def evaluate_metrics_from_files(pred_file: Union[Path, str],
ref_file: Union[Path, str]) \
-> Tuple[Dict[str, float], Dict[int, Dict[str, float]]]:
""" Evaluate the translation metrics from annotation files with the coco lib
Follows the example in the repo.
:param pred_file: File with predicted captions
:type pred_file: Path | str
:param ref_file: File with reference captions
:type ref_file: Path | str
:return: Tuple with metrics for the whole dataset and per-file metrics
:rtype: tuple[dict[str, float], dict[int, dict[str, float]]]
"""
# Load annotations from files
coco = COCO(str(ref_file))
cocoRes = coco.loadRes(str(pred_file))
# Create evaluation object and evaluate metrics
cocoEval = COCOEvalCap(coco, cocoRes)
cocoEval.params['audio_id'] = cocoRes.getAudioIds()
cocoEval.evaluate()
# Make dict from metrics
metrics = dict(
(m, s) for m, s in cocoEval.eval.items()
)
return metrics, cocoEval.audioToEval
def evaluate_on_coco_caption(res_file, label_file, outfile=None):
"""
res_tsv: TSV file, each row is [image_key, json format list of captions].
Each caption is a dict, with fields "caption", "conf".
label_file: JSON file of ground truth captions in COCO format.
"""
assert label_file.endswith('.json')
if res_file.endswith('.tsv'):
res_file_coco = op.splitext(res_file)[0] + '_coco_format.json'
convert_tsv_to_coco_format(res_file, res_file_coco)
else:
raise ValueError(
'unknown prediction result file format: {}'.format(res_file))
coco = COCO(label_file)
cocoRes = coco.loadRes(res_file_coco)
cocoEval = COCOEvalCap(coco, cocoRes, 'corpus')
# evaluate on a subset of images by setting
# cocoEval.params['image_id'] = cocoRes.getImgIds()
# please remove this line when evaluating the full validation set
cocoEval.params['image_id'] = cocoRes.getImgIds()
# evaluate results
# SPICE will take a few minutes the first time, but speeds up due to caching
cocoEval.evaluate()
result = cocoEval.eval
if not outfile:
print(result)
else:
with open(outfile, 'w') as fp:
json.dump(result, fp, indent=4)
return result
def get_metric(args_dict, results_file, ann_file):
coco = COCO(ann_file)
cocoRes = coco.loadRes(results_file)
cocoEval = COCOEvalCap(coco, cocoRes)
cocoEval.evaluate()
return cocoEval.eval[args_dict.es_metric]
def language_eval(preds, test_coco_ids, cache_path):
import sys
sys.path.insert(0, "coco_caption")
# generate target file
annFile = transform_annos(test_coco_ids)
from coco_caption.pycocotools.coco import COCO
from coco_caption.pycocoevalcap.eval import COCOEvalCap
encoder.FLOAT_REPR = lambda o: format(o, '.3f')
coco = COCO(annFile)
valids = coco.getImgIds()
# filter results to only those in MSCOCO validation set (will be about a third)
#preds_filt = [p for p in preds if p['image_id'] in valids]
preds_filt = []
image_id_filt = []
for p in preds:
if p['image_id'] in valids and p['image_id'] not in image_id_filt:
preds_filt.append(p)
image_id_filt.append(p['image_id'])
print('using %d/%d predictions' % (len(preds_filt), len(preds)))
json.dump(preds_filt,
open(cache_path,
'w')) # serialize to temporary json file. Sigh, COCO API...
cocoRes = coco.loadRes(cache_path)
cocoEval = COCOEvalCap(coco, cocoRes)
cocoEval.params['image_id'] = cocoRes.getImgIds()
cocoEval.evaluate()
# create output dictionary
out = {}
for metric, score in cocoEval.eval.items():
out[metric] = score
imgToEval = cocoEval.imgToEval
for p in preds_filt:
image_id, caption = p['image_id'], p['caption']
imgToEval[image_id]['caption'] = caption
with open(cache_path, 'w') as outfile:
json.dump({'overall': out, 'imgToEval': imgToEval}, outfile)
return out
def language_eval(preds, model_id, split):
annFile = 'coco_caption/annotations/captions_val2014.json'
from coco_caption.pycocotools.coco import COCO
from coco_caption.pycocoevalcap.eval import COCOEvalCap
# encoder.FLOAT_REPR = lambda o: format(o, '.3f')
if not os.path.isdir('eval_results'):
os.mkdir('eval_results')
cache_path = os.path.join('eval_results/',
model_id + '_' + split + '.json')
coco = COCO(annFile)
valids = coco.getImgIds()
# filter results to only those in MSCOCO validation set (will be about a third)
preds_filt = [p for p in preds if p['image_id'] in valids]
print(len(preds_filt))
print('using %d/%d predictions' % (len(preds_filt), len(preds)))
json.dump(preds_filt,
open(cache_path,
'w')) # serialize to temporary json file. Sigh, COCO API...
cocoRes = coco.loadRes(cache_path)
cocoEval = COCOEvalCap(coco, cocoRes)
cocoEval.params['image_id'] = cocoRes.getImgIds()
cocoEval.evaluate()
# create output dictionary
out = {}
for metric, score in cocoEval.eval.items():
out[metric] = score
imgToEval = cocoEval.imgToEval
for p in preds_filt:
image_id, caption = p['image_id'], p['caption']
imgToEval[image_id]['caption'] = caption
with open(cache_path, 'w') as outfile:
json.dump({'overall': out, 'imgToEval': imgToEval}, outfile)
return out
