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import os, json
from constants import DESCRIPTIVE_RESP_INST, DESCRIPTIVE_GRADING_PREFIX, \
DESCRIPTIVE_GRADING_QMAP, DESCRIPTIVE_GRADING_ICL
def get_rubric(qid):
instruction = None
if qid in [1]:
instruction = DESCRIPTIVE_GRADING_ICL['title']
if qid in [2, 3, 4, 5, 6, 7]:
instruction = DESCRIPTIVE_GRADING_ICL['ocr']
if qid in [8, 9, 10, 12, 14, 15, 17, 19]:
instruction = DESCRIPTIVE_GRADING_ICL['quant']
if qid in [11]:
instruction = DESCRIPTIVE_GRADING_ICL['bool']
if qid in [13]:
instruction = DESCRIPTIVE_GRADING_ICL['enum']
if qid in [16]:
instruction = DESCRIPTIVE_GRADING_ICL['trend']
if qid in [18]:
instruction = DESCRIPTIVE_GRADING_ICL['layout']
assert instruction is not None, f"Instruction for qid {qid} is not found."
return instruction
def get_descriptive_result_gpt(client, prompt, length, max_retries=10):
curr_retries = 0
max_tokens = 256
while curr_retries < max_retries:
try:
response = client.chat.completions.create(
messages=[
{
"role": "user",
"content": prompt,
}
],
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response_format={"type": "json_object"},
n=1,
max_tokens=max_tokens,
temperature=0,
top_p=1,
seed=42,
).choices[0].message.content
content = json.loads(response)
verify_grading_output(content, length)
break
except Exception as e:
print(f"Error: {e}")
# increase the max_tokens if the response is too long
if 'Unterminated string starting at' in str(e):
if max_tokens >= 1024:
print(f"Failed to get response for prompt: {prompt}")
content = build_dummy_output(length)
break
else:
max_tokens = min(1024, max_tokens * 2) # double the max_tokens
print(f"Retrying with max_tokens: {max_tokens}")
# otherwise, retry the request
curr_retries += 1
# if failed to get response, return dummy data
if curr_retries == max_retries:
print(f"Failed to get response for prompt: {prompt}")
content = build_dummy_output(length)
return content
def build_json_keys(length):
keys = []
# specify the keys for gpt-4o's json response
for i in range(1, length+1):
keys.append(f"extract_answer_T{i}")
keys.append(f"score_T{i}")
return str(keys)
def populate_grading_inputs(batch):
query = ""
for i, (_, response, answer) in enumerate(batch):
# index, response, answer
curr_query = "T{}:\nResponse {}: {}\nGround Truth {}: {}\n\n"\
.format(i+1, i+1, response, i+1, answer)
query += curr_query
return query
def verify_grading_output(data, length_data):
# check the integrity of keys and values
for i in range(1, length_data+1):
assert f"extract_answer_T{i}" in data, f"extract_answer_T{i} is not found in {d}"
assert f"score_T{i}" in data, f"score_T{i} is not found in {data}"
assert data[f"score_T{i}"] in [0, 1], f"score_T{i} is not in [0, 1]"
return True
def build_dummy_output(length_data):
# if failed to parse the response, return dummy data
data = {}
for i in range(1, length_data+1):
data[f"extract_answer_T{i}"] = "Failed to parse response"
data[f"score_T{i}"] = -1
return data
def preprocess_descriptive_grading_queries(input, resp, num_templates=19):
# group the responses based on the template id instead of figure id
groups = {i: [] for i in range(1, num_templates + 1)}
for _, data in input.items():
figure_id = data['figure_id']
qids = data['qids']
for i, qid in enumerate(qids):
# figure_id with question index
resp_key = f"{figure_id}_{i}"
response = resp[resp_key]['response']
answer = data['answers'][i]
groups[qid].append((resp_key, response, answer))
return groups
def build_descriptive_grading_queries(groups, nq_per_query=5):
queries = []
for qid, data in groups.items():
# batched evaluation based on number of questions per query (nq_per_query)
for i in range(0, len(data), nq_per_query):
# batch: list of tuples (resp_key, response, answer)
batch = data[i : i + nq_per_query]
# question based on the template id
question = DESCRIPTIVE_GRADING_QMAP[qid]
# build the json keys for GPT-4o's response
json_keys = build_json_keys(len(batch))
# populate batch size, question, and json keys spec
prefix = DESCRIPTIVE_GRADING_PREFIX\
.replace("<|NUM_TRIPLETS|>", str(len(batch)))\
.replace("<|OVERARCHING_QUESTION|>", question)\
.replace("<|JSON_KEYS|>", json_keys)
# add in-context grading example based on the template id
rubric_icl = get_rubric(qid)
# prompt + example + model responses
grading_query = prefix + rubric_icl + populate_grading_inputs(batch)
curr_query = {
'resp_keys': [d[0] for d in batch],
'grading_query': grading_query,
}
queries.append(curr_query)
return queries
def postprocess_descriptive_grading_queries(queries):
scores = {}
for query in queries:
# query contains resp_keys, grading_query, extract_answer and score
resp_keys = query['resp_keys']
for i, resp_key in enumerate(resp_keys):
# extract the answer and score for each response key
extracted_answer = query[f"extract_answer_T{i+1}"]
score = query[f"score_T{i+1}"]
# store the extracted answer and score
scores[resp_key] = {
'resp_id': resp_key,
'extracted_answer': extracted_answer,
'score': score,
}
return scores
def descriptive_query_helper(qid, subplot_loc):
if qid in [18, 19]:
# skip subplot location when asking about the layout of the subplots
return DESCRIPTIVE_RESP_INST[qid]
if isinstance(subplot_loc, list):
if subplot_loc[0] == 0:
# when there is only one subplot
prefix = "For the current plot, "
else:
# when there are multiple subplots
prefix = f"For the subplot at row {subplot_loc[0]} and column {subplot_loc[1]}, "
# when subplots do not form a grid
elif isinstance(subplot_loc, str):
prefix = f"For {subplot_loc}, "
else:
raise ValueError(f"Invalid subplot_loc: {subplot_loc}")
# return the question with the subplot location
return DESCRIPTIVE_RESP_INST[qid].format(prefix)
def build_descriptive_quries(data, image_dir):
queries = {}
for _, d in data.items():
figure_path = os.path.join(image_dir, f"{d['figure_id']}.jpg")
for i in range(len(d['qids'])):
# mapping from template id and subplot location to the question
question = descriptive_query_helper(d['qids'][i], d['subplot_loc'])
curr_query = {
'figure_id': d['figure_id'], # figure_id
'figure_path': figure_path, # figure_path (dropped later)
'subq_idx': i, # index of the (4) questions for the given figure
'qid': d['qids'][i], # template id
'question': question, # question content
}
queries[f"{d['figure_id']}_{i}"] = curr_query
return queries
