result = {}
+
+for line in open(args.src):
+    data = json.loads(line)
+    qid = data['question_id']
+    cur_result[f'v1_{qid}'] = data['text']
+
+with open(args.dst, 'w') as f:
+    json.dump(cur_result, f, indent=2)

scripts/convert_seed_for_submission.py

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+import os
+import json
+import argparse
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--annotation-file", type=str)
+    parser.add_argument("--result-file", type=str)
+    parser.add_argument("--result-upload-file", type=str)
+    return parser.parse_args()
+
+
+def eval_single(result_file, eval_only_type=None):
+    results = {}
+    for line in open(result_file):
+        row = json.loads(line)
+        results[row['question_id']] = row
+
+    type_counts = {}
+    correct_counts = {}
+    for question_data in data['questions']:
+        if eval_only_type is not None and question_data['data_type'] != eval_only_type: continue
+        data_type = question_data['question_type_id']
+        type_counts[data_type] = type_counts.get(data_type, 0) + 1
+        try:
+            question_id = int(question_data['question_id'])
+        except:
+            question_id = question_data['question_id']
+        if question_id not in results:
+            correct_counts[data_type] = correct_counts.get(data_type, 0)
+            continue
+        row = results[question_id]
+        if row['text'] == question_data['answer']:
+            correct_counts[data_type] = correct_counts.get(data_type, 0) + 1
+
+    total_count = 0
+    total_correct = 0
+    for data_type in sorted(type_counts.keys()):
+        accuracy = correct_counts[data_type] / type_counts[data_type] * 100
+        if eval_only_type is None:
+            print(f"{ques_type_id_to_name[data_type]}: {accuracy:.2f}%")
+
+        total_count += type_counts[data_type]
+        total_correct += correct_counts[data_type]
+
+    total_accuracy = total_correct / total_count * 100
+    if eval_only_type is None:
+        print(f"Total accuracy: {total_accuracy:.2f}%")
+    else:
+        print(f"{eval_only_type} accuracy: {total_accuracy:.2f}%")
+
+    return results
+
+if __name__ == "__main__":
+    args = get_args()
+    data = json.load(open(args.annotation_file))
+    ques_type_id_to_name = {id:n for n,id in data['question_type'].items()}
+
+    results = eval_single(args.result_file)
+    eval_single(args.result_file, eval_only_type='image')
+    eval_single(args.result_file, eval_only_type='video')
+
+    with open(args.result_upload_file, 'w') as fp:
+        for question in data['questions']:
+            qid = question['question_id']
+            if qid in results:
+                result = results[qid]
+            else:
+                result = results[int(qid)]
+            fp.write(json.dumps({
+                'question_id': qid,
+                'prediction': result['text']
+            }) + '\n')

scripts/convert_sqa_to_llava.py

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+import json
+import os
+import fire
+import re
+from convert_sqa_to_llava_base_prompt import build_prompt_chatbot
+
+
+def convert_to_llava(base_dir, split, prompt_format="QCM-LEA"):
+    split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[split]
+    problems = json.load(open(os.path.join(base_dir, "problems.json")))
+
+    split_problems = build_prompt_chatbot(
+        problems, split_indices, prompt_format,
+        use_caption=False, is_test=False)
+
+    target_format = []
+    for prob_id, (input, output) in split_problems.items():
+        if input.startswith('Question: '):
+            input = input.replace('Question: ', '')
+        if output.startswith('Answer: '):
+            output = output.replace('Answer: ', '')
+
+        raw_prob_data = problems[prob_id]
+        if raw_prob_data['image'] is None:
+            target_format.append({
+                "id": prob_id,
+                "conversations": [
+                    {'from': 'human', 'value': f"{input}"},
+                    {'from': 'gpt', 'value': f"{output}"},
+                ],
+            })
+
+        else:
+            target_format.append({
+                "id": prob_id,
+                "image": os.path.join(prob_id, raw_prob_data['image']),
+                "conversations": [
+                    {'from': 'human', 'value': f"{input}\n<image>"},
+                    {'from': 'gpt', 'value': f"{output}"},
+                ],
+            })
+
+    print(f'Number of samples: {len(target_format)}')
+
+    with open(os.path.join(base_dir, f"llava_{split}_{prompt_format}.json"), "w") as f:
+        json.dump(target_format, f, indent=2)
+
+
+def convert_to_jsonl(base_dir, split, prompt_format="QCM-LEPA"):
+    split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[split]
+    problems = json.load(open(os.path.join(base_dir, "problems.json")))
+
+    split_problems = build_prompt_chatbot(
+        problems, split_indices, prompt_format,
+        use_caption=False, is_test=False)
+
+    writer = open(os.path.join(base_dir, f"scienceqa_{split}_{prompt_format}.jsonl"), "w")
+    for prob_id, (input, output) in split_problems.items():
+        if input.startswith('Question: '):
+            input = input.replace('Question: ', '')
+        if output.startswith('Answer: '):
+            output = output.replace('Answer: ', '')
+
+        raw_prob_data = problems[prob_id]
+        if raw_prob_data['image'] is None:
+            data = {
+                "id": prob_id,
+                "instruction": f"{input}",
+                "output": f"{output}",
+            }
+
+        else:
+            data = {
+                "id": prob_id,
+                "image": os.path.join(prob_id, raw_prob_data['image']),
+                "instruction": f"{input}\n<image>",
+                "output": f"{output}",
+            }
+        writer.write(json.dumps(data) + '\n')
+    writer.close()
+
+
+def main(task, **kwargs):
+    globals()[task](**kwargs)
+
+
+if __name__ == "__main__":
+    fire.Fire(main)