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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 torch
import torch.distributed as dist
from itertools import chain
import minigpt4.common.dist_utils as dist_utils
from minigpt4.common.dist_utils import get_rank, get_world_size, is_main_process
from minigpt4.common.registry import registry
from minigpt4.common.vqa_tools.vqa_eval import VQAEval as VQATool
from minigpt4.tasks.vqa import VQATask
@registry.register_task("vqa_reading_comprehension")
class VQARCTask(VQATask):
def __init__(
self,
num_beams,
max_len,
min_len,
evaluate,
num_ans_candidates,
inference_method="rank",
**kwargs,
):
super().__init__(num_beams, max_len, min_len, evaluate, num_ans_candidates, inference_method)
self.config = kwargs.get('config')
@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)
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,
config=run_cfg,
)
def valid_step(self, model, samples):
answers, captions, gradcams = model.predict_answers(
samples=samples,
inference_method=self.inference_method,
num_beams=self.num_beams,
max_len=self.max_len,
min_len=self.min_len,
internal_bsz_fid=self.config['internal_bsz_fid'],
num_captions=self.config['num_captions'],
num_captions_fid=self.config['num_captions_fid'],
cap_max_length=self.config['cap_max_length'],
cap_min_length=self.config['cap_min_length'],
top_k=self.config['top_k'],
top_p=self.config['top_p'],
repetition_penalty=self.config['repetition_penalty'],
num_patches=self.config['num_patches'],
block_num=self.config['block_num'],
)
pred_qa_pairs = []
sample_captions = []
sample_gradcams = []
question_id = samples["question_id"]
for answer, caption, gradcam, ques_id in zip(answers, captions, gradcams, question_id):
ques_id = int(ques_id.item())
pred_qa_pairs.append({"question_id": ques_id, "answer": answer})
sample_captions.append({"question_id": ques_id, "caption": caption})
sample_gradcams.append({"question_id": ques_id, "gradcam": gradcam})
return [sample_gradcams, sample_captions, pred_qa_pairs]
def after_evaluation(self, val_result, split_name, **kwargs):
result_ = list(chain(*val_result[0::3]))
result_file = self.save_gradcam(
result_,
result_dir=registry.get_path("result_dir"),
filename=f"{split_name}_gradcam_result",
remove_duplicate="question_id",
)
result_ = list(chain(*val_result[1::3]))
result_file = self.save_result(
result_,
result_dir=registry.get_path("result_dir"),
filename=f"{split_name}_caption_result",
remove_duplicate="question_id",
)
result_ = list(chain(*val_result[2::3]))
result_file = self.save_result(
result_,
result_dir=registry.get_path("result_dir"),
filename=f"{split_name}_vqa_result",
remove_duplicate="question_id",
)
metrics = self._report_metrics(result_file=result_file, split=split_name)
return metrics
def save_gradcam(self, result, result_dir, filename, remove_duplicate=""):
result_file = os.path.join(result_dir, '%s_rank%d.pth' % (filename, get_rank()))
final_result_file = os.path.join(result_dir, '%s.pth' % filename)
torch.save({'result': result}, result_file)
dist.barrier()
if is_main_process():
logging.warning("rank %d starts merging results." % get_rank())
# combine results from all processes
result = []
for rank in range(get_world_size()):
result_file = os.path.join(result_dir, '%s_rank%d.pth' % (filename, rank))
res_ckpt = torch.load(result_file, map_location='cpu')
res = res_ckpt['result']
result += res
if remove_duplicate:
result_new = []
id_list = []
for res in result:
if res[remove_duplicate] not in id_list:
id_list.append(res[remove_duplicate])
result_new.append(res)
result = result_new
torch.save({'result': result}, final_result_file)
print("result file saved to %s" % final_result_file)
return final_result_file
@registry.register_task("gqa_reading_comprehension")
class GQARCTask(VQARCTask):
def valid_step(self, model, samples):
answers, captions, gradcams = model.predict_answers(
samples=samples,
inference_method=self.inference_method,
num_beams=self.num_beams,
max_len=self.max_len,
min_len=self.min_len,
internal_bsz_fid=self.config['internal_bsz_fid'],
num_captions=self.config['num_captions'],
num_captions_fid=self.config['num_captions_fid'],
cap_max_length=self.config['cap_max_length'],
cap_min_length=self.config['cap_min_length'],
top_k=self.config['top_k'],
top_p=self.config['top_p'],
repetition_penalty=self.config['repetition_penalty'],
num_patches=self.config['num_patches'],
block_num=self.config['block_num'],
)
pred_qa_pairs = []
sample_captions = []
sample_gradcams = []
question_id = samples["question_id"]
gt_answers = samples["answer"]
for pred_answer, caption, gradcam, ques_id, gt_answer in zip(answers, captions, gradcams, question_id, gt_answers):
ques_id = int(ques_id.item())
pred_qa_pairs.append({"question_id": ques_id, "pred_ans": pred_answer, "gt_ans": gt_answer})
sample_captions.append({"question_id": ques_id, "caption": caption})
sample_gradcams.append({"question_id": ques_id, "gradcam": gradcam})
return [sample_gradcams, sample_captions, 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 = VQATool()
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
@dist_utils.main_process
def _save_result_leaderboard(self, results):
"""
Saving the results in the format required for leaderboard evaluation.
"""
result_leaderboard = []
for res in results:
result_leaderboard.append({
"questionId": str(res['question_id']),
"prediction": str(res["pred_ans"]),
})
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}") |