references/classification/utils.py [36:155]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - t = t.tolist() self.count = int(t[0]) self.total = t[1] @property def median(self): d = torch.tensor(list(self.deque)) return d.median().item() @property def avg(self): d = torch.tensor(list(self.deque), dtype=torch.float32) return d.mean().item() @property def global_avg(self): return self.total / self.count @property def max(self): return max(self.deque) @property def value(self): return self.deque[-1] def __str__(self): return self.fmt.format( median=self.median, avg=self.avg, global_avg=self.global_avg, max=self.max, value=self.value ) class MetricLogger: def __init__(self, delimiter="\t"): self.meters = defaultdict(SmoothedValue) self.delimiter = delimiter def update(self, **kwargs): for k, v in kwargs.items(): if isinstance(v, torch.Tensor): v = v.item() assert isinstance(v, (float, int)) self.meters[k].update(v) def __getattr__(self, attr): if attr in self.meters: return self.meters[attr] if attr in self.__dict__: return self.__dict__[attr] raise AttributeError(f"'{type(self).__name__}' object has no attribute '{attr}'") def __str__(self): loss_str = [] for name, meter in self.meters.items(): loss_str.append(f"{name}: {str(meter)}") return self.delimiter.join(loss_str) def synchronize_between_processes(self): for meter in self.meters.values(): meter.synchronize_between_processes() def add_meter(self, name, meter): self.meters[name] = meter def log_every(self, iterable, print_freq, header=None): i = 0 if not header: header = "" start_time = time.time() end = time.time() iter_time = SmoothedValue(fmt="{avg:.4f}") data_time = SmoothedValue(fmt="{avg:.4f}") space_fmt = ":" + str(len(str(len(iterable)))) + "d" if torch.cuda.is_available(): log_msg = self.delimiter.join( [ header, "[{0" + space_fmt + "}/{1}]", "eta: {eta}", "{meters}", "time: {time}", "data: {data}", "max mem: {memory:.0f}", ] ) else: log_msg = self.delimiter.join( [header, "[{0" + space_fmt + "}/{1}]", "eta: {eta}", "{meters}", "time: {time}", "data: {data}"] ) MB = 1024.0 * 1024.0 for obj in iterable: data_time.update(time.time() - end) yield obj iter_time.update(time.time() - end) if i % print_freq == 0: eta_seconds = iter_time.global_avg * (len(iterable) - i) eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) if torch.cuda.is_available(): print( log_msg.format( i, len(iterable), eta=eta_string, meters=str(self), time=str(iter_time), data=str(data_time), memory=torch.cuda.max_memory_allocated() / MB, ) ) else: print( log_msg.format( i, len(iterable), eta=eta_string, meters=str(self), time=str(iter_time), data=str(data_time) ) ) i += 1 end = time.time() total_time = time.time() - start_time total_time_str = str(datetime.timedelta(seconds=int(total_time))) print(f"{header} Total time: {total_time_str}") - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - references/video_classification/utils.py [38:157]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - t = t.tolist() self.count = int(t[0]) self.total = t[1] @property def median(self): d = torch.tensor(list(self.deque)) return d.median().item() @property def avg(self): d = torch.tensor(list(self.deque), dtype=torch.float32) return d.mean().item() @property def global_avg(self): return self.total / self.count @property def max(self): return max(self.deque) @property def value(self): return self.deque[-1] def __str__(self): return self.fmt.format( median=self.median, avg=self.avg, global_avg=self.global_avg, max=self.max, value=self.value ) class MetricLogger: def __init__(self, delimiter="\t"): self.meters = defaultdict(SmoothedValue) self.delimiter = delimiter def update(self, **kwargs): for k, v in kwargs.items(): if isinstance(v, torch.Tensor): v = v.item() assert isinstance(v, (float, int)) self.meters[k].update(v) def __getattr__(self, attr): if attr in self.meters: return self.meters[attr] if attr in self.__dict__: return self.__dict__[attr] raise AttributeError(f"'{type(self).__name__}' object has no attribute '{attr}'") def __str__(self): loss_str = [] for name, meter in self.meters.items(): loss_str.append(f"{name}: {str(meter)}") return self.delimiter.join(loss_str) def synchronize_between_processes(self): for meter in self.meters.values(): meter.synchronize_between_processes() def add_meter(self, name, meter): self.meters[name] = meter def log_every(self, iterable, print_freq, header=None): i = 0 if not header: header = "" start_time = time.time() end = time.time() iter_time = SmoothedValue(fmt="{avg:.4f}") data_time = SmoothedValue(fmt="{avg:.4f}") space_fmt = ":" + str(len(str(len(iterable)))) + "d" if torch.cuda.is_available(): log_msg = self.delimiter.join( [ header, "[{0" + space_fmt + "}/{1}]", "eta: {eta}", "{meters}", "time: {time}", "data: {data}", "max mem: {memory:.0f}", ] ) else: log_msg = self.delimiter.join( [header, "[{0" + space_fmt + "}/{1}]", "eta: {eta}", "{meters}", "time: {time}", "data: {data}"] ) MB = 1024.0 * 1024.0 for obj in iterable: data_time.update(time.time() - end) yield obj iter_time.update(time.time() - end) if i % print_freq == 0: eta_seconds = iter_time.global_avg * (len(iterable) - i) eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) if torch.cuda.is_available(): print( log_msg.format( i, len(iterable), eta=eta_string, meters=str(self), time=str(iter_time), data=str(data_time), memory=torch.cuda.max_memory_allocated() / MB, ) ) else: print( log_msg.format( i, len(iterable), eta=eta_string, meters=str(self), time=str(iter_time), data=str(data_time) ) ) i += 1 end = time.time() total_time = time.time() - start_time total_time_str = str(datetime.timedelta(seconds=int(total_time))) print(f"{header} Total time: {total_time_str}") - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -