Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
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import json
import time
class TimeCounter:
def __init__(self) -> None:
pass
def clear(self):
self.timedict = {}
self.basetime = time.perf_counter()
def timeit(self, name):
nowtime = time.perf_counter() - self.basetime
self.timedict[name] = nowtime
self.basetime = time.perf_counter()
class TimeHolder:
def __init__(self) -> None:
self.timedict = {}
def update(self, _timedict: dict):
for k, v in _timedict.items():
if k not in self.timedict:
self.timedict[k] = AverageMeter(name=k, val_only=True)
self.timedict[k].update(val=v)
def final_res(self):
return {k: v.avg for k, v in self.timedict.items()}
def __str__(self):
return json.dumps(self.final_res(), indent=2)
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, name, fmt=":f", val_only=False):
self.name = name
self.fmt = fmt
self.val_only = val_only
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def __str__(self):
if self.val_only:
fmtstr = "{name} {val" + self.fmt + "}"
else:
fmtstr = "{name} {val" + self.fmt + "} ({avg" + self.fmt + "})"
return fmtstr.format(**self.__dict__)