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# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional
import mmengine
from mmpretrain.registry import METRICS
from mmpretrain.utils import require
from .caption import COCOCaption, save_result
try:
from pycocoevalcap.eval import COCOEvalCap
from pycocotools.coco import COCO
except ImportError:
COCOEvalCap = None
COCO = None
@METRICS.register_module()
class NocapsSave(COCOCaption):
"""Nocaps evaluation wrapper.
Save the generated captions and transform into coco format.
The dumped file can be submitted to the official evluation system.
Args:
collect_device (str): Device name used for collecting results from
different ranks during distributed training. Must be 'cpu' or
'gpu'. Defaults to 'cpu'.
prefix (str, optional): The prefix that will be added in the metric
names to disambiguate homonymous metrics of different evaluators.
If prefix is not provided in the argument, self.default_prefix
will be used instead. Should be modified according to the
`retrieval_type` for unambiguous results. Defaults to TR.
"""
@require('pycocoevalcap')
def __init__(self,
save_dir: str = './',
collect_device: str = 'cpu',
prefix: Optional[str] = None):
super(COCOCaption, self).__init__(
collect_device=collect_device, prefix=prefix)
self.save_dir = save_dir
def compute_metrics(self, results: List):
"""Compute the metrics from processed results.
Args:
results (dict): The processed results of each batch.
"""
mmengine.mkdir_or_exist(self.save_dir)
save_result(
result=results,
result_dir=self.save_dir,
filename='nocap_pred',
remove_duplicate='image_id',
)
return dict()
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