logger.py

import torch
import time, datetime
from collections import deque, defaultdict
import numpy as np
from typing import Optional, Tuple

epsilon = 1e-8

class SmoothedValue:
    """Track a series of values and provide access to smoothed values over a
    window or the global series average.
    """

    def __init__(self, window_size=20, fmt=None):
        if fmt is None:
            fmt = "{median:.4f} ({global_avg:.4f})"
        self.deque = deque(maxlen=window_size)
        self.total = 0.0
        self.count = 0
        self.fmt = fmt

    def update(self, value, n=1):
        self.deque.append(value)
        self.count += n
        self.total += value * n

    # def synchronize_between_processes(self):
    #     """
    #     Warning: does not synchronize the deque!
    #     """
    #     if not is_dist_avail_and_initialized():
    #         return
    #     t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda")
    #     dist.barrier()
    #     dist.all_reduce(t)
    #     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
        )


# def all_gather(data):
#     """
#     Run all_gather on arbitrary picklable data (not necessarily tensors)
#     Args:
#         data: any picklable object
#     Returns:
#         list[data]: list of data gathered from each rank
#     """
#     world_size = get_world_size()
#     if world_size == 1:
#         return [data]
#     data_list = [None] * world_size
#     dist.all_gather_object(data_list, data)
#     return data_list


# def reduce_dict(input_dict, average=True):
#     """
#     Args:
#         input_dict (dict): all the values will be reduced
#         average (bool): whether to do average or sum
#     Reduce the values in the dictionary from all processes so that all processes
#     have the averaged results. Returns a dict with the same fields as
#     input_dict, after reduction.
#     """
#     world_size = get_world_size()
#     if world_size < 2:
#         return input_dict
#     with torch.inference_mode():
#         names = []
#         values = []
#         # sort the keys so that they are consistent across processes
#         for k in sorted(input_dict.keys()):
#             names.append(k)
#             values.append(input_dict[k])
#         values = torch.stack(values, dim=0)
#         dist.all_reduce(values)
#         if average:
#             values /= world_size
#         reduced_dict = {k: v for k, v in zip(names, values)}
#     return reduced_dict

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 or i == len(iterable) - 1:
                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} ({total_time / len(iterable):.4f} s / it)")


def accuracy(output, target, topk=(1,)):
    """Computes the precision@k for the specified values of k"""
    maxk = max(topk)
    batch_size = target.size(0)
    _, pred = output.topk(maxk, 1, True, True)
    pred = pred.t()
    with torch.no_grad():
        correct = pred.eq(target.view(1, -1).expand_as(pred))
    return [correct[:k].view(-1).float().sum(0) * 100. / batch_size for k in topk]


def average_precision(output, target):
    # sort examples
    indices = output.argsort()[::-1]
    # Computes prec@i
    total_count_ = np.cumsum(np.ones((len(output), 1)))
    target_ = target[indices]
    ind = target_ == 1
    pos_count_ = np.cumsum(ind)
    total = pos_count_[-1]
    pos_count_[np.logical_not(ind)] = 0
    pp = pos_count_ / total_count_
    precision_at_i_ = np.sum(pp)
    precision_at_i = precision_at_i_/(total + epsilon)
    return precision_at_i


def mAP(targs, preds):
    """Returns the model's average precision for each class
    Return:
        ap (FloatTensor): 1xK tensor, with avg precision for each class k
    """
    if np.size(preds) == 0:
        return 0
    ap = np.zeros((preds.shape[1]))
    # compute average precision for each class
    for k in range(preds.shape[1]):
        # sort scores
        scores = preds[:, k]
        targets = targs[:, k]
        # compute average precision
        ap[k] = average_precision(scores, targets)
    return 100*ap.mean()