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import datetime |
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import time |
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from collections import defaultdict |
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from collections import deque |
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import torch |
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import torch.distributed as dist |
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class SmoothedValue: |
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"""Track a series of values and provide access to smoothed values over a |
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window or the global series average.""" |
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def __init__(self, window_size=20, fmt=None): |
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if fmt is None: |
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fmt = "{median:.4f} ({global_avg:.4f})" |
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self.deque = deque(maxlen=window_size) |
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self.total = 0.0 |
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self.count = 0 |
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self.fmt = fmt |
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def update(self, value, n=1): |
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self.deque.append(value) |
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self.count += n |
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self.total += value * n |
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def synchronize_between_processes(self): |
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""" |
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Warning: does not synchronize the deque! |
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""" |
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t = reduce_across_processes([self.count, self.total]) |
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t = t.tolist() |
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self.count = int(t[0]) |
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self.total = t[1] |
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@property |
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def median(self): |
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d = torch.tensor(list(self.deque)) |
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return d.median().item() |
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@property |
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def avg(self): |
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d = torch.tensor(list(self.deque), dtype=torch.float32) |
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return d.mean().item() |
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@property |
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def global_avg(self): |
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return self.total / self.count |
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@property |
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def max(self): |
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return max(self.deque) |
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@property |
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def value(self): |
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return self.deque[-1] |
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def __str__(self): |
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return self.fmt.format( |
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median=self.median, |
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avg=self.avg, |
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global_avg=self.global_avg, |
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max=self.max, |
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value=self.value, |
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) |
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class MetricLogger: |
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def __init__(self, delimiter="\t"): |
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self.meters = defaultdict(SmoothedValue) |
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self.delimiter = delimiter |
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def update(self, **kwargs): |
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for k, v in kwargs.items(): |
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if isinstance(v, torch.Tensor): |
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v = v.item() |
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assert isinstance(v, (float, int)) |
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self.meters[k].update(v) |
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def __getattr__(self, attr): |
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if attr in self.meters: |
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return self.meters[attr] |
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if attr in self.__dict__: |
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return self.__dict__[attr] |
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raise AttributeError( |
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f"'{type(self).__name__}' object has no attribute '{attr}'" |
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) |
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def __str__(self): |
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loss_str = [] |
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for name, meter in self.meters.items(): |
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loss_str.append(f"{name}: {str(meter)}") |
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return self.delimiter.join(loss_str) |
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def synchronize_between_processes(self): |
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for meter in self.meters.values(): |
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meter.synchronize_between_processes() |
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def add_meter(self, name, meter): |
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self.meters[name] = meter |
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def log_every(self, iterable, print_freq, header=None): |
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i = 0 |
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if not header: |
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header = "" |
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start_time = time.time() |
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end = time.time() |
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iter_time = SmoothedValue(fmt="{avg:.4f}") |
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data_time = SmoothedValue(fmt="{avg:.4f}") |
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space_fmt = ":" + str(len(str(len(iterable)))) + "d" |
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if torch.cuda.is_available(): |
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log_msg = self.delimiter.join( |
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[ |
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header, |
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"[{0" + space_fmt + "}/{1}]", |
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"eta: {eta}", |
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"{meters}", |
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"time: {time}", |
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"data: {data}", |
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"max mem: {memory:.0f}", |
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] |
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) |
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else: |
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log_msg = self.delimiter.join( |
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[ |
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header, |
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"[{0" + space_fmt + "}/{1}]", |
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"eta: {eta}", |
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"{meters}", |
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"time: {time}", |
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"data: {data}", |
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] |
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) |
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MB = 1024.0 * 1024.0 |
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for obj in iterable: |
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data_time.update(time.time() - end) |
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yield obj |
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iter_time.update(time.time() - end) |
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if i % print_freq == 0: |
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eta_seconds = iter_time.global_avg * (len(iterable) - i) |
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eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) |
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if torch.cuda.is_available(): |
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print( |
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log_msg.format( |
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i, |
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len(iterable), |
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eta=eta_string, |
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meters=str(self), |
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time=str(iter_time), |
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data=str(data_time), |
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memory=torch.cuda.max_memory_allocated() / MB, |
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) |
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) |
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else: |
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print( |
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log_msg.format( |
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i, |
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len(iterable), |
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eta=eta_string, |
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meters=str(self), |
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time=str(iter_time), |
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data=str(data_time), |
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) |
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) |
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i += 1 |
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end = time.time() |
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total_time = time.time() - start_time |
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total_time_str = str(datetime.timedelta(seconds=int(total_time))) |
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print(f"{header} Total time: {total_time_str}") |
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def is_dist_avail_and_initialized(): |
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if not dist.is_available(): |
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return False |
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if not dist.is_initialized(): |
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return False |
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return True |
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def reduce_across_processes(val): |
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if not is_dist_avail_and_initialized(): |
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return torch.tensor(val) |
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t = torch.tensor(val, device="cuda") |
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dist.barrier() |
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dist.all_reduce(t) |
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return t |
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def accuracy(output, target, topk=(1,)): |
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"""Computes the accuracy over the k top predictions for the specified |
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values of k.""" |
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with torch.inference_mode(): |
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maxk = max(topk) |
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batch_size = target.size(0) |
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if target.ndim == 2: |
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target = target.max(dim=1)[1] |
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_, pred = output.topk(maxk, 1, True, True) |
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pred = pred.t() |
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correct = pred.eq(target[None]) |
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res = [] |
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for k in topk: |
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correct_k = correct[:k].flatten().sum(dtype=torch.float32) |
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res.append(correct_k * (100.0 / batch_size)) |
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return res |
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