| 86 | |
| 87 | |
| 88 | class MetricLogger(object): |
| 89 | def __init__(self, delimiter="\t"): |
| 90 | self.meters = defaultdict(SmoothedValue) |
| 91 | self.delimiter = delimiter |
| 92 | |
| 93 | def update(self, **kwargs): |
| 94 | for k, v in kwargs.items(): |
| 95 | if isinstance(v, torch.Tensor): |
| 96 | v = v.item() |
| 97 | assert isinstance(v, (float, int)) |
| 98 | self.meters[k].update(v) |
| 99 | |
| 100 | def __getattr__(self, attr): |
| 101 | if attr in self.meters: |
| 102 | return self.meters[attr] |
| 103 | if attr in self.__dict__: |
| 104 | return self.__dict__[attr] |
| 105 | raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr)) |
| 106 | |
| 107 | def __str__(self): |
| 108 | loss_str = [] |
| 109 | for name, meter in self.meters.items(): |
| 110 | loss_str.append("{}: {}".format(name, str(meter))) |
| 111 | return self.delimiter.join(loss_str) |
| 112 | |
| 113 | def synchronize_between_processes(self): |
| 114 | for meter in self.meters.values(): |
| 115 | meter.synchronize_between_processes() |
| 116 | |
| 117 | def add_meter(self, name, meter): |
| 118 | self.meters[name] = meter |
| 119 | |
| 120 | def log_every(self, iterable, print_freq, header=None): |
| 121 | i = 0 |
| 122 | if not header: |
| 123 | header = "" |
| 124 | start_time = time.time() |
| 125 | end = time.time() |
| 126 | iter_time = SmoothedValue(fmt="{avg:.4f}") |
| 127 | data_time = SmoothedValue(fmt="{avg:.4f}") |
| 128 | space_fmt = ":" + str(len(str(len(iterable)))) + "d" |
| 129 | if torch.cuda.is_available(): |
| 130 | log_msg = self.delimiter.join( |
| 131 | [ |
| 132 | header, |
| 133 | "[{0" + space_fmt + "}/{1}]", |
| 134 | "eta: {eta}", |
| 135 | "{meters}", |
| 136 | "time: {time}", |
| 137 | "data: {data}", |
| 138 | "max mem: {memory:.0f}", |
| 139 | ] |
| 140 | ) |
| 141 | else: |
| 142 | log_msg = self.delimiter.join( |
| 143 | [ |
| 144 | header, |
| 145 | "[{0" + space_fmt + "}/{1}]", |