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"""
Copyright (c) 2022, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
import logging
import json
import os
import minigpt4.common.dist_utils as dist_utils
from minigpt4.common.registry import registry
from minigpt4.common.vqa_tools.vqa import VQA
from minigpt4.common.vqa_tools.vqa_eval import VQAEval
from minigpt4.tasks.base_task import BaseTask
@registry.register_task("vqa")
class VQATask(BaseTask):
def __init__(
self,
num_beams,
max_len,
min_len,
evaluate,
num_ans_candidates,
inference_method="rank",
prompt="",
):
super().__init__()
self.num_beams = num_beams
self.max_len = max_len
self.min_len = min_len
self.evaluate = evaluate
self.inference_method = inference_method
self.num_ans_candidates = num_ans_candidates
self.prompt = prompt
self.answer_list = None
self.ques_files = dict()
self.anno_files = dict()
@classmethod
def setup_task(cls, cfg):
run_cfg = cfg.run_cfg
num_beams = run_cfg.get("num_beams", 3)
max_len = run_cfg.get("max_len", 10)
min_len = run_cfg.get("min_len", 1)
evaluate = run_cfg.get("evaluate", False)
inference_method = run_cfg.get("inference_method", "rank")
num_ans_candidates = run_cfg.get("num_ans_candidates", 128)
prompt = run_cfg.get("prompt", "")
return cls(
num_beams=num_beams,
max_len=max_len,
min_len=min_len,
evaluate=evaluate,
num_ans_candidates=num_ans_candidates,
inference_method=inference_method,
prompt=prompt,
)
def build_datasets(self, cfg):
datasets = super().build_datasets(cfg)
# get question file, annotation file and anwser list in COCO format
for dataset in datasets.values():
for split in dataset:
if (
hasattr(dataset[split], "coco_fmt_qust_file")
and dataset[split].coco_fmt_qust_file is not None
):
self.ques_files[split] = dataset[split].coco_fmt_qust_file
self.anno_files[split] = dataset[split].coco_fmt_anno_file
try:
self.answer_list = dataset[split].answer_list
except AttributeError:
# if answer_list is not provided, then set it to None
pass
if len(self.ques_files) > 0:
assert len(self.ques_files) == len(
self.anno_files
), "Only support one split for evaluation."
return datasets
def valid_step(self, model, samples):
answers = model.predict_answers(
samples=samples,
answer_list=self.answer_list,
inference_method=self.inference_method,
num_beams=self.num_beams,
max_len=self.max_len,
min_len=self.min_len,
num_ans_candidates=self.num_ans_candidates,
prompt=self.prompt,
)
pred_qa_pairs = []
question_id = samples["question_id"]
for answer, ques_id in zip(answers, question_id):
ques_id = int(ques_id.item())
pred_qa_pairs.append({"question_id": ques_id, "answer": answer})
return pred_qa_pairs
def after_evaluation(self, val_result, split_name, result_dir):
result_file = self.save_result(
val_result,
result_dir=result_dir, #registry.get_path("result_dir"),
filename=split_name,
remove_duplicate="question_id",
)
# metrics = self._report_metrics(result_file=result_file, split=split_name)
# return metrics
@dist_utils.main_process
def _report_metrics(self, result_file, split):
"""
Use official VQA evaluation script to report metrics.
"""
metrics = {}
if split in self.ques_files and split in self.anno_files:
vqa = VQA(self.anno_files[split], self.ques_files[split])
vqa_result = vqa.loadRes(
resFile=result_file, quesFile=self.ques_files[split]
)
# create vqaEval object by taking vqa and vqaRes
# n is precision of accuracy (number of places after decimal), default is 2
vqa_scorer = VQAEval(vqa, vqa_result, n=2)
logging.info("Start VQA evaluation.")
vqa_scorer.evaluate()
# print accuracies
overall_acc = vqa_scorer.accuracy["overall"]
metrics["agg_metrics"] = overall_acc
logging.info("Overall Accuracy is: %.02f\n" % overall_acc)
logging.info("Per Answer Type Accuracy is the following:")
for ans_type in vqa_scorer.accuracy["perAnswerType"]:
logging.info(
"%s : %.02f"
% (ans_type, vqa_scorer.accuracy["perAnswerType"][ans_type])
)
metrics[ans_type] = vqa_scorer.accuracy["perAnswerType"][ans_type]
with open(
os.path.join(registry.get_path("output_dir"), "evaluate.txt"), "a"
) as f:
f.write(json.dumps(metrics) + "\n")
return metrics
@registry.register_task("gqa")
class GQATask(VQATask):
def valid_step(self, model, samples):
answers = model.predict_answers(
samples=samples,
answer_list=self.answer_list,
inference_method=self.inference_method,
num_beams=self.num_beams,
max_len=self.max_len,
min_len=self.min_len,
num_ans_candidates=self.num_ans_candidates,
prompt=self.prompt,
)
pred_qa_pairs = []
question_id = samples["question_id"]
gt_answers = samples["answer"]
for answer, ques_id, gt_answer in zip(answers, question_id, gt_answers):
ques_id = int(ques_id.item())
pred_qa_pairs.append({"question_id": ques_id, "pred_ans": answer, "gt_ans": gt_answer})
return pred_qa_pairs
@dist_utils.main_process
def _report_metrics(self, result_file, split):
"""
TODO: add other evaluation metrics for GQA
"""
results = json.load(open(result_file, "r"))
acc = []
vqa_tool = VQAEval()
for res in results:
if res["gt_ans"] is None:
# prepare test results for leaderboard evaluation
self._save_result_leaderboard(results)
return
gt_ans = res["gt_ans"]
pred = res["pred_ans"]
if self.inference_method == "generate":
pred = vqa_tool.processPunctuation(pred)
pred = vqa_tool.processDigitArticle(pred)
vqa_acc = 1 if pred == gt_ans else 0
acc.append(vqa_acc)
accuracy = sum(acc) / len(acc) * 100
metrics = {"agg_metrics": accuracy, "acc": accuracy}
with open(
os.path.join(registry.get_path("output_dir"), "evaluate.txt"), "a"
) as f:
f.write(json.dumps(metrics) + "\n")
logging.info(metrics)
return metrics
@registry.register_task("scienceqa")
class ScienceQATask(GQATask):
def valid_step(self, model, samples):
answers = model.predict_class(
samples=samples,
answer_list=self.answer_list,
inference_method=self.inference_method,
num_beams=self.num_beams,
max_len=self.max_len,
min_len=self.min_len,
num_ans_candidates=self.num_ans_candidates,
prompt=self.prompt,
)
pred_qa_pairs = []
question_id = samples["question_id"]
gt_answers = samples["answer"]
for answer, ques_id, gt_answer in zip(answers, question_id, gt_answers):
ques_id = int(ques_id.item())
pred_qa_pairs.append({"question_id": ques_id, "pred_ans": answer, "gt_ans": gt_answer})
return pred_qa_pairs
@registry.register_task("aok_vqa")
class AOKVQATask(VQATask):
def valid_step(self, model, samples):
answers = model.predict_answers(
samples=samples,
answer_list=self.answer_list,
inference_method=self.inference_method,
num_beams=self.num_beams,
max_len=self.max_len,
min_len=self.min_len,
num_ans_candidates=self.num_ans_candidates,
)
pred_qa_pairs = []
question_id = samples["question_id"]
gt_answers = samples["direct_answers"]
for pred_answer, ques_id, gt_answer in zip(answers, question_id, gt_answers):
pred_qa_pairs.append(
{"question_id": ques_id, "pred_ans": pred_answer, "gt_ans": gt_answer}
)
return pred_qa_pairs
@dist_utils.main_process
def _report_metrics(self, result_file, split):
"""
Implementing accuracy computation for AOKVQA, see
https://github.com/allenai/aokvqa/blob/main/evaluation/eval_predictions.py#L45 for details.
"""
# TODO add evaluation for multi-choice
results = json.load(open(result_file, "r"))
acc = []
for res in results:
if res["gt_ans"] is None:
# prepare test results for leaderboard evaluation
self._save_result_leaderboard(results)
return
pred = res["pred_ans"]
gt_ans = res["gt_ans"]
num_match = sum([pred == gt for gt in gt_ans])
vqa_acc = min(1.0, num_match / 3.0)
acc.append(vqa_acc)
accuracy = sum(acc) / len(acc) * 100
metrics = {"agg_metrics": accuracy, "acc": accuracy}
with open(
os.path.join(registry.get_path("output_dir"), "evaluate.txt"), "a"
) as f:
f.write(json.dumps(metrics) + "\n")
logging.info(metrics)
return metrics
@dist_utils.main_process
def _save_result_leaderboard(self, results):
"""
Saving the results in the format required for leaderboard evaluation.
[TODO] add support for multi-choice.
"""
result_leaderboard = dict()
for res in results:
result_leaderboard[res["question_id"]] = {
"direct_answer": res["pred_ans"],
"multiple_choice": "",
}
result_file = registry.get_path("result_dir") + "_leaderboard.json"
with open(result_file, "w") as f:
json.dump(result_leaderboard, f)
logging.info(f"Saved results for leaderboard evaluation at {result_file}")
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