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from model_inference import * |
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from config import dataset_files, cot_files |
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from result_parser import find_option_number_for_cot |
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from tqdm import tqdm |
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import json |
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import os |
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models = [gemma2b] |
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tasks = ["urban_region_function_recognition", "trajectory_region", "trajectory_trajectory", "trajectory_classification"] |
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if not os.path.exists("./logs"): |
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os.mkdir("./logs") |
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for fun in models: |
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model = fun() |
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for task in tasks: |
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error_writer = open("./logs/cot_{}.log".format(task), 'a') |
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error_writer.write(model.model_path+'\n') |
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context_samples = open(cot_files[task]) |
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prompt = "" |
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for _i, sample in enumerate(context_samples.readlines()): |
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sample = json.loads(sample) |
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prompt += "{}{}\n".format(sample['Question'], sample['Answer']) |
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for dataset_path in dataset_files[task]: |
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dataset = open(dataset_path, 'r') |
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dataset = dataset.readlines() |
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correct = 0 |
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total = 0 |
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exception = 0 |
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for i, item in tqdm(enumerate(dataset), total=len(dataset)): |
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item = json.loads(item) |
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if task=="urban_region_function_recognition": |
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question = item['Question'].replace("Please just answer the number of your option with no other texts. Answer: Option (", "") |
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elif task=="trajectory_trajectory": |
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question = item['Question'].replace(" with no other texts. Answer: Option (", ".") |
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elif task=="trajectory_region": |
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question = item['Question'].replace(" with no other texts. Answer: Option ", ".") |
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elif task=="trajectory_classification": |
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question = item['Question'].replace("Answer: The trajectory is most likely to be generated by", "") |
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response = model.generate(prompt+question, 100) |
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score = find_option_number_for_cot(response, item["Answer"], error_writer) |
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total +=1 |
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if score is None: |
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exception += 1 |
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else: |
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correct += score |
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if i%100==0: |
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print("Dataset: {}\nTotal: {}, correct:{}, exception:{}, accuracy:{}\n\n".format(dataset_path, total, correct, exception, correct/total)) |
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error_writer.write("Dataset: {}\nTotal: {}, correct:{}, exception:{}, accuracy:{}\n\n".format(dataset_path, total, correct, exception, correct/total)) |
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error_writer.flush() |
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error_writer.write("\n") |
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error_writer.close() |
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