| from ...smp import * | |
| from ...utils import can_infer | |
| FAIL_MSG = 'Failed to obtain answer via API.' | |
| def get_gpt4_ICE_for_qspatial(): | |
| example_1 = """ | |
| Hint: Please answer the question requiring in a tuple format. The tuple should contain a numeric value and a unit, | |
| e.g., (1, m), (2.2, cm), (3.12, meter), at the end.\n | |
| Model response: **Object Identification** | |
| * The object in question is a chair. | |
| * The chair is not visible in the image. | |
| **Conclusion** | |
| The height of the chair cannot be determined from the provided image.\n | |
| Extracted answer: (0, cm) | |
| """ | |
| example_2 = """ | |
| Hint: Please answer the question requiring in a tuple format. The tuple should contain a numeric value and a unit, | |
| e.g., (1, inch), (1.2, cm), (3.0, feet), at the end.\n | |
| Model response: **Step 1: Identify the stapler and the recycle bin in the image.** | |
| The stapler is located on the wooden table, and the recycle bin is located on the floor. | |
| **Step 2: Determine the distance between the stapler and the recycle bin.** | |
| The stapler is 0.5 meters from the edge of the table, and the recycle bin is 1.5 meters from the edge of the table. | |
| Therefore, the minimum distance between the stapler and the recycle bin is 1.5 - 0.5 = 1 meter. | |
| **Answer:** 1 m\n | |
| Extracted answer: (1, m) | |
| """ | |
| example_3 = """ | |
| Hint: Please answer the question requiring in a tuple format. The tuple should contain a numeric value and a unit, | |
| e.g., (1, foot), (2, cm), (4.3, meter), at the end.\n | |
| Model response: The mirror in the image is approximately 5 feet 4 inches tall.\n | |
| Extracted answer: (64, inch) | |
| """ | |
| example_4 = """ | |
| Hint: Please answer the question requiring in a tuple format. The tuple should contain a numeric value and a unit, | |
| e.g., (0.1, cm), (2.9, cm), (0.3, meter), at the end.\n | |
| Model response: The minimum distance between the wooden chair and the chair near the camera in the image is 1.7 feet.\n | |
| Extracted answer: (1.7, feet) | |
| """ | |
| example_5 = """ | |
| Hint: Please answer the question requiring in a tuple format. The tuple should contain a numeric value and a unit, | |
| e.g., (5.1, cm), (0.9, cm), (55, mm), at the end.\n | |
| Model response: The height of the painting's bottom edge from the floor is approximately 4.5 feet.\n | |
| Extracted answer: (4.5, feet) | |
| """ | |
| return [example_1, example_2, example_3, example_4, example_5] | |
| def list_to_dict(lst): | |
| return {chr(65 + i): val for i, val in enumerate(lst)} | |
| def post_check(line, prefetch=False): | |
| res = None | |
| ans = line['answer'] | |
| response = line['prediction'] if prefetch else line['res'] | |
| try: | |
| if line['question_type'] == 'multi_choice': | |
| ans = line['answer_option'] | |
| choices = list_to_dict(eval(line['choices'])) | |
| res = can_infer(response, choices) | |
| if prefetch: | |
| return res | |
| else: | |
| if line['answer_type'] == 'integer': | |
| res = int(response) | |
| ans = int(line['answer']) | |
| elif line['answer_type'] == 'float': | |
| res = float(response) | |
| ans = float(line['answer']) | |
| else: | |
| res = str(res) | |
| ans = str(ans) | |
| except ValueError: | |
| pass | |
| if res == ans: | |
| return res if prefetch else True | |
| else: | |
| return False | |
| def build_qspatial_gpt4_prompt(line): | |
| task_description = """ | |
| Please read the following example. | |
| Then extract the answer from the model response and type it at the end of the prompt.\n | |
| """ | |
| prediction = str(line['prediction']) | |
| prompt = task_description | |
| examples = get_gpt4_ICE_for_qspatial() | |
| for example in examples: | |
| prompt += example + '\n' | |
| prompt += 'Model respone: ' + prediction | |
| prompt += '\nExtracted answer:' | |
| return prompt | |
| def QSpatial_auxeval(model, line): | |
| prompt = build_qspatial_gpt4_prompt(line) | |
| log = '' | |
| retry = 5 | |
| for i in range(retry): | |
| prediction = line['prediction'] | |
| res = model.generate(prompt, temperature=i * 0.5) | |
| if FAIL_MSG in res: | |
| log += f'Try {i}: output is {prediction}, failed to parse.\n' | |
| else: | |
| log += 'Succeed' | |
| return dict(log=log, res=res) | |
| log += 'All 5 retries failed.\n' | |
| return dict(log=log, res='') | |