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import torch |
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import time |
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from collections import defaultdict, deque |
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import datetime |
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from typing import Optional, List |
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from torch import Tensor |
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@torch.no_grad() |
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def accuracy(output, target, topk=(1,)): |
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"""Computes the precision@k for the specified values of k""" |
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if target.numel() == 0: |
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return [torch.zeros([], device=output.device)] |
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maxk = max(topk) |
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batch_size = target.size(0) |
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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.view(1, -1).expand_as(pred)) |
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res = [] |
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for k in topk: |
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correct_k = correct[:k].view(-1).float().sum(0) |
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res.append(correct_k.mul_(100.0 / batch_size)) |
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return res |
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class SmoothedValue(object): |
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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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""" |
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def __init__(self, window_size=100, fmt=None): |
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if fmt is None: |
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fmt = "{median:.3f} ({global_avg:.3f})" |
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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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@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.avg |
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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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class MetricLogger(object): |
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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("'{}' object has no attribute '{}'".format( |
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type(self).__name__, attr)) |
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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( |
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"{}: {}".format(name, str(meter)) |
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) |
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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, length_total=None): |
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i = 0 |
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if length_total is None: |
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length_total = len(iterable) |
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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(length_total))) + 'd' |
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if torch.cuda.is_available(): |
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log_msg = self.delimiter.join([ |
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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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else: |
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log_msg = self.delimiter.join([ |
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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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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 or i == length_total - 1: |
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eta_seconds = iter_time.global_avg * (length_total - 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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try: |
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print(log_msg.format( |
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i, length_total, eta=eta_string, |
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meters=str(self), |
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time=str(iter_time), data=str(data_time), |
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memory=torch.cuda.max_memory_allocated() / MB)) |
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except Exception as e: |
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import pdb; pdb.set_trace() |
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else: |
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print(log_msg.format( |
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i, length_total, eta=eta_string, |
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meters=str(self), |
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time=str(iter_time), data=str(data_time))) |
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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('{} Total time: {} ({:.4f} s / it)'.format( |
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header, total_time_str, total_time / length_total)) |
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class NestedTensor(object): |
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def __init__(self, tensors, mask: Optional[Tensor]): |
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self.tensors = tensors |
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self.mask = mask |
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def to(self, device, non_blocking=False): |
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cast_tensor = self.tensors.to(device, non_blocking=non_blocking) |
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mask = self.mask |
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if mask is not None: |
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assert mask is not None |
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cast_mask = mask.to(device, non_blocking=non_blocking) |
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else: |
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cast_mask = None |
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return NestedTensor(cast_tensor, cast_mask) |
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def record_stream(self, *args, **kwargs): |
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self.tensors.record_stream(*args, **kwargs) |
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if self.mask is not None: |
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self.mask.record_stream(*args, **kwargs) |
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def decompose(self): |
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return self.tensors, self.mask |
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def __repr__(self): |
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return str(self.tensors) |
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